Clustering and Seasonal Variability of Weak Seismicity in the Lena Delta (Laptev Sea Region)

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A. Novikov, A. A. Krylov, E.A. Radyuk, W.H. Geissler, F. Krüger, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5362676/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jun, 2025 Read the published version in Pure and Applied Geophysics → Version 1 posted 9 You are reading this latest preprint version Abstract The present work is devoted to studying the characteristics of the spatiotemporal microearthquake distribution in the Lena Delta using data from local seismological monitoring conducted from 2016 to 2018. The results revealed the confinement of microearthquake sources to the Olenek segment of the Lena–Taimyr zone of boundary uplifts, marking the boundary of the Siberian Platform and the Laptev Sea Rift System. The Olenek segment fault zone is traced by hypocenters up to the Moho at a depth of about 40 km. Microearthquakes are distributed unevenly in both space and time, forming clusters in different parts of the fault zone. These clusters can be interpreted as originating from unstable stick-slip sliding during the process of background stable creeping. Seasonal variability in the number of recorded weak earthquakes in the Lena Delta has been revealed. An extended regional catalog (2003–2022) was also used to analyze the seasonal seismicity modulation. The average number of events per day increases by approximately a factor of two during cold seasons. Comparison of these results with snow cover thickness, the Lena water level, GNSS data, gravity data, and calculated additional Coulomb stresses revealed that the seasonal seismicity increase in the Lena Delta correlates with the positive additional Coulomb stresses under conditions of prevalence of normal faults in the Olenek segment. Additional Coulomb stress directly depends on equivalent water thickness, which, in turn, correlates with snow cover thickness. The summer flood does not have a significant impact on the seismicity rate, presumably due to its short duration. microearthquakes Lena Delta Siberian Platform Laptev Sea Rift System lower crust Gutenberg-Richter plot microearthquake clusters seasonal variability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1 Introduction The Laptev Sea region of the Arctic Ocean includes the Laptev Sea shelf and associated coastal structures of the Eurasian continent and extends from the Taimyr Peninsula in the west to the New Siberian Islands in the east (Fig. 1 ). Within the Laptev Sea region, there is a junction of the structures of the Siberian Platform, Taimyr, Verkhoyansk-Kolyma, and Novosibirsk-Chukotka fold-and-thrust systems (Parfenov et al., 2001 ). In the late Cretaceous and Cenozoic, this region became the scene of intensive riftogenesis associated with the opening of the oceanic Eurasian Basin. Continental rifting is reflected in the development of horst and graben structures on the Laptev Sea shelf, extending southeastward from the continental slope. Active seafloor spreading along the Gakkel Ridge in the Eurasia Basin is separated from the Laptev Sea rift system by the Khatanga-Lomonosov fault zone (Drachev, 2000a , 2002 ). The position of the Laptev Sea, on the one hand, at the boundary of the Eurasian and North American plates, and on the other hand, in the area of transition from seafloor spreading to continental rifting, determines its intensive seismic activity in the Russian sector of the Arctic (Drachev, 1998; Piskarev, 2016 ; Krylov et al., 2022 ). In the spatial distribution of earthquakes, one can distinguish a narrow seismic belt along the Gakkel Ridge and a wide area of epicenters between the Taimyr Peninsula, the Lena Delta, and the New Siberian Islands, which covers the main shelf structures of the Laptev Sea (Krylov et al., 2022 ). On the shelf, a seismic active zone is located in its eastern part and can be traced from the Gakkel Ridge towards the Yana Bay. It is confined to the East Laptev Sea province of horsts and grabens (the eastern part of the Laptev Sea rift system) and is located between two extensional detachments, i.e., the main flat or subhorizontal normal faults and sole thrusts along which simple shear deformations occur in the vertical plane (Krylov et al., 2022 ). Another zone of numerous epicenters is confined to the Lena-Taimyr zone of boundary uplifts and extends along the southern margin of the Laptev Sea from the Taimyr Peninsula through the Anabar and Olenek bays to the Lena Delta and the Buor-Khaya Bay (Imaeva et al., 2019 ). The Lena Delta is one of the key areas of the Laptev Sea region from the point of view of modern tectonic processes, as it is located at the junction of the ancient Siberian Platform with the structures of the Ust-Lena rift (western part of the Laptev Sea rift system) on the shelf and folded areas confined to the Lena-Taimyr zone of boundary uplifts and the Verkhoyansk fold-and-thrust system on land. Numerous earthquake epicenters in the Lena Delta vicinity form sublinear zones confined to existing faults (Avetisov, 1991 ; Seismotectonics ...,2017). The known focal mechanisms in the vicinity of the Lena Delta and the Buor-Khaya Bay are characterized by their diversity – structural segments of the crust with different types of stress-strain state are identified (Kovachev et al., 1994 ; Imaeva et al., 2019 ; Krylov et al., 2023 ). In addition, non-uniform vertical movements were revealed in the delta, expressed in the tilt of the delta towards the east – the difference in vertical velocities between the western and eastern parts was found to be approximately 1 mm/year (Bolshiyanov et al., 2019 ). Registration of weak local earthquakes is used to determine the position and activity of seismogenic structures, the structure of the crust, determination of its stress-strain state, and many other tasks. The most important practical application of such data is to clarify the seismic regime of the study area for seismic hazard assessment and prediction of strong earthquakes. The registration of weak seismic events in the Lena Delta has been carried out for several years using temporary networks of seismic stations (Geissler et al., 2017 , 2018 ) – these records were used in the present study. The purpose of the work is to describe the peculiarities of the spatial and temporal distribution of microearthquakes in the Lena Delta using data from local seismic monitoring. For this purpose, a semi-automated search for earthquake signals on seismograms, the determination of P- and S-wave arrival times, and then the determination of hypocenter coordinates and earthquake magnitudes were performed. Then maps of epicenter distribution, vertical sections with projections of foci, and diagrams of the temporal distribution of earthquake parameters were constructed. The results of the study can help in understanding the features of seismicity distribution and seismic regime in the fault regions of transition from platforms to younger geological structures under conditions of lithospheric stretching, using the selected region as an example. In addition, the paper considers the possible influence of exogenous factors on the natural seismicity in such unstable fault regions. 2 Methods and Data 2.1 Seismological monitoring data from the Lena Delta The present study is based on the results of seismological monitoring conducted in 2016–2018 in the Lena Delta within the framework of the SIOLA international project (Geissler et al., 2017 ; 2018 ). Monitoring was carried out using a temporary local network of seismic stations, equipped with MARK L4 (1 Hz, 3-component) seismometers and DATA-CUBE recorders provided by the GIPP instrument pool at GFZ Potsdam, Germany. The measurement season started in late August, the most suitable time in terms of weather and logistics, and lasted until late April/early May due to the limited data storage pool of the supplied seismographs. Figure 2 a demonstrates the network configuration during the 2016–2017 registration period (hereafter referred to as the first registration period). The seismic stations were installed along the Olenek and Bykov channels, as well as along the main channel of the Lena River. Figure 2 b demonstrates the network configuration during the registration period 2017–2018 (hereinafter referred to as the second registration period). In this period, the network was reduced to the area along the Bykov channel from the center of the Lena Delta in the southeastern direction. Additional information about the stations can be found in Appendix A . 2.2 Additional seismological, meteorological, gravity and GNSS data To discuss the peculiarities of the spatial and temporal distribution of seismic events in the Lena Delta, additional information on seismicity from the "Earthquakes of Russia" database (“Earthquakes of Russia”…) compiled by the Geophysical Survey of the Russian Academy of Sciences – it contains the information about seismic events occurred in Russia since the beginning of 2003. Snow cover thickness was accessed from the meteorological data archive for the Tiksi station (The electronic archive of the AARI). The gravity data was taken from GRACE and GRACE-FO experiments (Landerer, 2021 ). Anomalies of gravity are provided in centimeters of equivalent water thickness unit, which is the depth of a thin layer of water that could account for the observed gravity variation. The GNSS time series for TIXI station were acquired from the Nevada Geodesic Laboratory (Blewitt et al., 2016 ). The data was downloaded in tenv format, detrended, and smoothed using LOWESS (Cleveland, 1979 ). 2.3 Methods of seismic records and catalogs processing The acquired seismic records were processed in several stages: Data formatting; Semi-automated search for earthquake signals on seismograms; Determination of P- and S-wave arrival times on earthquake records; Determination of earthquake hypocenter positions; Determination of earthquake magnitudes. The records were reformatted from the internal binary raw data format of the DATA-CUBE recorder into the common seismological miniSeed format. The automated search for earthquakes was based on the algorithm (Krylov et. al., 2019 ). The presence of an earthquake signal on the records was determined by such features as a sharp increase in the amplitude of the STA/LTA characteristic function (Allen, 1978 ), signal duration, and correlation on different channels of different stations. Verification of the automatic detector results was performed visually by the operator. To reduce seismic noise in the frequency range characteristic of weak local earthquakes, filtering was performed by a 4 pole bandpass Butterworth filter with a bandwidth of 6–16 Hz.. The determination of P- and S-wave arrivals, hypocenter positions, and magnitudes was performed using the SEISAN software package (Havskov and Ottemoller, 1999 ). Wadati plots (Wadati, 1933 ) were used to check the correctness of the determination of arrival times. Hypocenter coordinates and origin times were determined by the HYPOCENTER subroutine (Lienert et al., 1986 ). The velocity model used for localization was compiled after Cherepanova et al. ( 2013 ), Laske et al., ( 2013 ), Kovachev et al. ( 1994 ) and is presented in Table 4 of Appendix B. Local magnitude was estimated using coefficients by Hutton and Boore ( 1987 ). For the “Earthquakes of Russia” database, K-class magnitude was converted to \(\:{M}_{L}\) using the coefficients from the paper (Rautian, 1960 ). Table 1 Mean, median and quantile values of the number of earthquakes (N) per day during warm and cold seasons for three declustered catalogs Mean N 25th quantile Median N 75th quantile First registration period (warm season) 0.662 0 0 1 First registration period (cold season) 1.385 0 1 2 Second registration period (warm season) 0.490 0 0 1 Second registration period (cold season) 1.093 0 1 2 “Earthquakes of Russia” (warm season) 0.012 0 0 0 “Earthquakes of Russia” (cold season) 0.024 0 0 0 Appendix A Table 2 Information about the stations deployed during the first registration period (2016–2017) Name Latitude Longitude Deployed End of recording LD004 72,65 124,35 26.07.2016 24.04.2017 LD007 72,18 124,89 25.07.2016 03.05.2017 LD008 72,44 125,31 25.07.2016 25.04.2017 LD010 72,33 125,76 26.07.2016 25.04.2017 LD011 72,48 126,27 01.08.2016 09.05.2017 LD012 72,17 126,11 28.07.2016 05.05.2017 LD014 71,89 126,04 25.07.2016 24.04.2017 LD015 72,12 126,98 31.07.2016 29.04.2017 LD016 72,40 127,15 02.08.2016 10.05.2017 LD018 71,93 127,31 29.07.2016 06.05.2017 LD019 72,07 128,32 04.08.2016 03.05.2017 LD021 71,40 127,25 30.07.2016 28.04.2017 TIK01 71,57 129,07 23.07.2016 22.04.2017 Table 3 Information about the stations deployed during the second registration period (2017–2018) Name Latitude Longitude Deployed End of recording LD011 72,48 126,27 29.07.2017 28.04.2018 LD015 72,12 126,98 29.07.2017 28.04.2018 LD016 72,40 127,15 02.08.2017 11.05.2018 LD019 72,07 128,32 02.08.2017 12.05.2018 LD031 72,40 126,82 31.07.2017 30.04.2018 LD032 72,40 126,81 01.08.2017 01.05.2018 LD033 72,40 126,80 02.08.2017 11.05.2018 TIK01 71,57 129,07 20.07.2017 19.04.2018 Appendix B Table 4 Velocity model used in the current study. \(\:{V}_{p}/{V}_{s}\) coefficient is set to 1.73 Depth, km \(\:{\varvec{V}}_{\varvec{p}}\) , km/s Comment 0 5.6 5 6.2 25 6.7 30 7.3 46 8.0 Expected Moho The catalogs were cleaned of frostquakes using three main criteria. First, frostquakes have a characteristic waveform with a predominant Rayleigh wave, which can be discerned from regular local weak tectonic earthquake waveform (Fig. 3 a-b). Second, frostquakes have a narrow low-frequency spectrum (Fig. 3 c-d), while an earthquake spectrum is broader and the signal decays longer. Third, in the case of frostquakes the particle motion of the P-wave tends to be horizontal in contrast to the more vertical entry of seismic rays of body waves from local weak tectonic earthquakes (Fig. 3 e-f). An elliptical particle motion trajectory, characteristic for Rayleigh wave, is observed instead of the S-wave particle motion typical for local weak tectonic earthquakes (Fig. 3 g-h) (Afonin, 2024). For statistical analysis and Gutenberg-Richter plots, the catalogs were declustered using Gardner and Knopoff procedure (Gardner and Knopoff, 1974). A one-day time window and a 0.1° spatial window were selected for all three catalogs (SIOLA16-17, SIOLA17-18, “Earthquakes of Russia” catalog) to form potential aftershock sequences. The overlapping sequences were united and treated as one cluster. The biggest clusters with sequence length L > 8 for the first season and L > 6 for the second season were mapped. Noise power spectral density plots were calculated using the direct Fourier transform method (McNamara and Buland, 2004 ). The spectra were calculated for 60-minute windows every 30 minutes for 45 days in autumn and 45 days in winter to determine seasonal variations in ambient noise. To analyze the spatial and temporal distribution of seismicity, we constructed epicenter distribution maps, vertical sections with projections of sources, and diagrams of the temporal distribution of earthquake parameters in the Matlab programming environment. The M_Map library (Pawlowicz, 2020 ) was used to construct the maps. 3 Results During each recording period, signals from about one thousand local and regional earthquakes were recorded. Figure 4 shows an example of typical local earthquake waveforms for three components, as well as their Fourier spectra in comparison with seismic noise spectra. The energy of signals is usually concentrated in the frequency range of 0.3–30 Hz with maximum signal-to-noise ratios in the range of 2–20 Hz. For further analysis of the spatiotemporal characteristics of seismicity, events were selected that were most accurately localized using the HYPOCENTER program - with error values in longitude, latitude, and depth of no more than 20 km. There were 598 such events in the first registration period and 390 in the second registration period. After catalog declustering the number of events decreased to 341 and 276, respectively. The lower number of events during the second period is expected due to the smaller network size and reduced number of stations. Figure 5 presents the distributions of epicenters of local earthquakes in the area of the Lena Delta and the Buor-Khaya Bay as well as corresponding distributions of projected depths of earthquake sources on vertical profiles along (A-B) and across (C-D) for both periods of record. The plotted boundary of the Siberian Platform is based on Drachev ( 2018 ), and the Moho depth values are derived from the CRUST 1.0 model (Laske et al., 2013 ). Most events form a cloud of epicenters of northwest-southeast trending, extending along the Olenek segment fault zone. Many events were also recorded at the southeastern rim of the Lena Delta in the Buor-Khaya Bay, where the epicenters tend to be located in the submeridional direction. The A-B section shows that from southeast to northwest, the maximum depth of hypocenters tends to increase (from approximately 20 km to 30 km). The C-D section shows that in the transition from shelf structures in the northeast to the Siberian Platform in the southwest, the density of hypocenter distribution drops sharply. Thus, the distribution of epicenters and projections of hypocenters on vertical sections show that the vast majority of events occurred outside the Siberian Platform. In addition, the distribution of the source depths shows that the overwhelming majority of events occurred within the Earth's crust - there are 4–6 hypocenters below the CRUST 1.0 Moho boundary per recording period. Figure 6 shows cumulative Gutenberg-Richter plots for the Lena Delta and the Buor-Khaya Bay, constructed separately for two local catalogs. In addition, the Gutenberg-Richter plot for the catalog created using the "Earthquakes of Russia" database ("Earthquakes of Russia" ...) is shown. The values of the Gutenberg-Richter parameters were determined, which are quite close for all three plots: the a-values are in the range of 2.7–3.7, and the b-values are in the range of 0.81–0.91. At the same time, the value of the minimum representative magnitude (magnitude of completeness \(\:{M}_{c}\) ) for the regional catalog is 2.8, a value much larger than the \(\:{M}_{c}\) of 0.5 for the local catalogs of both registration periods. Thus, the events registered by the local network of stations significantly increased the representativeness of the Gutenberg-Richter plot in the magnitude range of 0.5–2.8. In Fig. 5 , there are visible areas with high concentrations of earthquake epicenters for both recording periods (see Methods and Data section for definition of clusters). To study temporal characteristics of the clusters, time dependences of the depths (within 40 km) and magnitudes of earthquakes were also constructed (Figs. 7 – 8 ). Figure 9 shows maps of epicenters with marked earthquake clusters. Six clusters were identified for the first registration period (I1-I6) and four clusters for the second registration period (II1-II4). Many clusters (I1, I2, I6) are located along the fault zone near the Buor-Khaya bay. There are 5 clusters (I5 and II1-II4) in the active zone close to Tiksi. The b-values of the largest clusters I-3 and I-4 with 50 and 92 events were estimated to be 0.75 and 0.79 respectively. The b-values of other clusters were not analyzed due to the lack of events in them ( \(\:N\le\:20\) ). 4 Discussion The distribution of microearthquake foci projections in the horizontal and vertical planes (Fig. 5 ) revealed their location in the narrow elongated Olenek segment, marking the boundary between the Siberian Platform and the Laptev Sea Rift System in this area. In map view, this fault zone is elongated along the Olenek and Bykov channels, with the overwhelming number of foci located outside the Siberian Platform, which, in general, is not surprising, because platforms are stable and rigid environments, and usually aseismic (Wesnousky and Scholz, 1980 ). In depth, the fault zone is clearly traced by hypocenters up to the Moho boundary located in this area at depths of 40–45 km (Avetisov, 1991 ; Laske et al., 2013 , Cherepanova et al., 2013 , Atlas...). In addition, only 4–6 events occurred during each recording period below the Moho boundary at depths of 45–90 km. The presence of few presumably mantle sources in the Laptev Sea region, and in particular, in the area of the Lena Delta is confirmed by the ISC and USGS reviewed catalogs, as well as by local observations (Kovachev et al., 1994 ). Microearthquake clusters (Figs. 7 – 9 ) were recorded in both periods of registration and are located within the main cloud of epicenters of the Olenek segment. The b-values determined for the Gutenberg-Richter plot (Fig. 6 ) are in the range of 0.7–0.8 and are not high enough for classical earthquake swarms associated with magmatic and volcanic processes (Špičák et al, 1999 ; Cesca et al., 2022 , Yoshida et al., 2022). Such microearthquake clusters can occur in the fault plane in the presence of a background creeping process - these events are commonly interpreted as occurring on localized areas of faulting that undergo unstable stick-slip during the process of background stable creeping (Rubin et al, 1999 ). Figures 7 – 8 show the non-uniform distribution of microearthquakes in time for the declustered catalogs. To further analyze the features of the temporal distribution, we checked the number of earthquakes per day time histories for seasonal variability. The Fig. 10 shows that the number of earthquakes per day increases significantly with the onset of the cold season, characterized by temperature below 0°C and the presence of snow cover (hereafter referred as cold season). A quantitative assessment of this dependence was carried out, showing that in the cold season the average number of earthquakes per day was 2.09 times higher than in the warm season of the first registration period. For the second registration period this ratio was 2.23. For the catalog of "Earthquakes of Russia" database (2003–2022) – 2 times (Table 1 ). Thus, there is a pronounced seasonal variability in the number of recorded weak earthquakes in the Lena Delta. Further we consider several possible reasons for this. The variation in the number of recorded earthquakes per day may be associated with the intensity of the background seismic noise and, therefore, with the recording ability of seismic equipment (Morozova, 2019 ). The power spectral density (PSD) distributions of seismic noise were constructed for the time periods of the cold and warm seasons for the first registration period – Fig. 11 . It was found that in the frequency range of the sensors (1–50 Hz), PSD levels on most stations do not significantly depend on temperature or the presence of snow. One possible mechanism for seasonal seismicity variation is additional hydrological surface load caused by either snow (Heki, 2003 ; Ueda et al., 2004 ; Braunmiller, et al., 2014 ) or water column (Costain et al., 1987 ; Saar et al., 2003; Amos et al, 2014 ; Zhang et al., 2017 ; Craig et al., 2017 ; Bettinelli