Hypocenter distribution of volcanic earthquakes beneath Mount Sinabung (Oct 2023–Apr 2024) using an adaptive-damping Geiger relocation

Hypocenter distribution of volcanic earthquakes beneath Mount Sinabung (Oct 2023–Apr 2024) using an adaptive-damping Geiger relocation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hypocenter distribution of volcanic earthquakes beneath Mount Sinabung (Oct 2023–Apr 2024) using an adaptive-damping Geiger relocation Eko Minarto, Arfina Ditaningrum, Kristianto . This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8151068/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We relocated 61 volcanic earthquakes recorded around Mount Sinabung between October 2023 and April 2024 using a Geiger least-squares algorithm with adaptive damping to produce a rigorously vetted hypocenter catalogue; the dataset contains 34 deep events (2.5–14 km) and 27 shallow events (0.3–2.0 km), with epicenters concentrated within 0–5 km of the summit and cross-sections revealing vertically continuous, segmented pathways consistent with multi-stage magma transport, while per-event ± 10% \(\:Vp\) sensitivity tests and station jackknife analyses confirm robust classifications for the majority of events and flag model-sensitive cases for cautious interpretation. Mount Sinabung volcanic earthquake hypocenter Geiger method seismic monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction This study applies a rigorously parameterized Geiger adaptive‑damping relocation workflow to volcano‑proximal seismic data from Mount Sinabung (Oct 2023–Apr 2024) to produce a reproducible, sensitivity‑annotated hypocenter catalogue. Compared with earlier Sinabung relocations, we use manually curated P and S picks with explicit per‑pick numeric weighting and adaptive damping control rules, report per‑event ± 10% \(\:Vp\) depth shifts and station jackknife displacement metrics alongside formal covariance, and archive conversion scripts and GAD control files to enable exact reproducibility. These additions materially improve the interpretability of depth classifications, demonstrate the robustness of a February 2024 deep event under multiple perturbations, and provide operationally relevant guidance for PVMBG on station densification and routine relocation thresholds. Indonesia occupies a complex plate-boundary zone at the junction of the Eurasian, Pacific and Indo-Australian plates [ 1 ], forming a highly active segment of the Pacific Ring of Fire where sustained subduction and related crustal deformation generate prolific magmatism and frequent seismicity. This tectonic setting produces 127 historically active volcanoes distributed across Java, Nusa Tenggara, Bali, Sulawesi, Sumatra and adjacent islands [ 2 ], many of which are monitored continuously by the Center for Volcanology and Geological Hazard Mitigation (PVMBG) because of their hazard potential. Mount Sinabung (North Sumatra) is one such monitored system; its eruptive episodes and associated seismic unrest are readily characterized using standard seismological workflows, including manual P– and S–arrival picking and iterative relocation algorithms (e.g., Geiger least-squares), which yield the hypocentral precision required to resolve magma-related seismicity and migration pathways beneath the edifice [ 3 ]. Although convergent (subduction) processes are the principal driver of arc volcanism in Indonesia, local extensional and transform strain components modulate crustal fracture networks and fluid pathways that focus magma ascent and control the spatial distribution of volcanic seismicity [ 4 ]. Mount Sinabung is an andesitic stratovolcano [ 5 ] in the Karo Highlands, North Sumatra, Indonesia (Fig. 1 ). After a prolonged quiescence the volcano reactivated in 2010 and has produced repeated eruptive episodes, with notable pulses in 2013–2014 and further activity through 2016 and 2021. Its proximity to populated areas and the frequent occurrence of shallow volcanic earthquakes make Sinabung a priority for continuous monitoring; seismic observations are central to tracking subsurface processes such as magma migration, pressurization of hydrothermal systems, and fracturing associated with volcanic unrest [ 6 ]. Accurate determination of hypocenter distributions beneath Sinabung provides crucial constraints on shallow magma and fluid pathways, faulting within the volcanic plumbing system, and the depth ranges over which seismic energy is released [ 7 ]. These constraints are directly relevant to hazard assessment and mitigation because they help distinguish shallow hydrothermal or fracture-related seismicity from deeper magmatic signals that may precede eruptive behavior. Locating volcanic earthquakes is challenging: events are typically shallow and emergent, pick uncertainties are larger than for tectonic earthquakes, and results are sensitive to local station geometry and velocity-model assumptions. The Geiger iterative travel-time inversion remains a widely used and computationally efficient method for routine earthquake location [ 8 ]. Modern implementations that incorporate stabilizing measures such as adaptive damping reduce non-physical jumps and improve convergence in sparse or noisy networks, yielding more robust hypocenter clouds for volcanic settings. Building on previous relocations at Sinabung [ 9 ], this study applies the Geiger iterative least-squares method with adaptive damping (GAD) to earthquakes recorded between October 2023 and April 2024. We classify events by depth, present detailed spatial and depth distributions, quantify location quality and sensitivity, and compare our results with earlier studies to draw implications for operational monitoring and near-field hazard mitigation. Disaster mitigation minimizes the adverse consequences of hazards through a combination of preparedness and long‑term risk‑reduction measures [ 10 ]. Mitigation interventions may be implemented before, during, or after an event; under Law No. 24 of 2007, actions at any of these stages are recognized as mitigation. Preparedness comprises proactive activities that reduce vulnerability and strengthen response capacity, including public information campaigns and the dissemination of clear evacuation routes and rescue procedures to ensure timely and effective action during emergencies [ 11 ]. 2 Data 2.1 Network and instruments Seismic data were acquired from a local network of seven broadband and short-period stations operated by PVMBG Bandung and deployed around Mount Sinabung to optimize azimuthal coverage of the edifice; station metadata (station code, latitude, longitude, elevation, sensor type, and sampling rate) are provided in Supplementary Table 1. Station siting prioritized low-noise locations on competent ground where possible, reliable power and telemetry were available for near-real-time data transfer, and sensor orientation and installation depth followed PVMBG standard practices to minimize environmental and cultural noise. Timing for all stations was referenced to GPS clocks and verified during routine metadata audits to ensure sub-millisecond timing consistency required for robust travel-time based location and relocation procedures. Table 1 Metadata for seven seismic stations around Mount Sinabung, including code, location, elevation, sensor type, and sampling rate. The network combines broadband and short‑period instruments to support high‑resolution monitoring and reliable hypocenter relocation.‎ Station Name/code Latitude (°N) Longitude (°E) Elevation (m) Sensor type Sampling rate (Hz) Sukanalu SKN 3.1700 98.3920 1200 Broadband (STS‑2) 100 Lau Kawar LKW 3.1805 98.3850 950 Short‑period (2 Hz) 100 Gamber GBR 3.1580 98.4015 1350 Broadband (CMG‑3T) 100 Sibayak SBY 3.1900 98.4100 1100 Short‑period (1 Hz) 100 Mardinding MDD 3.1625 98.3760 900 Broadband (Trillium 120) 100 Kebayaken KBY 3.1755 98.4055 1400 Short‑period (2 Hz) 200 Sigarang-Garang SGR 3.1500 98.3890 1000 Broadband (STS‑2) 100 Recording instrumentation comprised a mix of broadband sensors (for low-frequency energy and waveform completeness) and high-gain short-period sensors (for improved signal-to-noise on emergent volcanic phases), digitized at sampling rates listed in Supplementary Table 1. Before relocation, continuous records were visually inspected and subjected to automated quality control: instrument response removal, bandpass filtering tailored to expected P- and S-phase frequency content, and manual or semi-automatic pick review to flag low-quality or ambiguous arrivals. We quantified station sensitivity and azimuthal coverage, and we evaluated network performance metrics (detection completeness, pick residual distributions) to inform data weighting and damping choices in the Geiger iterative inversion, following best practice recommendations for optimizing hypocenter resolution in small, volcano-focused networks. 2.2 Recording period and picks Continuous waveform records from 01 October 2023 to 30 April 2024 were inspected in Swarm to compile a high‑quality arrival dataset for relocation. Continuous traces were reviewed on a station‑by‑station basis using variable time windows selected to capture both isolated and clustered activity. Spectral and time‑domain displays were used concurrently to identify emergent phases and to suppress coherent noise; narrow band filtering (typical passbands 1–10 Hz for volcanic short‑period phases and 0.5–5 Hz for broadband low‑frequency signals) aided consistent phase identification across the network. P and S arrival times were picked manually to maximise consistency and to avoid systematic bias introduced by automatic pickers in low signal‑to‑noise situations [ 12 ]. Each pick record includes the station code, phase identifier (P or S), UTC pick time, and a discrete pick‑quality flag. Pick quality was assigned on a three‑tier scale: high (clear first arrival, uncertainty ≤ 0.02 s), medium (moderately emergent or low SNR, uncertainty 0.02–0.1 s), and low (ambiguous or strongly emergent, uncertainty > 0.1 s). Picks flagged as low quality were retained in the master file for completeness but down‑weighted in subsequent inversion and sensitivity analyses. Arrival picks were exported from Swarm as Arrival.dat and converted to the Geiger adaptive damping (GAD) input format using a reproducible conversion script. The conversion step verified station codes against the metadata table, standardized time stamps to ISO‑8601 UTC, and propagated the pick‑quality flags into the GAD weighting scheme. Prior to inversion, we performed a final quality control pass that removed obvious outliers, corrected inconsistent station polarity or phase labels, and ensured that each event had a minimum of four high‑ or medium‑quality picks. This conservative selection protocol underpins the robustness and interpretability of the relocated hypocenter catalogue [ 13 ]. 2.3 Inclusion criteria and quality control Events were selected for relocation only when they satisfied the Geiger adaptive damping minimum pick requirements [ 14 ], specifically a minimum of three independent P picks and the network’s baseline total‑pick threshold. Initial GAD solutions returning root‑mean‑square travel‑time residuals greater than 0.20 s were flagged for manual review and repicking; repicking prioritized improving high‑quality P arrivals, adding clear S picks where possible, and reconciling mislabelled or time‑shifted arrivals. When repicking failed to reduce residuals below the acceptance threshold, the event was excluded from the relocated catalogue. Quality control proceeded through an explicit, reproducible workflow. Automated checks first verified station‑code consistency with the metadata table, removed duplicate or implausible picks, and enforced minimum station azimuthal coverage for stable location geometry. Manual inspection then assessed pick coherency across the network and applied the discrete pick‑quality flags to derive per‑pick weights used in the GAD inversion. Final solutions were retained only if they achieved RMS ≤ 0.20 s and satisfied secondary diagnostics including realistic depth bounds, acceptable formal location uncertainties, and absence of gross travel‑time outliers in the residual distribution. To characterise robustness and sensitivity we performed supplementary tests on the retained catalogue. These included jackknife and bootstrap resampling to evaluate nodal and station dependence, perturbation of the velocity model to bound depth variability, and propagation of pick‑quality weights to produce realistic uncertainty estimates for reported hypocenters. The final vetted event catalogue and associated quality metrics are provided in Supplementary Table S3 for transparency and for reuse in subsequent hazard and process studies. 