Revealing Seismic Sequence Characteristics in the South-eastern Alps and the Western Dinarides by clustering analysis and refined location

Revealing Seismic Sequence Characteristics in the South-eastern Alps and the Western Dinarides by clustering analysis and refined location | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Revealing Seismic Sequence Characteristics in the South-eastern Alps and the Western Dinarides by clustering analysis and refined location Piero Brondi, Matteo Picozzi, Grazia De Landro, Antonio Giovanni Iaccarino, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9169863/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract The characterization of seismic sequences provides key constraints on fault geometry, rupture processes, and seismic hazard. In this study we investigate seismicity recorded between 2015–2024 in the South-eastern Alps and Western Dinarides (SEAWD), one of the most tectonically complex regions of the Alpine–Adriatic domain. Starting from the bulletin seismic catalogue of the Northeastern Italy Seismometer Network, we implement a multiscale relocation workflow. The catalogue is first relocated at regional scale using a multi-iterative NonLinLoc Source-Specific Station Term (NLL-SSST) approach, progressively refining residual grids to account for velocity-model uncertainties. Seismic sequences are then identified through nearest-neighbour clustering. Selected sequences are finally relocated at local scale using waveform-coherence–based NonLinLoc (NLL-coherence), enforcing spatial consistency among similar events. This workflow yields locations with ~hundreds-of-meters resolution and resolves fine-scale fault structures. We identify 71 sequences, mainly foreshock–mainshock–aftershock and mainshock–aftershock types, with limited swarm-like activity. Principal component analysis quantifies sequence volume and orientation of seismicity distributions. Most clusters define tabular structures: larger volumes correspond to planar fault zones, whereas compact clusters involve low-magnitude and low seismic activity. These patterns highlight the structural complexity of the Alpine–Dinaric transition zone and provide new constraints on active seismogenic structures and seismic hazard in northeastern Italy and adjacent regions. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Solid earth sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The progressive increase in seismic station density, together with advances in computational capabilities and the development of improved detection and location techniques (Mousavi et al., 2020 ; Tan et al., 2021 ; Adinolfi et al., 2023 ), has significantly enhanced our ability to identify seismic sources and to resolve the physical processes governing earthquake occurrence on faults (Petersen et al., 2025 ). Consequently, analysis of seismicity has become a key tool for constraining fault geometry and its temporal evolution (e.g., Picozzi et al., 2019 , 2022 ; Tan et al., 2021 ; Liu et al., 2022 ; Muzellec et al., 2025 ; Poggiali et al., 2025 ; Sollai et al., 2025 ). The characterization of seismicity patterns is central to seismic hazard assessment, as changes in earthquake occurrence often reflect variations in stress loading and fault interactions. In this context, seismic sequences, defined as earthquakes clustered in space and time relative to background seismicity, provide essential information on fault activation and triggering processes (Riga and Balocchi, 2015 ; Petersen et al., 2025 ). Recent studies have shown that both static stress transfer and fault-zone complexity strongly influence the spatial and temporal evolution of seismic sequences (King et al., 1994 ; Stein, 1999 ; Nandan et al., 2017 ). Large cascading events, such as the 2023 Kahramanmaraş sequence, illustrate how stress interactions between adjacent fault segments can control earthquake occurrence (Jia et al., 2023 ), while swarm-like activity along the Maacama, Northern California fault, highlights the role of small, distributed fault structures in generating complex seismicity (Shelly et al., 2023 ). In Italy, seismic sequence analysis has provided important insights into faults structure and rupture processes. The 2016–2017 Central Italy sequence revealed the progressive activation of complex fault systems and clarified the role of fault segmentation and cascading rupture during major earthquakes (Michele et al., 2020 ; Tan et al., 2021 ; Picozzi et al., 2018 , 2019 , 2022 , 2024 ). Other studies documented intermittent fault activity, fluid-driven seismic migration, and static stress-controlled sequences in different tectonic settings, including the Altotiberina fault, Molise, and Irpinia (Vuan et al., 2020 ; Gentili et al., 2024 ; Scotto di Uccio et al., 2024; Palo et al., 2023 ; Camanni et al., 2025 ). The South-eastern Alps and Western Dinarides (SEAWD) constitute a region of high tectonic complexity and moderate-to-high seismic hazard, with the 1976 M W 6.5 Friuli earthquake marking the most recent destructive event. Subsequent investigations of major sequences, such as the 1998 and 2004 Bovec–Krn earthquakes, revealed complex fault geometries and stress heterogeneities controlled by regional tectonics (Bressan et al., 2009 ). Later studies showed that mainshock-induced stress changes can promote static fatigue and subcritical crack growth, processes enhanced by mechanical contrasts and unclamping effects (Bressan et al., 2018b ), and highlighted the role of fluid transients bound to strong to moderate earthquakes in the stress field variation and in modulating the background seismicity (Rossi et al., 2021 ). More recent analyses in the SEAWD region have emphasized the influence of fault interactions and mechanical heterogeneity on the spatial and temporal distribution of seismicity (Bressan et al., 2021 ). In this study, we analyze the seismic sequences that have occurred in the SEAWD region over the past decade using approximately 17,000 seismic events from the National Institute of Oceanography and Applied Geophysics - OGS high-quality catalogue (Fig. 1 a) recorded by stations belonging to the OGS (Bragato et al., 2021 ) and other nearby seismometric networks (Fig. 1 b; see the Data section for more details). Focusing on the period after 2015 ensures an improved hypocentral accuracy in terms of location uncertainties and azimuthal coverage (Sandron et al., 2023 ). Based on various geophysical characteristics observed in previous studies, we organize our discussion of results dividing the SEAWD region into three subdomains (Fig. 1 b): Western SEAWD (WS), Central SEAWD (CS), and Eastern SEAWD (ES). The WS area experienced over the last decade low seismicity, characterized by swarm activity and structures with subvertical strike-slip (NNE–SSW, N–S) and normal NW–SE focal mechanisms with a southwest dip (Petersen et al., 2025 ). The CS area has the highest seismicity in the entire Alpine arc, close to the epicenter of the M W 6.5 1976 Friuli earthquake, and is characterized by a predominance of mainshock-aftershock sequences with a mainly thrust faulting regime (Saraò et al., 2021 ; Petersen et al., 2025 ). Finally, the ES area, located in the transition zone between the Alps, the Dinarides, and the Pannonian Basin, is characterized by active faults with a NW–SE orientation, parallel to the Dinarides chain, exhibiting right-lateral strike-slip movement that extends for tens of kilometers and produces both swarms and sequences (Vičič et al., 2019 ; Atanockov et al., 2021). (see section “Data”). The outline of our analyses in the SEAWD area is presented in Fig. 2 . Starting from the OGS bulletin seismic catalogue and its corresponding 1D velocity model (Slejko et al., 1989 ), seismicity is first relocated at the regional scale using a multi-scale approach based on NonLinLoc-source-specific station travel-time corrections (NLL-SSST; Lomax and Savvaidis, 2022 ), which progressively refines the time residual grids to mitigate velocity-model uncertainties. On the resulting high-precision catalogue, seismic sequences are identified through nearest-neighbour clustering and subsequently relocated to higher precision at the local scale with a waveform-coherence-based NonLinLoc approach (NLL-coherence; Lomax and Savvaidis, 2022 ). Specifically, we will use the term NLL-SC to refer to the application of the NLL-SSST methodology and, consequently, to the NLL-coherence application. Our strategy enables a detailed characterization of the spatial and temporal evolution of seismic sequences. Sequences are subsequently classified according to their temporal behavior (e.g., foreshock–mainshock–aftershock, mainshock–aftershock, and swarm-like activity), while their spatial extent is quantitatively characterized through principal component analysis, used to derive a first-order estimation of sequence volume (Fig. 2 ). Overall, this approach provides a consistent basis to investigate sequence geometry, rupture processes, and fault-zone structure, with implications for seismic hazard assessment in northeastern Italy and adjacent regions. Results Selection and relocation of the sequences We applied the NLL-SSST analysis to the OGS bulletin catalogue and we relocated 16053 events (i.e., representing 93% of the original dataset, while the remaining events were discarded due to low quality; Fig. 3 a). Comparing Figs. 1 a and 3 a, we see that the most significant difference between the locations of the OGS bulletin and those of NLL-SSST is in hypocentral depth, with the difference Δz between the depth of events relocated by NLL-SSST and the depth of events in the bulletin being mostly negative in the longitude ranges 10.2°–11° and 11.5°–14° (Fig. 3 a). This result suggests that in all three areas - WS, CS and ES -, NLL-SSST implies a medium with a higher wavespeeds than that used for the creation of the OGS bulletin. Figure 3 also shows north-south cross sections at longitudes 13.0° and 13.25° (CS area) for the OGS bulletin (Figs. 3 b and 3 c) and NLL-SSST locations (Figs. 3 d and 3 e). By comparing them, we observe that even at this stage of relocation, the structures are geometrically more defined with NLL-SSST than in the initial bulletin data. In the next step, we separated background seismicity from clustered seismicity in the relocated catalogue using the rescaled generalized distance method by Zaliapin et al. ( 2008 ). As a result of the clustering analysis (details are provided in the Methods section), 76 seismic sequences, comprising approximately 2,200 events, were identified (Fig. 4 ; see Supplementary Fig. S1 ). Looking at the nearest-neighbour proximity parameter log(η) we observe as sequences occur rather regularly distributed in time (Figs. 4 a, 4 b), but not in space (Figs. 4 c, 4 d). The highest density of sequences is observed in the eastern part of the CS region (Fig. 4 c), while apparent gaps between sequences are observed in coincidence with the western part of the CS region (i.e., longitude ~ 12°) and at the junction of the CS and ES areas. A look at moment magnitude as a function of time (Fig. 4 b) does not highlight specific temporal patterns, leading us to exclude, at least in a first approximation, the presence of external forcing driving the sequences occurrence. Clusters concentrate along the main seismogenic belt of the South-Eastern Alps, particularly along the southern Alpine thrust front and the Friuli–Slovenia transition zone, while background seismicity (grey) appears more diffusely distributed. Figures (4d) presents the depth distribution versus longitude, indicating that most clustered seismicity occurs within the upper crust (≈ 5–15 km depth) and follows the along-strike geometry of the Alpine front. The clustering highlights localized zones of repeated seismic interaction embedded within a broader background seismicity field. The clustering parameters adopted in this study follow previous applications of the nearest-neighbour method (Zaliapin and Ben-Zion, 2013 ; 2020 ). Although different parameter choices may slightly modify the number of identified clusters, the main spatial patterns and the concentration of sequences in the Central SEAWD remain stable. For each indicated sequence, we applied the NLL-coherence stage of NLL-SC (details are provided in the Methods section), using waveforms from at least four stations near the median epicentral location to achieve a good balance between signal quality and network coverage. Due to limited stations coverage in the southwestern part of the CS area, this criterion was not met for the M L 4.4 2023 Ceneselli earthquake sequence (the southernmost sequence in Fig. 4 c), resulting in its exclusion from subsequent analyses. Because the seismicity of four sequences in the ES appears to be located near the southeast corner of the SE area, to avoid inaccuracies in characterizing these sequences due to partial event detection, we also excluded them from subsequent analyses. Consequently, our final SEAWD sequence database consists of 71 sequences: 11 within WS, 47 within CS, and 13 within ES. Starting from the absolute location of the OGS bulletin, Fig. 5 shows the effect of the intermediate NLL-SSST relocation procedure and the final NLL-SC relocation procedure for three sequences characterized by one of the strongest mainshock: the M L 3.8 Ledro sequence (WS) in 2015, the M L 4.3 Bovec sequence (ES) in 2020, and the M L 4.6 Socchieve sequence (CS) in 2024 (similar figures for all the sequences are available as Supplemental Material). In all three cases, applying NLL-SSST results in more clustered seismicity (Fig. 5 b, 5 e, 5 h) than the OGS bulletin data (Fig. 5 a, 5 d, 5 g). For the 2015 Ledro and 2024 Socchieve sequences, the events also appear to decrease in depth by a few kilometers. The subsequent application of the NLL-coherence procedure further clusters the seismicity, revealing finer geometric structures (Fig. 5 c, 5 f, 5 i). Specifically, seismicity that, with NLL-SSST, spans 2 km in latitude, 2 km in longitude, and 5 km in depth is subsequently concentrated within 1 km in latitude and longitude and 3 km in depth. Figure S2 in the Supplementary Information shows the root mean square residual (RMS) and the horizontal and vertical errors of the events from the three sequences obtained at the initial iteration of NLL-SSST, the final iteration of NLL-SSST, and the NLL-coherence procedure. In all three cases, the significant reduction in RMS at the last iteration of NLL-SSST compared to the first suggests that the SSST corrections to the initial velocity model allowed better consideration of the medium's heterogeneity, leading to improved location quality (see Supplementary Figures S2a, S2d, S2g). This decrease in RMS results in horizontal and vertical error values that remain the same or are slightly higher than those in the initial iteration (See Supplementary Figures S2b, S2c, S2e, S2f, S2h, S2i). This outcome may be because SSST implicitly introduces a more realistic model, revealing heterogeneities that the initial simple model had previously absorbed. The subsequent application of NLL-SC results in an expected slight increase in RMS and a significant decrease in both horizontal and vertical error. For most events in the three sequences, the horizontal error is less than 0.5 km and the vertical error is less than 1 km. Sequences distribution and properties Figure 6 a shows the distribution of the 71 sequences located in the SEAWD area in terms of epicentral position and vertical sections along longitude and latitude. Most of these sequences are found in the CS area (63%), while a significantly lower percentage is found in the WS (15%) and ES (21%). In Fig. 6 a the epicentral location of the sequences that occurred in the SEAWD is also compared with surface fault traces from the Database of Individual Seismogenic Sources [DISS, version 3.3.1, (DISS Working Group, 2025 )]. Following Ogata & Katsura ( 2012 ), we classify the seismic sequences on the base of the relative magnitude difference (ΔM) between the mainshock and the largest preceding event: sequences characterized by a dominant mainshock followed by aftershocks were defined as Mainshock–Aftershock (MA) type; sequences including a preparatory phase were classified as foreshock–mainshock–aftershock (FMA) type when ΔM > 0.45; conversely, sequences with ΔM < 0.45, lacking a clearly dominant mainshock, were classified as swarm-type (SW) sequences. We found that 49 sequences are FMA type (65%), 25 are MA type (32%), and 1 is SW type (3%). Table S1 in the Supplemental Information presents information on the time, epicenter location, and magnitude of the mainshocks, as well as the type, number of events, and duration of the identified sequences. Interestingly, the FMA sequences are distributed in both the pre-Alpine and Alpine areas of CS, while the Mainshock-Aftershock sequences are mainly found in the CS Alpine area (Fig. 6 b). Figure 6 b also shows that 60% of MA sequences are located in the latitude range 46–46.25, and their number is comparable to that of FMA sequences which are predominant in the longitude range 10°–12.5° (WS and western CS areas), 13°–13.5° (CS area), and 14.25°–14.5° (ES area). Conversely, MA sequences dominate in the central part of the CS area. Figure 7 shows the sequences for different attributes: duration, magnitude, and depth of the mainshock for the FMA (Figs. 7 a, 7 b, 7 c) and MA (Figs. 7 d, 7 e, 7 f) sequences, respectively. The two types of FMA and MA sequences do not appear to differ significantly in cluster duration (Fig. 7 g) or mainshock depth (Fig. 7 i). Although the three longest sequences are of the FMA type (the February and June 2017 Alpago sequences in the CS area and the 2020 Bovec sequence in ES area; see the Supplementary Table S1 ), 61% of the 28 sequences lasting more than 30 days are FMA, and 39% are MA. Regarding the mainshock depth, the FMA and MA sequences show almost completely overlapping distributions (Fig. 7 i) and lower values in the WS, in the northern part of CS and northwestern part of ES (Fig. 7 c- 7 f). The depth of the mainshock in the sequences increases toward the Adriatic Sea and ES area (Figs. 7 c- 7 f). Figure 7 h shows that, on average, MA sequences have a greater mainshock magnitude than FMA sequences. In particular, the three sequences with the strongest mainshocks (i.e., the 2024 Socchieve sequence in CS, the 2015 and 2020 Bovec sequences in ES; see the Supplementary Table S1 ) are of the MA type. Principal Component Analysis for Sequences To characterize the spatiotemporal evolution of the seismicity in each of the 71 analyzed sequences, we conducted a Principal Component Analysis (PCA) on the spatial coordinates of each sequence converted in a local cartesian space, as previously done in the region (Michelini and Bolt, 1986 ; Rossi and Ebblin, 1990 ; Bressan et al., 2018a ; 2021 ). To perform the analysis, we use the code PyCEFeaX (Iaccarino & Picozzi, 2026 ) that utilizes the LAPACK method for the Singular Value Decomposition (Halko et al., 2011 ). The procedure produces 3 eigenvectors E 1 , E 2 , E 3 with 3 associated eigenvalues λ 1 , λ 2 , λ 3 that provide information on the spatial distribution directions of seismicity. To exclude eventual outlier effects in the calculation, we select only the 90% of events for each sequence by finding the smallest cylinder with radius r perc and height 2*r perc centered on the median location of the sequence that contains most of the sequence seismicity. The volume of the sequence (V seq ) is computed as the volume of the 3D Delaunay convex hull (Delaunay, 1934 ; Iaccarino and Picozzi, 2026 ). We classified the sequences as “Planar,” “Linear,” and “Highly Clustered” based on the ratio of the PCA eigenvalues (as in Michelini and Bolt, 1986 ), and on the V seq volume, calculated from them (Figure S3): $$\:PCA\:classification\left\{\begin{array}{c}\:\:\:\:\:\:\:\:planar\:\:\:\:\:\:\:\:\:\:\::\:\frac{{\lambda\:}_{1}}{{\lambda\:}_{3}}>2.5,\:\:\frac{{\lambda\:}_{2}}{{\lambda\:}_{3}}>1.75,\:\:{V}_{seq}\gg\:0\:\\\:\:\:\:\:\:\:\:\:linear\:\:\:\:\:\:\:\:\:\:\:\:\::\:\frac{{\lambda\:}_{1}}{{\lambda\:}_{3}}>2.5,\:\:\frac{{\lambda\:}_{2}}{{\lambda\:}_{3}}\le\:1.75,\:\:{V}_{seq}\gg\:0\:\:\\\:highly\:clustered\:\:\:\:\:\:\:\::\:\frac{{\lambda\:}_{1}}{{\lambda\:}_{3}}\le\:2.5,\:\:\frac{{\lambda\:}_{2}}{{\lambda\:}_{3}}\le\:1.75\end{array}\right.