et al., 2008 ). To test the applicability of this hypothesis for the Lena Delta, the time series of monthly number of earthquakes according to “Earthquakes of Russia” database for 2003–2022 was analyzed (Fig. 12 a, Fig. 13 a). Since the Mc for that catalog is 2.8, these events can be considered as relatively strong earthquakes for this area. It is worth noting a large increase in the yearly number of recorded earthquakes in “Earthquakes of Russia” catalog starting from 2014–2015, but it’s unclear whether that process is due to the change in the processing method and equipment or the real change in regional seismicity. As was stated earlier, the vast majority of earthquakes in the catalog (Table 1 ) occur in the cold season. Moreover, according to the GNSS time series from TIXI station, cold seasons correspond to the maximum downward displacement (ground subsidence), while warm seasons are characterized by maximum upward lift (Fig. 12 b, Fig. 13 b). This clear seasonality in the vertical displacement can be at least partially explained by changes in the hydrological surface loading (Michel et al, 2021 ; Xue et al, 2021 ). To investigate this relationship further, the time series of mass flux in cm of equivalent water thickness (EWT) from GRACE and GRACE-FO missions (Fig. 12 c, Fig. 13 c) was incorporated. The break in data in 2017–2018 is due to the end of GRACE operation and the start of GRACE-FO in July 2018. The cold season periods correspond to the rise in EWT, while it steadily declines during the warm season. The Lena water level sharp rise in June (Fig. 13 d) also corresponds to the characteristic EWT spike. The snow thickness is also rising throughout the cold season from October to May with rapid decrease towards the end of the cold period in June (Fig. 12 d, Fig. 13 e). The rise in EWT throughout the cold season can be caused by accumulation of snow in the region. The average annual peak-to-peak amplitude of EWT is around 24 cm with its maximum in June and minimum in September. According to the GRACE and GRACE-FO data, in the central part of the Lena Delta and areas around the Bykov and Olenek channels the EWT changes simultaneously throughout the year with roughly the same 24 cm peak-to-peak amplitude. To model the Coulomb stress changes in this area, the surface loading field was chosen to be 160 km long and 120 km wide rectangle along the Olenek channel with additional pressure of 2.4 kPa (corresponds to the EWT peak-to-peak amplitude). The three-dimensional stress tensor resulting from additional surface loading was computed using Bossinesq equations for the point load in cylindrical coordinates (Dang et al, 2010, Timoshenko and Goudier, 1970). The full stress tensor was obtained by integrating these equations over the loading area. To estimate the effect of this additional stress on the faults in the Olenek segment, the Coulomb failure stress formula \(\:\varDelta\:{\sigma\:}_{c}=\varDelta\:\tau\:+\mu\:\varDelta\:{\sigma\:}_{n}\) , \(\:\mu\:=0.3\) was used. According to the Active Faults of Eurasia Database (AFEAD) (Zelenin et al., 2022 ) there are numerous normal faults with 60° dip in the most active central western part of the segment. The calculation of additional Coulomb stress on the fault with \(\:\delta\:=60^\circ\:,\lambda\:=-90^\circ\:\) was carried out. The time series of additional Coulomb stress at typical seismogenic depths of 15 km, which approximately corresponds to the median source depths value for all three catalogs used in this study, were calculated (Fig. 12 e and Fig. 13 f). The average annual peak-to-peak amplitude of additional Coulomb stress is estimated to be ~ 1.2 kPa. Some studies have shown that several kPa positive change in Coulomb stresses on the fault can be sufficient to cause additional seismicity (Heki, 2003 ; Craig et al, 2017 ; Shiddiqi et al, 2023 ). For example, hydrological load that corresponds to ~ 1 kPa peak-to-peak seasonal variation in additional Coulomb stress is shown to modulate the seismicity in the New Madrid zone (Craig et al, 2017 ). The 2–4 kPa change in Coulomb stress due to changes in water level within the Ganges basin increased the seismicity rate by the factor of 2 in winter in the Nepal Himalaya (Bettinelli et al., 2008 ). Moreover, the study of hydrological load found that changes of Coulomb stress up to 1 kPa increased the number of both dip-slip and strike-slip events in Northern California (Johnson et al, 2017 ). Coulomb failure criterion implies that positive change in Coulomb stress should result in increased seismic activity and negative change should delay earthquakes. Whether surface loading will result in increase or decrease in Coulomb stress is heavily determined by the fault geometry. For shallow faults, normal stress induced by surface pressure plays a key role and for steeper faults shear stress can be dominant. Moreover, additional shear stress along the slip would be positive for reverse fault and negative for normal. The location of the fault relative to the loading area also plays a key role in determining the effect on Coulomb stress. According to the modeling results, in our case the surface loading corresponds to the positive Coulomb stress change. According to Fig. 13 , there is higher seismicity in months with high Coulomb stress. Figure 14 shows a general linear upward trend in number of earthquakes a month with increase in Coulomb stress. 5 Conclusion During local instrumental observations using a temporary network of seismographs installed in the Lena Delta, a large number of microearthquakes were recorded. The spatial and temporal distribution of these events revealed a number of patterns. The sources of recorded microearthquakes are concentrated in the area of the Olenek segment fault zone, marking in this area the boundary of the Siberian platform and the Laptev Sea rift system. The fault zone is clearly traced by hypocenters up to the Moho boundary located in this area at depths of 40–45 km. Microearthquakes are distributed unevenly both in space and in time, forming clusters, which indicates the unstable and dynamic nature of processes in the fault zone. Such a pattern can occur in the fault plane in the presence of a background creeping process – microearthquake clusters can be interpreted as originating from unstable stick-slip sliding during the process of background stable creeping. Seasonal variability in the number of recorded weak earthquakes in the Lena Delta was revealed – the average number of events per day increases two times during the cold seasons, characterized by temperature drops below 0°C and the presence of snow cover. This relationship is also present in the temporal distribution of relatively strong ( \(\:{M}_{L}\) > 2.8) earthquakes in the regional earthquake catalog from 2003 to 2022. Additional Coulomb stress was quantified by numerical modeling of a three-dimensional stress tensor and its peak-to-peak annual variation was estimated to be 1.2 kPa. Seasonal seismicity increase in the Lena Delta corresponds to positive additional Coulomb stress under conditions of the prevalence of normal faults in the Olenek segment. Additional Coulomb stresses directly depends on calculated equivalent water thickness, which, in turn, correlates with the snow cover thickness. Summer flood, in turn, does not seem to have a significant correlation with seismicity rate, presumably due to its short duration. Though we obtained indications of seasonal variability caused by hydrological loading, the evidence can hardly be considered complete – both more local seismological observations and better extensive coverage of GNSS measurements will be required to better understand the nature of these processes. Declarations Author Contributions The project was initiated by Geissler W.H., Baranov B.V., Krüger F., Haberland C., Shibaev S.V. Data processing and analysis were designed by Krylov A.A. and were performed by Novikov M.A., Krylov A.A. and Radiuk E.A. The first draft of the manuscript was written by Novikov M.A. and Krylov A.A. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding The processing of seismic records, the determination of the main parameters of earthquakes and the description of the seismic regime of the Lena Delta area were supported by RSF grant No. 23-17-00125. The analysis of the seasonal character of seismicity was performed under the state assignment No. FMWE-2024-0018. Numerical modeling of crustal response caused by surface loading was partially supported by the grant of the state program of the «Sirius» Federal Territory «Scientific and technological development of the «Sirius» Federal Territory» (Agreement №18-03 date 10.09.2024). German funding was provided by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, the Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, and the University of Potsdam. Acknowledgements We are grateful to all of our Russian and German colleagues organized and executed the fieldwork. We would like to thank the colleagues who participated in the SIOLA project: Aline Plötz, Haberland C., Waldemar Schneider, Mikhail Grigoriev, Anne Morgenstern, Daniel Vollmer, Karl-Heinz Jäckel, Mike Hönig, Stepan Gukov, Dmitry Peresypkin, Sergey Petrunin, Tanja Fromm, Joelund Asseng. Stations and equipment were provided by the Geophysical Instrument Pool Potsdam (GIPP). Data availability The seismological data are archived in the mseed format at the GEOFON data center at the GFZ Potsdam (https://geofon.gfz-potsdam.de/waveform/archive/network.php?ncode=8B&year=2016, doi:10.14470/3O7561738646). Data request should be sent to [email protected] . References Adushkin, V.V., Loktev, D.N., Spivak, A.A. (2008). Influence of baric perturbations of the atmosphere on microseismic processes in the Earth's crust. Physics of the Earth, (6), 77–85. https://doi.org/10.1134/S1069351308060086 Afonin, N., Kozlovskaya, E., Moisio, K., Kokko, E.R., Okkonen, J. (2024). Frost quakes in wetlands in northern Finland during extreme winter weather conditions and related hazard to urban infrastructure. The Cryosphere, 18, 2223–2238. https://doi.org/10.5194/tc-18-2223-2024 Allen, R.V. (1978). Automatic earthquake recognition and timing from single traces. 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A short-term survey of microseismicity in the Buor-Khaya Bay, Laptev Sea using bottom seismographs. Izvestiya, Physics of the Solid Earth, 7–8, 65–76 (in Russian) Krylov A. A., L. I. Lobkovsky, A. I. Ivashchenko. (2019). Automated detection of microearthquakes in continuous noisy records produced by local ocean bottom seismographs or coastal networks. Russian Journal of Earth Science, 19, ES2001. https://doi.org/10.2205/2018ES000649 . Krylov, A.A., Lobkovsky, L.I., Rukavishnikova, D.D., et al. (2022). New Data on Seismotectonics of the Laptev Sea from Observations by Ocean Bottom Seismographs. Doklady Earth Sciences, 507, 936–940. https://doi.org/10.1134/S1028334X22600591 Krylov, A.A., Lobkovskii, L.I., Kovachev, S.A. et al. (2023) Geodynamic Regimes in the Laptev Sea Region According to the Latest Seismological Data. Doklady Earth Sciences, 513, 1338–1343. https://doi.org/10.1134/S1028334X23602031 Laske, G., Masters, G., Ma, Z., Pasyanos, M. (2013). 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Cite Share Download PDF Status: Published Journal Publication published 19 Jun, 2025 Read the published version in Pure and Applied Geophysics → Version 1 posted Editorial decision: Revision requested 23 Jan, 2025 Reviews received at journal 22 Jan, 2025 Reviews received at journal 18 Dec, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviewers invited by journal 04 Nov, 2024 Editor assigned by journal 02 Nov, 2024 Submission checks completed at journal 02 Nov, 2024 First submitted to journal 30 Oct, 2024 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-5362676","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376438012,"identity":"2d3d0575-5122-4cef-a073-89fce6bf166a","order_by":0,"name":"M. A. 