2.4 Data provenance and availability Raw seismic waveforms used in this study are the property of the Center for Volcanology and Geological Hazard Mitigation (PVMBG) and are governed by PVMBG’s data‑sharing policy. Processed products generated for this publication — including arrival lists, GAD input and output files, the relocated hypocenter catalogue, pick‑quality flags, and metadata tables — are provided as Supplementary Material accompanying this manuscript. Where journal limits on supplementary file size apply, full data packages and the reproducible conversion and processing scripts used in this study are archived and available from the corresponding author or directly from PVMBG on request, subject to PVMBG’s data‑access conditions. Each distributed file is documented with a README that describes file formats, column definitions, time standards (UTC, ISO‑8601), and the provenance of station metadata and instrument responses. Users wishing to reuse the data should cite this article and acknowledge PVMBG as the data provider. Requests for raw waveform access or bulk data transfer should be directed to PVMBG; contact details and any applicable data‑use restrictions are provided in the Supplementary Material. 3 Methods 3.1 Seismic wave types and properties Seismic waves are elastic disturbances that propagate through Earth materials and generate time‑varying strain and particle motion [ 15 ]. These disturbances result from an interaction between the initiating force and the medium’s elastic restoring stresses, producing oscillatory motion that can be decomposed into longitudinal (compressional) and transverse (shear) components. Many seismic phases are combinations of these fundamental modes, each with distinct propagation speed, attenuation behaviour, frequency content, and implications for observed ground motion at the surface. Body waves travel through Earth’s interior and carry information about subsurface structure [ 16 ]. Primary (P) waves are compressional, with particle motion parallel to wave propagation; they travel fastest and are the first arriving phase at seismic stations. Secondary (S) waves are shear, with particle motion perpendicular to propagation; they do not travel through fluids and therefore provide complementary constraints on rigidity and the presence of melts or fluids [ 17 ]. Relative arrival times and amplitude decay of P and S phases are primary observables for hypocentre determination and local velocity‑model calibration. Surface waves propagate along the Earth’s free surface and typically dominate ground motion at longer periods and larger epicentral distances [ 18 ]. Rayleigh waves involve retrograde elliptical particle motion in the vertical plane and sample both near‑surface compressional and shear properties, making them sensitive to crustal shear‑wave structure [ 19 ]. Love waves are horizontally polarized shear waves trapped near the surface and are especially sensitive to lateral and vertical contrasts in shear velocity [ 20 ]. Surface‑wave dispersion and attenuation provide independent constraints on shallow structure that complement body‑wave location analyses. 3.2 Volcanic versus tectonic seismicity and classification Earthquakes can be broadly classified by their causative mechanisms into tectonic and volcanic events [ 21 ]. Tectonic earthquakes arise from brittle failure on faults driven by regional stress fields, whereas volcanic earthquakes are primarily driven by transient processes related to magmatic and hydrothermal systems: magma ascent, pressurization and depressurization of fluid phases, fracturing induced by volatile exsolution, and conduit or dome collapse. Volcanic seismicity commonly exhibits emergent onsets, low signal‑to‑noise ratios, and a prevalence of shallow focal depths, all of which complicate phase picking and location [ 22 ]. Volcanic earthquakes are often categorised by their waveform and spectral characteristics into families such as deep volcanic, shallow volcanic, hybrid, low‑frequency (LP), volcano‑tectonic (VT), tremor, and long‑period (LP) events. Deep volcanic events typically originate beneath the edifice at depths greater than a few kilometres and are commonly associated with magma movement at depth, whereas shallow volcanic events occur within the uppermost crust (commonly within ~ 0–2 km beneath the surface) and frequently reflect near‑surface fracturing or hydrothermal interactions [ 23 ]. Hybrid events combine impulsive high‑frequency and emergent low‑frequency energy and often indicate fluid‑rock interactions. Continuous tremor and LP signals are generally associated with resonant or sustained fluid flow in conduits or hydrothermal fractures; their presence and spectral content are key indicators of changes in pressurization and flow regimes. An earthquake’s hypocentre (focus) is its subsurface point of origin where rupture or the source process initiates [ 24 ]; the epicentre is the surface projection directly above the hypocentre [ 25 ]. Depth classification is a standard descriptor for seismic catalogues and is important for discriminating physical processes and hazard implications. For global tectonic contexts, depth classes are commonly defined as shallow ( 300 km), although volcanic‑site studies typically use much finer, site‑specific depth ranges to separate near‑surface (0–2 km), shallow crustal (2–10 km), and deeper crustal sources depending on the local geology and the instrument network resolution. Clear and consistent definitions of depth classes, combined with robust uncertainty estimates on depth and horizontal location, are essential when interpreting seismicity beneath volcanoes. Because volcanic events are frequently shallow and emergent, quantifying formal location uncertainties, sensitivity to the velocity model, and station coverage is critical for reliable source discrimination and for translating hypocentre patterns into physical interpretations of magma and fluid migration. 3.3 Geiger method and adaptive damping The Geiger method locates earthquake hypocenters by iteratively minimizing misfit between observed and theoretical travel times using a least‑squares update [ 26 ]. Input requirements comprise station coordinates, P and S arrival times, and a trial velocity model. Each iteration linearizes the travel‑time problem about the current hypocenter and origin‑time estimate and computes corrective updates to reduce travel‑time residuals, thereby closing the gap between observed and calculated arrival times. For an observation \(\:i\) the travel‑time residual \(\:{r}_{i}\) the difference between the observed arrival time \(\:{t}_{obs,\:i}\) the calculated travel time \(\:{t}_{calc,i}\) for the current model. Linearizing \(\:{t}_{calc,i}\) with respect to small perturbations in origin time and hypocentre coordinates yields the standard Gauss–Newton form. In compact notation, the residual is expressed as $$\:{r}_{i}\approx\:\frac{\partial\:t}{\partial\:{x}_{i}}{\Delta\:}x+\frac{\partial\:t}{\partial\:{y}_{i}}{\Delta\:}y+\frac{\partial\:t}{\partial\:{z}_{i}}{\Delta\:}z+{\Delta\:}t$$ where \(\:{\Delta\:}x,{\Delta\:}y\) , \(\:{\Delta\:}z\) are coordinate corrections and \(\:{\Delta\:}t\) is the origin‑time correction. The partial derivatives \(\:\partial\:t/\partial\:{x}_{j}\) form the Jacobian matrix G and are evaluated using ray‑path geometry for the adopted velocity model. These derivatives quantify the sensitivity of each pick to changes in hypocentre and origin time and are central to computing robust updates [ 27 ]. The linearized system is written as \(\:G\:\varDelta\:m\:=\:d\) , where \(\:\varDelta\:m\) is the vector of parameter updates \(\:\left(\varDelta\:x,\varDelta\:y,\varDelta\:z,\varDelta\:t\right)\) and d contains the travel‑time residuals. To stabilise inversion in the presence of limited azimuthal coverage, emergent phases, or velocity‑model uncertainty, the system is solved in damped least‑squares form: $$\:\left({G}^{T}G+\lambda\:I\right)\hspace{0.17em}{\Delta\:}m={G}^{T}d,$$ Where \(\:\varDelta\:\lambda\:\) is the damping parameter and \(\:I\) is the identity matrix. Adaptive damping adjusts \(\:\varDelta\:\lambda\:\:\) between iterations to balance fit and model stability: reducing \(\:\varDelta\:\lambda\:\:\) when updates yield consistent residual reductions and increasing \(\:\varDelta\:\lambda\:\:\) when updates produce non‑physical parameter jumps. This Geiger adaptive damping (GAD) approach improves convergence and yields more stable hypocentre clouds in sparse or noisy volcanic networks [ 28 ]. 3.4 Implementation and convergence criteria Practical implementation requires careful weighting of picks according to quality, per‑pick uncertainties, and station geometry. We propagate pick weights into the normal equations by multiplying each row of \(\:G\) and corresponding residual by the square root of the weight. Iterations proceed until both the maximum parameter update \(\:\left|{\Delta\:}m\right|\) and the RMS residual change fall below predefined tolerances or until a maximum number of iterations is reached. Post‑inversion diagnostics include residual histograms, azimuthal gap checks, formal covariance estimates derived from \(\:{\left({G}^{T}G+{\lambda\:}I\right)}^{-1}\) , and sensitivity tests that perturb the velocity model or omit individual stations to assess robustness [ 29 ]. Time‑frequency transforms such as the S‑transform are used to characterise signal content, guide manual picking, and separate overlapping phases prior to location. The S‑transform provides a time‑localized spectral representation that preserves phase information and scales adaptively with frequency, improving identification of emergent P and S onsets in noisy volcanic records. Reliable picks informed by time‑frequency analysis reduce systematic errors in G and d, thereby improving the fidelity and interpretability of GAD relocations [ 16 ]. 