\:\:\:\:\:\:\left(1\right)\:\:\:$$ We assume that planar sequences are characterized by seismicity distribution within planar geometric structures. For linear sequences, seismicity distributes along a dominant direction defined by the E1 eigenvector. If the major component lies approximately within the horizontal plane and aligns preferentially along the E–W or N–S directions, we classify the sequence as ‘horizontal linear’; otherwise, we classify it as “vertical linear.” Finally, we consider sequences with very small volumes, where no significant seismicity extent is observed in any direction, to be “highly clustered”. Figure 8 shows that 74% of sequences are planar, 21% are highly clustered, and 5% are linear. Of the four linear sequences observed, three are vertical: the Vidor sequence (western part of the CS) in 2015, the Cividale sequence (eastern part of the CS) in 2021, and the Stelvio sequence (northern part of the WS) in 2019. For the last case, the E1 autovector shows a significant component along the south-north direction. The only horizontal linear sequence observed is the Saviore dell'Adamello sequence in 2024 in the WS. Our results show no clear correlation between the sequence type and the seismicity volume geometry. In the case of planar distribution, FMA sequences slightly outnumber MA sequences. For highly clustered sequences, the proportion of FMA sequences increases to 70% and reaches 100% for linear sequences (Figure S4a). The only Swarm sequence observed, which occurred in Stregna (ES) in 2018, appears highly clustered, confirming the presence of repeater events in the ES area (Vičič et al., 2019 ). The highest V seq values were observed for the 2019 Verzegnis sequence (V seq =0.4 km 3 ) and for the 2021 Valdobbiadene sequence (V seq =0.13 km 3 ) in CS. It should be noted that the V seq values of some sequences near the SEAWD border area may be affected by overestimation due to greater uncertainty regarding their location. However, this effect does not appear to be systematic for sequences in this area, as small, highly clustered planar sequences are also observed within it (Fig. 8 ). Figure S4b in the Supplementary Information shows that the logarithm of V seq for planar and linear sequences has, as a first approximation, a linear relationship with the magnitude of the mainshock. Comparing the number of events in the sequence with the magnitude of the mainshock, we observe that while planar and linear sequences show a wide range of variation, highly clustered sequences are characterized by fewer than 30 events and a mainshock magnitude of less than 3.0 (See the supplementary Figure S4c). Discussion The detailed relocation and characterization of seismic sequences in the SEAWD area between 2015 and 2024 provide new insights into the spatiotemporal organization of seismicity in one of the most complex tectonic domains of the Eastern Alps–Western Dinarides region. Compared to previous studies based on standard locations and declustering approaches (e.g., Peresan and Gentili, 2018 ), the combined application of the rescaled generalized distance and the NLL-SC relocation procedures allows a more robust identification of sequence geometry and depth distribution. A first regional screening of the seismic sequence characteristics suggested predominantly burst-like behavior in the Alpine and pre-Alpine domains and swarm-like activity in the Veneto–Friuli plain (CS area) and along the CS and ES border (Peresan and Gentili, 2018 ). High-resolution observations from the temporary Swath-D deployment further identified multiple sequences with both swarm-like and mainshock–aftershock behavior, and revealed short fault structures correlated with zones of high P-wave attenuation (Heit et al., 2017 , 2021 ; Hofman et al., 2023 ; Petersen et al., 2025 ). The spatial distribution of the identified sequences highlights a clear concentration of seismic clustering within the Central SEAWD region, while significantly fewer sequences occur in the western and eastern sectors of SEAWD. This pattern likely reflects the structural complexity and heterogeneous stress distribution within the southeastern Alpine–Dinaric transition zone, which was also highlighted by Picozzi et al. ( 2026 ). Although not all sequences align exactly with mapped DISS sources, several clusters occur close to the trace of known thrust and strike-slip structures. This suggests that part of the seismicity may illuminate secondary fault segments or portions of larger structures that are not yet fully mapped at or do not reach the surface. The Central SEAWD corresponds to the region affected by the 1976 Friuli seismic sequence and is characterized by a dense network of south-verging thrust and transpressional structures (Marchesini et al., 2023; Poli et al., 2024 ) accommodating the convergence between the Adria microplate and the Eurasian plate. The concentration of sequences in the Central SEAWD coinciding with the 1976 M W 6.5 Friuli earthquake area suggests that the present-day microseismicity may still reflect the segmentation and mechanical complexity of the fault system activated during that event. The alignment of many sequences with mapped seismogenic sources from the DISS database suggests that a significant fraction of the observed seismicity occurs along known active fault systems or their associated damage zones. The depth distribution, mostly confined between 5 and 15 km, is consistent with previous estimates of the seismogenic layer thickness in the southeastern Alps (Slejko et al., 1989 ; Viganò et al., 2015 ; Picozzi et al., 2026 ) and indicates that these sequences likely activate structures within the upper crustal portion of the Alpine wedge. Previous studies highlighted the mechanical heterogeneity and segmentation that can characterize active fault systems, as revealed by the spatial distribution and clustering of microseismicity (Picozzi et al., 2019 , 2022 ). In this work, we found a predominance of Foreshock–Mainshock–Aftershock (FMA) and Mainshock–Aftershock (MA) sequences, with a minor contribution of swarm-like activity, which is consistent with the results of Petersen et al. ( 2025 ), who also reported a limited occurrence of pure swarm sequences in the CS region. The spatial variability between FMA and MA sequences may reflect differences in fault maturity and structural complexity. More mature structures within the Alpine domain may favor the occurrence of dominant mainshocks followed by aftershock sequences, whereas more fragmented fault systems in the pre-Alpine domain may promote preparatory seismic activity preceding the largest event. This behaviour differs from what has been observed in some sectors of the northwestern Dinarides (Eastern SEAWD), where swarm-like seismicity is relatively common. For example, seismic swarms recorded along the Predjama Fault (Eastern SEAWD) have been interpreted as the result of transient deformation processes, possibly linked to slow slip occurring on the deeper portion of the fault system (Vičič et al., 2019 ). On the other hand, in this region we observe events that are highly clustered, characterized by a high cross-correlation coefficient, which could be interpreted as swarms using a classification different from that used in this work. Furthermore, differences in sequence classification in the WS and CS areas compared to previous studies (Peresan and Gentili, 2018 ; Petersen et al., 2025 ) may be also related to both the different parameterization of the clustering methods used and the data, which partially coincides or does not coincide with that used in this study. However, the lower proportion of swarm-like sequences observed in the SEAWD catalogue may indicate that seismic clustering in northeastern Italy is primarily controlled by stress changes associated with moderate mainshocks rather than by aseismic processes. This interpretation is consistent with previous studies showing that the seismicity of the southeastern Alps is influenced by a complex stress field and by the interaction of multiple fault systems (e.g., Bressan et al., 2018b , 2021 ). In agreement with Petersen et al. ( 2025 ), we observed a significant percentage of MA sequences in the CS area, where they are more concentrated than in other areas of the SEAWD. However, the higher percentage of FMA sequences identified in this study (65%) suggests that improved relative locations, based on event similarity, enhance the detectability of foreshock activity, which may be partially underestimated by analyzing standard, lower precision catalogues. In particular, the initial regional relocation mitigates uncertainties associated with the velocity model and enables a more reliable selection of candidate sequences. The resulting travel-time residuals indicate that, on average, the observed arrivals are slightly earlier than predicted by the OGS bulletin velocity model, suggesting that the effective propagation velocities sampled by the relocation are somewhat faster, although the same initial velocity model is retained. This behavior is expected when applying SSST corrections, which can compensate for epistemic errors in the velocity model, particularly when a 1D model is used in structurally heterogeneous regions (Lomax et al., 2024 ). The predominance of FMA sequences across both Alpine and pre-Alpine domains suggests that earthquake nucleation in the region frequently involves preparatory phases characterized by small precursor activity, which may reflect the progressive loading of complex fault systems. Conversely, MA sequences appear preferentially concentrated within the Alpine sector of the Central SEAWD, where the fault system is structurally mature and capable of producing more clearly defined mainshock–aftershock patterns. This spatial differentiation may reflect variations in fault maturity, stress regime, and mechanical heterogeneity between the internal Alpine belt and the external thrust-and-fold system toward the foreland. Some uncertainties remain due to the use of a regional 1D velocity model and to the variable station coverage near the borders of the network. Future analyses based on 3D velocity models and expanded station coverage could further improve the resolution of sequence geometry. Comparing Figs. 1 a and 3 a, the effect of relocation with NLL-SSST is more evident in depth than in epicentral location. This may be related to the influence of the initial 1D velocity model, which covers an area of approximately 360 × 280 km², on the ability to model lateral heterogeneities in the medium. We expect that relocating the events with a 3D velocity model for SEAWD in the future will result in even more precise epicentral relocation. Subsequent waveform-similarity-based relocation within each sequence further refines their spatial clustering, allowing a more accurate reconstruction of their spatiotemporal evolution. The lack of a clear spatial dominance of a single sequence type, already noted by Petersen et al. ( 2025 ), is confirmed here. Nevertheless, our results show a tendency for MA sequences to be preferentially located in the Alpine domain of Friuli region (northern part of the CS area), whereas FMA sequences are distributed across both Alpine and pre-Alpine regions in the CS area. This spatial pattern may reflect differences in fault maturity, stress regime, and rheological properties between the internal Alpine belt and the external thrust-and-fold system of Friuli and Veneto (CS area). Interestingly, we found that MA sequences are, on average, characterized by a stronger mainshock than FMA sequences. This suggests that within the SEAWD, foreshock analysis is not significant for forecasting mainshocks with M > 3.5. However, in the same area, it has been observed that analyzing aftershock characteristics can provide a high success rate for forecasting strong aftershocks following an initial strong event (Brondi et al., 2025a ). The observed increase in sequence depth toward the Adriatic foreland and eastern Slovenia is consistent with the regional deepening of the seismogenic layer reported in earlier studies and reflects the transition from the Alpine orogenic wedge to the Adriatic indenter (Zupančič et al., 2024 ). The Principal Component Analysis performed on the spatial distribution of events within each sequence reveals that most clusters exhibit a planar geometry, while a smaller fraction appears highly clustered and only a few sequences display a linear structure. Planar distributions of hypocentres are commonly interpreted as reflecting the activation of fault planes or damage zones surrounding them. The predominance of planar geometries in our dataset therefore suggests that most sequences likely occur along well-defined structural planes within the seismogenic crust. In contrast, highly clustered sequences likely reflect small asperities or limited rupture patches within the fault network. The few linear sequences identified in our analysis show near-vertical trends. Bressan et al. ( 2021 ), using PCA and fractal dimension analyses, also found vertical distributions of seismicity in the CS area; however, these appear to have a much higher incidence than observed in our work. The relationship observed between the logarithm of the estimated sequence volume V seq and the magnitude of the mainshock suggests a first-order scaling between the spatial extent of clustered seismicity and the size of the largest event. Although the volume derived from the PCA analysis represents only a first-order measurement for the true rupture sequence volume, the observed trend is consistent with empirical scaling relationships between rupture dimensions and earthquake magnitude documented in many tectonic environments. In this context, larger mainshocks are expected to activate wider portions of the surrounding fault network, generating broader aftershock clouds and larger seismic clusters. The variability observed within this relationship may reflect differences in fault-zone structure, mechanical heterogeneity, or stress conditions across the SEAWD region. Furthermore, this scaling could be partly explained by the diffusion of fluids observed in the SEAWD area, related to the occurrence of a moderate-intensity mainshock that creates transient deformation and modulates background seismicity (Rossi et al., 2021 ). Fluid diffusion processes may, in fact, contribute to the observed clustering behaviour. Transient increases in pore pressure can locally reduce effective normal stress on faults, promoting the activation of small clusters of earthquakes even in the absence of large stress perturbations. The absence of a clear relationship between sequence type and the spatial geometry of seismicity suggests that the temporal evolution of earthquake clusters may be largely independent from the structural geometry of the activated fault zone. Numerical and statistical models of earthquake triggering have shown that foreshock, aftershock, and swarm sequences can emerge from the same cascading stress-transfer processes within complex fault networks (e.g., Helmstetter and Sornette, 2002 ; Saichev and Sornette, 2004 ; Im and Avouac, 2023 ). Overall, this study confirms and refines previous findings while demonstrating that high-precision relocation and geometrical analysis significantly improve our understanding of seismic sequence behavior. Even though most sequences involve small-to-moderate earthquakes, their spatial organization provides valuable constraints on the geometry of active fault structures that may host larger future earthquakes. High-precision sequence locations provide indirect constraints on the geometry of active structures that may not be fully resolved at the surface. Such information can contribute to improving the characterization of seismogenic sources and therefore to refining regional seismic hazard models. The ability to rapidly identify and characterize seismic sequences using clustering and relocation techniques may provide valuable information for operational seismic monitoring, particularly in regions such as northeastern Italy where moderate earthquakes can generate damaging ground shaking. Data This study analyzes approximately 17,000 seismic events recorded between 2015 and 2024 and included in the seismic bulletins published by the National Institute of Oceanography and Experimental Geophysics – OGS (Fig. 1 a). The progressive evolution of the OGS seismic network, particularly in terms of station density and operational performance, has significantly influenced the magnitude of completeness (Mc) of the catalogue over time (Gentili et al., 2011 ). Over the last 15 years, the coverage provided by the Struttura di Monitoraggio dell’Italia Nord Orientale (SMINO) network (Bragato et al., 2021 ) has allowed the achievement of a Mc below 1 in the Friuli Venezia Giulia region (Brondi et al., 2025a ). Since its establishment in 1977, the SMINO network has supported the compilation of annual seismic bulletins by OGS, which are publicly available online. The dataset used in this study includes events published in the bulletins from 2015 to 2023 (Snidarcig et al., 2016 , 2017 , 2018 , 2019 , 2020 , 2021 , 2022 ; Brondi et al., 2024 , 2025b ; https://www.crs.ogs.it/bollettino_new/ ), as well as events recorded in 2024 and reviewed by the Review Group of the Seismological Research Center (CRS, https://terremoti.ogs.it/ ) of the National Institute of Oceanography and Experimental Geophysics – OGS. These bulletins include manually reviewed phase picks from stations operated by OGS and collaborating institutions in Italy and Europe. In this study, we used data from approximately 150 seismic stations (Fig. 1 a). For Italy, the dataset includes stations belonging to networks managed by the CRS [international code OX, (National Institute of Oceanography and Experimental Geophysics – OGS, 2016)]; the North-East Italy Broadband Network [international code NI, (National Institute of Oceanography and Experimental Geophysics, University of Trieste, 2002)]; the Italian National Seismic Network [international code IV, (National Institute of Geophysics and Volcanology, 2005)]; the Collalto Seismic Network [international code EV, (Priolo et al., 2015 )]; the Friuli Venezia Giulia Accelerometric Network [international code RF, (University of Trieste, 1993 )]; the Trentino Seismic Network [international code ST, (Geological Survey – Provincia Autonoma di Trento, 1981)]; the Province Südtirol Seismic Network [international code SI]; the Mediterranean Very Broadband Seismographic Network [international code MN, (MedNet Project Partner Institutions, 1990 )]; and the Interreg IV HAREIA project [hereinafter HA, (University of Trieste, 1993 )] (Fig. 1 b). Additional data were obtained from the Austrian Seismic Network [international code OE, GeoSphere, ZAMG – Central Institute for Meteorology and Geodynamics, 1987], the Seismic Network of the Republic of Slovenia [international code SL, Agency of the Republic of Slovenia for the Environment (ARSO), 1990], the Croatian Seismograph Network [international code CR, University of Zagreb, 2001 ], and the Swiss National Seismic Network [Swiss Seismological Service (SED) at ETH Zurich, 1983] (Fig. 1 b). These collaborations are essential for obtaining accurate earthquake locations, particularly near the edges of the network, where the availability of additional stations helps reduce azimuthal gaps to values below 180° and ensures a typical depth uncertainty of about 2 km (Bragato et al., 2021 ). Hypocentral locations are computed using the HYPO71 algorithm (Lee and Lahr, 1975 ), adopting a regional velocity model and parameterization specifically developed for the study area (Slejko et al., 1989 ). For each seismic event, the bulletin reports the duration magnitude (M D ), calculated using the empirical relationships proposed by Rebez and Renner ( 1991 ) and calibrated to be consistent with the local magnitude (M L ) derived from the Wood–Anderson seismograph installed in Trieste (Bragato and Tento, 2005 ). Methods We followed a multi-step procedure to identify seismic sequences and accurately relocate them using the NLL-SC technique (Lomax, 2020 ; Lomax and Savvaidis, 2022 ) (Fig. 2 ). This algorithm has been adapted to enhance relative location precision by incorporating spatially variable, source-specific station travel-time corrections (SSST) and a multi-event relocation strategy based on waveform coherence (NLL-SC; Lomax and Savvaidis, 2022 ). This method builds upon the NonLinLoc (NLL) probabilistic global-search algorithm (Lomax et al., 2000 , 2014 ), which estimates hypocenter locations by constructing a three-dimensional probability density function (PDF). This approach is particularly robust to the presence of outliers in the input data (Lomax and Savvaidis, 2022 ) and was used to obtain high-resolution location data for volcanic (De Landro et al., 2025 ) and tectonic (Sollai et al., 2025 ) structures. In our procedure, using data from the OGS bulletin and monitoring group catalogue, we relocated the entire catalogue with the initial NLL-SSST procedure. Then, the relocated catalogue has been used to identify seismic sequences by applying the nearest-neighbour method (Zaliapin and Ben-Zion, 2013 ). After identifying the sequences, we applied a second relocation analysis to each of them based on waveform coherence with NLL-coherence (Fig. 2 ). Finally, we classified and characterized the sequences as discussed in the Results section. Since HYPO71 and NLL use a perturbative and probabilistic approach for location, respectively, we verified with a preliminary test that, to a first approximation, the two methods reproduce the same results using the 1D velocity model with V P /V S =1.78 used for the OGS bulletin (Slejko et al., 1989 ). In addition, we conducted a localization test with NLL, comparing the previous velocity model with two more complex 1D models for SEAWD: the 1D velocity model by Gentile et al. ( 2000 ) and a 1D velocity profile derived from the 3D model by Magrin and Rossi ( 2020 ). Comparing the residual distributions of P- and S-wave arrivals at stations, the model by Slejko et al. ( 1989 ) showed the best performance. SSST Analysis We initially relocated the 2015–2024 OGS seismic catalogue iteratively while generating smoothly varying SSST corrections throughout a three-dimensional volume with dimensions of 361 km in longitude, 281 km in latitude, 45 km in depth, and using a constant spatial step of 0.5 km. These corrections, dependent on the position of the events relocated in the previous iteration, are computed for P- and S-wave seismic phases of 83 seismic