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Shibaev","email":"","orcid":"","institution":"Yakut (Sakha) Regional Seismological Centre, Geophysical Survey of Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"S.V.","middleName":"","lastName":"Shibaev","suffix":""}],"badges":[],"createdAt":"2024-10-30 17:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5362676/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5362676/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00024-025-03755-6","type":"published","date":"2025-06-19T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68975423,"identity":"f911fd69-f5cb-4c0f-bd6d-e38bc5e7bcdc","added_by":"auto","created_at":"2024-11-14 06:39:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289903,"visible":true,"origin":"","legend":"\u003cp\u003ePrinciple structural scheme based on (Drachev, 2000b; Tectonographic atlas…, 2020; Krylov et al., 2023) and seismicity of the Laptev Sea region: (1) Siberian Platform; (2) Eastern Laptev Sea horst and graben province; (3) Lena–Taimyr zone of boundary uplifts; (4) Kotelnicheskii uplift; (5) Ust’–Lena rift; (6) Verkhoyansk fold-and-thrust system; (7) Eurasian Basin; (8) Gakkel Ridge; (9) Khatanga–Lomonosov fault zone; (10) extensional detachments. Purple circles mark earthquake epicenters from the combined catalog of the Geophysical Survey of the Russian Academy of Sciences, the International Seismological Center, and the United States Geological Survey (1909‒2020) (“Earthquakes of Russia”; ISC…; USGS…). The red rectangle outlines the region of interest for this study\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/6073aeeccc40be74785d9e08.jpeg"},{"id":68975415,"identity":"c5380296-1853-486d-96c5-2340274e9961","added_by":"auto","created_at":"2024-11-14 06:39:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":388741,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration of the local network of seismic stations in the Lena Delta within the framework of the SIOLA project: \u003cstrong\u003e(a)\u003c/strong\u003e during first registration period (August 2016 – May 2017); \u003cstrong\u003e(b)\u003c/strong\u003e during second registration period (August 2017 – May 2018)\u003c/p\u003e","description":"","filename":"floatimage249.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/523fbaf1f41742a0b12e5987.png"},{"id":68974521,"identity":"4fe3dbb9-7a1e-40d7-a09e-a26bc7e620c6","added_by":"auto","created_at":"2024-11-14 06:31:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":392421,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristic features of earthquakes (left column) and frostquakes (right column): \u003cstrong\u003e(a)\u003c/strong\u003e, \u003cstrong\u003e(b)\u003c/strong\u003e waveforms; \u003cstrong\u003e(c)\u003c/strong\u003e, \u003cstrong\u003e(d)\u003c/strong\u003e spectrograms; \u003cstrong\u003e(e)\u003c/strong\u003e, \u003cstrong\u003e(f)\u003c/strong\u003e P-wave particle motion diagrams; \u003cstrong\u003e(g)\u003c/strong\u003e, \u003cstrong\u003e(h)\u003c/strong\u003e S-wave and Rayleigh particle motion diagrams. The recordings of vertical components of 2017-11-01T21:18:32 earthquake and 2018-02-06T13:49:42 frostquake are used in the picture. Time window of particle motion diagram is 1.5 seconds for the earthquake and 1 second for the frostquake event.\u003c/p\u003e","description":"","filename":"floatimage341.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/ef08b9e7c25f2b9c0c9b011d.png"},{"id":68974524,"identity":"6e0cb39c-ff2b-46c1-b6d4-94c7670b4493","added_by":"auto","created_at":"2024-11-14 06:31:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207160,"visible":true,"origin":"","legend":"\u003cp\u003eTypical waveforms for three components and corresponding Fourier spectra of local earthquakes on the example of the event with magnitude M\u003csub\u003eL\u003c/sub\u003e\u0026nbsp;= 1.7 occurred on 2017-08-07 10:18 (UTC) and recorded by the station LD011 in the Lena Delta. The blue line shows the spectra of earthquake signals and the green line shows the spectra of seismic noise before the P-waves onset.\u003c/p\u003e","description":"","filename":"floatimage434.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/131171b40d4e509a584bbab7.png"},{"id":68976074,"identity":"6754dad5-a735-4aeb-8f91-f7461036acbf","added_by":"auto","created_at":"2024-11-14 06:47:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":510564,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of earthquakes in the Lena Delta obtained during the SIOLA project: \u003cstrong\u003e(a)\u003c/strong\u003e epicenter distribution for the first registration; \u003cstrong\u003e(b)\u003c/strong\u003e epicenter distribution for the second registration period; \u003cstrong\u003e(c)\u003c/strong\u003e projections of earthquake sources on the vertical section A-B for the first registration period; \u003cstrong\u003e(d)\u003c/strong\u003eprojections of earthquake sources on the vertical section A-B for the second registration period; \u003cstrong\u003e(e)\u003c/strong\u003e projections of earthquake sources on the vertical section C-D for the first registration; \u003cstrong\u003e(f)\u003c/strong\u003e projections of earthquake sources on the vertical section C-D for the second registration period. The black line in (c)-(f) is the Moho depth (Laske et al., 2013). The orange line is the Siberian platform boundary (Drachev, 2018). The shaded rectangle indicates the Olenek segment fault zone\u003c/p\u003e","description":"","filename":"floatimage526.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/87e9352c49f353e57440a2e2.png"},{"id":68977721,"identity":"65a24249-8bb1-4b29-8810-97ed7e8bb2be","added_by":"auto","created_at":"2024-11-14 06:55:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80695,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative normalized Gutenberg-Richter plots for the area of the Lena Delta and the Buor-Khaya Bay corresponding to the local catalogs of the two periods of registration, as well as to the regional catalog (“Earthquakes of Russia\" database) after declustering\u003c/p\u003e","description":"","filename":"floatimage614.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/683b0224a0fe9cb97ab5457c.png"},{"id":68975419,"identity":"50aeccf9-46d2-41e4-bb7e-e32e4682b64e","added_by":"auto","created_at":"2024-11-14 06:39:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":376102,"visible":true,"origin":"","legend":"\u003cp\u003eTime dependences of the depths of crustal earthquakes recorded during the first registration period \u003cstrong\u003e(a)\u003c/strong\u003eand during the second registration period \u003cstrong\u003e(b)\u003c/strong\u003ewith the indicated positions of earthquake clusters (only events with depths less than 40 km are shown).\u003c/p\u003e","description":"","filename":"floatimage76.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/9516cb1fe02984cb27b834cd.png"},{"id":68974529,"identity":"0b13c70c-803c-4313-9ecf-cbf4214019e6","added_by":"auto","created_at":"2024-11-14 06:31:27","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":271730,"visible":true,"origin":"","legend":"\u003cp\u003eTime dependences of crustal earthquake magnitudes recorded during the first registration period \u003cstrong\u003e(a)\u003c/strong\u003e and during the second registration period \u003cstrong\u003e(b)\u003c/strong\u003e with indicated positions of earthquake clusters.\u003c/p\u003e","description":"","filename":"floatimage82.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/945f78448b1f205c2e7cdee4.png"},{"id":68975416,"identity":"316ffb46-5d5a-4423-bf76-60a89a67f086","added_by":"auto","created_at":"2024-11-14 06:39:27","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":307636,"visible":true,"origin":"","legend":"\u003cp\u003eEarthquake epicenters with identified clusters: \u003cstrong\u003e(a)\u003c/strong\u003e for the first registration period; \u003cstrong\u003e(b)\u003c/strong\u003e for the second registration period\u003c/p\u003e","description":"","filename":"floatimage92.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/fd52c9a2828baaaa054138cc.png"},{"id":68976076,"identity":"da4c583f-c904-4e6d-b127-116bd9b567ea","added_by":"auto","created_at":"2024-11-14 06:47:27","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":173754,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal distribution of the number of earthquakes per week for the first registration period \u003cstrong\u003e(a)\u003c/strong\u003e, for the second registration period \u003cstrong\u003e(b) \u003c/strong\u003eand for regional catalog\u003cstrong\u003e (c) \u003c/strong\u003eafter declustering. Blue shading indicates cold seasons\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/8c5a16bc20a50aab75c3ba57.png"},{"id":68974534,"identity":"efd74cf9-ca9b-41bd-ac0b-5debf4f6b63e","added_by":"auto","created_at":"2024-11-14 06:31:27","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":400999,"visible":true,"origin":"","legend":"\u003cp\u003ePower spectral density (PSD) of seismic noise for different components of the LD004 station for separate cold and warm season time intervals of the first recording period. The solid red and brown lines indicate the NHNM (new high-noise model) and NLNM (new low-noise model) respectively (Peterson, 1993). The red and brown dotted lines indicate the extension of the NHNM and NLNM curves respectively according to (Wolin and McNamara, 2020). The color scale denotes the probability of occurrence of a given power at a particular frequency\u003c/p\u003e","description":"","filename":"floatimage111.