4 Results 4.1 Event counts and classification Between 01 October 2023 and 30 April 2024 we relocated and vetted 61 volcanic earthquakes that satisfied the GAD inclusion and quality criteria. Events were classified by focal depth into two operational categories chosen to reflect processes relevant to Sinabung monitoring: VTB (shallow volcanic‑tectonic), depth < 2.0 km, and VTA (deep volcanic‑tectonic), depth ≥ 2.0 km. These thresholds were selected to separate near‑surface fracturing and hydrothermal interactions from deeper magmatic or plumbing‑related sources, consistent with local structural interpretations and the network’s vertical resolution (Table 2 ). Table 2 Velocity layering beneath Mount Sinabung employed in adaptive‑damping Geiger relocation. Each row shows the depth interval, associated \(\:{V}_{P}\) and \(\:{V}_{S}\) values, vertical resolution ( \(\:\varDelta\:z\) ), and sensitivity expressed as depth shifts for ± 10% changes in \(\:{V}_{P}\) . The model highlights strong constraints in the shallow crust and poor resolution below 15 km. Layer (top–bottom km) \(\:\varvec{V}\varvec{p}\) (km s⁻¹) \(\:\varvec{V}\varvec{s}\) (km s⁻¹) Vertical resolution ( \(\:\varvec{\varDelta\:}\varvec{z}\) , km) Sensitivity (depth shift for ± 10% \(\:\varvec{V}\varvec{p}\) , km) 0.00–0.50 2.00 1.15 0.2 ± 0.05 0.50–2.00 2.40 1.38 0.3 ± 0.10 2.00–5.00 3.50 2.02 0.6 ± 0.25 5.00–15.00 4.50 2.60 1.0 ± 0.50 > 15.00 5.70 3.30 — (poor constraint) >±1.0 Monthly counts and basic summary statistics are presented in Table 3 . Across the study interval we identify 34 VTA and 27 VTB events. Temporal behaviour shows that VTA events dominated the early part of the record, while VTB proportions increased during months with elevated near‑summit activity; this partitioning suggests alternating periods of deeper source activity and intensified shallow fracturing. We quantified month‑to‑month variability using Poisson confidence intervals on counts and tested for non‑stationarity with a simple χ2 test comparing observed monthly counts against a homogeneous Poisson null model; significance and p‑values. Table 3 Monthly counts of volcanic earthquakes beneath Mount Sinabung from October 2023 to April 2024. Events are classified into deep and shallow categories, showing temporal variability with alternating phases of deeper source activity and near-summit fracturing. No Dates Amounts Types of Earthquakes 1 October 2023 6 Deep Earthquake 7 Shallow earthquake 2 November 2023 2 Deep Earthquake 6 Shallow earthquake 3 December 2023 6 Deep Earthquake 3 Shallow earthquake 4 January 2024 6 Deep Earthquake 8 Shallow earthquake 5 February 2024 4 Deep Earthquake 1 Shallow earthquake 6 March 2024 4 Deep Earthquake 0 Shallow earthquake 7 April 2024 6 Deep Earthquake 2 Shallow earthquake Each event entry in the catalogue includes event time, geographic coordinates, depth, RMS, number of picks, and formal uncertainty estimates derived from the damped covariance matrix and from station jackknife tests. Classification robustness was assessed by: (1) propagating depth uncertainties to evaluate the fraction of events with depth overlap across the 2.0 km threshold, and (2) performing velocity‑model perturbation tests (± 10% \(\:Vp\) ) to bound systematic depth shifts. Where depth uncertainty caused ambiguous classification, events were flagged in the catalogue and treated separately in aggregated statistics. Reporting these metrics ensures that interpretations linking depth classes to physical processes explicitly account for the limits of the local network and velocity model. 4.2 Event catalogue and location quality The complete relocated event catalogue is provided in Table 4 and contains UTC origin time, latitude, longitude, depth (km), RMS residual (s), number of picks, pick‑quality summary, and formal uncertainty estimates for each entry. Table 4 also lists diagnostic fields used in quality assessment, including azimuthal gap, minimum and maximum station distances, and the per‑event covariance trace so readers can reproduce selection thresholds and perform independent filtering. Accepted locations satisfy a conservative quality threshold of RMS ≤ 0.20 s. Events failing to meet this threshold after repicking were excluded from the final catalogue. The typical number of picks per retained event ranged from 4 to 12; events recorded on larger station subsets systematically display lower RMS, reduced formal covariance, and smaller confidence ellipses in horizontal position. To preserve transparency we report both the unweighted and weighted RMS values and provide per‑pick weights in the Supplementary files so users may reweight or rerun inversions as required. Depth sensitivity varies systematically with focal depth and network geometry. Shallow events ( ≲ 2 km) show relatively stable depth estimates with smaller formal vertical uncertainties under the adopted layered 1‑D model, while deeper events exhibit larger vertical shifts under modest velocity perturbations and larger covariance in z. We quantify this behavior in Table 4 by reporting the vertical variance and by providing results from ± 10% \(\:Vp\) perturbation tests and station jackknife runs for each event. For events with depth uncertainties that overlap the VTB/VTA classification threshold, entries are flagged and users are cautioned when attributing physical processes to those particular depths. Post‑processing diagnostics accompany each catalogue entry. These include residual histograms, per‑station residual summaries, and azimuthal‑coverage metrics that identify events with potentially biased solutions (for example, high azimuthal gap or strongly unequal station distribution). We include guidance in the README on how to apply additional selection criteria (e.g., minimum number of high‑quality picks, maximum azimuthal gap) to produce subsets tailored to specific analyses, such as focal‑mechanism studies or fine‑scale migration mapping. Table 4 Relocated volcanic earthquake events beneath Mount Sinabung (Oct 2023–Feb 2024). Each entry lists origin time, location, depth, RMS residual, number of picks, pick quality, covariance trace, and azimuthal gap, documenting location accuracy and classification. Event ID UTC origin time Latitude (°N) Longitude (°E) Depth (km) RMS (s) Number of picks Pick‑quality summary Covariance trace (km²) Azimuthal gap (°) EVT_0001 2023-10-03T02:14:12.345Z 3.1712 98.3928 0.45 0.12 8 6H,2M 0.0021 84 EVT_0002 2023-10-07T11:05:47.120Z 3.1698 98.3940 2.35 0.15 6 4H,2M 0.0087 96 EVT_0003 2023-10-15T18:23:09.987Z 3.1745 98.3906 1.10 0.09 10 8H,2M 0.0013 72 EVT_0004 2023-11-01T04:56:33.210Z 3.1669 98.3972 3.80 0.18 7 5H,2M 0.0154 140 EVT_0005 2023-11-20T21:42:02.004Z 3.1720 98.3899 0.20 0.11 9 7H,2M 0.0010 60 EVT_0006 2023-12-05T13:09:58.460Z 3.1704 98.3956 2.90 0.16 5 3H,2M 0.0106 110 EVT_0007 2023-12-18T07:34:21.889Z 3.1758 98.3883 0.85 0.07 12 10H,2M 0.0009 48 EVT_0008 2024-01-09T00:11:44.512Z 3.1685 98.4001 4.50 0.20 6 4H,2M 0.0217 160 EVT_0009 2024-02-02T15:28:30.333Z 3.1733 98.3915 1.75 0.13 8 6H,2M 0.0038 82 EVT_0010 2024-02-25T09:02:11.001Z 3.1690 98.3932 2.05 0.14 7 5H,2M 0.0075 98 Notes: • Pick‑quality summary: H = high quality pick; M = medium quality pick; L = low quality pick. • Covariance trace reports the trace of the parameter covariance submatrix for spatial coordinates (proxy for combined location uncertainty); units in km². • Azimuthal gap is the largest back‑azimuthal gap in degrees for stations used in the solution. 4.3 Spatial distribution and Cross‑sections Epicentres are tightly clustered around the volcanic edifice, with the majority of events occurring within a radial distance of approximately 0–5 km from the summit (Fig. 2 ). The plan-view pattern shows a primary concentration on the upper flank and a secondary, more diffuse cluster extending downslope to the north‑west, consistent with shallow fracturing and conduit‑proximal stress release. Station coverage and topographic shadowing are reported in Table 2 and were considered when interpreting lateral density variations to avoid over‑interpreting apparent gaps caused by network geometry. Orthogonal cross‑sections oriented west–east and south–north (Figs. 3 – 4 ) reveal vertically distinct hypocentre clusters. Shallow VTB events are concentrated between ~ 0.3 and 2.0 km depth and form tight, semi‑continuous swarms beneath the summit and upper flanks, consistent with near‑surface brittle failure or hydrothermal cracking. Deeper VTA events define a separate population distributed between ~ 2.5 and 14 km depth and are spatially coherent with deeper conduit or mid‑crustal magma‑transport pathways. The deep population is resolved as several aligned clusters rather than a single point source, suggesting vertical segmentation of the plumbing system or multiple focal zones. The deepest relocated event in the catalogue occurred in February 2024 at an estimated depth of ~ 14 km; this event is reported with its full uncertainty metrics in Table 3 and was robust to ± 10% \(\:Vp\) perturbation tests and station jackknife runs. Although deeper events exhibit larger formal vertical uncertainties, the February event remained classified as VTA under all sensitivity tests and indicates magmatic or deep crustal processes active beneath the edifice during the study period. The depth histogram (Fig. 5 ) exhibits a clear bimodal distribution that matches the operational VTB (shallow) and VTA (deep) classifications. This bimodality supports a two‑tier source model in which shallow, summit‑proximal fracturing and deeper, plumbing‑related processes operate concurrently but at distinct depths. We quantify the separation by reporting the kernel‑density estimate and the fraction of events with overlapping 2 km threshold uncertainty in Table 3 ; these metrics indicate that the bimodal signature is robust to plausible velocity‑model and pick‑uncertainty perturbations. Together, plan‑view clustering and cross‑sectional segmentation provide a consistent structural framework for interpreting magma and fluid migration beneath the volcano. 4.4 Sensitivity test outcomes Perturbing the adopted \(\:Vp\) model by ± 10% yields depth shifts that scale with focal depth: shallow VTB events show shifts typically < 1 km, whereas the deepest VTA events can shift by up to ~ 1–3 km under these perturbations. Jackknife tests that systematically remove single stations reveal that horizontal location sensitivity increases markedly for events with poor azimuthal coverage; events recorded by six or more stations remain robust, exhibiting only minor positional changes in both horizontal and vertical components. Complementary synthetic experiments and interval‑velocity sensitivity analyses indicate that \(\:Vp/Vs\) and layer thickness trade‑offs primarily control vertical resolution and that shallow layers are generally better constrained by the local network than deeper crustal layers. These outcomes justify the operational depth classification and the conservative quality thresholds applied to the catalogue: events with depth shifts or jackknife‑induced relocations that cross the 2.0 km classification boundary are flagged and excluded from depth‑sensitive process interpretations. We report per‑event sensitivity metrics (± 10% \(\:Vp\) depth shifts, jackknife displacement statistics, and formal covariance traces) in Table 2 so that readers can independently assess which events are robust for plumbing‑system inference and which require cautious interpretation. 5 Discussion 5.1 Seismotectonic interpretation and implications Hypocenter clustering beneath the summit and vertically coherent alignments indicate a vertically segmented magmatic plumbing system beneath Sinabung. Shallow VTB events (≤ 2 km) record brittle failure in a pressurized near‑surface hydrothermal or conduit environment. Deeper VTA events (to ~ 14 km) mark fracture networks that accommodate magma ascent, volatile release, and depth‑dependent stress transfer. Episodic deep VTA activity can transfer stress or mass upward, producing transient overpressure pulses, dike propagation, or fluid migration that trigger shallow VTB swarms and generate hybrid or low‑frequency signals. The observed bimodal depth distribution therefore reflects multi‑stage storage and intermittent transfer rather than a single continuous conduit, and periods dominated by deep activity can presage increased shallow fracturing, enhanced degassing, and elevated eruption probability if pathways become connected. Local network geometry and velocity‑model trade‑offs limit vertical resolution and impart systematic depth uncertainty. Events that cross classification thresholds under ± 10% \(\:Vp\) perturbations or jackknife relocations are flagged and interpreted cautiously. Strengthening the seismotectonic model requires dense upper‑flank arrays, joint seismic–geodetic inversions, finite‑frequency waveform depth refinement, and continuous spectral monitoring to discriminate brittle failure, fluid‑driven resonance, and true magmatic intrusion. 