stations. In this NLL-SSST analysis, we considered 6 iterations and, at each iteration, only residuals from P- and S-phase picks and events that meet a fixed quality thresholdare utilized for the updates (Lomax and Savvaidis, 2022 ). The final refined NLL-SSST locations have been obtained by the application of the Gaussian SSST smoothing kernels with progressively smaller widths which correspond to the smoothing distances of 32, 16, 8, 4, 2 and 1 km. The quality criteria for location and station-phase to be used for calculating are: 68% error-ellipsoid principle-axis half-width ≤ 5.0 km, root mean square of residuals (RMS) ≤ 0.15 s, number of readings ≥ 10, azimuth gap ≤ 120°, P residual ≤ 0.5 s, S residual ≤ 1.0 s. Clustering by Nearest-neighbour Distance, η The catalogue obtained from the SSST analysis is used to identify the main sequences that occurred between 2015 and 2024 in our study area using the clustering method of Zaliapin et al. ( 2008 ). This approach computes the generalized distance between pairs of earthquakes, η, by an analysis of the time-space distances between pairs of earthquakes, which consists in estimating the distances in time (i.e., rescaled time, T η ) and space (i.e., rescaled distance, R η ) between an event i and its parent j, where both distances are normalized by the magnitude of the parent event. Rescaled time and distance are computed as follows: $$\:{T}_{ij}={t}_{ij}{10}^{-b\frac{{m}_{i}}{2}}$$ 2 $$\:{R}_{ij}={\left({r}_{ij}\right)}^{{D}_{c}}{10}^{-b\frac{{m}_{i}}{2}}$$ 3 where, m is the magnitude, b is the parameter of the Gutenberg–Richter law, which plays the role of exponential weight of the earlier event i by its magnitude, and D c is the fractal dimension. Finally, η is defined as: $$\:log{\:(\eta\:}_{ij})=\:log({R}_{ij})+\:log({T}_{ij})$$ 4 Following Zaliapin and Ben-Zion ( 2020 ), we set the b equal to 0 to mitigate the presence of artifacts due to the overlap of earthquakes’ domain of attraction with background seismicity, and we use D c equal to 1.5. To create a family of events (i.e., a cluster) we required a minimum number of events equal to 10. Waveform based Analysis In this stage, the relative precision of the NLL-SSST event locations is further refined by integrating waveform similarity across events for each sequence. This second relocation step, NLL-coherence, relies on the principle that, when seismograms from different events show strong similarity at a given station—i.e., high coherence across a broad frequency band—the spatial separation between such "multiplet" events must be small compared to the seismic wavelength at the highest coherent frequency (Lomax and Savvaidis, 2022 ). These events are likely to represent slip on the same, localized fault patch (Geller & Mueller, 1980 ; Poupinet et al., 1982 , 1984 ; Nadeau et al., 1994 ). For our NLL-coherence relocations, we calculated the coherence using channel data from the horizontal and vertical components of velocimeter sensors located around the immediate vicinity of the sequence. In particular, in order to ensure a good azimuthal coverage of the stations around the seismicity cluster of each sequence, we decided to use a minimum of 4 stations up to a maximum of 8 stations located around the midpoint of the epicentral distribution of the sequence. In calculating coherence, according to Lomax and Savvaidis ( 2022 ) we applied a 2–10 Hz bandpass filter in a signal window starting 4 seconds before the arrival of the predicted P-wave and ending 4 seconds after the theoretical arrival of the S-wave. We perform cross-correlation on waveform windows that slide within the range from − 2.0 to 2.0 s. Stacking weights, ranging from 0 to 1, are assigned based on the NLL-SC procedure of Lomax and Savvaidis ( 2022 ) and applied to coherency values between Cmin = 0.5 and 1.0. This method is implemented for the D = 1 km NLL-SSST relocations of the 75 sequences of SEAWD, considering all event pairs whose hypocentral distance does not exceed 5.0 km. Declarations Data Availability The seismic catalogue analyzed in this study is derived from the seismic bulletins of the National Institute of Oceanography and Experimental Geophysics – OGS, produced by the Seismological Research Center (CRS) within the Struttura di Monitoraggio dell’Italia Nord Orientale (SMINO). The bulletins for the period 2015–2023 are publicly available athttps://www.crs.inogs.it/bollettino_new/. The dataset for 2024, reviewed by the CRS, is publicly available at https://terremoti.ogs.it/ . Code availability Earthquake relocation analyses were carried out using the source-specific station travel-time corrections Coherence algorithm (NLL-SSST) implemented in the NonLinLoc software developed by Anthony Lomax. The software is publicly available athttps://github.com/ut-beg-texnet/NonLinLoc. PCA analysis and the computation of V seq have been done using the Python code PyCEFeaX (Iaccarino & Picozzi, 2026). The software is publicly available at https://github.com/AGIaccarino/PyCEFeaX. Acknowledgements The authors gratefully acknowledge Giuliana Rossi for valuable discussions and Enrico Magrin for providing the computational resources used in this work. Funding This research was supported by the PRIN 2022 project ‘2022ZHXWC9’—Intercepting the preparatory phase of large earthquakes from seismic information and geodetic displacement (PREPARED). This study was partially funded by the Joint Research Unit (JRU) of the European Plate Observing System (EPOS) Italia (https://www.epos-italia.it/, last accessed September 2024). Author Contributions Statement PB and MP conceived and designed the study. GDL contributed to its conception. PB acquired the data and, together with MP, performed the data analysis. PB carried out the precise relocation analysis, MP conducted the cluster identification analysis, and AGI performed the PCA. All authors contributed to the interpretation of the results and to manuscript writing. Competing interests The authors declare no competing interests. References Adinolfi, G. M. et al. Comprehensive study of micro-seismicity by using an automatic monitoring platform. Front. Earth Sci. 11 , 1073684. 10.3389/feart.2023.1073684 (2023). Atanackov, J. et al. Database of active faults in Slovenia: compiling a new active fault database at the junction between the Alps, the Dinarides and the Pannonian Basin tectonic domains. Front. Earth Sci. 9 , 604388 (2021). Bragato, P. L. & Tento, A. Local magnitude in northeastern Italy. Bull. Seismol. Soc. Am. 95 (2), 579–591 (2005). Bragato, P. L. et al. The OGS–Northeastern Italy Seismic and Deformation Network: Current Status and Outlook. Seismol. Res. Lett. 92 (3), 1704–1716. https://doi.org/10.1785/0220200372 (2021). Bressan, G., Gentile, G. F., Perniola, B. & Urban, S. The 1998 and 2004 Bovec-Krn (Slovenia) seismic sequences:Aftershock pattern, focal mechanisms and static stress changes. Geophys. J. Int. 179 , 231–253 (2009). Bressan, G., Barnaba, C., Magrin, A. & Rossi, G. A study on off-fault aftershock pattern N-Adria microplate. J. Seismolog. 22 , 863–888. https://doi.org/10.1007/s10950-018-9737-x (2018a). Bressan, G., Barnaba, C., Bragato, P., Ponton, M. & Restivo, A. Revised seismotectonic model of NE Italy and W Slovenia based on focal mechanism inversion. J. Seismolog. 22 (6), 1563–1578 (2018b). Bressan, G., Barnaba, C., Peresan, A. & Rossi, G. Anatomy of seismicity clustering from parametric space-time analysis. Phys. Earth Planet. Inter. 320 , 106787 (2021). Brondi, P., Snidarcig, A., Bernardi, P., Bragato, P. L. & Di Bartolomeo, P. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2022 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: 10.13120/w1vp-b578 (2024). Brondi, P., Gentili, S. & Di Giovambattista, R. Forecasting strong subsequent events in the Italian territory: a national and regional application for NESTOREv1.0. Nat. Hazards . 121 , 3499–3531. https://doi.org/10.1007/s11069-024-06913-6 (2025a). Brondi, P. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO) (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, 2025b). 10.13120/m4pk-nd81 Anno 2023 [Data set]. Camanni, G. et al. Remobilization of inverted normal faults drives active extension in the axial zone of the southern Apennine mountain belt (Italy). Journal of the Geological Society , 182 (2), pp.jgs2024-184. (2025). Cesca, S. et al. Massive earthquake swarm driven by magmatic intrusion at the Bransfield Strait, Antarctica. Commun. Earth Environ. 3 , 89. https://doi.org/10.1038/s43247-022-00418-5 (2022). De Landro, G. et al. 3D structure and dynamics of Campi Flegrei enhance multi-hazard assessment. Nat. Commun. 16 (1), 1–12 (2025). Delaunay, B. Sur la sphere vide (On the empty sphere). Bulletin de l’Académie des Sciences de l’URSS, Classe des Sciences Mathématiques et Naturelles, 6, 793–800. (1934). DISS Working Group. Database of Individual Seismogenic Sources (DISS), version 3.3.1: A compilation of potential sources for earthquakes larger than M 5.5 in Italy and surrounding areas. Istituto Nazionale di Geofisica e Vulcanologia (INGV). (2025)., March 28 https://doi.org/10.13127/diss3.3.1 Fischer, T. et al. Intra-continental earthquake swarms in west-Bohemia and Vogtland: A review. Tectonophysics 611 , 1–27. https://doi.org/10.1016/j.tecto.2013.11.001 (2014). Fischer, T. & Hainzl, S. Effective Stress Drop of Earthquake Clusters. Bull. Seismol. Soc. Am. 107 , 2247–2257 (2017). Geller, R. J. & Mueller, C. S. Four similar earthquakes in central California. Geophys. Res. Lett. 7 (10), 821–824 (1980). Gentile, G. F., Bressan, G., Burlini, L. & De Franco, R. Three-dimensional Vp and Vp/Vs models of the upper crust in the Friuli area (northeastern Italy). Geophys. J. Int. 141 (2), 457–478 (2000). Gentili, S., Sugan, M., Peruzza, L. & Schorlemmer, D. Probabilistic completeness assessment of the past 30 years of seismic monitoring in northeastern Italy. Phys. Earth Planet. Inter. 186 (1–2), 81–96 (2011). Gentili, S. et al. Seismic clusters and fluids diffusion: a lesson from the 2018 Molise (Southern Italy) earthquake sequence Earth Planets Space 76, 157 (2024). (2024). https://doi.org/10.1186/s40623-024-02096-3 Geological Survey-Provincia Autonoma di Trento. Trentino Seismic Network [Data set]. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/ST (1981). Halko, N., Martinsson, P. G. & Tropp, J. A. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions. Https://Doi.Org/10.1137/090771806, 53 (2), 217–288. (2011). https://doi.org/10.1137/090771806 Heit, B. et al. The Swath-D seismic network in Italy and Austria. GFZ Data Serv. https://doi.org/10.14470/mf7562601148 (2017). Heit, B. et al. The SWATH-D seismological network in the eastern Alps. Seismol. Res. Lett. 92 (3), 1592–1609. https://doi.org/10.1785/0220200377 (2021). Helmstetter, A. & Sornette, D. Subcritical and supercritical regimes in epidemic models of earthquake aftershocks. J. Geophys. Research: Solid Earth . 107 (B10), ESE–10 (2002). Hofman, L. J., Kummerow, J., Cesca, S., the AlpArray-Swath-D Working Group. & & A new seismicity catalogue of the eastern Alps using the temporary Swath-D network. Solid Earth . 14 (10), 1053–1066. https://doi.org/10.5194/se-14-1053-2023 (2023). Iaccarino, A. G. & Picozzi, M. PyCEFeaX. Zenodo. https://doi.org/10.5281/zenodo.18548970 (2026). Im, K. & Avouac, J. P. Cascading foreshocks, aftershocks and earthquake swarms in a discrete fault network. Geophys. J. Int. 235 (1), 831–852 (2023). Istituto Nazionale di Geofisica e Vulcanologia (INGV). Rete Sismica Nazionale (RSN) [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/sd/x0fxnh7qfy (2005). Istituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS. North-East Italy Seismic Network [Data set]. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/OX (2016). Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, University of Trieste. North-East Italy Broadband Network [Data set]. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/NI (2002). Jia, Z. et al. The complex dynamics of the 2023 Kahramanmaraş, Turkey, Mw 7.8–7.7 earthquake doublet. Science 381 (6661), 985–990. https://doi.org/10.1126/science.adi0685 (2023). King, G. C. P., Stein, R. S. & Lin, J. Static stress changes and the triggering of earthquakes. (1994). Bulletin of the Seismological Society of America. 84(3), 935–953. https://doi.org/10.1785/BSSA0840030935 Lee, W. H. & Lahr, J. C. HYPO71 (revised; a computer program for determining hypocenter, magnitude, and first motion pattern of local earthquakes (No. 75–311). US Dept. of the Interior, Geological Survey, National Center for Earthquake Research. (1975). Liu, Y. K., Ross, Z. E., Cochran, E. S. & Lapusta, N. A unified perspective of seismicity and fault coupling along the San Andreas fault. Sci. Adv. 8 (8), eabk1167 (2022). Lomax, A., Virieux, J., Volant, P. & Berge-Thierry, C. Probabilistic earthquake location in 3D and layered models: Introduction of a Metropolis-Gibbs method and comparison with linear locations. In Advances in seismic event location (101–134). Dordrecht: Springer Netherlands. (2000). Lomax, A., Michelini, A. & Curtis, A. Earthquake location, direct, global-search methods. In R. A. Meyers (Ed.), Encyclopedia of complexity and systems science (pp. 1–33). Springer New York. (2014). https://doi.org/10.1007/978-3-642-27737-5_150-2 Lomax, A. The 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake: relocated seismicity shows rupture of a complete shear-crack system. EarthArXiv. (2020). https://doi.org/10.31223/X5X015 Lomax, A. & Savvaidis, A. High-precision earthquake location using source‐specific station terms and inter‐event waveform similarity. Journal of Geophysical Research: Solid Earth , 127 (1), e2021JB023190. (2022). Lomax, A., Tuvè, T., Giampiccolo, E. & Cocina, O. A new view of seismicity under Mt. Etna volcano, Italy, 2014–2023 from multi-scale high-precision earthquake relocations. Ann. Geophys. 67 (4), S437–S437 (2024). Magrin, A. & Rossi, G. Deriving a new crustal model of Northern Adria: the Northern Adria Crust (NAC) model. Front. Earth Sci. 8 , 89 (2020). Marchesini, A. et al. e Gruppo di lavoro Faglie Attive FVG, 2023. Linee guida per l’utilizzo della banca dati georiferita delle faglie attive della Regione Friuli Venezia Giulia. Servizio Geologico - Regione Autonoma Friuli Venezia Giulia, 64 pp. MedNet Project Partner Institutions. Mediterranean Very Broadband Seismographic Network (MedNet) [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/SD/FBBBTDTD6Q (1990). Michele, M., Chiaraluce, L., Di Stefano, R. & Waldhauser, F. Fine-scale structure of the 2016–2017 Central Italy seismic sequence from data recorded at the Italian National Network. Journal of Geophysical Research: Solid Earth, 125(4), e2019JB018440. (2020). Michelini, A. & Bolt, B. Application of the principal parameters method to the Coalinga, California, aftershock sequence. Bull. Seis Soc. Am. 76 , 409–420 (1986). Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y. & Beroza, G. C. Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat. Commun. 11 (1), 3952 (2020). Muzellec, T., De Landro, G., Camanni, G., Adinolfi, G. M. & Zollo, A. The complex 4D multi-segmented rupture of the 2014 Mw 6.2 Northern Nagano Earthquake revealed by high-precision aftershock locations. Tectonophysics 898 , 230641 (2025). Nadeau, R., Antolik, M., Johnson, P. A., Foxall, W. & McEvilly, T. V. Seismological studies at Parkfield III: Microearthquake clusters in the study of fault-zone dynamics. Bull. Seismol. Soc. Am. 84 (2), 247–263 (1994). Nandan, S., Ouillon, G., Woessner, J., Sornette, D. & Wiemer, S. Systematic assessment of the static stress-triggering hypothesis using inter-earthquake time statistics. J. Geophys. Research: Solid Earth . 122 , 6271–6291 (2017). Ogata, Y. & Katsura, K. Prospective foreshock forecast experiment during the last 17 years, Geophysical Journal International , Volume 191, Issue 3, December 2012, Pages 1237–1244, (2012). https://doi.org/10.1111/j.1365-246X.2012.05645.x Palo, M., Picozzi, M., De Landro, G. & Zollo, A. Microseismicity clustering and mechanic properties reveal fault segmentation in southern Italy. Tectonophysics , 856 , p.229849. (2023). Peresan, A. & Gentili, S. Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach. Phys. Earth Planet. Inter. 274 , 87–104 (2018). Petersen, G. M., Hofman, L. J., Kummerow, J. & Cesca, S. Microseismicity in the large-N Swath‐D network: Revealing seismic sequences and active faults in the eastern Alps. J. Geophys. Research: Solid Earth . 130 , e2024JB030516. https://doi.org/10.1029/2024JB030516 (2025). Picozzi, M. et al. A rapid response magnitude scale for timely assessment of the high frequency seismic radiation. Sci. Rep. 8 , 8562. https://doi.org/10.1038/s41598-018-26938-9 (2018). Picozzi, M., Bindi, D., Zollo, A., Festa, G. & Spallarossa, D. Detecting long-lasting transients of earthquake activity on a fault system by monitoring apparent stress, ground motion and clustering. Sci. Rep. 9 (1), 16268. https://doi.org/10.1038/s41598-019-52756-8 (2019). Picozzi, M., Spallarossa, D., Bindi, D., Iaccarino, A. G. & Rivalta, E. Detection of spatial and temporal stress changes during the 2016 central Italy seismic sequence by monitoring the evolution of the energy index. J. Geophys. Research: Solid Earth . 127 https://doi.org/10.1029/2022JB025100 (2022). e2022JB025100. Picozzi, M. et al. Event-specific ground motion anomalies highlight the preparatory phase of earthquakes during the 2016–2017 Italian seismicity. Commun. Earth Environ. 5 , 289. https://doi.org/10.1038/s43247-024-01455-y (2024). Picozzi, M. et al. Seismic energy from small earthquakes maps fault segmentation in the Southeastern Alps. Sci. Rep. 16 , 5731. https://doi.org/10.1038/s41598-026-35618-y (2026). Poggiali, G., Chiaraluce, L., Ross, Z. E., Zhu, W. & Marone, C. Fault Geometry and Source Mechanics of the Altotiberina Fault System from a High-Resolution Machine-Learning Earthquake Catalog. Bull. Seismol. Soc. Am. 115 , 2181–2201. 10.1785/0120250072 (2025). Poli, M. E., Patricelli, G., Monegato, G. & Zanferrari, A. Structural inheritances, fault segmentation and seismogenic potential at the front of the eastern Southern Alps (central Carnic Prealps, NE Italy). Tectonophysics 883 , 230390 (2024). Poupinet, G., Glangeaud, F. & Cote, P. P-Time delay measurement of a doublet of microearthquakes. In ICASSP'82. IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 7, pp. 1516–1519). IEEE. (1982), May. Poupinet, G., Ellsworth, W. L. & Fréchet, J. Monitoring velocity variations in the crust using earthquake doublets: An application to the Calaveras Fault, California. J. Geophys. Research: Solid Earth . 89 (B7), 5719–5731 (1984). Priolo, E. et al. Seismic Monitoring of an Underground Natural Gas Storage Facility: The Collalto Seismic Network. Seismol. Res. Lett. 86 (1), 109–123. https://doi.org/10.1785/0220140087 (2015). Rebez, A. & Renner, G. Duration magnitude for the northeastern Italy seismometric network. Bollettino di Geofis. teorica ed. Appl. 33 (130 – 31), 177–186 (1991). Riga, G. & Balocchi, P. Seismic sequence structure and earthquakes triggering patterns. Open. J. Earthq. Res. 5 (1), 20–34 (2015). Rossi, G. & Ebblin, C. Space (3-D) and space-time (4-D) analysis of aftershock sequences: the Friuli (NE Italy) case. Boll Geof Teor Appl. 22 , 37–49 (1990). Rossi, G., Pastorutti, A., Nagy, I., Braitenberg, C. & Parolai, S. Recurrence of Fault Valve Behavior in a Continental Collision Area: Evidence From Tilt/Strain Measurements in Northern Adria. Front. Earth Sci. 9 , 641416. 10.3389/feart.2021.641416 (2021). Saichev, A. & Sornette, D. Anomalous power law distribution of total lifetimes of branching processes: Application to earthquake aftershock sequences. Phys. Rev. E—Statistical Nonlinear Soft Matter Phys. 70 (4), 046123 (2004). Sandron, D., Rebez, A., Tamaro, A. & Slejko, D. Recent earthquakes (2000–2021) in and around the Friuli Venezia Giulia region (NE Italy) and quality improvements of the OGS network monitoring capabilities. Bull. Geoph Ocean. 64 , 237–258. 10.4430/bgo00421 (2023). Saraò, A., Sugan, M., Bressan, G., Renner, G. & Restivo, A. A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019. Earth Syst. Sci. Data . 13 (5), 2245–2258 (2021). di Scotto, F. et al. Delineation and fine-scale structure of fault zones activated during the 2014–2024 unrest at the Campi Flegrei caldera (Southern Italy) from high‐ precision earthquake locations. Geophys. Res. Lett. 51 https://doi.org/10.1029/2023GL107680 (2024). e2023GL107680. Shelly, D. R., Ellsworth, W. L. & Hill, D. P. Fluid-faulting evolution in high definition: Connecting fault structure and frequency-magnitude variations during the 2014 Long Valley Caldera, California, earthquake swarm. J. Geophys. Research: Solid Earth . 121 (3), 1776–1795. https://doi.org/10.1002/2015JB012719 (2016). Shelly, D. R., Skoumal, R. J. & Hardebeck, J. L. Fracture-mesh faulting in the swarm-like 2020 Maacama sequence revealed by high-precision earthquake detection, location, and focal mechanisms. Geophys. Res. Lett. 50 , e2022GL101233. https://doi.org/10.1029/2022GL101233 (2023). Slejko, D. et al. Seismotectonics of the Eastern Southern Alps: a review. Bollettino di Geofis. Teorica e Appl. 31 (122), 109–136 (1989). Slovenian Environment Agency. Seismic Network of the Republic of Slovenia [Data set]. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/SL (1990). Snidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2015 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: 10.6092/58ff169a-2f02-46ae-908a-bdfcacea069c (2016). Snidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2016 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. (2017). 