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/3409522dd1b6d6f87d0fe6e6.png"},{"id":68975421,"identity":"869c1a99-64ae-4d44-aecc-17b425caaa38","added_by":"auto","created_at":"2024-11-14 06:39:27","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":321424,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal distribution of the number of earthquakes per month according to the catalog from the \"Earthquakes of Russia\" database in the period from 2003 to 2022 \u003cstrong\u003e(a)\u003c/strong\u003e, GNSS weekly vertical displacement time series for TIXI station (blue line) with LOWESS smoothed curve (black dashed line)\u003cstrong\u003e(b)\u003c/strong\u003e, equivalent water thickness according to GRACE and GRACE-FO \u003cstrong\u003e(c)\u003c/strong\u003e, snow thickness at Tiksi \u003cstrong\u003e(d)\u003c/strong\u003e, additional Coulomb stress on the fault \u003cstrong\u003e(e)\u003c/strong\u003e. Blue shading indicates cold seasons\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/72bc8f31e962838894fae764.png"},{"id":68974532,"identity":"d46863f7-67a8-40bf-abd3-f49da1b485fa","added_by":"auto","created_at":"2024-11-14 06:31:27","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":98508,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of various parameters aggregated by month. Cumulative number of earthquakes by month for 2003-2022 period (orange for M\u003csub\u003eL\u003c/sub\u003e\u0026nbsp;≥ M\u003csub\u003eC\u003c/sub\u003e \u0026nbsp;= 2.8, blue for M\u003csub\u003eL\u003c/sub\u003e\u0026nbsp;\u0026lt; M\u003csub\u003eC\u003c/sub\u003e \u0026nbsp;= 2.8 ) \u003cstrong\u003e(a), \u003c/strong\u003eGNSS vertical displacement time series for TIXI station \u003cstrong\u003e(b)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eequivalent water thickness according to GRACE and GRACE-FO \u003cstrong\u003e(c)\u003c/strong\u003e, Lena water level (Medvedev et al, 2021) \u003cstrong\u003e(d)\u003c/strong\u003e, snow cover thickness at Tiksi \u003cstrong\u003e(e)\u003c/strong\u003e, modeled additional Coulomb stress on the fault\u003cstrong\u003e (f)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/0e82f9dfe9796ae49557587f.png"},{"id":68976077,"identity":"06fa6316-db63-464e-b9ae-7386929afd07","added_by":"auto","created_at":"2024-11-14 06:47:27","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":78700,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship of number of earthquakes in a month and Coulomb stress change. Values were plotted for the months with N\u0026gt;1 earthquakes\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/a8f0f99dc777d5c1fb6da56f.png"},{"id":85231281,"identity":"84cb3c78-39cc-4a23-b599-9c37ac36c799","added_by":"auto","created_at":"2025-06-23 16:04:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4906982,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5362676/v1/0c68dede-4355-4980-bccd-cd7ee7a06c1e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eClustering and Seasonal Variability of Weak Seismicity in the Lena Delta (Laptev Sea Region)\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe Laptev Sea region of the Arctic Ocean includes the Laptev Sea shelf and associated coastal structures of the Eurasian continent and extends from the Taimyr Peninsula in the west to the New Siberian Islands in the east (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Within the Laptev Sea region, there is a junction of the structures of the Siberian Platform, Taimyr, Verkhoyansk-Kolyma, and Novosibirsk-Chukotka fold-and-thrust systems (Parfenov et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In the late Cretaceous and Cenozoic, this region became the scene of intensive riftogenesis associated with the opening of the oceanic Eurasian Basin. Continental rifting is reflected in the development of horst and graben structures on the Laptev Sea shelf, extending southeastward from the continental slope. Active seafloor spreading along the Gakkel Ridge in the Eurasia Basin is separated from the Laptev Sea rift system by the Khatanga-Lomonosov fault zone (Drachev, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000a\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe position of the Laptev Sea, on the one hand, at the boundary of the Eurasian and North American plates, and on the other hand, in the area of transition from seafloor spreading to continental rifting, determines its intensive seismic activity in the Russian sector of the Arctic (Drachev, 1998; Piskarev, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Krylov et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the spatial distribution of earthquakes, one can distinguish a narrow seismic belt along the Gakkel Ridge and a wide area of epicenters between the Taimyr Peninsula, the Lena Delta, and the New Siberian Islands, which covers the main shelf structures of the Laptev Sea (Krylov et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the shelf, a seismic active zone is located in its eastern part and can be traced from the Gakkel Ridge towards the Yana Bay. It is confined to the East Laptev Sea province of horsts and grabens (the eastern part of the Laptev Sea rift system) and is located between two extensional detachments, i.e., the main flat or subhorizontal normal faults and sole thrusts along which simple shear deformations occur in the vertical plane (Krylov et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another zone of numerous epicenters is confined to the Lena-Taimyr zone of boundary uplifts and extends along the southern margin of the Laptev Sea from the Taimyr Peninsula through the Anabar and Olenek bays to the Lena Delta and the Buor-Khaya Bay (Imaeva et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Lena Delta is one of the key areas of the Laptev Sea region from the point of view of modern tectonic processes, as it is located at the junction of the ancient Siberian Platform with the structures of the Ust-Lena rift (western part of the Laptev Sea rift system) on the shelf and folded areas confined to the Lena-Taimyr zone of boundary uplifts and the Verkhoyansk fold-and-thrust system on land. Numerous earthquake epicenters in the Lena Delta vicinity form sublinear zones confined to existing faults (Avetisov, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Seismotectonics ...,2017). The known focal mechanisms in the vicinity of the Lena Delta and the Buor-Khaya Bay are characterized by their diversity \u0026ndash; structural segments of the crust with different types of stress-strain state are identified (Kovachev et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Imaeva et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Krylov et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, non-uniform vertical movements were revealed in the delta, expressed in the tilt of the delta towards the east \u0026ndash; the difference in vertical velocities between the western and eastern parts was found to be approximately 1 mm/year (Bolshiyanov et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegistration of weak local earthquakes is used to determine the position and activity of seismogenic structures, the structure of the crust, determination of its stress-strain state, and many other tasks. The most important practical application of such data is to clarify the seismic regime of the study area for seismic hazard assessment and prediction of strong earthquakes. The registration of weak seismic events in the Lena Delta has been carried out for several years using temporary networks of seismic stations (Geissler et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) \u0026ndash; these records were used in the present study. The purpose of the work is to describe the peculiarities of the spatial and temporal distribution of microearthquakes in the Lena Delta using data from local seismic monitoring. For this purpose, a semi-automated search for earthquake signals on seismograms, the determination of P- and S-wave arrival times, and then the determination of hypocenter coordinates and earthquake magnitudes were performed. Then maps of epicenter distribution, vertical sections with projections of foci, and diagrams of the temporal distribution of earthquake parameters were constructed.\u003c/p\u003e \u003cp\u003eThe results of the study can help in understanding the features of seismicity distribution and seismic regime in the fault regions of transition from platforms to younger geological structures under conditions of lithospheric stretching, using the selected region as an example. In addition, the paper considers the possible influence of exogenous factors on the natural seismicity in such unstable fault regions.\u003c/p\u003e"},{"header":"2 Methods and Data","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Seismological monitoring data from the Lena Delta\u003c/h2\u003e \u003cp\u003eThe present study is based on the results of seismological monitoring conducted in 2016\u0026ndash;2018 in the Lena Delta within the framework of the SIOLA international project (Geissler et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Monitoring was carried out using a temporary local network of seismic stations, equipped with MARK L4 (1 Hz, 3-component) seismometers and DATA-CUBE recorders provided by the GIPP instrument pool at GFZ Potsdam, Germany. The measurement season started in late August, the most suitable time in terms of weather and logistics, and lasted until late April/early May due to the limited data storage pool of the supplied seismographs. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea demonstrates the network configuration during the 2016\u0026ndash;2017 registration period (hereafter referred to as the first registration period). The seismic stations were installed along the Olenek and Bykov channels, as well as along the main channel of the Lena River. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb demonstrates the network configuration during the registration period 2017\u0026ndash;2018 (hereinafter referred to as the second registration period). In this period, the network was reduced to the area along the Bykov channel from the center of the Lena Delta in the southeastern direction. Additional information about the stations can be found in \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003eAppendix A\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Additional seismological, meteorological, gravity and GNSS data\u003c/h2\u003e \u003cp\u003eTo discuss the peculiarities of the spatial and temporal distribution of seismic events in the Lena Delta, additional information on seismicity from the \"Earthquakes of Russia\" database (\u0026ldquo;Earthquakes of Russia\u0026rdquo;\u0026hellip;) compiled by the Geophysical Survey of the Russian Academy of Sciences \u0026ndash; it contains the information about seismic events occurred in Russia since the beginning of 2003. Snow cover thickness was accessed from the meteorological data archive for the Tiksi station (The electronic archive of the AARI).\u003c/p\u003e \u003cp\u003eThe gravity data was taken from GRACE and GRACE-FO experiments (Landerer, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Anomalies of gravity are provided in centimeters of equivalent water thickness unit, which is the depth of a thin layer of water that could account for the observed gravity variation.\u003c/p\u003e \u003cp\u003eThe GNSS time series for TIXI station were acquired from the Nevada Geodesic Laboratory (Blewitt et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The data was downloaded in tenv format, detrended, and smoothed using LOWESS (Cleveland, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1979\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Methods of seismic records and catalogs processing\u003c/h2\u003e \u003cp\u003eThe acquired seismic records were processed in several stages:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eData formatting;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSemi-automated search for earthquake signals on seismograms;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDetermination of P- and S-wave arrival times on earthquake records;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDetermination of earthquake hypocenter positions;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDetermination of earthquake magnitudes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe records were reformatted from the internal binary raw data format of the DATA-CUBE recorder into the common seismological miniSeed format. The automated search for earthquakes was based on the algorithm (Krylov et. al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The presence of an earthquake signal on the records was determined by such features as a sharp increase in the amplitude of the STA/LTA characteristic function (Allen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), signal duration, and correlation on different channels of different stations. Verification of the automatic detector results was performed visually by the operator. To reduce seismic noise in the frequency range characteristic of weak local earthquakes, filtering was performed by a 4 pole bandpass Butterworth filter with a bandwidth of 6\u0026ndash;16 Hz.. The determination of P- and S-wave arrivals, hypocenter positions, and magnitudes was performed using the SEISAN software package (Havskov and Ottemoller, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Wadati plots (Wadati, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1933\u003c/span\u003e) were used to check the correctness of the determination of arrival times. Hypocenter coordinates and origin times were determined by the HYPOCENTER subroutine (Lienert et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The velocity model used for localization was compiled after Cherepanova et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Laske et al., (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Kovachev et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e of Appendix B. Local magnitude was estimated using coefficients by Hutton and Boore (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). For the \u0026ldquo;Earthquakes of Russia\u0026rdquo; database, K-class magnitude was converted to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{L}\\)\u003c/span\u003e\u003c/span\u003e using the coefficients from the paper (Rautian, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1960\u003c/span\u003e).