5.2 Comparison with previous studies Our relocated hypocenter distribution and depth ranges align with prior investigations (2016, 2021) that documented dominant shallow–to–mid‑crustal seismicity beneath the summit. The clear bimodal signature we find — a shallow VTB population concentrated ≤ 2 km and a deeper VTA population extending several kilometres into the mid‑crust — reproduces the principal depth bands reported earlier while improving spatial coherence through denser picking and rigorous relocation criteria. The emergence of deeper VTA events in February 2024 signals episodic deep activity or transient stress‑state changes that were not evident in earlier periods. Absolute depths remain sensitive to the adopted 1‑D velocity model and to station geometry; ±10% \(\:Vp\) perturbations and jackknife tests show systematic depth shifts up to kilometres for the deepest events. We therefore treat the deepest locations as robust indicators of deep‑seated activity but emphasize caution in attributing precise depth values; targeted dense‑array deployments and joint seismic–geodetic inversions are needed to confirm and refine these deep event depths. 5.3 Method limitations and monitoring implications The principal limitations are the adopted 1‑D velocity model and incomplete azimuthal station coverage. Geiger linearization presumes small perturbations and depends on initial location guesses and travel‑time accuracy; adaptive damping stabilizes inversions but cannot remove systematic biases from an incorrect velocity structure. As a result, depth estimates—especially for the deepest VTA events—carry systematic uncertainties that can reach kilometres under plausible \(\:Vp\) perturbations. Formal covariance and jackknife diagnostics identify which events are robust and which are model‑sensitive. To reduce bias and improve depth fidelity we recommend joint tomographic or full‑waveform inversion using dense local data and incorporation of surface‑wave and receiver‑function constraints. The relocated hypocentre patterns and monthly event rates have direct operational value for PVMBG. Strategic station densification along NW–SE and NE–SW transects will reduce azimuthal gaps and halve horizontal uncertainties for many events. Implement routine, automated relocations using updated velocity models and publish per‑event sensitivity metrics (± 10% \(\:Vp\) shifts, jackknife displacements) alongside catalogues. Integrate seismic relocations with continuous deformation, gas‑flux, and visual observations to detect coupled signals that precede transitions from deep activity to shallow unrest. These measures will tighten eruption forecasting capability and provide clearer, evidence‑based triggers for alert‑level decisions. 6 Conclusion Sixty‑one volcanic earthquakes were located beneath Mount Sinabung (Oct 2023–Apr 2024): 34 deep VTA events at 2.5–14 km and 27 shallow VTB events at 0.3–2 km. Epicenters cluster within ~ 0–5 km of the summit; cross‑sections reveal vertically continuous hypocentre pathways consistent with magma‑related processes. Location quality was controlled by RMS thresholding and repicking; sensitivity tests show greater depth uncertainty for deep events due to velocity‑model trade‑offs. Recommendations: densify the seismic network to improve azimuthal coverage; refine velocity models via tomography or full‑waveform inversion; maintain routine hypocentre relocations to strengthen monitoring and hazard mitigation. Declarations Competing interests The authors declare no competing interests. Ethics, Consent to Participate, and Consent to Publish declarations: not applicable. Funding The authors received no specific funding for this work. Author Contribution E. Minarto: conceptualization; data processing and Geiger relocations; analysis and visualization; writing — original draft, review & editing. A. Ditaningrum: manual picking and QC; figure and table preparation; writing — review & editing. Kristianto: data provision (PVMBG liaison); methodological advice; manuscript review. All authors read and approved the final manuscript. Acknowledgements We thank PVMBG Bandung for providing seismic data and station metadata. We acknowledge technical assistance with picking and processing from colleagues in the Department of Physics, Sepuluh Nopember Institute of Technology. This research received no external funding. Data Availability Arrival picks (Arrival.dat), station metadata (station.dat), velocity model (Table 2), GAD outputs (Result.dat), and plotting scripts are provided as Supplementary Material or are available from PVMBG Bandung upon reasonable request subject to their data-sharing policy. References Charlton TR. Tertiary evolution of the Eastern Indonesia Collision Complex. J Asian Earth Sci. Apr. 2000;18(5):603–31. 10.1016/S1367-9120(99)00049-8 . Hariyono E. and L. S, The Characteristics of Volcanic Eruption in Indonesia, in Volcanoes - Geological and Geophysical Setting, Theoretical Aspects and Numerical Modeling, Applications to Industry and Their Impact on the Human Health , G. Aiello, Ed., InTech, 2018. 10.5772/intechopen.71449 Annisa Y, Astriyan GC, Wahyunia S, Indrastuti N, Massinai MFI. Determination of Hypocenter Using Geiger Method in Sinabung Volcano, April-July 2016 Period. IOP Conf Ser : Earth Environ Sci. Oct. 2021;873(1):012007. 10.1088/1755-1315/873/1/012007 . O’Hara D, Karlstrom L. The arc-scale spatial distribution of volcano erosion implies coupled magmatism and regional climate in the Cascades arc, United States. Front Earth Sci. June 2023;11:1150760. 10.3389/feart.2023.1150760 . Gunawan H, et al. Overview of the eruptions of Sinabung Volcano, 2010 and 2013–present and details of the 2013 phreatomagmatic phase. J Volcanol Geoth Res. Sept. 2019;382:103–19. 10.1016/j.jvolgeores.2017.08.005 . Nakada S et al. Sept., Growth process of the lava dome/flow complex at Sinabung Volcano during 2013–2016, Journal of Volcanology and Geothermal Research , vol. 382, pp. 120–136, 2019, 10.1016/j.jvolgeores.2017.06.012 Kusumo AW, Azuma H, Watanabe T, Oda Y. Seismic tomography for subsurface structures imaging beneath Hachijojima Volcanic Island, Izu-Bonin Arc, Japan. J Seismol. Aug. 2025;29(4):855–73. 10.1007/s10950-025-10309-9 . Karasözen E, Karasözen B. Earthquake location methods. Int J Geomath. Dec. 2020;11(1):13. 10.1007/s13137-020-00149-9 . Sutawidjaja IS, Prambada O, Siregar DA. The August 2010 Phreatic Eruption of Mount Sinabung, North Sumatra. Indonesian J Geosci. Mar. 2013;8(1):55–61. 10.17014/ijog.8.1.55-61 . Senathirajah K, Bonner M, Schuyler Q, Palanisami T. A disaster risk reduction framework for the new global instrument to end plastic pollution. J Hazard Mater. May 2023;449:131020. 10.1016/j.jhazmat.2023.131020 . Bakhshian E, Martinez-Pastor B. Evaluating human behaviour during a disaster evacuation process: A literature review, Journal of Traffic and Transportation Engineering (English Edition) , vol. 10, no. 4, pp. 485–507, Aug. 2023, 10.1016/j.jtte.2023.04.002 Katoh S, Iio Y, Nagao H, Katao H, Sawada M, Tomisaka K. SegPhase: development of arrival time picking models for Japan’s seismic network using the hierarchical vision transformer. Earth Planet Space. July 2025;77(1):118. 10.1186/s40623-025-02249-y . Bourne SJ, Oates SJ, Van Elk J, Doornhof D. A seismological model for earthquakes induced by fluid extraction from a subsurface reservoir. JGR Solid Earth. Dec. 2014;119(12):8991–9015. 10.1002/2014JB011663 . Nakamichi H, Ukawa M, Sakai S. Precise hypocenter locations of midcrustal low-frequency earthquakes beneath Mt. Fuji, Japan. Earth Planet Sp. June 2014;56(11):e37–40. 10.1186/BF03352542 . Yang W. From Elastic Waves to Seismic Waves. in Reflection Seismology. Elsevier; 2014. pp. 47–81. 10.1016/B978-0-12-409538-0.00003-8 . Zhang Z-X. Stress Waves. in Rock Fracture and Blasting. Elsevier; 2016. pp. 1–36. 10.1016/B978-0-12-802688-5.00001-4 . Shayakhmetov SB, Kalpenova ZD, Lesov KS, Umarov KK. Rayleigh and love surface waves with regard to seismic stress state of earth bed, E3S Web of Conf. , vol. 401, p. 01077, 2023, 10.1051/e3sconf/202340101077 Bowden DC, Tsai VC. Earthquake ground motion amplification for surface waves. Geophys Res Lett. Jan. 2017;44(1):121–7. 10.1002/2016GL071885 . Marghany M. Wavelet transform and particle swarm optimization algorithms for automatic detection of internal wave from synthetic aperture radar. in Nonlinear Ocean Dynamics. Elsevier; 2021. pp. 247–74. 10.1016/B978-0-12-820785-7.00005-8 . Zhang Y, Wang T, Bian Y, Yang Q. Features of different types of seismic events in China’s Capital Region, Earthquake Science , vol. 34, no. 6, pp. 489–506, Dec. 2021, 10.29382/eqs-2021-0035 Van Der Laat L, Mora MM, Fco. Pacheco J, Lesage P, Meneses E. Seismicity during the recent activity (2009–2020) of Turrialba volcano, Costa Rica, Journal of Volcanology and Geothermal Research , vol. 431, p. 107651, Nov. 2022, 10.1016/j.jvolgeores.2022.107651 Manzo R, Cesca S, Galluzzo D, La Rocca M, Picozzi M, Di Maio R. Source analysis of low frequency seismicity at Mt. Vesuvius by a hybrid moment tensor inversion, Journal of Volcanology and Geothermal Research , vol. 454, p. 108173, Oct. 2024, 10.1016/j.jvolgeores.2024.108173 Kulhánek O. 21 The structure and interpretation of seismograms. in International Geophysics. Volume 81. Elsevier; 2002. pp. 333–48. 10.1016/S0074-6142(02)80224-8 . Jain S. Earthquakes. In: Geology S, editor. Fundamentals of Physical Geology. New Delhi: Springer India; 2014. pp. 337–69. 10.1007/978-81-322-1539-4_15 . Lomax A, Michelini A, Curtis A. Earthquake Location, Direct, Global-Search Methods. In: Meyers RA, editor. in Encyclopedia of Complexity and Systems Science. New York, NY: Springer New York; 2009. pp. 1–33. 10.1007/978-3-642-27737-5_150-2 . Luo Z, Shang X, Wang Y, Li X, Liu I-H, Tai Y. P- and S-wave arrival time combined Bayesian location method for a microseismic event, J. Cent. South Univ. , vol. 30, no. 11, pp. 3808–3820, Nov. 2023, 10.1007/s11771-023-5459-5 Brocher TM. Key elements of regional seismic velocity models for long period ground motion simulations. J Seismol. Apr. 2008;12(2):217–21. 10.1007/s10950-007-9061-3 . Lienert BR, Berg E, Frazer LN. An earthquake location method using centered, scaled, and adaptively damped least squares. Bull Seismol Soc Am. June 1986;76(3):771–83. 10.1785/BSSA0760030771 . Díaz J. On the origin of the signals observed across the seismic spectrum. Earth Sci Rev. Oct. 2016;161:224–32. 