10.6092/a608d853-755e-4177-aada-992857ccb44e Snidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2017 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: 10.6092/3ff3c323-d7a4-4183-bea0-1a53814ac8b9 (2018). Snidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2018 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: 10.6092/c53f37ce-bcf3-453c-a2cf-1894d48cfbbb (2019). Snidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2019 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: 10.6092/58ff169a-2f02-46ae-908a-bdfcacea069c (2020). Snidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2020 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: 10.13120/108b8d94-361a-45f3-8195-fc4e8f73d264 (2021). Snidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2021 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: 10.13120/8b252b09-314f-456f-812a-b05268ecd001 (2022). Sollai, A. et al. April. High-Precision Earthquake Locations Reveal Detailed Fault Geometries in the western Corinth Rift. In EGU General Assembly Conference Abstracts (pp. EGU25-21500). (2025). Stein, R. S. The role of stress transfer in earthquake occurrence. Nature 402 , 605–609. https://doi.org/10.1038/45144 (1999). Swiss Seismological Service (SED), At, E. T. H. & Zurich National Seismic Networks of Switzerland. ETH Zürich. (1983). https://doi.org/10.12686/sed/networks/ch Tan, Y. J. et al. Machine-learning‐based high‐resolution earthquake catalog reveals how complex fault structures were activated during the 2016–2017 central Italy sequence. Seismic Record . 1 (1), 11–19 (2021). University of Trieste. Friuli Venezia Giulia Accelerometric Network [Data set]. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/RF (1993). University of Zagreb. Croatian Seismograph Network [Data set]. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/CR (2001). Valoroso, L., Chiaraluce, L., Di Stefano, R. & Monachesi, G. Mixed-mode slip behavior of the Altotiberina low‐angle normal fault system (Northern Apennines, Italy) through high‐resolution earthquake locations and repeating events. J. Geophys. Research: Solid Earth . 122 (12), 10–220 (2017). Vičič, B., Aoudia, A., Javed, F., Foroutan, M. & Costa, G. Geometry and mechanics of the active fault system in western Slovenia. Geophys. J. Int. 217 (3), 1755–1766 (2019). Viganò, A. et al. Earthquake relocations, crustal rheology, and active deformation in the central–eastern Alps (N Italy). Tectonophysics 661 , 81–98. https://doi.org/10.1016/j.tecto.2015.08.017 (2015). Villegas-Lanza, J. C. et al. A mixed seismic–aseismic stress release episode in the andean subduction zone. Nat. Geosci. 9 (2), 150–154. https://doi.org/10.1038/ngeo2620 (2016). Vuan, A. et al. Intermittent slip along the Alto Tiberina low-angle normal fault in central Italy. Geophys. Res. Lett. 47 , e2020GL089039. https://doi.org/10.1029/2020GL089039 (2020). Waldhauser, F. & Ellsworth, W. L. A Double-Difference Earthquake Location Algorithm: Method and Application to the Northern Hayward Fault, California. Seismological Soc. Am. 90 , 1353–1368. https://doi.org/10.1785/0120000006 (2000). Zaliapin, I., Gabrielov, A., Keilis-Borok, V. & Wong, H. Clustering analysis of seismicity and aftershock identification. Phys. Rev. Lett. 101 (1), 018501 (2008). Zaliapin, I. & Ben-Zion, Y. Earthquake clusters in southern California I: identification and stability. J. Geophys. Res. Solid Earth . 118 , 2847–2864. https://doi.org/10.1002/jgrb.50179 (2013). ZAMG - Zentralanstalt für Meterologie und Geodynamik. Austrian Seismic Network [Data set]. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/OE (1987). Zaliapin, I. & Ben-Zion, Y. Earthquake declustering using the nearest‐neighbor approach in space‐time‐magnitude domain. Journal Geophys. Research: Solid Earth , 125 (4), (2020). e2018JB017120. Zupančič, P. et al. Seismogenic depth and seismic coupling estimation in the transition zone between Alps, Dinarides and Pannonian Basin for the new Slovenian seismic hazard model, Nat. Hazards Earth Syst. Sci., 24, 651–672, (2024). https://doi.org/10.5194/nhess-24-651-2024 , 2024. Additional Declarations No competing interests reported. Supplementary Files BrondietalSEAWDsequencesSupplementaryInfo.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 25 Mar, 2026 Editor invited by journal 25 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 19 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9169863","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":612470337,"identity":"ff06fc7c-68ba-4882-bea7-6ac7c1cdb1ea","order_by":0,"name":"Piero Brondi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYDACCRDBJiEH5iSQosUYooUoPRAtDIkNRFtjLt378HFBmUV6P//yBwwPfxChxXLOcWPjGeckcmfOeGNAnMMMbqSxSfO2SeRuuHGGSL/AtKQb3Dj+gDQtCQbnG4h0mOWcY8zGPOckDGfO4DE4kJBGhBZz6TbGxzxldfL8/McfPvxhQ4zD4CyJBIYDRGhA1sJPnIZRMApGwSgYgQAAje0yNf1nOGAAAAAASUVORK5CYII=","orcid":"","institution":"National Institute of Oceanography and Applied Geophysics – OGS","correspondingAuthor":true,"prefix":"","firstName":"Piero","middleName":"","lastName":"Brondi","suffix":""},{"id":612470338,"identity":"475cb8af-5cd1-490c-931b-e340e135b4bd","order_by":1,"name":"Matteo Picozzi","email":"","orcid":"","institution":"National Institute of Oceanography and Applied Geophysics – OGS","correspondingAuthor":false,"prefix":"","firstName":"Matteo","middleName":"","lastName":"Picozzi","suffix":""},{"id":612470339,"identity":"d4074529-bfb0-4f41-ab7d-5644a37b0c89","order_by":2,"name":"Grazia De Landro","email":"","orcid":"","institution":"University of Naples “Federico II”","correspondingAuthor":false,"prefix":"","firstName":"Grazia","middleName":"","lastName":"De Landro","suffix":""},{"id":612470340,"identity":"2e7e6a9d-4b62-46b6-abae-663bd02dac10","order_by":3,"name":"Antonio Giovanni Iaccarino","email":"","orcid":"","institution":"University of Naples “Federico II”","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"Giovanni","lastName":"Iaccarino","suffix":""},{"id":612470341,"identity":"681a05d3-d9a2-41f9-9208-2b358e97ae2b","order_by":4,"name":"Anthony Lomax","email":"","orcid":"","institution":"ALomax Scientific","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Lomax","suffix":""},{"id":612470342,"identity":"aba1436d-f4ad-473f-88f9-f52096535e5f","order_by":5,"name":"Andrea Magrin","email":"","orcid":"","institution":"National Institute of Oceanography and Applied Geophysics – OGS","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Magrin","suffix":""},{"id":612470343,"identity":"bd17d7ca-bb76-4568-a87c-39c7454d321c","order_by":6,"name":"Luigi Zampa","email":"","orcid":"","institution":"National Institute of Oceanography and Applied Geophysics – OGS","correspondingAuthor":false,"prefix":"","firstName":"Luigi","middleName":"","lastName":"Zampa","suffix":""},{"id":612470344,"identity":"30cb7716-0ed5-42d9-b74d-9e65208cea10","order_by":7,"name":"Maddalena Michele","email":"","orcid":"","institution":"National Institute of Geophysics and Volcanology","correspondingAuthor":false,"prefix":"","firstName":"Maddalena","middleName":"","lastName":"Michele","suffix":""}],"badges":[],"createdAt":"2026-03-19 13:09:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9169863/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9169863/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105543799,"identity":"f8b046b6-85fb-4914-b02f-d3985dda1663","added_by":"auto","created_at":"2026-03-27 08:42:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":585587,"visible":true,"origin":"","legend":"\u003cp\u003ea) OGS bulletin data in the 0–20 km depth range reported for the SEAWD area and b) the subdivision of SEAWD in 3 subregions shown with the stations of the different seismic networks used\u003c/p\u003e\n\u003cp\u003e(see section “Data”).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9169863/v1/ab1f9bd60b13ce33dd6a2bc6.png"},{"id":105543811,"identity":"f37f2bac-05e6-4e77-a7e0-c1c45bb35b8c","added_by":"auto","created_at":"2026-03-27 08:42:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68153,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the analyses for seismic sequences\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9169863/v1/96710747f198935cd64d20b7.png"},{"id":105543796,"identity":"bfd766b6-0b53-4b66-a075-e6fea1bbccdd","added_by":"auto","created_at":"2026-03-27 08:42:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":294388,"visible":true,"origin":"","legend":"\u003cp\u003ea) OGS bulletin data relocated by NLL-SSST (top) in the 0–20 km depth range and difference between the depth of the NLL-SSST relocated catalogue and the depth value of the original catalogue (bottom). b) Cross section of seismicity at longitudes 13.0° and c) 13.25° for the OGS bulletin. d) Cross-section of the seismicity at longitudes 13.0° and e) 13.25° for the relocated catalogue with NLL-SSST\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9169863/v1/ee1fd87e0de1885749f63548.png"},{"id":105543804,"identity":"5ad5b9f9-7fb2-4d58-b584-af405b3ab796","added_by":"auto","created_at":"2026-03-27 08:42:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":330795,"visible":true,"origin":"","legend":"\u003cp\u003eSequences identification. a) distribution of log (η) in time for the background seismicity (gray dots) and the identified sequences (dots with the same colour represent a sequence) b) the same as a), but in magnitude. c) Spatial distribution of sequences (background seismicity shown as black dots). d) distribution of background seismicity and seismic sequences for a cross section along longitude.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9169863/v1/c3d4f0bc773c701926ac82e6.png"},{"id":105543814,"identity":"f307d954-a8b0-449a-8510-0987a2e74197","added_by":"auto","created_at":"2026-03-27 08:42:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":357032,"visible":true,"origin":"","legend":"\u003cp\u003eExample of three sequences’ location from OGS bulletin, NLL-SSST and NLL-SC. a-c) location for the M\u003csub\u003eL \u003c/sub\u003e3.8 2015 Ledro Sequence. d-f) M\u003csub\u003eL \u003c/sub\u003e4.2 2020 Bovec Sequence.\u0026nbsp; g-i) M\u003csub\u003eL\u003c/sub\u003e 4.6 2024 Socchieve Sequence.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9169863/v1/0ce521287cb37279d6d638aa.png"},{"id":105543897,"identity":"d73e18ce-da57-4b82-86ed-2258687c38ab","added_by":"auto","created_at":"2026-03-27 08:43:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":319910,"visible":true,"origin":"","legend":"\u003cp\u003ea) Sequences relocated with NLL-SC (map) and their distribution with respect to longitude and \u0026nbsp;(histograms). b) The same as a), but for sequence typology.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9169863/v1/03bbeb6b1793b72472cd427e.png"},{"id":105543706,"identity":"be9b858a-1687-4e3e-9194-e25ecca0524e","added_by":"auto","created_at":"2026-03-27 08:42:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":326456,"visible":true,"origin":"","legend":"\u003cp\u003eLogarithm of duration, mainshock magnitude and mainshock depth for the FMA sequences (a,b,c) and the MA sequences (d,e,f), respectively. Distributions of (g) logarithm of duration, (h) mainshock magnitude, and (i) mainshock depth for the FMA sequences (in red) and the MA sequences (in blue).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9169863/v1/eb8e1a1c7c878ebf7803940c.png"},{"id":105543880,"identity":"eaf0e963-4237-4b55-b9a1-f089a44d8f04","added_by":"auto","created_at":"2026-03-27 08:43:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":551452,"visible":true,"origin":"","legend":"\u003cp\u003eVolume of the seismicity of the sequences (V\u003csub\u003eseq\u003c/sub\u003e). Color indicates the geometry of the spatio-temporal distribution of the sequences.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9169863/v1/dc3ab76e880ef73f76c1f5a4.png"},{"id":105543797,"identity":"6e48a703-3c8d-48a0-9cf4-244d5d54e2b9","added_by":"auto","created_at":"2026-03-27 08:42:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6082594,"visible":true,"origin":"","legend":"","description":"","filename":"BrondietalSEAWDsequencesSupplementaryInfo.docx","url":"https://assets-eu.researchsquare.com/files/rs-9169863/v1/2e1541803d84191ddf272dee.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revealing Seismic Sequence Characteristics in the South-eastern Alps and the Western Dinarides by clustering analysis and refined location","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe progressive increase in seismic station density, together with advances in computational capabilities and the development of improved detection and location techniques (Mousavi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Adinolfi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), has significantly enhanced our ability to identify seismic sources and to resolve the physical processes governing earthquake occurrence on faults (Petersen et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Consequently, analysis of seismicity has become a key tool for constraining fault geometry and its temporal evolution (e.g., Picozzi et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Muzellec et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Poggiali et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sollai et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe characterization of seismicity patterns is central to seismic hazard assessment, as changes in earthquake occurrence often reflect variations in stress loading and fault interactions. In this context, seismic sequences, defined as earthquakes clustered in space and time relative to background seismicity, provide essential information on fault activation and triggering processes (Riga and Balocchi, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Petersen et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recent studies have shown that both static stress transfer and fault-zone complexity strongly influence the spatial and temporal evolution of seismic sequences (King et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Stein, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Nandan et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Large cascading events, such as the 2023 Kahramanmaraş sequence, illustrate how stress interactions between adjacent fault segments can control earthquake occurrence (Jia et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while swarm-like activity along the Maacama, Northern California fault, highlights the role of small, distributed fault structures in generating complex seismicity (Shelly et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Italy, seismic sequence analysis has provided important insights into faults structure and rupture processes. The 2016\u0026ndash;2017 Central Italy sequence revealed the progressive activation of complex fault systems and clarified the role of fault segmentation and cascading rupture during major earthquakes (Michele et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Picozzi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Other studies documented intermittent fault activity, fluid-driven seismic migration, and static stress-controlled sequences in different tectonic settings, including the Altotiberina fault, Molise, and Irpinia (Vuan et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gentili et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Scotto di Uccio et al., 2024; Palo et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Camanni et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe South-eastern Alps and Western Dinarides (SEAWD) constitute a region of high tectonic complexity and moderate-to-high seismic hazard, with the 1976 M\u003csub\u003eW\u003c/sub\u003e 6.5 Friuli earthquake marking the most recent destructive event. Subsequent investigations of major sequences, such as the 1998 and 2004 Bovec\u0026ndash;Krn earthquakes, revealed complex fault geometries and stress heterogeneities controlled by regional tectonics (Bressan et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Later studies showed that mainshock-induced stress changes can promote static fatigue and subcritical crack growth, processes enhanced by mechanical contrasts and unclamping effects (Bressan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e), and highlighted the role of fluid transients bound to strong to moderate earthquakes in the stress field variation and in modulating the background seismicity (Rossi et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More recent analyses in the SEAWD region have emphasized the influence of fault interactions and mechanical heterogeneity on the spatial and temporal distribution of seismicity (Bressan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we analyze the seismic sequences that have occurred in the SEAWD region over the past decade using approximately 17,000 seismic events from the National Institute of Oceanography and Applied Geophysics - OGS high-quality catalogue (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) recorded by stations belonging to the OGS (Bragato et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and other nearby seismometric networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb; see the Data section for more details). Focusing on the period after 2015 ensures an improved hypocentral accuracy in terms of location uncertainties and azimuthal coverage (Sandron et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on various geophysical characteristics observed in previous studies, we organize our discussion of results dividing the SEAWD region into three subdomains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb): Western SEAWD (WS), Central SEAWD (CS), and Eastern SEAWD (ES). The WS area experienced over the last decade low seismicity, characterized by swarm activity and structures with subvertical strike-slip (NNE\u0026ndash;SSW, N\u0026ndash;S) and normal NW\u0026ndash;SE focal mechanisms with a southwest dip (Petersen et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The CS area has the highest seismicity in the entire Alpine arc, close to the epicenter of the M\u003csub\u003eW\u003c/sub\u003e 6.5 1976 Friuli earthquake, and is characterized by a predominance of mainshock-aftershock sequences with a mainly thrust faulting regime (Sara\u0026ograve; et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Petersen et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, the ES area, located in the transition zone between the Alps, the Dinarides, and the Pannonian Basin, is characterized by active faults with a NW\u0026ndash;SE orientation, parallel to the Dinarides chain, exhibiting right-lateral strike-slip movement that extends for tens of kilometers and produces both swarms and sequences (Vičič et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Atanockov et al., 2021).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(see section \u0026ldquo;Data\u0026rdquo;).\u003c/p\u003e \u003cp\u003eThe outline of our analyses in the SEAWD area is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Starting from the OGS bulletin seismic catalogue and its corresponding 1D velocity model (Slejko et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), seismicity is first relocated at the regional scale using a multi-scale approach based on NonLinLoc-source-specific station travel-time corrections (NLL-SSST; Lomax and Savvaidis, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which progressively refines the time residual grids to mitigate velocity-model uncertainties. On the resulting high-precision catalogue, seismic sequences are identified through nearest-neighbour clustering and subsequently relocated to higher precision at the local scale with a waveform-coherence-based NonLinLoc approach (NLL-coherence; Lomax and Savvaidis, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Specifically, we will use the term NLL-SC to refer to the application of the NLL-SSST methodology and, consequently, to the NLL-coherence application. Our strategy enables a detailed characterization of the spatial and temporal evolution of seismic sequences. Sequences are subsequently classified according to their temporal behavior (e.g., foreshock\u0026ndash;mainshock\u0026ndash;aftershock, mainshock\u0026ndash;aftershock, and swarm-like activity), while their spatial extent is quantitatively characterized through principal component analysis, used to derive a first-order estimation of sequence volume (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, this approach provides a consistent basis to investigate sequence geometry, rupture processes, and fault-zone structure, with implications for seismic hazard assessment in northeastern Italy and adjacent regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSelection and relocation of the sequences\u003c/h2\u003e \u003cp\u003eWe applied the NLL-SSST analysis to the OGS bulletin catalogue and we relocated 16053 events (i.e., representing 93% of the original dataset, while the remaining events were discarded due to low quality; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Comparing Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, we see that the most significant difference between the locations of the OGS bulletin and those of NLL-SSST is in hypocentral depth, with the difference Δz between the depth of events relocated by NLL-SSST and the depth of events in the bulletin being mostly negative in the longitude ranges 10.2\u0026deg;\u0026ndash;11\u0026deg; and 11.5\u0026deg;\u0026ndash;14\u0026deg; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). This result suggests that in all three areas - WS, CS and ES -, NLL-SSST implies a medium with a higher wavespeeds than that used for the creation of the OGS bulletin. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e also shows north-south cross sections at longitudes 13.0\u0026deg; and 13.25\u0026deg; (CS area) for the OGS bulletin (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) and NLL-SSST locations (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). By comparing them, we observe that even at this stage of relocation, the structures are geometrically more defined with NLL-SSST than in the initial bulletin data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the next step, we separated background seismicity from clustered seismicity in the relocated catalogue using the rescaled generalized distance method by Zaliapin et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). As a result of the clustering analysis (details are provided in the Methods section), 76 seismic sequences, comprising approximately 2,200 events, were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; see Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Looking at the nearest-neighbour proximity parameter log(η) we observe as sequences occur rather regularly distributed in time (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), but not in space (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The highest density of sequences is observed in the eastern part of the CS region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), while apparent gaps between sequences are observed in coincidence with the western part of the CS region (i.e., longitude\u0026thinsp;~\u0026thinsp;12\u0026deg;) and at the junction of the CS and ES areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA look at\u0026nbsp;moment magnitude as a function of time (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) does not highlight specific temporal patterns, leading us to exclude, at least in a first approximation, the presence of external forcing driving the sequences occurrence. Clusters concentrate along the main seismogenic belt of the\u0026nbsp;South-Eastern Alps, particularly along the\u0026nbsp;southern Alpine thrust front and the Friuli\u0026ndash;Slovenia transition zone, while background seismicity (grey) appears more diffusely distributed. Figures\u0026nbsp;(4d)\u0026nbsp;presents the\u0026nbsp;depth distribution versus longitude, indicating that most clustered seismicity occurs within the\u0026nbsp;upper crust (\u0026asymp;\u0026thinsp;5\u0026ndash;15 km depth)\u0026nbsp;and follows the along-strike geometry of the Alpine front. The clustering highlights localized zones of repeated seismic interaction embedded within a broader background seismicity field. The clustering parameters adopted in this study follow previous applications of the nearest-neighbour method (Zaliapin and Ben-Zion, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although different parameter choices may slightly modify the number of identified clusters, the main spatial patterns and the concentration of sequences in the Central SEAWD remain stable.\u003c/p\u003e \u003cp\u003eFor each indicated sequence, we applied the NLL-coherence stage of NLL-SC (details are provided in the Methods section), using waveforms from at least four stations near the median epicentral location to achieve a good balance between signal quality and network coverage. Due to limited stations coverage in the southwestern part of the CS area, this criterion was not met for the M\u003csub\u003eL\u003c/sub\u003e 4.4 2023 Ceneselli earthquake sequence (the southernmost sequence in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), resulting in its exclusion from subsequent analyses. Because the seismicity of four sequences in the ES appears to be located near the southeast corner of the SE area, to avoid inaccuracies in characterizing these sequences due to partial event detection, we also excluded them from subsequent analyses. Consequently, our final SEAWD sequence database consists of 71 sequences: 11 within WS, 47 within CS, and 13 within ES. Starting from the absolute location of the OGS bulletin, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the effect of the intermediate NLL-SSST relocation procedure and the final NLL-SC relocation procedure for three sequences characterized by one of the strongest mainshock: the M\u003csub\u003eL\u003c/sub\u003e 3.8 Ledro sequence (WS) in 2015, the M\u003csub\u003eL\u003c/sub\u003e 4.3 Bovec sequence (ES) in 2020, and the M\u003csub\u003eL\u003c/sub\u003e 4.6 Socchieve sequence (CS) in 2024 (similar figures for all the sequences are available as Supplemental Material). In all three cases, applying NLL-SSST results in more clustered seismicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh) than the OGS bulletin data (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg). For the 2015 Ledro and 2024 Socchieve sequences, the events also appear to decrease in depth by a few kilometers. The subsequent application of the NLL-coherence procedure further clusters the seismicity, revealing finer geometric structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei). Specifically, seismicity that, with NLL-SSST, spans 2 km in latitude, 2 km in longitude, and 5 km in depth is subsequently concentrated within 1 km in latitude and longitude and 3 km in depth. Figure S2 in the Supplementary Information shows the root mean square residual (RMS) and the horizontal and vertical errors of the events from the three sequences obtained at the initial iteration of NLL-SSST, the final iteration of NLL-SSST, and the NLL-coherence procedure. In all three cases, the significant reduction in RMS at the last iteration of NLL-SSST compared to the first suggests that the SSST corrections to the initial velocity model allowed better consideration of the medium's heterogeneity, leading to improved location quality (see Supplementary Figures S2a, S2d, S2g). This decrease in RMS results in horizontal and vertical error values that remain the same or are slightly higher than those in the initial iteration (See Supplementary Figures S2b, S2c, S2e, S2f, S2h, S2i). This outcome may be because SSST implicitly introduces a more realistic model, revealing heterogeneities that the initial simple model had previously absorbed. The subsequent application of NLL-SC results in an expected slight increase in RMS and a significant decrease in both horizontal and vertical error. For most events in the three sequences, the horizontal error is less than 0.5 km and the vertical error is less than 1 km.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSequences distribution and properties\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea shows the distribution of the 71 sequences located in the SEAWD area in terms of epicentral position and vertical sections along longitude and latitude. Most of these sequences are found in the CS area (63%), while a significantly lower percentage is found in the WS (15%) and ES (21%). In Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea the epicentral location of the sequences that occurred in the SEAWD is also compared with surface fault traces from the Database of Individual Seismogenic Sources [DISS, version 3.3.1, (DISS Working Group, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)]. Following Ogata \u0026amp; Katsura (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), we classify the seismic sequences on the base of the relative magnitude difference (ΔM) between the mainshock and the largest preceding event: sequences characterized by a dominant mainshock followed by aftershocks were defined as Mainshock\u0026ndash;Aftershock (MA) type; sequences including a preparatory phase were classified as foreshock\u0026ndash;mainshock\u0026ndash;aftershock (FMA) type when ΔM\u0026thinsp;\u0026gt;\u0026thinsp;0.45; conversely, sequences with ΔM\u0026thinsp;\u0026lt;\u0026thinsp;0.45, lacking a clearly dominant mainshock, were classified as swarm-type (SW) sequences. We found that 49 sequences are FMA type (65%), 25 are MA type (32%), and 1 is SW type (3%). Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the Supplemental Information presents information on the time, epicenter location, and magnitude of the mainshocks, as well as the type, number of events, and duration of the identified sequences. Interestingly, the FMA sequences are distributed in both the pre-Alpine and Alpine areas of CS, while the Mainshock-Aftershock sequences are mainly found in the CS Alpine area (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb also shows that 60% of MA sequences are located in the latitude range 46\u0026ndash;46.25, and their number is comparable to that of FMA sequences which are predominant in the longitude range 10\u0026deg;\u0026ndash;12.5\u0026deg; (WS and western CS areas), 13\u0026deg;\u0026ndash;13.5\u0026deg; (CS area), and 14.25\u0026deg;\u0026ndash;14.5\u0026deg; (ES area). Conversely, MA sequences dominate in the central part of the CS area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the sequences for different attributes: duration, magnitude, and depth of the mainshock for the FMA (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec) and MA (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef) sequences, respectively. The two types of FMA and MA sequences do not appear to differ significantly in cluster duration (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg) or mainshock depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei). Although the three longest sequences are of the FMA type (the February and June 2017 Alpago sequences in the CS area and the 2020 Bovec sequence in ES area; see the Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), 61% of the 28 sequences lasting more than 30 days are FMA, and 39% are MA. Regarding the mainshock depth, the FMA and MA sequences show almost completely overlapping distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei) and lower values in the WS, in the northern part of CS and northwestern part of ES (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef). The depth of the mainshock in the sequences increases toward the Adriatic Sea and ES area (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef). Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eh shows that, on average, MA sequences have a greater mainshock magnitude than FMA sequences. In particular, the three sequences with the strongest mainshocks (i.e., the 2024 Socchieve sequence in CS, the 2015 and 2020 Bovec sequences in ES; see the Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) are of the MA type.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePrincipal Component Analysis for Sequences\u003c/h3\u003e\n\u003cp\u003eTo characterize the spatiotemporal evolution of the seismicity in each of the 71 analyzed sequences, we conducted a Principal Component Analysis (PCA) on the spatial coordinates of each sequence converted in a local cartesian space, as previously done in the region (Michelini and Bolt, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Rossi and Ebblin, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Bressan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To perform the analysis, we use the code PyCEFeaX (Iaccarino \u0026amp; Picozzi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) that utilizes the LAPACK method for the Singular Value Decomposition (Halko et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The procedure produces 3 eigenvectors E\u003csub\u003e1\u003c/sub\u003e, E\u003csub\u003e2\u003c/sub\u003e, E\u003csub\u003e3\u003c/sub\u003e with 3 associated eigenvalues λ\u003csub\u003e1\u003c/sub\u003e, λ\u003csub\u003e2\u003c/sub\u003e, λ\u003csub\u003e3\u003c/sub\u003e that provide information on the spatial distribution directions of seismicity. To exclude eventual outlier effects in the calculation, we select only the 90% of events for each sequence by finding the smallest cylinder with radius r\u003csub\u003eperc\u003c/sub\u003e and height 2*r\u003csub\u003eperc\u003c/sub\u003e centered on the median location of the sequence that contains most of the sequence seismicity. The volume of the sequence (V\u003csub\u003eseq\u003c/sub\u003e) is computed as the volume of the 3D Delaunay convex hull (Delaunay, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1934\u003c/span\u003e; Iaccarino and Picozzi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). We classified the sequences as \u0026ldquo;Planar,\u0026rdquo; \u0026ldquo;Linear,\u0026rdquo; and \u0026ldquo;Highly Clustered\u0026rdquo; based on the ratio of the PCA eigenvalues (as in Michelini and Bolt, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), and on the V\u003csub\u003eseq\u003c/sub\u003e volume, calculated from them (Figure S3):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:PCA\\:classification\\left\\{\\begin{array}{c}\\:\\:\\:\\:\\:\\:\\:\\:planar\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\::\\:\\frac{{\\lambda\\:}_{1}}{{\\lambda\\:}_{3}}\u0026gt;2.5,\\:\\:\\frac{{\\lambda\\:}_{2}}{{\\lambda\\:}_{3}}\u0026gt;1.75,\\:\\:{V}_{seq}\\gg\\:0\\:\\\\\\:\\:\\:\\:\\:\\:\\:\\:\\:linear\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\::\\:\\frac{{\\lambda\\:}_{1}}{{\\lambda\\:}_{3}}\u0026gt;2.5,\\:\\:\\frac{{\\lambda\\:}_{2}}{{\\lambda\\:}_{3}}\\le\\:1.75,\\:\\:{V}_{seq}\\gg\\:0\\:\\:\\\\\\:highly\\:clustered\\:\\:\\:\\:\\:\\:\\:\\::\\:\\frac{{\\lambda\\:}_{1}}{{\\lambda\\:}_{3}}\\le\\:2.5,\\:\\:\\frac{{\\lambda\\:}_{2}}{{\\lambda\\:}_{3}}\\le\\:1.75\\end{array}\\right.\\:\\:\\:\\:\\:\\:\\left(1\\right)\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWe assume that planar sequences are characterized by seismicity distribution within planar geometric structures. For linear sequences, seismicity distributes along a dominant direction defined by the E1 eigenvector. If the major component lies approximately within the horizontal plane and aligns preferentially along the E\u0026ndash;W or N\u0026ndash;S directions, we classify the sequence as \u0026lsquo;horizontal linear\u0026rsquo;; otherwise, we classify it as \u0026ldquo;vertical linear.\u0026rdquo; Finally, we consider sequences with very small volumes, where no significant seismicity extent is observed in any direction, to be \u0026ldquo;highly clustered\u0026rdquo;. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that 74% of sequences are planar, 21% are highly clustered, and 5% are linear. Of the four linear sequences observed, three are vertical: the Vidor sequence (western part of the CS) in 2015, the Cividale sequence (eastern part of the CS) in 2021, and the Stelvio sequence (northern part of the WS) in 2019. For the last case, the E1 autovector shows a significant component along the south-north direction. The only horizontal linear sequence observed is the Saviore dell'Adamello sequence in 2024 in the WS. Our results show no clear correlation between the sequence type and the seismicity volume geometry. In the case of planar distribution, FMA sequences slightly outnumber MA sequences. For highly clustered sequences, the proportion of FMA sequences increases to 70% and reaches 100% for linear sequences (Figure S4a). The only Swarm sequence observed, which occurred in Stregna (ES) in 2018, appears highly clustered, confirming the presence of repeater events in the ES area (Vičič et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The highest V\u003csub\u003eseq\u003c/sub\u003e values were observed for the 2019 Verzegnis sequence (V\u003csub\u003eseq\u003c/sub\u003e=0.4 km\u003csup\u003e3\u003c/sup\u003e) and for the 2021 Valdobbiadene sequence (V\u003csub\u003eseq\u003c/sub\u003e=0.13 km\u003csup\u003e3\u003c/sup\u003e) in CS. It should be noted that the V\u003csub\u003eseq\u003c/sub\u003e values of some sequences near the SEAWD border area may be affected by overestimation due to greater uncertainty regarding their location. However, this effect does not appear to be systematic for sequences in this area, as small, highly clustered planar sequences are also observed within it (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Figure S4b in the Supplementary Information shows that the logarithm of V\u003csub\u003eseq\u003c/sub\u003e for planar and linear sequences has, as a first approximation, a linear relationship with the magnitude of the mainshock. Comparing the number of events in the sequence with the magnitude of the mainshock, we observe that while planar and linear sequences show a wide range of variation, highly clustered sequences are characterized by fewer than 30 events and a mainshock magnitude of less than 3.0 (See the supplementary Figure S4c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe detailed relocation and characterization of seismic sequences in the SEAWD area between 2015 and 2024 provide new insights into the spatiotemporal organization of seismicity in one of the most complex tectonic domains of the Eastern Alps\u0026ndash;Western Dinarides region. Compared to previous studies based on standard locations and declustering approaches (e.g., Peresan and Gentili, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the combined application of the rescaled generalized distance and the NLL-SC relocation procedures allows a more robust identification of sequence geometry and depth distribution. A first regional screening of the seismic sequence characteristics suggested predominantly burst-like behavior in the Alpine and pre-Alpine domains and swarm-like activity in the Veneto\u0026ndash;Friuli plain (CS area) and along the CS and ES border (Peresan and Gentili, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). High-resolution observations from the temporary Swath-D deployment further identified multiple sequences with both swarm-like and mainshock\u0026ndash;aftershock behavior, and revealed short fault structures correlated with zones of high P-wave attenuation (Heit et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hofman et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Petersen et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe spatial distribution of the identified sequences highlights a clear concentration of seismic clustering within the Central SEAWD region, while significantly fewer sequences occur in the western and eastern sectors of SEAWD. This pattern likely reflects the structural complexity and heterogeneous stress distribution within the southeastern Alpine\u0026ndash;Dinaric transition zone, which was also highlighted by Picozzi et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Although not all sequences align exactly with mapped DISS sources, several clusters occur close to the trace of known thrust and strike-slip structures. This suggests that part of the seismicity may illuminate secondary fault segments or portions of larger structures that are not yet fully mapped at or do not reach the surface.\u003c/p\u003e \u003cp\u003eThe Central SEAWD corresponds to the region affected by the 1976 Friuli seismic sequence and is characterized by a dense network of south-verging thrust and transpressional structures (Marchesini et al., 2023; Poli et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) accommodating the convergence between the Adria microplate and the Eurasian plate. The concentration of sequences in the Central SEAWD coinciding with the 1976 M\u003csub\u003eW\u003c/sub\u003e 6.5 Friuli earthquake area suggests that the present-day microseismicity may still reflect the segmentation and mechanical complexity of the fault system activated during that event. The alignment of many sequences with mapped seismogenic sources from the DISS database suggests that a significant fraction of the observed seismicity occurs along known active fault systems or their associated damage zones. The depth distribution, mostly confined between 5 and 15 km, is consistent with previous estimates of the seismogenic layer thickness in the southeastern Alps (Slejko et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Vigan\u0026ograve; et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Picozzi et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) and indicates that these sequences likely activate structures within the upper crustal portion of the Alpine wedge. Previous studies highlighted the mechanical heterogeneity and segmentation that can characterize active fault systems, as revealed by the spatial distribution and clustering of microseismicity (Picozzi et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this work, we found a predominance of Foreshock\u0026ndash;Mainshock\u0026ndash;Aftershock (FMA) and Mainshock\u0026ndash;Aftershock (MA) sequences, with a minor contribution of swarm-like activity, which is consistent with the results of Petersen et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who also reported a limited occurrence of pure swarm sequences in the CS region. The spatial variability between FMA and MA sequences may reflect differences in fault maturity and structural complexity. More mature structures within the Alpine domain may favor the occurrence of dominant mainshocks followed by aftershock sequences, whereas more fragmented fault systems in the pre-Alpine domain may promote preparatory seismic activity preceding the largest event.