\u003c/p\u003e \u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean, median and quantile values of the number of earthquakes (N) per day during warm and cold seasons for three declustered catalogs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean N\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25th quantile\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian N\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75th quantile\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirst registration period (warm season)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirst registration period (cold season)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.385\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecond registration period (warm season)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecond registration period (cold season)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.093\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e“Earthquakes of Russia” (warm season)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e“Earthquakes of Russia” (cold season)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAppendix A\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation about the stations deployed during the first registration period (2016\u0026ndash;2017)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeployed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnd of recording\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.07.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.04.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.07.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e03.05.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.07.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.04.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.07.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.04.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e01.08.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e09.05.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.07.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e05.05.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.07.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.04.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.07.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.04.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e02.08.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.05.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.07.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e06.05.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e04.08.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e03.05.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.07.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.04.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIK01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.07.2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.04.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation about the stations deployed during the second registration period (2017\u0026ndash;2018)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeployed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnd of recording\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.07.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.04.2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.07.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.04.2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e02.08.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.05.2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e02.08.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.05.2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.07.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.04.2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e01.08.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e01.05.2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e02.08.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.05.2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIK01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.07.2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.04.2018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAppendix B\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVelocity model used in the current study. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{p}/{V}_{s}\\)\u003c/span\u003e\u003c/span\u003e coefficient is set to 1.73\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth, km\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{V}}_{\\varvec{p}}\\)\u003c/span\u003e\u003c/span\u003e, km/s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected Moho\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe catalogs were cleaned of frostquakes using three main criteria. First, frostquakes have a characteristic waveform with a predominant Rayleigh wave, which can be discerned from regular local weak tectonic earthquake waveform (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b). Second, frostquakes have a narrow low-frequency spectrum (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d), while an earthquake spectrum is broader and the signal decays longer. Third, in the case of frostquakes the particle motion of the P-wave tends to be horizontal in contrast to the more vertical entry of seismic rays of body waves from local weak tectonic earthquakes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee-f). An elliptical particle motion trajectory, characteristic for Rayleigh wave, is observed instead of the S-wave particle motion typical for local weak tectonic earthquakes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg-h) (Afonin, 2024).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor statistical analysis and Gutenberg-Richter plots, the catalogs were declustered using Gardner and Knopoff procedure (Gardner and Knopoff, 1974). A one-day time window and a 0.1\u0026deg; spatial window were selected for all three catalogs (SIOLA16-17, SIOLA17-18, \u0026ldquo;Earthquakes of Russia\u0026rdquo; catalog) to form potential aftershock sequences. The overlapping sequences were united and treated as one cluster. The biggest clusters with sequence length L\u0026thinsp;\u0026gt;\u0026thinsp;8 for the first season and L\u0026thinsp;\u0026gt;\u0026thinsp;6 for the second season were mapped.\u003c/p\u003e \u003cp\u003eNoise power spectral density plots were calculated using the direct Fourier transform method (McNamara and Buland, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The spectra were calculated for 60-minute windows every 30 minutes for 45 days in autumn and 45 days in winter to determine seasonal variations in ambient noise.\u003c/p\u003e \u003cp\u003eTo analyze the spatial and temporal distribution of seismicity, we constructed epicenter distribution maps, vertical sections with projections of sources, and diagrams of the temporal distribution of earthquake parameters in the Matlab programming environment. The M_Map library (Pawlowicz, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was used to construct the maps.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eDuring each recording period, signals from about one thousand local and regional earthquakes were recorded. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows an example of typical local earthquake waveforms for three components, as well as their Fourier spectra in comparison with seismic noise spectra. The energy of signals is usually concentrated in the frequency range of 0.3–30 Hz with maximum signal-to-noise ratios in the range of 2–20 Hz.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor further analysis of the spatiotemporal characteristics of seismicity, events were selected that were most accurately localized using the HYPOCENTER program - with error values in longitude, latitude, and depth of no more than 20 km. There were 598 such events in the first registration period and 390 in the second registration period. After catalog declustering the number of events decreased to 341 and 276, respectively. The lower number of events during the second period is expected due to the smaller network size and reduced number of stations.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the distributions of epicenters of local earthquakes in the area of the Lena Delta and the Buor-Khaya Bay as well as corresponding distributions of projected depths of earthquake sources on vertical profiles along (A-B) and across (C-D) for both periods of record. The plotted boundary of the Siberian Platform is based on Drachev (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the Moho depth values are derived from the CRUST 1.0 model (Laske et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost events form a cloud of epicenters of northwest-southeast trending, extending along the Olenek segment fault zone. Many events were also recorded at the southeastern rim of the Lena Delta in the Buor-Khaya Bay, where the epicenters tend to be located in the submeridional direction.\u003c/p\u003e \u003cp\u003eThe A-B section shows that from southeast to northwest, the maximum depth of hypocenters tends to increase (from approximately 20 km to 30 km). The C-D section shows that in the transition from shelf structures in the northeast to the Siberian Platform in the southwest, the density of hypocenter distribution drops sharply.\u003c/p\u003e \u003cp\u003eThus, the distribution of epicenters and projections of hypocenters on vertical sections show that the vast majority of events occurred outside the Siberian Platform. In addition, the distribution of the source depths shows that the overwhelming majority of events occurred within the Earth's crust - there are 4–6 hypocenters below the CRUST 1.0 Moho boundary per recording period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows cumulative Gutenberg-Richter plots for the Lena Delta and the Buor-Khaya Bay, constructed separately for two local catalogs. In addition, the Gutenberg-Richter plot for the catalog created using the \"Earthquakes of Russia\" database (\"Earthquakes of Russia\" ...) is shown.