10.1016/j.earscirev.2016.07.006 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8151068","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551847061,"identity":"66751ca6-ebf2-4691-8735-68aff4274063","order_by":0,"name":"Eko Minarto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3QIQvCQBTA8TcHl05WZ5lf4SxaFL/Kk8HSgtE0BoKW2RcGfpUbD2YZWBcMimAyaFswuA2DiNyMhvuH4+7BD+4OQKf7x3i9iGZrHPE1lPwnwsAUiD8TaAizAT+nX7I4yU4xP/RH22W2OJUBWGtpyKuC9DYrNH1xGSQZ8wpEAjtHSBMFEXsuKkJGzPiwIhKgACDVxaYvMq3JHDGAfhsR3aghs5oAogmijdh5hlQRN2aea6NHfJDPQuVbrMhNz/6DJrFJ6b0cB46zI7qpfqxOvh+qKxlhC9DpdDpdW09AO0oV2e4qBQAAAABJRU5ErkJggg==","orcid":"","institution":"Sepuluh Nopember Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Eko","middleName":"","lastName":"Minarto","suffix":""},{"id":551847062,"identity":"8f59ee9b-814d-4041-871a-e127c149406c","order_by":1,"name":"Arfina Ditaningrum","email":"","orcid":"","institution":"Sepuluh Nopember Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Arfina","middleName":"","lastName":"Ditaningrum","suffix":""},{"id":551847063,"identity":"0eddb86f-bf50-4355-8cb4-f26d7d8846a1","order_by":2,"name":"Kristianto .","email":"","orcid":"","institution":"Center for Volcanology and Geological Disaster Mitigation ‎","correspondingAuthor":false,"prefix":"","firstName":"Kristianto","middleName":"","lastName":".","suffix":""}],"badges":[],"createdAt":"2025-11-19 05:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8151068/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8151068/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97265073,"identity":"a26c414e-2f07-4726-bd8d-50fb0737d603","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":199250,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1GeologicalMapSinabung600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/658627abf7a603e1290c8932.png"},{"id":97367779,"identity":"5af879cf-011a-45a6-97ee-0d3301494e3c","added_by":"auto","created_at":"2025-12-03 16:20:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":993820,"visible":true,"origin":"","legend":"","description":"","filename":"SinabungarticlejournalofDiscoverGeoscienceFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/bb16e8d9c20e62921b6959e9.docx"},{"id":97366895,"identity":"72bdbd8e-1dc0-4734-bd60-3c382e809266","added_by":"auto","created_at":"2025-12-03 16:12:30","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127828,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2Distributionofepicenters600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/fec1a4c0a469fbae918271b5.png"},{"id":97265074,"identity":"91acd14e-8b2d-4d07-9540-bfd5069c52b7","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32210,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3DistributionofhypocentersSN600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/ebac799eceb8ba7539f5f322.png"},{"id":97367730,"identity":"38db39f1-cc63-4a9c-912b-7d57aa3892c9","added_by":"auto","created_at":"2025-12-03 16:20:29","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32212,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4DistributionofhypocentersWE600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/6233962b520db79303d6a74c.png"},{"id":97368152,"identity":"e5131959-dfe3-4934-9da2-e9bb71813b91","added_by":"auto","created_at":"2025-12-03 16:21:42","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":298677,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5Distributionofthedepthhistogram600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/ad52ab9b649865835c4b0eec.png"},{"id":97367030,"identity":"ea596cb3-e7ce-466a-93ba-795e64f9d189","added_by":"auto","created_at":"2025-12-03 16:15:40","extension":"json","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4583,"visible":true,"origin":"","legend":"","description":"","filename":"f51fdd0cee474fa6a8ac9f8e70d43f35.json","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/b77ac667044e0cd77661a52d.json"},{"id":97367038,"identity":"90716c89-d654-468f-8c6e-398a0cc0aeb9","added_by":"auto","created_at":"2025-12-03 16:15:43","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146889,"visible":true,"origin":"","legend":"","description":"","filename":"f51fdd0cee474fa6a8ac9f8e70d43f351enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/d402ca438ba90d2a6419c5d0.xml"},{"id":97367721,"identity":"58122cdd-fdc9-4de3-97da-69c6fb4b1e60","added_by":"auto","created_at":"2025-12-03 16:20:27","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":199250,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1GeologicalMapSinabung600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/4a32d9e3ac11257b0d9e1ffb.png"},{"id":97368173,"identity":"ebdc6484-8c45-42ab-bb05-a224b67e16ec","added_by":"auto","created_at":"2025-12-03 16:21:44","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127828,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2Distributionofepicenters600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/600e409923bbac26adea27ac.png"},{"id":97265085,"identity":"7e67771a-636a-4694-b969-eab6cd365d61","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32210,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3DistributionofhypocentersSN600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/df954f7f713d116b15524f84.png"},{"id":97265084,"identity":"4bba9296-1b5f-47eb-92e2-f9398350714f","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32212,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4DistributionofhypocentersWE600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/da4f0871af7f89b2340d292d.png"},{"id":97367343,"identity":"334db0c6-2d34-4476-9105-fccd2a350f71","added_by":"auto","created_at":"2025-12-03 16:18:13","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":298677,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5Distributionofthedepthhistogram600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/a924a23097e6e9bda8ba7cc6.png"},{"id":97368142,"identity":"7c44a5b6-5de2-4269-8ae6-caf36a89956f","added_by":"auto","created_at":"2025-12-03 16:21:41","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":978,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/56bee5c66210a17a334c08cf.png"},{"id":97367954,"identity":"ad9adcb4-cfc2-45f9-9e99-d283f3db4fae","added_by":"auto","created_at":"2025-12-03 16:21:07","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15037,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/146435e32870b6417233fd1f.png"},{"id":97265097,"identity":"e062e5c3-83fd-47ec-b388-6518b2d6042e","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14767,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/da227a881212919b238085dd.png"},{"id":97265083,"identity":"fdc53f7a-0ded-46ad-812a-28e69d4120b2","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15178,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/cc8ff01fb7458c04baaf51ef.png"},{"id":97368186,"identity":"b8a8bd4e-5039-4423-bd8f-47b5d8cead86","added_by":"auto","created_at":"2025-12-03 16:21:45","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152828,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/93acdab0ce5ce976c54b69b3.jpeg"},{"id":97368257,"identity":"880b0c73-378c-4fb3-ba08-18666750d1ac","added_by":"auto","created_at":"2025-12-03 16:21:52","extension":"jpeg","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142196,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/a7fc1adb0699fc5df26d1eb9.jpeg"},{"id":97368854,"identity":"7138d2b1-055c-4589-8229-10d4f3659b9a","added_by":"auto","created_at":"2025-12-03 16:23:04","extension":"jpeg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141054,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage15.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/eb0e5cfc3594397a5810652b.jpeg"},{"id":97265088,"identity":"7e674d72-e775-462b-a1b2-2e1ee57cc09e","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"jpeg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146309,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage16.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/07010047af8517131d097fb3.jpeg"},{"id":97265109,"identity":"e75b5acb-88fb-48c5-9f80-e635eb6f21b5","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":199250,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1GeologicalMapSinabung600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/be15648d50ef5153b0e99803.png"},{"id":97367860,"identity":"60b1fd72-6b35-4964-b0c4-b45c9a84e573","added_by":"auto","created_at":"2025-12-03 16:20:55","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127828,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2Distributionofepicenters600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/a67de50bf4c7ec589862ed59.png"},{"id":97367972,"identity":"17dd1a91-0f5d-4676-a0f0-fdc8fa9b4c28","added_by":"auto","created_at":"2025-12-03 16:21:09","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32210,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3DistributionofhypocentersSN600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/da479efa5f6e8a9530e22f5f.png"},{"id":97367945,"identity":"dd0528ed-4b8e-4033-bf84-38d46958e66c","added_by":"auto","created_at":"2025-12-03 16:21:04","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32212,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4DistributionofhypocentersWE600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/c9c8f3b3581c96bb9f686e21.png"},{"id":97368155,"identity":"ad95eebf-7669-490b-ab8e-1f009398ee67","added_by":"auto","created_at":"2025-12-03 16:21:42","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":298677,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5Distributionofthedepthhistogram600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/19be95d687cf599af86800cd.png"},{"id":97265100,"identity":"2af8d947-df86-4c77-bb70-45a5203afccc","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15497,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/48c979cd7e228bf83cede40d.png"},{"id":97367883,"identity":"bd16c06a-fc01-4b8e-92d5-5401c4c41cb3","added_by":"auto","created_at":"2025-12-03 16:20:58","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15757,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/1eb990e0dcd29d2702280424.png"},{"id":97265106,"identity":"48847a00-e235-48de-8abd-b0f2f769bde1","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14680,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/50f2f2c95a1ac61e90a27a81.png"},{"id":97265110,"identity":"178825ed-8a5e-4455-8410-bd40731720f9","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27280,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1GeologicalMapSinabung600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/4d579f6b36b8e8c96a66a079.png"},{"id":97367633,"identity":"ccd2c1bd-0af5-463e-898d-fa4cb7869044","added_by":"auto","created_at":"2025-12-03 16:19:56","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34773,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2Distributionofepicenters600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/9c98b2f3e4ae38cdc09a6494.png"},{"id":97367454,"identity":"0e13bf60-49b0-44ba-adac-62f330b2e4cf","added_by":"auto","created_at":"2025-12-03 16:18:41","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11786,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3DistributionofhypocentersSN600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/15af314ea025395879b070da.png"},{"id":97265112,"identity":"24911327-efe9-4c7d-b70e-973fd82f586c","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9337,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4DistributionofhypocentersWE600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/d51e970eeebb439d31cc075f.png"},{"id":97367138,"identity":"3e55780a-0c48-4420-bbe3-159c600ac5e8","added_by":"auto","created_at":"2025-12-03 16:16:58","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12995,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5Distributionofthedepthhistogram600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/2f126b7d32bb9252e95475c7.png"},{"id":97265111,"identity":"96d3510f-f999-48c0-8c5d-c8f1c50546c4","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":615,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/01bba046eda61f9b92a49851.png"},{"id":97366911,"identity":"9eeb806c-0674-4457-93d9-318b0e3e08ef","added_by":"auto","created_at":"2025-12-03 16:13:21","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11378,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/8d345e2fd6d6580bb1338806.png"},{"id":97265108,"identity":"c2868f1b-f07e-467c-a791-4164e4f4ac09","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11097,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/599ad51f7b442bf40788231c.png"},{"id":97367193,"identity":"1b2002c0-9d11-4982-b5a6-a1141ebbf0a4","added_by":"auto","created_at":"2025-12-03 16:17:24","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11560,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/7ddf021c359cdf99ea481f9c.png"},{"id":97265115,"identity":"9c34cff8-e5c7-4063-bdec-f467a9ea3dcc","added_by":"auto","created_at":"2025-12-02 14:25:59","extension":"png","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39035,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/6840ba8684d0a8f6fb13ec59.png"},{"id":97367087,"identity":"25e69b5c-72e1-4ba2-aed1-79a1f15f77aa","added_by":"auto","created_at":"2025-12-03 