\u003c/p\u003e \u003cp\u003eThis behaviour differs from what has been observed in some sectors of the northwestern Dinarides (Eastern SEAWD), where swarm-like seismicity is relatively common. For example, seismic swarms recorded along the Predjama Fault (Eastern SEAWD) have been interpreted as the result of transient deformation processes, possibly linked to slow slip occurring on the deeper portion of the fault system (Vičič et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the other hand, in this region we observe events that are highly clustered, characterized by a high cross-correlation coefficient, which could be interpreted as swarms using a classification different from that used in this work. Furthermore, differences in sequence classification in the WS and CS areas compared to previous studies (Peresan and Gentili, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Petersen et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) may be also related to both the different parameterization of the clustering methods used and the data, which partially coincides or does not coincide with that used in this study. However, the lower proportion of swarm-like sequences observed in the SEAWD catalogue may indicate that seismic clustering in northeastern Italy is primarily controlled by stress changes associated with moderate mainshocks rather than by aseismic processes. This interpretation is consistent with previous studies showing that the seismicity of the southeastern Alps is influenced by a complex stress field and by the interaction of multiple fault systems (e.g., Bressan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn agreement with Petersen et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we observed a significant percentage of MA sequences in the CS area, where they are more concentrated than in other areas of the SEAWD. However, the higher percentage of FMA sequences identified in this study (65%) suggests that improved relative locations, based on event similarity, enhance the detectability of foreshock activity, which may be partially underestimated by analyzing standard, lower precision catalogues. In particular, the initial regional relocation mitigates uncertainties associated with the velocity model and enables a more reliable selection of candidate sequences. The resulting travel-time residuals indicate that, on average, the observed arrivals are slightly earlier than predicted by the OGS bulletin velocity model, suggesting that the effective propagation velocities sampled by the relocation are somewhat faster, although the same initial velocity model is retained. This behavior is expected when applying SSST corrections, which can compensate for epistemic errors in the velocity model, particularly when a 1D model is used in structurally heterogeneous regions (Lomax et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe predominance of FMA sequences across both Alpine and pre-Alpine domains suggests that earthquake nucleation in the region frequently involves preparatory phases characterized by small precursor activity, which may reflect the progressive loading of complex fault systems. Conversely, MA sequences appear preferentially concentrated within the Alpine sector of the Central SEAWD, where the fault system is structurally mature and capable of producing more clearly defined mainshock\u0026ndash;aftershock patterns. This spatial differentiation may reflect variations in fault maturity, stress regime, and mechanical heterogeneity between the internal Alpine belt and the external thrust-and-fold system toward the foreland.\u003c/p\u003e \u003cp\u003eSome uncertainties remain due to the use of a regional 1D velocity model and to the variable station coverage near the borders of the network. Future analyses based on 3D velocity models and expanded station coverage could further improve the resolution of sequence geometry. Comparing Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, the effect of relocation with NLL-SSST is more evident in depth than in epicentral location. This may be related to the influence of the initial 1D velocity model, which covers an area of approximately 360 \u0026times; 280 km\u0026sup2;, on the ability to model lateral heterogeneities in the medium. We expect that relocating the events with a 3D velocity model for SEAWD in the future will result in even more precise epicentral relocation. Subsequent waveform-similarity-based relocation within each sequence further refines their spatial clustering, allowing a more accurate reconstruction of their spatiotemporal evolution. The lack of a clear spatial dominance of a single sequence type, already noted by Petersen et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), is confirmed here. Nevertheless, our results show a tendency for MA sequences to be preferentially located in the Alpine domain of Friuli region (northern part of the CS area), whereas FMA sequences are distributed across both Alpine and pre-Alpine regions in the CS area. This spatial pattern may reflect differences in fault maturity, stress regime, and rheological properties between the internal Alpine belt and the external thrust-and-fold system of Friuli and Veneto (CS area). Interestingly, we found that MA sequences are, on average, characterized by a stronger mainshock than FMA sequences. This suggests that within the SEAWD, foreshock analysis is not significant for forecasting mainshocks with M\u0026thinsp;\u0026gt;\u0026thinsp;3.5. However, in the same area, it has been observed that analyzing aftershock characteristics can provide a high success rate for forecasting strong aftershocks following an initial strong event (Brondi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed increase in sequence depth toward the Adriatic foreland and eastern Slovenia is consistent with the regional deepening of the seismogenic layer reported in earlier studies and reflects the transition from the Alpine orogenic wedge to the Adriatic indenter (Zupančič et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Principal Component Analysis performed on the spatial distribution of events within each sequence reveals that most clusters exhibit a planar geometry, while a smaller fraction appears highly clustered and only a few sequences display a linear structure. Planar distributions of hypocentres are commonly interpreted as reflecting the activation of fault planes or damage zones surrounding them. The predominance of planar geometries in our dataset therefore suggests that most sequences likely occur along well-defined structural planes within the seismogenic crust. In contrast, highly clustered sequences likely reflect small asperities or limited rupture patches within the fault network. The few linear sequences identified in our analysis show near-vertical trends. Bressan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), using PCA and fractal dimension analyses, also found vertical distributions of seismicity in the CS area; however, these appear to have a much higher incidence than observed in our work.\u003c/p\u003e \u003cp\u003eThe relationship observed between the logarithm of the estimated sequence volume V\u003csub\u003eseq\u003c/sub\u003e and the magnitude of the mainshock suggests a first-order scaling between the spatial extent of clustered seismicity and the size of the largest event. Although the volume derived from the PCA analysis represents only a first-order measurement for the true rupture sequence volume, the observed trend is consistent with empirical scaling relationships between rupture dimensions and earthquake magnitude documented in many tectonic environments. In this context, larger mainshocks are expected to activate wider portions of the surrounding fault network, generating broader aftershock clouds and larger seismic clusters. The variability observed within this relationship may reflect differences in fault-zone structure, mechanical heterogeneity, or stress conditions across the SEAWD region. Furthermore, this scaling could be partly explained by the diffusion of fluids observed in the SEAWD area, related to the occurrence of a moderate-intensity mainshock that creates transient deformation and modulates background seismicity (Rossi et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fluid diffusion processes may, in fact, contribute to the observed clustering behaviour. Transient increases in pore pressure can locally reduce effective normal stress on faults, promoting the activation of small clusters of earthquakes even in the absence of large stress perturbations.\u003c/p\u003e \u003cp\u003eThe absence of a clear relationship between sequence type and the spatial geometry of seismicity suggests that the temporal evolution of earthquake clusters may be largely independent from the structural geometry of the activated fault zone. Numerical and statistical models of earthquake triggering have shown that foreshock, aftershock, and swarm sequences can emerge from the same cascading stress-transfer processes within complex fault networks (e.g., Helmstetter and Sornette, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Saichev and Sornette, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Im and Avouac, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, this study confirms and refines previous findings while demonstrating that high-precision relocation and geometrical analysis significantly improve our understanding of seismic sequence behavior. Even though most sequences involve small-to-moderate earthquakes, their spatial organization provides valuable constraints on the geometry of active fault structures that may host larger future earthquakes. High-precision sequence locations provide indirect constraints on the geometry of active structures that may not be fully resolved at the surface. Such information can contribute to improving the characterization of seismogenic sources and therefore to refining regional seismic hazard models. The ability to rapidly identify and characterize seismic sequences using clustering and relocation techniques may provide valuable information for operational seismic monitoring, particularly in regions such as northeastern Italy where moderate earthquakes can generate damaging ground shaking.\u003c/p\u003e\n\u003ch3\u003eData\u003c/h3\u003e\n\u003cp\u003eThis study analyzes approximately 17,000 seismic events recorded between 2015 and 2024 and included in the seismic bulletins published by the National Institute of Oceanography and Experimental Geophysics \u0026ndash; OGS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The progressive evolution of the OGS seismic network, particularly in terms of station density and operational performance, has significantly influenced the magnitude of completeness (Mc) of the catalogue over time (Gentili et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Over the last 15 years, the coverage provided by the Struttura di Monitoraggio dell\u0026rsquo;Italia Nord Orientale (SMINO) network (Bragato et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) has allowed the achievement of a Mc below 1 in the Friuli Venezia Giulia region (Brondi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince its establishment in 1977, the SMINO network has supported the compilation of annual seismic bulletins by OGS, which are publicly available online. The dataset used in this study includes events published in the bulletins from 2015 to 2023 (Snidarcig et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Brondi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.crs.ogs.it/bollettino_new/\u003c/span\u003e\u003cspan address=\"https://www.crs.ogs.it/bollettino_new/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), as well as events recorded in 2024 and reviewed by the Review Group of the Seismological Research Center (CRS, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://terremoti.ogs.it/\u003c/span\u003e\u003cspan address=\"https://terremoti.ogs.it/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) of the National Institute of Oceanography and Experimental Geophysics \u0026ndash; OGS. These bulletins include manually reviewed phase picks from stations operated by OGS and collaborating institutions in Italy and Europe.\u003c/p\u003e \u003cp\u003eIn this study, we used data from approximately 150 seismic stations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). For Italy, the dataset includes stations belonging to networks managed by the CRS [international code OX, (National Institute of Oceanography and Experimental Geophysics \u0026ndash; OGS, 2016)]; the North-East Italy Broadband Network [international code NI, (National Institute of Oceanography and Experimental Geophysics, University of Trieste, 2002)]; the Italian National Seismic Network [international code IV, (National Institute of Geophysics and Volcanology, 2005)]; the Collalto Seismic Network [international code EV, (Priolo et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)]; the Friuli Venezia Giulia Accelerometric Network [international code RF, (University of Trieste, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1993\u003c/span\u003e)]; the Trentino Seismic Network [international code ST, (Geological Survey \u0026ndash; Provincia Autonoma di Trento, 1981)]; the Province S\u0026uuml;dtirol Seismic Network [international code SI]; the Mediterranean Very Broadband Seismographic Network [international code MN, (MedNet Project Partner Institutions, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1990\u003c/span\u003e)]; and the Interreg IV HAREIA project [hereinafter HA, (University of Trieste, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1993\u003c/span\u003e)] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Additional data were obtained from the Austrian Seismic Network [international code OE, GeoSphere, ZAMG \u0026ndash; Central Institute for Meteorology and Geodynamics, 1987], the Seismic Network of the Republic of Slovenia [international code SL, Agency of the Republic of Slovenia for the Environment (ARSO), 1990], the Croatian Seismograph Network [international code CR, University of Zagreb, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2001\u003c/span\u003e], and the Swiss National Seismic Network [Swiss Seismological Service (SED) at ETH Zurich, 1983] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThese collaborations are essential for obtaining accurate earthquake locations, particularly near the edges of the network, where the availability of additional stations helps reduce azimuthal gaps to values below 180\u0026deg; and ensures a typical depth uncertainty of about 2 km (Bragato et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hypocentral locations are computed using the HYPO71 algorithm (Lee and Lahr, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), adopting a regional velocity model and parameterization specifically developed for the study area (Slejko et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). For each seismic event, the bulletin reports the duration magnitude (M\u003csub\u003eD\u003c/sub\u003e), calculated using the empirical relationships proposed by Rebez and Renner (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and calibrated to be consistent with the local magnitude (M\u003csub\u003eL\u003c/sub\u003e) derived from the Wood\u0026ndash;Anderson seismograph installed in Trieste (Bragato and Tento, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cp\u003eWe followed a multi-step procedure to identify seismic sequences and accurately relocate them using the NLL-SC technique (Lomax, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lomax and Savvaidis, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This algorithm has been adapted to enhance relative location precision by incorporating spatially variable, source-specific station travel-time corrections (SSST) and a multi-event relocation strategy based on waveform coherence (NLL-SC; Lomax and Savvaidis, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This method builds upon the NonLinLoc (NLL) probabilistic global-search algorithm (Lomax et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which estimates hypocenter locations by constructing a three-dimensional probability density function (PDF). This approach is particularly robust to the presence of outliers in the input data (Lomax and Savvaidis, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and was used to obtain high-resolution location data for volcanic (De Landro et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and tectonic (Sollai et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) structures.\u003c/p\u003e \u003cp\u003eIn our procedure, using data from the OGS bulletin and monitoring group catalogue, we relocated the entire catalogue with the initial NLL-SSST procedure. Then, the relocated catalogue has been used to identify seismic sequences by applying the nearest-neighbour method (Zaliapin and Ben-Zion, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). After identifying the sequences, we applied a second relocation analysis to each of them based on waveform coherence with NLL-coherence (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Finally, we classified and characterized the sequences as discussed in the Results section. Since HYPO71 and NLL use a perturbative and probabilistic approach for location, respectively, we verified with a preliminary test that, to a first approximation, the two methods reproduce the same results using the 1D velocity model with V\u003csub\u003eP\u003c/sub\u003e/V\u003csub\u003eS\u003c/sub\u003e=1.78 used for the OGS bulletin (Slejko et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). In addition, we conducted a localization test with NLL, comparing the previous velocity model with two more complex 1D models for SEAWD: the 1D velocity model by Gentile et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and a 1D velocity profile derived from the 3D model by Magrin and Rossi (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Comparing the residual distributions of P- and S-wave arrivals at stations, the model by Slejko et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) showed the best performance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSSST Analysis\u003c/h3\u003e\n\u003cp\u003eWe initially relocated the 2015\u0026ndash;2024 OGS seismic catalogue iteratively while generating smoothly varying SSST corrections throughout a three-dimensional volume with dimensions of 361 km in longitude, 281 km in latitude, 45 km in depth, and using a constant spatial step of 0.5 km. These corrections, dependent on the position of the events relocated in the previous iteration, are computed for P- and S-wave seismic phases of 83 seismic stations. In this NLL-SSST analysis, we considered 6 iterations and, at each iteration, only residuals from P- and S-phase picks and events that meet a fixed quality thresholdare utilized for the updates (Lomax and Savvaidis, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The final refined NLL-SSST locations have been obtained by the application of the Gaussian SSST smoothing kernels with progressively smaller widths which correspond to the smoothing distances of 32, 16, 8, 4, 2 and 1 km. The quality criteria for location and station-phase to be used for calculating are: 68% error-ellipsoid principle-axis half-width\u0026thinsp;\u0026le;\u0026thinsp;5.0 km, root mean square of residuals (RMS)\u0026thinsp;\u0026le;\u0026thinsp;0.15 s, number of readings\u0026thinsp;\u0026ge;\u0026thinsp;10, azimuth gap\u0026thinsp;\u0026le;\u0026thinsp;120\u0026deg;, P residual\u0026thinsp;\u0026le;\u0026thinsp;0.5 s, S residual\u0026thinsp;\u0026le;\u0026thinsp;1.0 s.\u003c/p\u003e\n\u003ch3\u003eClustering by Nearest-neighbour Distance, η\u003c/h3\u003e\n\u003cp\u003eThe catalogue obtained from the SSST analysis is used to identify the main sequences that occurred between 2015 and 2024 in our study area using the clustering method of Zaliapin et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This approach computes the generalized distance between pairs of earthquakes, η, by an analysis of the time-space distances between pairs of earthquakes, which consists in estimating the distances in time (i.e., rescaled time, T\u003csub\u003eη\u003c/sub\u003e) and space (i.e., rescaled distance, R\u003csub\u003eη\u003c/sub\u003e) between an event i and its parent j, where both distances are normalized by the magnitude of the parent event.\u003c/p\u003e \u003cp\u003eRescaled time and distance are computed as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{T}_{ij}={t}_{ij}{10}^{-b\\frac{{m}_{i}}{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{R}_{ij}={\\left({r}_{ij}\\right)}^{{D}_{c}}{10}^{-b\\frac{{m}_{i}}{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, m is the magnitude, b is the parameter of the Gutenberg\u0026ndash;Richter law, which plays the role of exponential weight of the earlier event i by its magnitude, and D\u003csub\u003ec\u003c/sub\u003e is the fractal dimension. Finally, η is defined as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:log{\\:(\\eta\\:}_{ij})=\\:log({R}_{ij})+\\:log({T}_{ij})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFollowing Zaliapin and Ben-Zion (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we set the b equal to 0 to mitigate the presence of artifacts due to the overlap of earthquakes\u0026rsquo; domain of attraction with background seismicity, and we use D\u003csub\u003ec\u003c/sub\u003e equal to 1.5. To create a family of events (i.e., a cluster) we required a minimum number of events equal to 10.