\u003c/p\u003e \u003cp\u003eThe values of the Gutenberg-Richter parameters were determined, which are quite close for all three plots: the a-values are in the range of 2.7–3.7, and the b-values are in the range of 0.81–0.91. At the same time, the value of the minimum representative magnitude (magnitude of completeness \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{c}\\)\u003c/span\u003e\u003c/span\u003e) for the regional catalog is 2.8, a value much larger than the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{c}\\)\u003c/span\u003e\u003c/span\u003e of 0.5 for the local catalogs of both registration periods. Thus, the events registered by the local network of stations significantly increased the representativeness of the Gutenberg-Richter plot in the magnitude range of 0.5–2.8.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, there are visible areas with high concentrations of earthquake epicenters for both recording periods (see \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods and Data\u003c/span\u003e section for definition of clusters). To study temporal characteristics of the clusters, time dependences of the depths (within 40 km) and magnitudes of earthquakes were also constructed (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e–\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows maps of epicenters with marked earthquake clusters. Six clusters were identified for the first registration period (I1-I6) and four clusters for the second registration period (II1-II4). Many clusters (I1, I2, I6) are located along the fault zone near the Buor-Khaya bay. There are 5 clusters (I5 and II1-II4) in the active zone close to Tiksi. The b-values of the largest clusters I-3 and I-4 with 50 and 92 events were estimated to be 0.75 and 0.79 respectively. The b-values of other clusters were not analyzed due to the lack of events in them (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\le\\:20\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e "},{"header":"4 Discussion","content":"\u003cp\u003eThe distribution of microearthquake foci projections in the horizontal and vertical planes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed their location in the narrow elongated Olenek segment, marking the boundary between the Siberian Platform and the Laptev Sea Rift System in this area. In map view, this fault zone is elongated along the Olenek and Bykov channels, with the overwhelming number of foci located outside the Siberian Platform, which, in general, is not surprising, because platforms are stable and rigid environments, and usually aseismic (Wesnousky and Scholz, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1980\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn depth, the fault zone is clearly traced by hypocenters up to the Moho boundary located in this area at depths of 40–45 km (Avetisov, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Laske et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Cherepanova et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Atlas...). In addition, only 4–6 events occurred during each recording period below the Moho boundary at depths of 45–90 km. The presence of few presumably mantle sources in the Laptev Sea region, and in particular, in the area of the Lena Delta is confirmed by the ISC and USGS reviewed catalogs, as well as by local observations (Kovachev et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMicroearthquake clusters (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e–\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) were recorded in both periods of registration and are located within the main cloud of epicenters of the Olenek segment. The b-values determined for the Gutenberg-Richter plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) are in the range of 0.7–0.8 and are not high enough for classical earthquake swarms associated with magmatic and volcanic processes (Špičák et al, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Cesca et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Yoshida et al., 2022). Such microearthquake clusters can occur in the fault plane in the presence of a background creeping process - these events are commonly interpreted as occurring on localized areas of faulting that undergo unstable stick-slip during the process of background stable creeping (Rubin et al, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e–\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e show the non-uniform distribution of microearthquakes in time for the declustered catalogs. To further analyze the features of the temporal distribution, we checked the number of earthquakes per day time histories for seasonal variability. The Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows that the number of earthquakes per day increases significantly with the onset of the cold season, characterized by temperature below 0°C and the presence of snow cover (hereafter referred as cold season).\u003c/p\u003e\u003cp\u003eA quantitative assessment of this dependence was carried out, showing that in the cold season the average number of earthquakes per day was 2.09 times higher than in the warm season of the first registration period. For the second registration period this ratio was 2.23. For the catalog of \"Earthquakes of Russia\" database (2003–2022) – 2 times (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Thus, there is a pronounced seasonal variability in the number of recorded weak earthquakes in the Lena Delta. Further we consider several possible reasons for this.\u003c/p\u003e\u003cp\u003eThe variation in the number of recorded earthquakes per day may be associated with the intensity of the background seismic noise and, therefore, with the recording ability of seismic equipment (Morozova, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The power spectral density (PSD) distributions of seismic noise were constructed for the time periods of the cold and warm seasons for the first registration period – Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. It was found that in the frequency range of the sensors (1–50 Hz), PSD levels on most stations do not significantly depend on temperature or the presence of snow.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cp\u003eOne possible mechanism for seasonal seismicity variation is additional hydrological surface load caused by either snow (Heki, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ueda et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Braunmiller, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) or water column (Costain et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Saar et al., 2003; Amos et al, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Craig et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bettinelli et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). To test the applicability of this hypothesis for the Lena Delta, the time series of monthly number of earthquakes according to “Earthquakes of Russia” database for 2003–2022 was analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ea). Since the Mc for that catalog is 2.8, these events can be considered as relatively strong earthquakes for this area. It is worth noting a large increase in the yearly number of recorded earthquakes in “Earthquakes of Russia” catalog starting from 2014–2015, but it’s unclear whether that process is due to the change in the processing method and equipment or the real change in regional seismicity.\u003c/p\u003e\u003cp\u003eAs was stated earlier, the vast majority of earthquakes in the catalog (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e1\u003c/span\u003e) occur in the cold season. Moreover, according to the GNSS time series from TIXI station, cold seasons correspond to the maximum downward displacement (ground subsidence), while warm seasons are characterized by maximum upward lift (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eb, Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eb). This clear seasonality in the vertical displacement can be at least partially explained by changes in the hydrological surface loading (Michel et al, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xue et al, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eTo investigate this relationship further, the time series of mass flux in cm of equivalent water thickness (EWT) from GRACE and GRACE-FO missions (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ec, Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ec) was incorporated. The break in data in 2017–2018 is due to the end of GRACE operation and the start of GRACE-FO in July 2018. The cold season periods correspond to the rise in EWT, while it steadily declines during the warm season. The Lena water level sharp rise in June (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ed) also corresponds to the characteristic EWT spike. The snow thickness is also rising throughout the cold season from October to May with rapid decrease towards the end of the cold period in June (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ed, Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ee). The rise in EWT throughout the cold season can be caused by accumulation of snow in the region. The average annual peak-to-peak amplitude of EWT is around 24 cm with its maximum in June and minimum in September.\u003c/p\u003e\u003cp\u003eAccording to the GRACE and GRACE-FO data, in the central part of the Lena Delta and areas around the Bykov and Olenek channels the EWT changes simultaneously throughout the year with roughly the same 24 cm peak-to-peak amplitude. To model the Coulomb stress changes in this area, the surface loading field was chosen to be 160 km long and 120 km wide rectangle along the Olenek channel with additional pressure of 2.4 kPa (corresponds to the EWT peak-to-peak amplitude). The three-dimensional stress tensor resulting from additional surface loading was computed using Bossinesq equations for the point load in cylindrical coordinates (Dang et al, 2010, Timoshenko and Goudier, 1970). The full stress tensor was obtained by integrating these equations over the loading area.\u003c/p\u003e\u003cp\u003eTo estimate the effect of this additional stress on the faults in the Olenek segment, the Coulomb failure stress formula \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{\\sigma\\:}_{c}=\\varDelta\\:\\tau\\:+\\mu\\:\\varDelta\\:{\\sigma\\:}_{n}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:=0.3\\)\u003c/span\u003e\u003c/span\u003e was used. According to the Active Faults of Eurasia Database (AFEAD) (Zelenin et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) there are numerous normal faults with 60° dip in the most active central western part of the segment. The calculation of additional Coulomb stress on the fault with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\delta\\:=60^\\circ\\:,\\lambda\\:=-90^\\circ\\:\\)\u003c/span\u003e\u003c/span\u003e was carried out.