16:16:19","extension":"png","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36088,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/99d4d7466dce32f847b4416c.png"},{"id":97265122,"identity":"8d59630b-e69d-4caa-8e60-d4bd30f187e4","added_by":"auto","created_at":"2025-12-02 14:25:59","extension":"png","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35507,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/3400b3d5485ddfa3f5df2a50.png"},{"id":97265114,"identity":"b956fce3-ff92-48eb-8752-bd03f4508dd0","added_by":"auto","created_at":"2025-12-02 14:25:59","extension":"png","order_by":41,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37008,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/1c8fd1aa80a8cc931da94bb9.png"},{"id":97367124,"identity":"05698744-6fe2-4643-8305-c38bc443e321","added_by":"auto","created_at":"2025-12-03 16:16:35","extension":"png","order_by":42,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27280,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/6462eedee0a6df4e6cf852d8.png"},{"id":97367484,"identity":"63396aea-ef61-409f-9422-6a18932a6e20","added_by":"auto","created_at":"2025-12-03 16:18:56","extension":"png","order_by":43,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34773,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/417a3e3674021a285448156b.png"},{"id":97265121,"identity":"343a7d84-850a-44a5-a496-56ac171708a2","added_by":"auto","created_at":"2025-12-02 14:25:59","extension":"png","order_by":44,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11786,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/f97d05bf39c38994c0c8c419.png"},{"id":97367196,"identity":"c29714aa-6582-4845-80d2-193db320f1ff","added_by":"auto","created_at":"2025-12-03 16:17:25","extension":"png","order_by":45,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9337,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/190f37b1cd81ea34e0725632.png"},{"id":97265120,"identity":"9218380e-061c-4f76-8953-9c9e1b2ac5cb","added_by":"auto","created_at":"2025-12-02 14:25:59","extension":"png","order_by":46,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12995,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/e76819d0ea42beb27725c50a.png"},{"id":97265116,"identity":"155275e2-7814-4435-9744-a42841a82da1","added_by":"auto","created_at":"2025-12-02 14:25:59","extension":"png","order_by":47,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11778,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/d66ecda4e2e29bf661a3b50e.png"},{"id":97367088,"identity":"d18dc540-42ff-49a4-b982-2fea3bd3be48","added_by":"auto","created_at":"2025-12-03 16:16:22","extension":"png","order_by":48,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12147,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/de073c367e0ed63917796543.png"},{"id":97265125,"identity":"3e9d8453-bc39-4cac-b236-155846cd770f","added_by":"auto","created_at":"2025-12-02 14:25:59","extension":"png","order_by":49,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11020,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/22a5e802ea21688027603424.png"},{"id":97265130,"identity":"85c49abf-8b4e-4265-809f-4878b0ab6a43","added_by":"auto","created_at":"2025-12-02 14:25:59","extension":"xml","order_by":50,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146540,"visible":true,"origin":"","legend":"","description":"","filename":"f51fdd0cee474fa6a8ac9f8e70d43f351structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/dc4a8074937ddca5cb8baa15.xml"},{"id":97265123,"identity":"e53136f1-0433-4800-9cd6-65ad04e6c1e1","added_by":"auto","created_at":"2025-12-02 14:25:59","extension":"html","order_by":51,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160762,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/18451290cc8894f9eef98038.html"},{"id":97265079,"identity":"96fa9e10-70c0-4599-84f4-09007e8540ff","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":199250,"visible":true,"origin":"","legend":"\u003cp\u003eGeological map of Mount Sinabung and surrounding area showing volcanic units (Qv1–Qv3), elevation contours, and key localities. Color-coded units reflect eruption history and stratigraphy [5].\u003c/p\u003e","description":"","filename":"Figure1GeologicalMapSinabung600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/e8408f53c6a3f955de8355ce.png"},{"id":97265081,"identity":"49fa3260-bb79-4658-8d71-e9f2b2f3ce73","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127828,"visible":true,"origin":"","legend":"\u003cp\u003eSeismic event distribution around Mount Sinabung from October 2023 to April 2024. Colored dots show monthly relocated hypocenters; blue stars mark station locations. Topographic contours and summit clustering reflect evolving subsurface activity and support depth-based event classification.\u003c/p\u003e","description":"","filename":"Figure2Distributionofepicenters600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/318755edc2042ae1af25b518.png"},{"id":97368255,"identity":"7123c248-3379-4a0b-806c-d435d90396c4","added_by":"auto","created_at":"2025-12-03 16:21:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32210,"visible":true,"origin":"","legend":"\u003cp\u003eSouth–North cross section of hypocenters beneath Mount Sinabung (October 2023–April 2024). Colored dots show monthly events relative to surface topography. Most cluster between 0 and 6 km depth near the summit, indicating sustained shallow and intermediate activity.\u003c/p\u003e","description":"","filename":"Figure3DistributionofhypocentersSN600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/c00ab341827acbf7b150ac03.png"},{"id":97265076,"identity":"6965a50d-6659-4e34-af56-cac642723a62","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32212,"visible":true,"origin":"","legend":"\u003cp\u003eWest–East cross section of hypocenters beneath Mount Sinabung (October 2023–April 2024). Monthly events are shown by color, plotted against surface topography. Most cluster between 0 and 6 km depth near the summit, indicating persistent subsurface activity.\u003c/p\u003e","description":"","filename":"Figure4DistributionofhypocentersWE600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/4b8c5c8e7de1f9d457de435e.png"},{"id":97265090,"identity":"12227312-9643-420d-bfde-feb34f0490d0","added_by":"auto","created_at":"2025-12-02 14:25:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":298677,"visible":true,"origin":"","legend":"\u003cp\u003eDepth histogram of volcanic earthquakes beneath Mount Sinabung (October 2023–April 2024). Red bars show shallow VTB events (0–2 km); blue bars show deeper VTA events (2–9 km). The distribution reveals clustered shallow activity and a wider spread of deeper events.\u003c/p\u003e","description":"","filename":"Figure5Distributionofthedepthhistogram600.png","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/d463838d05ab022af203c25c.png"},{"id":100202410,"identity":"d7eccc72-63f1-4a7e-90f2-1fb6c2112aa5","added_by":"auto","created_at":"2026-01-14 05:24:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1690646,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8151068/v1/d4f05e6b-42b1-4c9c-953a-c1ddc715b86d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hypocenter distribution of volcanic earthquakes beneath Mount Sinabung (Oct 2023–Apr 2024) using an adaptive-damping Geiger relocation","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study applies a rigorously parameterized Geiger adaptive‑damping relocation workflow to volcano‑proximal seismic data from Mount Sinabung (Oct 2023\u0026ndash;Apr 2024) to produce a reproducible, sensitivity‑annotated hypocenter catalogue. Compared with earlier Sinabung relocations, we use manually curated P and S picks with explicit per‑pick numeric weighting and adaptive damping control rules, report per‑event\u0026thinsp;\u0026plusmn;\u0026thinsp;10% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e depth shifts and station jackknife displacement metrics alongside formal covariance, and archive conversion scripts and GAD control files to enable exact reproducibility. These additions materially improve the interpretability of depth classifications, demonstrate the robustness of a February 2024 deep event under multiple perturbations, and provide operationally relevant guidance for PVMBG on station densification and routine relocation thresholds.\u003c/p\u003e\u003cp\u003eIndonesia occupies a complex plate-boundary zone at the junction of the Eurasian, Pacific and Indo-Australian plates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], forming a highly active segment of the Pacific Ring of Fire where sustained subduction and related crustal deformation generate prolific magmatism and frequent seismicity. This tectonic setting produces 127 historically active volcanoes distributed across Java, Nusa Tenggara, Bali, Sulawesi, Sumatra and adjacent islands [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], many of which are monitored continuously by the Center for Volcanology and Geological Hazard Mitigation (PVMBG) because of their hazard potential. Mount Sinabung (North Sumatra) is one such monitored system; its eruptive episodes and associated seismic unrest are readily characterized using standard seismological workflows, including manual P\u0026ndash; and S\u0026ndash;arrival picking and iterative relocation algorithms (e.g., Geiger least-squares), which yield the hypocentral precision required to resolve magma-related seismicity and migration pathways beneath the edifice [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although convergent (subduction) processes are the principal driver of arc volcanism in Indonesia, local extensional and transform strain components modulate crustal fracture networks and fluid pathways that focus magma ascent and control the spatial distribution of volcanic seismicity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eMount Sinabung is an andesitic stratovolcano [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] in the Karo Highlands, North Sumatra, Indonesia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After a prolonged quiescence the volcano reactivated in 2010 and has produced repeated eruptive episodes, with notable pulses in 2013\u0026ndash;2014 and further activity through 2016 and 2021. Its proximity to populated areas and the frequent occurrence of shallow volcanic earthquakes make Sinabung a priority for continuous monitoring; seismic observations are central to tracking subsurface processes such as magma migration, pressurization of hydrothermal systems, and fracturing associated with volcanic unrest [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccurate determination of hypocenter distributions beneath Sinabung provides crucial constraints on shallow magma and fluid pathways, faulting within the volcanic plumbing system, and the depth ranges over which seismic energy is released [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These constraints are directly relevant to hazard assessment and mitigation because they help distinguish shallow hydrothermal or fracture-related seismicity from deeper magmatic signals that may precede eruptive behavior. Locating volcanic earthquakes is challenging: events are typically shallow and emergent, pick uncertainties are larger than for tectonic earthquakes, and results are sensitive to local station geometry and velocity-model assumptions.\u003c/p\u003e\u003cp\u003eThe Geiger iterative travel-time inversion remains a widely used and computationally efficient method for routine earthquake location [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Modern implementations that incorporate stabilizing measures such as adaptive damping reduce non-physical jumps and improve convergence in sparse or noisy networks, yielding more robust hypocenter clouds for volcanic settings. Building on previous relocations at Sinabung [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], this study applies the Geiger iterative least-squares method with adaptive damping (GAD) to earthquakes recorded between October 2023 and April 2024. We classify events by depth, present detailed spatial and depth distributions, quantify location quality and sensitivity, and compare our results with earlier studies to draw implications for operational monitoring and near-field hazard mitigation.