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWaveform based Analysis\u003c/h2\u003e \u003cp\u003eIn this stage, the relative precision of the NLL-SSST event locations is further refined by integrating waveform similarity across events for each sequence. This second relocation step, NLL-coherence, relies on the principle that, when seismograms from different events show strong similarity at a given station\u0026mdash;i.e., high coherence across a broad frequency band\u0026mdash;the spatial separation between such \"multiplet\" events must be small compared to the seismic wavelength at the highest coherent frequency (Lomax and Savvaidis, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These events are likely to represent slip on the same, localized fault patch (Geller \u0026amp; Mueller, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Poupinet et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1982\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Nadeau et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor our NLL-coherence relocations, we calculated the coherence using channel data from the horizontal and vertical components of velocimeter sensors located around the immediate vicinity of the sequence. In particular, in order to ensure a good azimuthal coverage of the stations around the seismicity cluster of each sequence, we decided to use a minimum of 4 stations up to a maximum of 8 stations located around the midpoint of the epicentral distribution of the sequence. In calculating coherence, according to Lomax and Savvaidis (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) we applied a 2\u0026ndash;10 Hz bandpass filter in a signal window starting 4 seconds before the arrival of the predicted P-wave and ending 4 seconds after the theoretical arrival of the S-wave. We perform cross-correlation on waveform windows that slide within the range from \u0026minus;\u0026thinsp;2.0 to 2.0 s. Stacking weights, ranging from 0 to 1, are assigned based on the NLL-SC procedure of Lomax and Savvaidis (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and applied to coherency values between Cmin\u0026thinsp;=\u0026thinsp;0.5 and 1.0. This method is implemented for the D\u0026thinsp;=\u0026thinsp;1 km NLL-SSST relocations of the 75 sequences of SEAWD, considering all event pairs whose hypocentral distance does not exceed 5.0 km.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe seismic catalogue analyzed in this study is derived from the seismic bulletins of the National Institute of Oceanography and Experimental Geophysics \u0026ndash; OGS, produced by the Seismological Research Center (CRS) within the Struttura di Monitoraggio dell\u0026rsquo;Italia Nord Orientale (SMINO). The bulletins for the period 2015\u0026ndash;2023 are publicly available athttps://www.crs.inogs.it/bollettino_new/. The dataset for 2024, reviewed by the CRS, is publicly available at https://terremoti.ogs.it/\u003cu\u003e.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarthquake relocation analyses were carried out using the source-specific station travel-time corrections Coherence algorithm (NLL-SSST) implemented in the NonLinLoc software developed by Anthony Lomax. The software is publicly available athttps://github.com/ut-beg-texnet/NonLinLoc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA analysis and the computation of V\u003csub\u003eseq\u003c/sub\u003e have been done using the Python code PyCEFeaX (Iaccarino \u0026amp; Picozzi, 2026). The software is publicly available at https://github.com/AGIaccarino/PyCEFeaX.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge Giuliana Rossi for valuable discussions and Enrico Magrin for providing the computational resources used in this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the PRIN 2022 project \u0026lsquo;2022ZHXWC9\u0026rsquo;\u0026mdash;Intercepting the preparatory phase of large earthquakes from seismic information and geodetic displacement (PREPARED). This study was partially funded by the Joint Research Unit (JRU) of the European Plate Observing System (EPOS) Italia (https://www.epos-italia.it/, last accessed September 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePB and MP conceived and designed the study. GDL contributed to its conception. PB acquired the data and, together with MP, performed the data analysis. PB carried out the precise relocation analysis, MP conducted the cluster identification analysis, and AGI performed the PCA. All authors contributed to the interpretation of the results and to manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdinolfi, G. M. et al. Comprehensive study of micro-seismicity by using an automatic monitoring platform. \u003cem\u003eFront. Earth Sci.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1073684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/feart.2023.1073684\u003c/span\u003e\u003cspan address=\"10.3389/feart.2023.1073684\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtanackov, J. et al. Database of active faults in Slovenia: compiling a new active fault database at the junction between the Alps, the Dinarides and the Pannonian Basin tectonic domains. \u003cem\u003eFront. Earth Sci.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 604388 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBragato, P. L. \u0026amp; Tento, A. Local magnitude in northeastern Italy. \u003cem\u003eBull. Seismol. Soc. Am.\u003c/em\u003e \u003cb\u003e95\u003c/b\u003e (2), 579\u0026ndash;591 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBragato, P. L. et al. The OGS\u0026ndash;Northeastern Italy Seismic and Deformation Network: Current Status and Outlook. \u003cem\u003eSeismol. Res. Lett.\u003c/em\u003e \u003cb\u003e92\u003c/b\u003e (3), 1704\u0026ndash;1716. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1785/0220200372\u003c/span\u003e\u003cspan address=\"10.1785/0220200372\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBressan, G., Gentile, G. F., Perniola, B. \u0026amp; Urban, S. The 1998 and 2004 Bovec-Krn (Slovenia) seismic sequences:Aftershock pattern, focal mechanisms and static stress changes. \u003cem\u003eGeophys. J. Int.\u003c/em\u003e \u003cb\u003e179\u003c/b\u003e, 231\u0026ndash;253 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBressan, G., Barnaba, C., Magrin, A. \u0026amp; Rossi, G. A study on off-fault aftershock pattern N-Adria microplate. \u003cem\u003eJ. Seismolog.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 863\u0026ndash;888. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10950-018-9737-x\u003c/span\u003e\u003cspan address=\"10.1007/s10950-018-9737-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018a).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBressan, G., Barnaba, C., Bragato, P., Ponton, M. \u0026amp; Restivo, A. Revised seismotectonic model of NE Italy and W Slovenia based on focal mechanism inversion. \u003cem\u003eJ. Seismolog.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (6), 1563\u0026ndash;1578 (2018b).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBressan, G., Barnaba, C., Peresan, A. \u0026amp; Rossi, G. Anatomy of seismicity clustering from parametric space-time analysis. \u003cem\u003ePhys. Earth Planet. Inter.\u003c/em\u003e \u003cb\u003e320\u003c/b\u003e, 106787 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrondi, P., Snidarcig, A., Bernardi, P., Bragato, P. L. \u0026amp; Di Bartolomeo, P. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2022 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13120/w1vp-b578\u003c/span\u003e\u003cspan address=\"10.13120/w1vp-b578\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrondi, P., Gentili, S. \u0026amp; Di Giovambattista, R. Forecasting strong subsequent events in the Italian territory: a national and regional application for NESTOREv1.0. \u003cem\u003eNat. Hazards\u003c/em\u003e. \u003cb\u003e121\u003c/b\u003e, 3499\u0026ndash;3531. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-024-06913-6\u003c/span\u003e\u003cspan address=\"10.1007/s11069-024-06913-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025a).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrondi, P. et al. \u003cem\u003eBollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO)\u003c/em\u003e (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, 2025b). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13120/m4pk-nd81\u003c/span\u003e\u003cspan address=\"10.13120/m4pk-nd81\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eAnno 2023 [Data set].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamanni, G. et al. Remobilization of inverted normal faults drives active extension in the axial zone of the southern Apennine mountain belt (Italy). \u003cem\u003eJournal of the Geological Society\u003c/em\u003e, \u003cem\u003e182\u003c/em\u003e(2), pp.jgs2024-184. (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCesca, S. et al. Massive earthquake swarm driven by magmatic intrusion at the Bransfield Strait, Antarctica. \u003cem\u003eCommun. Earth Environ.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43247-022-00418-5\u003c/span\u003e\u003cspan address=\"10.1038/s43247-022-00418-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Landro, G. et al. 3D structure and dynamics of Campi Flegrei enhance multi-hazard assessment. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (1), 1\u0026ndash;12 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelaunay, B. Sur la sphere vide (On the empty sphere). Bulletin de l\u0026rsquo;Acad\u0026eacute;mie des Sciences de l\u0026rsquo;URSS, Classe des Sciences Math\u0026eacute;matiques et Naturelles, 6, 793\u0026ndash;800. (1934).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDISS Working Group. Database of Individual Seismogenic Sources (DISS), version 3.3.1: A compilation of potential sources for earthquakes larger than M 5.5 in Italy and surrounding areas. Istituto Nazionale di Geofisica e Vulcanologia (INGV). (2025)., March 28 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13127/diss3.3.1\u003c/span\u003e\u003cspan address=\"10.13127/diss3.3.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischer, T. et al. Intra-continental earthquake swarms in west-Bohemia and Vogtland: A review. \u003cem\u003eTectonophysics\u003c/em\u003e \u003cb\u003e611\u003c/b\u003e, 1\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tecto.2013.11.001\u003c/span\u003e\u003cspan address=\"10.1016/j.tecto.2013.11.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischer, T. \u0026amp; Hainzl, S. Effective Stress Drop of Earthquake Clusters. \u003cem\u003eBull. Seismol. Soc. Am.\u003c/em\u003e \u003cb\u003e107\u003c/b\u003e, 2247\u0026ndash;2257 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeller, R. J. \u0026amp; Mueller, C. S. Four similar earthquakes in central California. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (10), 821\u0026ndash;824 (1980).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGentile, G. F., Bressan, G., Burlini, L. \u0026amp; De Franco, R. Three-dimensional Vp and Vp/Vs models of the upper crust in the Friuli area (northeastern Italy). \u003cem\u003eGeophys. J. Int.\u003c/em\u003e \u003cb\u003e141\u003c/b\u003e (2), 457\u0026ndash;478 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGentili, S., Sugan, M., Peruzza, L. \u0026amp; Schorlemmer, D. Probabilistic completeness assessment of the past 30 years of seismic monitoring in northeastern Italy. \u003cem\u003ePhys. Earth Planet. Inter.\u003c/em\u003e \u003cb\u003e186\u003c/b\u003e (1\u0026ndash;2), 81\u0026ndash;96 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGentili, S. et al. Seismic clusters and fluids diffusion: a lesson from the 2018 Molise (Southern Italy) earthquake sequence Earth Planets Space 76, 157 (2024). (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40623-024-02096-3\u003c/span\u003e\u003cspan address=\"10.1186/s40623-024-02096-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeological Survey-Provincia Autonoma di Trento. Trentino Seismic Network [Data set]. International Federation of Digital Seismograph Networks. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7914/SN/ST\u003c/span\u003e\u003cspan address=\"10.7914/SN/ST\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1981).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalko, N., Martinsson, P. G. \u0026amp; Tropp, J. A. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions. Https://Doi.Org/10.1137/090771806, \u003cb\u003e53\u003c/b\u003e(2), 217\u0026ndash;288. (2011). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1137/090771806\u003c/span\u003e\u003cspan address=\"10.1137/090771806\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeit, B. et al. The Swath-D seismic network in Italy and Austria. \u003cem\u003eGFZ Data Serv.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14470/mf7562601148\u003c/span\u003e\u003cspan address=\"10.14470/mf7562601148\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeit, B. et al. The SWATH-D seismological network in the eastern Alps. \u003cem\u003eSeismol. Res. Lett.\u003c/em\u003e \u003cb\u003e92\u003c/b\u003e (3), 1592\u0026ndash;1609. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1785/0220200377\u003c/span\u003e\u003cspan address=\"10.1785/0220200377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelmstetter, A. \u0026amp; Sornette, D. Subcritical and supercritical regimes in epidemic models of earthquake aftershocks. \u003cem\u003eJ. Geophys. Research: Solid Earth\u003c/em\u003e. \u003cb\u003e107\u003c/b\u003e (B10), ESE\u0026ndash;10 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofman, L. J., Kummerow, J., Cesca, S., the AlpArray-Swath-D Working Group. \u0026amp; \u0026amp; A new seismicity catalogue of the eastern Alps using the temporary Swath-D network. \u003cem\u003eSolid Earth\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e (10), 1053\u0026ndash;1066. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/se-14-1053-2023\u003c/span\u003e\u003cspan address=\"10.5194/se-14-1053-2023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIaccarino, A. G. \u0026amp; Picozzi, M. PyCEFeaX. Zenodo. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.18548970\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.18548970\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2026).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIm, K. \u0026amp; Avouac, J. P. Cascading foreshocks, aftershocks and earthquake swarms in a discrete fault network. \u003cem\u003eGeophys. J. Int.\u003c/em\u003e \u003cb\u003e235\u003c/b\u003e (1), 831\u0026ndash;852 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIstituto Nazionale di Geofisica e Vulcanologia (INGV). Rete Sismica Nazionale (RSN) [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13127/sd/x0fxnh7qfy\u003c/span\u003e\u003cspan address=\"10.13127/sd/x0fxnh7qfy\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIstituto Nazionale di Oceanografia e di Geofisica Sperimentale \u0026ndash; OGS. North-East Italy Seismic Network [Data set]. International Federation of Digital Seismograph Networks. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7914/SN/OX\u003c/span\u003e\u003cspan address=\"10.7914/SN/OX\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIstituto Nazionale di Oceanografia e di Geofisica Sperimentale, University of Trieste. North-East Italy Broadband Network [Data set]. International Federation of Digital Seismograph Networks. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7914/SN/NI\u003c/span\u003e\u003cspan address=\"10.7914/SN/NI\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia, Z. et al. The complex dynamics of the 2023 Kahramanmaraş, Turkey, Mw 7.8\u0026ndash;7.7 earthquake doublet. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e381\u003c/b\u003e (6661), 985\u0026ndash;990. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.adi0685\u003c/span\u003e\u003cspan address=\"10.1126/science.adi0685\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKing, G. C. P., Stein, R. S. \u0026amp; Lin, J. Static stress changes and the triggering of earthquakes. (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulletin of the Seismological Society of America. 84(3), 935\u0026ndash;953. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1785/BSSA0840030935\u003c/span\u003e\u003cspan address=\"10.1785/BSSA0840030935\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, W. H. \u0026amp; Lahr, J. C. \u003cem\u003eHYPO71 (revised; a computer program for determining hypocenter, magnitude, and first motion pattern of local earthquakes\u003c/em\u003e (No. 75\u0026ndash;311). US Dept. of the Interior, Geological Survey, National Center for Earthquake Research. (1975).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, Y. K., Ross, Z. E., Cochran, E. S. \u0026amp; Lapusta, N. A unified perspective of seismicity and fault coupling along the San Andreas fault. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (8), eabk1167 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLomax, A., Virieux, J., Volant, P. \u0026amp; Berge-Thierry, C. Probabilistic earthquake location in 3D and layered models: Introduction of a Metropolis-Gibbs method and comparison with linear locations. In Advances in seismic event location (101\u0026ndash;134). Dordrecht: Springer Netherlands. (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLomax, A., Michelini, A. \u0026amp; Curtis, A. Earthquake location, direct, global-search methods. In R. A. Meyers (Ed.), Encyclopedia of complexity and systems science (pp. 1\u0026ndash;33). Springer New York. (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-642-27737-5_150-2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-642-27737-5_150-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLomax, A. The 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake: relocated seismicity shows rupture of a complete shear-crack system. EarthArXiv. (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31223/X5X015\u003c/span\u003e\u003cspan address=\"10.31223/X5X015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLomax, A. \u0026amp; Savvaidis, A. High-precision earthquake location using source‐specific station terms and inter‐event waveform similarity. \u003cem\u003eJournal of Geophysical Research: Solid Earth\u003c/em\u003e, \u003cem\u003e127\u003c/em\u003e(1), e2021JB023190. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLomax, A., Tuv\u0026egrave;, T., Giampiccolo, E. \u0026amp; Cocina, O. A new view of seismicity under Mt. Etna volcano, Italy, 2014\u0026ndash;2023 from multi-scale high-precision earthquake relocations. \u003cem\u003eAnn. Geophys.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e (4), S437\u0026ndash;S437 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagrin, A. \u0026amp; Rossi, G. Deriving a new crustal model of Northern Adria: the Northern Adria Crust (NAC) model. \u003cem\u003eFront. Earth Sci.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 89 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarchesini, A. et al. e Gruppo di lavoro Faglie Attive FVG, 2023. Linee guida per l\u0026rsquo;utilizzo della banca dati georiferita delle faglie attive della Regione Friuli Venezia Giulia. Servizio Geologico - Regione Autonoma Friuli Venezia Giulia, 64 pp.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedNet Project Partner Institutions. Mediterranean Very Broadband Seismographic Network (MedNet) [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13127/SD/FBBBTDTD6Q\u003c/span\u003e\u003cspan address=\"10.13127/SD/FBBBTDTD6Q\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichele, M., Chiaraluce, L., Di Stefano, R. \u0026amp; Waldhauser, F. Fine-scale structure of the 2016\u0026ndash;2017 Central Italy seismic sequence from data recorded at the Italian National Network. Journal of Geophysical Research: Solid Earth, 125(4), e2019JB018440. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichelini, A. \u0026amp; Bolt, B. Application of the principal parameters method to the Coalinga, California, aftershock sequence. \u003cem\u003eBull. Seis Soc. Am.\u003c/em\u003e \u003cb\u003e76\u003c/b\u003e, 409\u0026ndash;420 (1986).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y. \u0026amp; Beroza, G. C. Earthquake transformer\u0026mdash;an attentive deep-learning model for simultaneous earthquake detection and phase picking. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (1), 3952 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuzellec, T., De Landro, G., Camanni, G., Adinolfi, G. M. \u0026amp; Zollo, A. The complex 4D multi-segmented rupture of the 2014 Mw 6.2 Northern Nagano Earthquake revealed by high-precision aftershock locations. \u003cem\u003eTectonophysics\u003c/em\u003e \u003cb\u003e898\u003c/b\u003e, 230641 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNadeau, R., Antolik, M., Johnson, P. A., Foxall, W. \u0026amp; McEvilly, T. V. Seismological studies at Parkfield III: Microearthquake clusters in the study of fault-zone dynamics. \u003cem\u003eBull. Seismol. Soc. Am.