\u003c/p\u003e\u003cp\u003eThe time series of additional Coulomb stress at typical seismogenic depths of 15 km, which approximately corresponds to the median source depths value for all three catalogs used in this study, were calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ee and Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ef). The average annual peak-to-peak amplitude of additional Coulomb stress is estimated to be ~ 1.2 kPa. Some studies have shown that several kPa positive change in Coulomb stresses on the fault can be sufficient to cause additional seismicity (Heki, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Craig et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shiddiqi et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor example, hydrological load that corresponds to ~ 1 kPa peak-to-peak seasonal variation in additional Coulomb stress is shown to modulate the seismicity in the New Madrid zone (Craig et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The 2–4 kPa change in Coulomb stress due to changes in water level within the Ganges basin increased the seismicity rate by the factor of 2 in winter in the Nepal Himalaya (Bettinelli et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Moreover, the study of hydrological load found that changes of Coulomb stress up to 1 kPa increased the number of both dip-slip and strike-slip events in Northern California (Johnson et al, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCoulomb failure criterion implies that positive change in Coulomb stress should result in increased seismic activity and negative change should delay earthquakes. Whether surface loading will result in increase or decrease in Coulomb stress is heavily determined by the fault geometry. For shallow faults, normal stress induced by surface pressure plays a key role and for steeper faults shear stress can be dominant. Moreover, additional shear stress along the slip would be positive for reverse fault and negative for normal. The location of the fault relative to the loading area also plays a key role in determining the effect on Coulomb stress. According to the modeling results, in our case the surface loading corresponds to the positive Coulomb stress change.\u003c/p\u003e\u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e, there is higher seismicity in months with high Coulomb stress. Figure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e shows a general linear upward trend in number of earthquakes a month with increase in Coulomb stress.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eDuring local instrumental observations using a temporary network of seismographs installed in the Lena Delta, a large number of microearthquakes were recorded. The spatial and temporal distribution of these events revealed a number of patterns.\u003c/p\u003e \u003cp\u003eThe sources of recorded microearthquakes are concentrated in the area of the Olenek segment fault zone, marking in this area the boundary of the Siberian platform and the Laptev Sea rift system. The fault zone is clearly traced by hypocenters up to the Moho boundary located in this area at depths of 40\u0026ndash;45 km. Microearthquakes are distributed unevenly both in space and in time, forming clusters, which indicates the unstable and dynamic nature of processes in the fault zone. Such a pattern can occur in the fault plane in the presence of a background creeping process \u0026ndash; microearthquake clusters can be interpreted as originating from unstable stick-slip sliding during the process of background stable creeping.\u003c/p\u003e \u003cp\u003eSeasonal variability in the number of recorded weak earthquakes in the Lena Delta was revealed \u0026ndash; the average number of events per day increases two times during the cold seasons, characterized by temperature drops below 0\u0026deg;C and the presence of snow cover. This relationship is also present in the temporal distribution of relatively strong (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{L}\\)\u003c/span\u003e\u003c/span\u003e \u0026gt; 2.8) earthquakes in the regional earthquake catalog from 2003 to 2022. Additional Coulomb stress was quantified by numerical modeling of a three-dimensional stress tensor and its peak-to-peak annual variation was estimated to be 1.2 kPa. Seasonal seismicity increase in the Lena Delta corresponds to positive additional Coulomb stress under conditions of the prevalence of normal faults in the Olenek segment. Additional Coulomb stresses directly depends on calculated equivalent water thickness, which, in turn, correlates with the snow cover thickness. Summer flood, in turn, does not seem to have a significant correlation with seismicity rate, presumably due to its short duration. Though we obtained indications of seasonal variability caused by hydrological loading, the evidence can hardly be considered complete \u0026ndash; both more local seismological observations and better extensive coverage of GNSS measurements will be required to better understand the nature of these processes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eThe project was\u0026nbsp;initiated by Geissler W.H., Baranov B.V., Kr\u0026uuml;ger F., Haberland C., Shibaev S.V.\u0026nbsp;Data processing and analysis were designed by Krylov A.A. and\u0026nbsp;were performed by Novikov M.A., Krylov A.A. and Radiuk E.A.\u0026nbsp;The first draft of the manuscript was written by Novikov M.A. and Krylov A.A. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe processing of seismic records, the determination of the main parameters of earthquakes and the description of the seismic regime of the Lena Delta area were supported by RSF grant No. 23-17-00125. The analysis of the seasonal character of seismicity was performed under the state assignment No. FMWE-2024-0018. Numerical modeling of crustal response caused by surface loading was partially supported by the grant of the state program of the \u0026laquo;Sirius\u0026raquo; Federal Territory \u0026laquo;Scientific and technological development of the \u0026laquo;Sirius\u0026raquo; Federal Territory\u0026raquo; (Agreement №18-03 date 10.09.2024). German funding was provided by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, the Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, and the University of Potsdam.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe are grateful to all of our Russian and German colleagues organized and executed the fieldwork. We would like to thank the colleagues who participated in the SIOLA project: Aline Pl\u0026ouml;tz, Haberland C.,\u0026nbsp;Waldemar Schneider, Mikhail Grigoriev, Anne Morgenstern, Daniel Vollmer, Karl-Heinz J\u0026auml;ckel, Mike H\u0026ouml;nig, Stepan Gukov, Dmitry Peresypkin, Sergey Petrunin, Tanja Fromm, Joelund Asseng. Stations and equipment were provided by the Geophysical Instrument Pool Potsdam (GIPP).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe seismological data are archived in the mseed format at the GEOFON data center at the GFZ Potsdam (https://geofon.gfz-potsdam.de/waveform/archive/network.php?ncode=8B\u0026amp;year=2016, doi:10.14470/3O7561738646). Data request should be sent to [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdushkin, V.V., Loktev, D.N., Spivak, A.A. (2008). Influence of baric perturbations of the atmosphere on microseismic processes in the Earth's crust. 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(2017). Analysis of the relationship between water level fluctuation and seismicity in the Three Gorges Reservoir (China). Geodesy and Geodynamics, 8(2), 96\u0026ndash;102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geog.2017.02.004\u003c/span\u003e\u003cspan address=\"10.1016/j.geog.2017.02.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pure-and-applied-geophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paag","sideBox":"Learn more about [Pure and Applied Geophysics](https://www.springer.com/journal/24)","snPcode":"24","submissionUrl":"https://submission.nature.com/new-submission/24/3","title":"Pure and Applied Geophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"microearthquakes, Lena Delta, Siberian Platform, Laptev Sea Rift System, lower crust, Gutenberg-Richter plot, microearthquake clusters, seasonal variability","lastPublishedDoi":"10.21203/rs.3.rs-5362676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5362676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present work is devoted to studying the characteristics of the spatiotemporal microearthquake distribution in the Lena Delta using data from local seismological monitoring conducted from 2016 to 2018. The results revealed the confinement of microearthquake sources to the Olenek segment of the Lena\u0026ndash;Taimyr zone of boundary uplifts, marking the boundary of the Siberian Platform and the Laptev Sea Rift System. The Olenek segment fault zone is traced by hypocenters up to the Moho at a depth of about 40 km. Microearthquakes are distributed unevenly in both space and time, forming clusters in different parts of the fault zone. These clusters can be interpreted as originating from unstable stick-slip sliding during the process of background stable creeping. Seasonal variability in the number of recorded weak earthquakes in the Lena Delta has been revealed. An extended regional catalog (2003\u0026ndash;2022) was also used to analyze the seasonal seismicity modulation. The average number of events per day increases by approximately a factor of two during cold seasons. Comparison of these results with snow cover thickness, the Lena water level, GNSS data, gravity data, and calculated additional Coulomb stresses revealed that the seasonal seismicity increase in the Lena Delta correlates with the positive additional Coulomb stresses under conditions of prevalence of normal faults in the Olenek segment. Additional Coulomb stress directly depends on equivalent water thickness, which, in turn, correlates with snow cover thickness. The summer flood does not have a significant impact on the seismicity rate, presumably due to its short duration.\u003c/p\u003e","manuscriptTitle":"Clustering and Seasonal Variability of Weak Seismicity in the Lena Delta (Laptev Sea Region)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-14 06:31:22","doi":"10.21203/rs.3.rs-5362676/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-23T06:52:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-22T21:11:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-19T04:23:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109555542410027095793772761733766002511","date":"2024-11-28T14:31:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147765938495057199788567816096497281534","date":"2024-11-28T10:32:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-04T08:34:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-02T14:58:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-02T14:42:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pure and Applied Geophysics","date":"2024-10-30T17:06:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pure-and-applied-geophysics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paag","sideBox":"Learn more about [Pure and Applied Geophysics](https://www.springer.com/journal/24)","snPcode":"24","submissionUrl":"https://submission.nature.com/new-submission/24/3","title":"Pure and Applied Geophysics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"20d51243-ba89-4349-8fdf-84a6280f647d","owner":[],"postedDate":"November 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T15:58:51+00:00","versionOfRecord":{"articleIdentity":"rs-5362676","link":"https://doi.org/10.1007/s00024-025-03755-6","journal":{"identity":"pure-and-applied-geophysics","isVorOnly":false,"title":"Pure and Applied Geophysics"},"publishedOn":"2025-06-19 15:56:53","publishedOnDateReadable":"June 19th, 2025"},"versionCreatedAt":"2024-11-14 06:31:22","video":"","vorDoi":"10.1007/s00024-025-03755-6","vorDoiUrl":"https://doi.org/10.1007/s00024-025-03755-6","workflowStages":[]},"version":"v1","identity":"rs-5362676","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5362676","identity":"rs-5362676","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}