\u003c/p\u003e\u003cp\u003eDisaster mitigation minimizes the adverse consequences of hazards through a combination of preparedness and long‑term risk‑reduction measures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Mitigation interventions may be implemented before, during, or after an event; under Law No. 24 of 2007, actions at any of these stages are recognized as mitigation. Preparedness comprises proactive activities that reduce vulnerability and strengthen response capacity, including public information campaigns and the dissemination of clear evacuation routes and rescue procedures to ensure timely and effective action during emergencies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2 Data","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Network and instruments\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSeismic data were acquired from a local network of seven broadband and short-period stations operated by PVMBG Bandung and deployed around Mount Sinabung to optimize azimuthal coverage of the edifice; station metadata (station code, latitude, longitude, elevation, sensor type, and sampling rate) are provided in Supplementary Table\u0026nbsp;1. Station siting prioritized low-noise locations on competent ground where possible, reliable power and telemetry were available for near-real-time data transfer, and sensor orientation and installation depth followed PVMBG standard practices to minimize environmental and cultural noise. Timing for all stations was referenced to GPS clocks and verified during routine metadata audits to ensure sub-millisecond timing consistency required for robust travel-time based location and relocation procedures.\u003c/p\u003e\u003c/div\u003e\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 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMetadata for seven seismic stations around Mount Sinabung, including code, location, elevation, sensor type, and sampling rate. The network combines broadband and short‑period instruments to support high‑resolution monitoring and reliable hypocenter relocation.\u0026lrm;\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStation Name/code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLatitude (\u0026deg;N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLongitude (\u0026deg;E)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElevation (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensor type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSampling rate (Hz)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSukanalu\u003c/p\u003e\u003cp\u003eSKN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.1700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.3920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBroadband (STS‑2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLau Kawar\u003c/p\u003e\u003cp\u003eLKW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.1805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.3850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eShort‑period (2 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGamber\u003c/p\u003e\u003cp\u003eGBR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.1580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.4015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBroadband (CMG‑3T)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSibayak\u003c/p\u003e\u003cp\u003eSBY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.1900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.4100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eShort‑period (1 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMardinding\u003c/p\u003e\u003cp\u003eMDD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.1625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.3760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBroadband (Trillium 120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKebayaken\u003c/p\u003e\u003cp\u003eKBY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.1755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.4055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eShort‑period (2 Hz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSigarang-Garang\u003c/p\u003e\u003cp\u003eSGR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.3890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBroadband (STS‑2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e100\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=\"BlockQuote\"\u003e\u003cp\u003eRecording instrumentation comprised a mix of broadband sensors (for low-frequency energy and waveform completeness) and high-gain short-period sensors (for improved signal-to-noise on emergent volcanic phases), digitized at sampling rates listed in Supplementary Table\u0026nbsp;1. Before relocation, continuous records were visually inspected and subjected to automated quality control: instrument response removal, bandpass filtering tailored to expected P- and S-phase frequency content, and manual or semi-automatic pick review to flag low-quality or ambiguous arrivals. We quantified station sensitivity and azimuthal coverage, and we evaluated network performance metrics (detection completeness, pick residual distributions) to inform data weighting and damping choices in the Geiger iterative inversion, following best practice recommendations for optimizing hypocenter resolution in small, volcano-focused networks.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Recording period and picks\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eContinuous waveform records from 01 October 2023 to 30 April 2024 were inspected in Swarm to compile a high‑quality arrival dataset for relocation. Continuous traces were reviewed on a station‑by‑station basis using variable time windows selected to capture both isolated and clustered activity. Spectral and time‑domain displays were used concurrently to identify emergent phases and to suppress coherent noise; narrow band filtering (typical passbands 1\u0026ndash;10 Hz for volcanic short‑period phases and 0.5\u0026ndash;5 Hz for broadband low‑frequency signals) aided consistent phase identification across the network.\u003c/p\u003e\u003cp\u003eP and S arrival times were picked manually to maximise consistency and to avoid systematic bias introduced by automatic pickers in low signal‑to‑noise situations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Each pick record includes the station code, phase identifier (P or S), UTC pick time, and a discrete pick‑quality flag. Pick quality was assigned on a three‑tier scale: high (clear first arrival, uncertainty\u0026thinsp;\u0026le;\u0026thinsp;0.02 s), medium (moderately emergent or low SNR, uncertainty 0.02\u0026ndash;0.1 s), and low (ambiguous or strongly emergent, uncertainty\u0026thinsp;\u0026gt;\u0026thinsp;0.1 s). Picks flagged as low quality were retained in the master file for completeness but down‑weighted in subsequent inversion and sensitivity analyses.\u003c/p\u003e\u003cp\u003eArrival picks were exported from Swarm as Arrival.dat and converted to the Geiger adaptive damping (GAD) input format using a reproducible conversion script. The conversion step verified station codes against the metadata table, standardized time stamps to ISO‑8601 UTC, and propagated the pick‑quality flags into the GAD weighting scheme. Prior to inversion, we performed a final quality control pass that removed obvious outliers, corrected inconsistent station polarity or phase labels, and ensured that each event had a minimum of four high‑ or medium‑quality picks. This conservative selection protocol underpins the robustness and interpretability of the relocated hypocenter catalogue [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Inclusion criteria and quality control\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEvents were selected for relocation only when they satisfied the Geiger adaptive damping minimum pick requirements [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], specifically a minimum of three independent P picks and the network\u0026rsquo;s baseline total‑pick threshold. Initial GAD solutions returning root‑mean‑square travel‑time residuals greater than 0.20 s were flagged for manual review and repicking; repicking prioritized improving high‑quality P arrivals, adding clear S picks where possible, and reconciling mislabelled or time‑shifted arrivals. When repicking failed to reduce residuals below the acceptance threshold, the event was excluded from the relocated catalogue.\u003c/p\u003e\u003cp\u003eQuality control proceeded through an explicit, reproducible workflow. Automated checks first verified station‑code consistency with the metadata table, removed duplicate or implausible picks, and enforced minimum station azimuthal coverage for stable location geometry. Manual inspection then assessed pick coherency across the network and applied the discrete pick‑quality flags to derive per‑pick weights used in the GAD inversion. Final solutions were retained only if they achieved RMS\u0026thinsp;\u0026le;\u0026thinsp;0.20 s and satisfied secondary diagnostics including realistic depth bounds, acceptable formal location uncertainties, and absence of gross travel‑time outliers in the residual distribution.\u003c/p\u003e\u003cp\u003eTo characterise robustness and sensitivity we performed supplementary tests on the retained catalogue. These included jackknife and bootstrap resampling to evaluate nodal and station dependence, perturbation of the velocity model to bound depth variability, and propagation of pick‑quality weights to produce realistic uncertainty estimates for reported hypocenters. The final vetted event catalogue and associated quality metrics are provided in Supplementary Table S3 for transparency and for reuse in subsequent hazard and process studies.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data provenance and availability\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eRaw seismic waveforms used in this study are the property of the Center for Volcanology and Geological Hazard Mitigation (PVMBG) and are governed by PVMBG\u0026rsquo;s data‑sharing policy. Processed products generated for this publication \u0026mdash; including arrival lists, GAD input and output files, the relocated hypocenter catalogue, pick‑quality flags, and metadata tables \u0026mdash; are provided as Supplementary Material accompanying this manuscript. Where journal limits on supplementary file size apply, full data packages and the reproducible conversion and processing scripts used in this study are archived and available from the corresponding author or directly from PVMBG on request, subject to PVMBG\u0026rsquo;s data‑access conditions.\u003c/p\u003e\u003cp\u003eEach distributed file is documented with a README that describes file formats, column definitions, time standards (UTC, ISO‑8601), and the provenance of station metadata and instrument responses. Users wishing to reuse the data should cite this article and acknowledge PVMBG as the data provider. Requests for raw waveform access or bulk data transfer should be directed to PVMBG; contact details and any applicable data‑use restrictions are provided in the Supplementary Material.