\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e (2), 247\u0026ndash;263 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNandan, S., Ouillon, G., Woessner, J., Sornette, D. \u0026amp; Wiemer, S. Systematic assessment of the static stress-triggering hypothesis using inter-earthquake time statistics. \u003cem\u003eJ. Geophys. Research: Solid Earth\u003c/em\u003e. \u003cb\u003e122\u003c/b\u003e, 6271\u0026ndash;6291 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgata, Y. \u0026amp; Katsura, K. Prospective foreshock forecast experiment during the last 17 years, \u003cem\u003eGeophysical Journal International\u003c/em\u003e, Volume 191, Issue 3, December 2012, Pages 1237\u0026ndash;1244, (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-246X.2012.05645.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-246X.2012.05645.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalo, M., Picozzi, M., De Landro, G. \u0026amp; Zollo, A. Microseismicity clustering and mechanic properties reveal fault segmentation in southern Italy. \u003cem\u003eTectonophysics\u003c/em\u003e, \u003cem\u003e856\u003c/em\u003e, p.229849. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeresan, A. \u0026amp; Gentili, S. Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach. \u003cem\u003ePhys. Earth Planet. Inter.\u003c/em\u003e \u003cb\u003e274\u003c/b\u003e, 87\u0026ndash;104 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen, G. M., Hofman, L. J., Kummerow, J. \u0026amp; Cesca, S. Microseismicity in the large-N Swath‐D network: Revealing seismic sequences and active faults in the eastern Alps. \u003cem\u003eJ. Geophys. Research: Solid Earth\u003c/em\u003e. \u003cb\u003e130\u003c/b\u003e, e2024JB030516. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2024JB030516\u003c/span\u003e\u003cspan address=\"10.1029/2024JB030516\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicozzi, M. et al. A rapid response magnitude scale for timely assessment of the high frequency seismic radiation. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 8562. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-26938-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-26938-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicozzi, M., Bindi, D., Zollo, A., Festa, G. \u0026amp; Spallarossa, D. Detecting long-lasting transients of earthquake activity on a fault system by monitoring apparent stress, ground motion and clustering. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (1), 16268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-52756-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-52756-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicozzi, M., Spallarossa, D., Bindi, D., Iaccarino, A. G. \u0026amp; Rivalta, E. Detection of spatial and temporal stress changes during the 2016 central Italy seismic sequence by monitoring the evolution of the energy index. \u003cem\u003eJ. Geophys. Research: Solid Earth\u003c/em\u003e. \u003cb\u003e127\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2022JB025100\u003c/span\u003e\u003cspan address=\"10.1029/2022JB025100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022). e2022JB025100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicozzi, M. et al. Event-specific ground motion anomalies highlight the preparatory phase of earthquakes during the 2016\u0026ndash;2017 Italian seismicity. \u003cem\u003eCommun. Earth Environ.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43247-024-01455-y\u003c/span\u003e\u003cspan address=\"10.1038/s43247-024-01455-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicozzi, M. et al. Seismic energy from small earthquakes maps fault segmentation in the Southeastern Alps. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 5731. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-026-35618-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-026-35618-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2026).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoggiali, G., Chiaraluce, L., Ross, Z. E., Zhu, W. \u0026amp; Marone, C. Fault Geometry and Source Mechanics of the Altotiberina Fault System from a High-Resolution Machine-Learning Earthquake Catalog. \u003cem\u003eBull. Seismol. Soc. Am.\u003c/em\u003e \u003cb\u003e115\u003c/b\u003e, 2181\u0026ndash;2201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1785/0120250072\u003c/span\u003e\u003cspan address=\"10.1785/0120250072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoli, M. E., Patricelli, G., Monegato, G. \u0026amp; Zanferrari, A. Structural inheritances, fault segmentation and seismogenic potential at the front of the eastern Southern Alps (central Carnic Prealps, NE Italy). \u003cem\u003eTectonophysics\u003c/em\u003e \u003cb\u003e883\u003c/b\u003e, 230390 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoupinet, G., Glangeaud, F. \u0026amp; Cote, P. P-Time delay measurement of a doublet of microearthquakes. In ICASSP'82. IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 7, pp. 1516\u0026ndash;1519). IEEE. (1982), May.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoupinet, G., Ellsworth, W. L. \u0026amp; Fr\u0026eacute;chet, J. Monitoring velocity variations in the crust using earthquake doublets: An application to the Calaveras Fault, California. \u003cem\u003eJ. Geophys. Research: Solid Earth\u003c/em\u003e. \u003cb\u003e89\u003c/b\u003e (B7), 5719\u0026ndash;5731 (1984).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePriolo, E. et al. Seismic Monitoring of an Underground Natural Gas Storage Facility: The Collalto Seismic Network. \u003cem\u003eSeismol. Res. Lett.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e (1), 109\u0026ndash;123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1785/0220140087\u003c/span\u003e\u003cspan address=\"10.1785/0220140087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRebez, A. \u0026amp; Renner, G. Duration magnitude for the northeastern Italy seismometric network. \u003cem\u003eBollettino di Geofis. teorica ed. Appl.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e (130\u0026thinsp;\u0026ndash;\u0026thinsp;31), 177\u0026ndash;186 (1991).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiga, G. \u0026amp; Balocchi, P. Seismic sequence structure and earthquakes triggering patterns. \u003cem\u003eOpen. J. Earthq. Res.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (1), 20\u0026ndash;34 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossi, G. \u0026amp; Ebblin, C. Space (3-D) and space-time (4-D) analysis of aftershock sequences: the Friuli (NE Italy) case. \u003cem\u003eBoll Geof Teor Appl.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 37\u0026ndash;49 (1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossi, G., Pastorutti, A., Nagy, I., Braitenberg, C. \u0026amp; Parolai, S. Recurrence of Fault Valve Behavior in a Continental Collision Area: Evidence From Tilt/Strain Measurements in Northern Adria. \u003cem\u003eFront. Earth Sci.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 641416. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/feart.2021.641416\u003c/span\u003e\u003cspan address=\"10.3389/feart.2021.641416\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaichev, A. \u0026amp; Sornette, D. Anomalous power law distribution of total lifetimes of branching processes: Application to earthquake aftershock sequences. \u003cem\u003ePhys. Rev. E\u0026mdash;Statistical Nonlinear Soft Matter Phys.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e (4), 046123 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandron, D., Rebez, A., Tamaro, A. \u0026amp; Slejko, D. Recent earthquakes (2000\u0026ndash;2021) in and around the Friuli Venezia Giulia region (NE Italy) and quality improvements of the OGS network monitoring capabilities. \u003cem\u003eBull. Geoph Ocean.\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e, 237\u0026ndash;258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4430/bgo00421\u003c/span\u003e\u003cspan address=\"10.4430/bgo00421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSara\u0026ograve;, A., Sugan, M., Bressan, G., Renner, G. \u0026amp; Restivo, A. A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928\u0026ndash;2019. \u003cem\u003eEarth Syst. Sci. Data\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e (5), 2245\u0026ndash;2258 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edi Scotto, F. et al. Delineation and fine-scale structure of fault zones activated during the 2014\u0026ndash;2024 unrest at the Campi Flegrei caldera (Southern Italy) from high‐ precision earthquake locations. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2023GL107680\u003c/span\u003e\u003cspan address=\"10.1029/2023GL107680\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024). e2023GL107680.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShelly, D. R., Ellsworth, W. L. \u0026amp; Hill, D. P. Fluid-faulting evolution in high definition: Connecting fault structure and frequency-magnitude variations during the 2014 Long Valley Caldera, California, earthquake swarm. \u003cem\u003eJ. Geophys. Research: Solid Earth\u003c/em\u003e. \u003cb\u003e121\u003c/b\u003e (3), 1776\u0026ndash;1795. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/2015JB012719\u003c/span\u003e\u003cspan address=\"10.1002/2015JB012719\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShelly, D. R., Skoumal, R. J. \u0026amp; Hardebeck, J. L. Fracture-mesh faulting in the swarm-like 2020 Maacama sequence revealed by high-precision earthquake detection, location, and focal mechanisms. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, e2022GL101233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2022GL101233\u003c/span\u003e\u003cspan address=\"10.1029/2022GL101233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlejko, D. et al. Seismotectonics of the Eastern Southern Alps: a review. \u003cem\u003eBollettino di Geofis. Teorica e Appl.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (122), 109\u0026ndash;136 (1989).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlovenian Environment Agency. Seismic Network of the Republic of Slovenia [Data set]. International Federation of Digital Seismograph Networks. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7914/SN/SL\u003c/span\u003e\u003cspan address=\"10.7914/SN/SL\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2015 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6092/58ff169a-2f02-46ae-908a-bdfcacea069c\u003c/span\u003e\u003cspan address=\"10.6092/58ff169a-2f02-46ae-908a-bdfcacea069c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2016 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6092/a608d853-755e-4177-aada-992857ccb44e\u003c/span\u003e\u003cspan address=\"10.6092/a608d853-755e-4177-aada-992857ccb44e\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2017 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6092/3ff3c323-d7a4-4183-bea0-1a53814ac8b9\u003c/span\u003e\u003cspan address=\"10.6092/3ff3c323-d7a4-4183-bea0-1a53814ac8b9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2018 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6092/c53f37ce-bcf3-453c-a2cf-1894d48cfbbb\u003c/span\u003e\u003cspan address=\"10.6092/c53f37ce-bcf3-453c-a2cf-1894d48cfbbb\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2019 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6092/58ff169a-2f02-46ae-908a-bdfcacea069c\u003c/span\u003e\u003cspan address=\"10.6092/58ff169a-2f02-46ae-908a-bdfcacea069c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2020 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13120/108b8d94-361a-45f3-8195-fc4e8f73d264\u003c/span\u003e\u003cspan address=\"10.13120/108b8d94-361a-45f3-8195-fc4e8f73d264\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnidarcig, A. et al. Bollettino della Rete Sismometrica dell'Italia Nord Orientale (RSINO), Anno 2021 [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13120/8b252b09-314f-456f-812a-b05268ecd001\u003c/span\u003e\u003cspan address=\"10.13120/8b252b09-314f-456f-812a-b05268ecd001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSollai, A. et al. April. High-Precision Earthquake Locations Reveal Detailed Fault Geometries in the western Corinth Rift. In EGU General Assembly Conference Abstracts (pp. EGU25-21500). (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStein, R. S. The role of stress transfer in earthquake occurrence. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e402\u003c/b\u003e, 605\u0026ndash;609. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/45144\u003c/span\u003e\u003cspan address=\"10.1038/45144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwiss Seismological Service (SED), At, E. T. H. \u0026amp; Zurich National Seismic Networks of Switzerland. ETH Z\u0026uuml;rich. (1983). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.12686/sed/networks/ch\u003c/span\u003e\u003cspan address=\"10.12686/sed/networks/ch\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan, Y. J. et al. Machine-learning‐based high‐resolution earthquake catalog reveals how complex fault structures were activated during the 2016\u0026ndash;2017 central Italy sequence. \u003cem\u003eSeismic Record\u003c/em\u003e. \u003cb\u003e1\u003c/b\u003e (1), 11\u0026ndash;19 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUniversity of Trieste. Friuli Venezia Giulia Accelerometric Network [Data set]. International Federation of Digital Seismograph Networks. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7914/SN/RF\u003c/span\u003e\u003cspan address=\"10.7914/SN/RF\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUniversity of Zagreb. Croatian Seismograph Network [Data set]. International Federation of Digital Seismograph Networks. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7914/SN/CR\u003c/span\u003e\u003cspan address=\"10.7914/SN/CR\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValoroso, L., Chiaraluce, L., Di Stefano, R. \u0026amp; Monachesi, G. Mixed-mode slip behavior of the Altotiberina low‐angle normal fault system (Northern Apennines, Italy) through high‐resolution earthquake locations and repeating events. \u003cem\u003eJ. Geophys. Research: Solid Earth\u003c/em\u003e. \u003cb\u003e122\u003c/b\u003e (12), 10\u0026ndash;220 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVičič, B., Aoudia, A., Javed, F., Foroutan, M. \u0026amp; Costa, G. Geometry and mechanics of the active fault system in western Slovenia. \u003cem\u003eGeophys. J. Int.\u003c/em\u003e \u003cb\u003e217\u003c/b\u003e (3), 1755\u0026ndash;1766 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVigan\u0026ograve;, A. et al. Earthquake relocations, crustal rheology, and active deformation in the central\u0026ndash;eastern Alps (N Italy). \u003cem\u003eTectonophysics\u003c/em\u003e \u003cb\u003e661\u003c/b\u003e, 81\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tecto.2015.08.017\u003c/span\u003e\u003cspan address=\"10.1016/j.tecto.2015.08.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillegas-Lanza, J. C. et al. A mixed seismic\u0026ndash;aseismic stress release episode in the andean subduction zone. \u003cem\u003eNat. Geosci.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (2), 150\u0026ndash;154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ngeo2620\u003c/span\u003e\u003cspan address=\"10.1038/ngeo2620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVuan, A. et al. Intermittent slip along the Alto Tiberina low-angle normal fault in central Italy. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, e2020GL089039. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020GL089039\u003c/span\u003e\u003cspan address=\"10.1029/2020GL089039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaldhauser, F. \u0026amp; Ellsworth, W. L. A Double-Difference Earthquake Location Algorithm: Method and Application to the Northern Hayward Fault, California. \u003cem\u003eSeismological Soc. Am.\u003c/em\u003e \u003cb\u003e90\u003c/b\u003e, 1353\u0026ndash;1368. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1785/0120000006\u003c/span\u003e\u003cspan address=\"10.1785/0120000006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaliapin, I., Gabrielov, A., Keilis-Borok, V. \u0026amp; Wong, H. Clustering analysis of seismicity and aftershock identification. \u003cem\u003ePhys. Rev. Lett.\u003c/em\u003e \u003cb\u003e101\u003c/b\u003e (1), 018501 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaliapin, I. \u0026amp; Ben-Zion, Y. Earthquake clusters in southern California I: identification and stability. \u003cem\u003eJ. Geophys. Res. Solid Earth\u003c/em\u003e. \u003cb\u003e118\u003c/b\u003e, 2847\u0026ndash;2864. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jgrb.50179\u003c/span\u003e\u003cspan address=\"10.1002/jgrb.50179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZAMG - Zentralanstalt f\u0026uuml;r Meterologie und Geodynamik. Austrian Seismic Network [Data set]. International Federation of Digital Seismograph Networks. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7914/SN/OE\u003c/span\u003e\u003cspan address=\"10.7914/SN/OE\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1987).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaliapin, I. \u0026amp; Ben-Zion, Y. Earthquake declustering using the nearest‐neighbor approach in space‐time‐magnitude domain. \u003cem\u003eJournal Geophys. Research: Solid Earth\u003c/em\u003e, \u003cb\u003e125\u003c/b\u003e(4), (2020). e2018JB017120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZupančič, P. et al. Seismogenic depth and seismic coupling estimation in the transition zone between Alps, Dinarides and Pannonian Basin for the new Slovenian seismic hazard model, Nat. Hazards Earth Syst. Sci., 24, 651\u0026ndash;672, (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/nhess-24-651-2024\u003c/span\u003e\u003cspan address=\"10.5194/nhess-24-651-2024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9169863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9169863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe characterization of seismic sequences provides key constraints on fault geometry, rupture processes, and seismic hazard. In this study we investigate seismicity recorded between 2015\u0026ndash;2024 in the South-eastern Alps and Western Dinarides (SEAWD), one of the most tectonically complex regions of the Alpine\u0026ndash;Adriatic domain. Starting from the bulletin seismic catalogue of the Northeastern Italy Seismometer Network, we implement a multiscale relocation workflow. The catalogue is first relocated at regional scale using a multi-iterative NonLinLoc Source-Specific Station Term (NLL-SSST) approach, progressively refining residual grids to account for velocity-model uncertainties. Seismic sequences are then identified through nearest-neighbour clustering. Selected sequences are finally relocated at local scale using waveform-coherence\u0026ndash;based NonLinLoc (NLL-coherence), enforcing spatial consistency among similar events.\u003c/p\u003e \u003cp\u003eThis workflow yields locations with ~hundreds-of-meters resolution and resolves fine-scale fault structures. We identify 71 sequences, mainly foreshock\u0026ndash;mainshock\u0026ndash;aftershock and mainshock\u0026ndash;aftershock types, with limited swarm-like activity. Principal component analysis quantifies sequence volume and orientation of seismicity distributions. Most clusters define tabular structures: larger volumes correspond to planar fault zones, whereas compact clusters involve low-magnitude and low seismic activity. These patterns highlight the structural complexity of the Alpine\u0026ndash;Dinaric transition zone and provide new constraints on active seismogenic structures and seismic hazard in northeastern Italy and adjacent regions.\u003c/p\u003e","manuscriptTitle":"Revealing Seismic Sequence Characteristics in the South-eastern Alps and the Western Dinarides by clustering analysis and refined location","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 08:40:39","doi":"10.21203/rs.3.rs-9169863/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-07T03:54:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T09:50:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T07:44:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209513663689989658971717652229218301249","date":"2026-04-08T07:33:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272261272654258838862810003277631793088","date":"2026-04-01T06:55:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-25T12:26:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-25T07:28:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T14:11:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T14:11:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-19T12:59:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b0b79c0-336d-45bc-ad00-30a24cd0f725","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-07T03:54:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T09:50:48+00:00","index":33,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":65168555,"name":"Earth and environmental sciences/Natural hazards"},{"id":65168556,"name":"Earth and environmental sciences/Solid earth sciences"}],"tags":[],"updatedAt":"2026-05-07T04:11:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 08:40:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9169863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9169863","identity":"rs-9169863","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}