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Seismic wave types and properties\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSeismic waves are elastic disturbances that propagate through Earth materials and generate time‑varying strain and particle motion [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These disturbances result from an interaction between the initiating force and the medium\u0026rsquo;s elastic restoring stresses, producing oscillatory motion that can be decomposed into longitudinal (compressional) and transverse (shear) components. Many seismic phases are combinations of these fundamental modes, each with distinct propagation speed, attenuation behaviour, frequency content, and implications for observed ground motion at the surface.\u003c/p\u003e\u003cp\u003eBody waves travel through Earth\u0026rsquo;s interior and carry information about subsurface structure [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Primary (P) waves are compressional, with particle motion parallel to wave propagation; they travel fastest and are the first arriving phase at seismic stations. Secondary (S) waves are shear, with particle motion perpendicular to propagation; they do not travel through fluids and therefore provide complementary constraints on rigidity and the presence of melts or fluids [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Relative arrival times and amplitude decay of P and S phases are primary observables for hypocentre determination and local velocity‑model calibration.\u003c/p\u003e\u003cp\u003eSurface waves propagate along the Earth\u0026rsquo;s free surface and typically dominate ground motion at longer periods and larger epicentral distances [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Rayleigh waves involve retrograde elliptical particle motion in the vertical plane and sample both near‑surface compressional and shear properties, making them sensitive to crustal shear‑wave structure [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Love waves are horizontally polarized shear waves trapped near the surface and are especially sensitive to lateral and vertical contrasts in shear velocity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Surface‑wave dispersion and attenuation provide independent constraints on shallow structure that complement body‑wave location analyses.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Volcanic versus tectonic seismicity and classification\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEarthquakes can be broadly classified by their causative mechanisms into tectonic and volcanic events [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Tectonic earthquakes arise from brittle failure on faults driven by regional stress fields, whereas volcanic earthquakes are primarily driven by transient processes related to magmatic and hydrothermal systems: magma ascent, pressurization and depressurization of fluid phases, fracturing induced by volatile exsolution, and conduit or dome collapse. Volcanic seismicity commonly exhibits emergent onsets, low signal‑to‑noise ratios, and a prevalence of shallow focal depths, all of which complicate phase picking and location [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eVolcanic earthquakes are often categorised by their waveform and spectral characteristics into families such as deep volcanic, shallow volcanic, hybrid, low‑frequency (LP), volcano‑tectonic (VT), tremor, and long‑period (LP) events. Deep volcanic events typically originate beneath the edifice at depths greater than a few kilometres and are commonly associated with magma movement at depth, whereas shallow volcanic events occur within the uppermost crust (commonly within ~\u0026thinsp;0\u0026ndash;2 km beneath the surface) and frequently reflect near‑surface fracturing or hydrothermal interactions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Hybrid events combine impulsive high‑frequency and emergent low‑frequency energy and often indicate fluid‑rock interactions. Continuous tremor and LP signals are generally associated with resonant or sustained fluid flow in conduits or hydrothermal fractures; their presence and spectral content are key indicators of changes in pressurization and flow regimes.\u003c/p\u003e\u003cp\u003eAn earthquake\u0026rsquo;s hypocentre (focus) is its subsurface point of origin where rupture or the source process initiates [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; the epicentre is the surface projection directly above the hypocentre [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Depth classification is a standard descriptor for seismic catalogues and is important for discriminating physical processes and hazard implications. For global tectonic contexts, depth classes are commonly defined as shallow (\u0026lt;\u0026thinsp;70 km), intermediate (70\u0026ndash;300 km), and deep (\u0026gt;\u0026thinsp;300 km), although volcanic‑site studies typically use much finer, site‑specific depth ranges to separate near‑surface (0\u0026ndash;2 km), shallow crustal (2\u0026ndash;10 km), and deeper crustal sources depending on the local geology and the instrument network resolution.\u003c/p\u003e\u003cp\u003eClear and consistent definitions of depth classes, combined with robust uncertainty estimates on depth and horizontal location, are essential when interpreting seismicity beneath volcanoes. Because volcanic events are frequently shallow and emergent, quantifying formal location uncertainties, sensitivity to the velocity model, and station coverage is critical for reliable source discrimination and for translating hypocentre patterns into physical interpretations of magma and fluid migration.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Geiger method and adaptive damping\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe Geiger method locates earthquake hypocenters by iteratively minimizing misfit between observed and theoretical travel times using a least‑squares update [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Input requirements comprise station coordinates, P and S arrival times, and a trial velocity model. Each iteration linearizes the travel‑time problem about the current hypocenter and origin‑time estimate and computes corrective updates to reduce travel‑time residuals, thereby closing the gap between observed and calculated arrival times.\u003c/p\u003e\u003cp\u003eFor an observation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e the travel‑time residual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{i}\\)\u003c/span\u003e\u003c/span\u003e the difference between the observed arrival time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{obs,\\:i}\\)\u003c/span\u003e\u003c/span\u003e the calculated travel time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{calc,i}\\)\u003c/span\u003e\u003c/span\u003e for the current model. Linearizing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{calc,i}\\)\u003c/span\u003e\u003c/span\u003e with respect to small perturbations in origin time and hypocentre coordinates yields the standard Gauss\u0026ndash;Newton form. In compact notation, the residual is expressed as\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{r}_{i}\\approx\\:\\frac{\\partial\\:t}{\\partial\\:{x}_{i}}{\\Delta\\:}x+\\frac{\\partial\\:t}{\\partial\\:{y}_{i}}{\\Delta\\:}y+\\frac{\\partial\\:t}{\\partial\\:{z}_{i}}{\\Delta\\:}z+{\\Delta\\:}t$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}x,{\\Delta\\:}y\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}z\\)\u003c/span\u003e\u003c/span\u003e are coordinate corrections and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}t\\)\u003c/span\u003e\u003c/span\u003e is the origin‑time correction. The partial derivatives \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\partial\\:t/\\partial\\:{x}_{j}\\)\u003c/span\u003e\u003c/span\u003e form the Jacobian matrix G and are evaluated using ray‑path geometry for the adopted velocity model. These derivatives quantify the sensitivity of each pick to changes in hypocentre and origin time and are central to computing robust updates [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe linearized system is written as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:G\\:\\varDelta\\:m\\:=\\:d\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:m\\)\u003c/span\u003e\u003c/span\u003e is the vector of parameter updates \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\varDelta\\:x,\\varDelta\\:y,\\varDelta\\:z,\\varDelta\\:t\\right)\\)\u003c/span\u003e\u003c/span\u003e and d contains the travel‑time residuals. To stabilise inversion in the presence of limited azimuthal coverage, emergent phases, or velocity‑model uncertainty, the system is solved in damped least‑squares form:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\left({G}^{T}G+\\lambda\\:I\\right)\\hspace{0.17em}{\\Delta\\:}m={G}^{T}d,$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e is the damping parameter and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\)\u003c/span\u003e\u003c/span\u003e is the identity matrix. Adaptive damping adjusts \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\lambda\\:\\:\\)\u003c/span\u003e\u003c/span\u003ebetween iterations to balance fit and model stability: reducing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\lambda\\:\\:\\)\u003c/span\u003e\u003c/span\u003ewhen updates yield consistent residual reductions and increasing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\lambda\\:\\:\\)\u003c/span\u003e\u003c/span\u003ewhen updates produce non‑physical parameter jumps. This Geiger adaptive damping (GAD) approach improves convergence and yields more stable hypocentre clouds in sparse or noisy volcanic networks [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Implementation and convergence criteria\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePractical implementation requires careful weighting of picks according to quality, per‑pick uncertainties, and station geometry. We propagate pick weights into the normal equations by multiplying each row of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:G\\)\u003c/span\u003e\u003c/span\u003e and corresponding residual by the square root of the weight. Iterations proceed until both the maximum parameter update \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left|{\\Delta\\:}m\\right|\\)\u003c/span\u003e\u003c/span\u003e and the RMS residual change fall below predefined tolerances or until a maximum number of iterations is reached. Post‑inversion diagnostics include residual histograms, azimuthal gap checks, formal covariance estimates derived from \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left({G}^{T}G+{\\lambda\\:}I\\right)}^{-1}\\)\u003c/span\u003e\u003c/span\u003e, and sensitivity tests that perturb the velocity model or omit individual stations to assess robustness [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTime‑frequency transforms such as the S‑transform are used to characterise signal content, guide manual picking, and separate overlapping phases prior to location. The S‑transform provides a time‑localized spectral representation that preserves phase information and scales adaptively with frequency, improving identification of emergent P and S onsets in noisy volcanic records. Reliable picks informed by time‑frequency analysis reduce systematic errors in G and d, thereby improving the fidelity and interpretability of GAD relocations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Event counts and classification\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eBetween 01 October 2023 and 30 April 2024 we relocated and vetted 61 volcanic earthquakes that satisfied the GAD inclusion and quality criteria. Events were classified by focal depth into two operational categories chosen to reflect processes relevant to Sinabung monitoring: VTB (shallow volcanic‑tectonic), depth\u0026thinsp;\u0026lt;\u0026thinsp;2.0 km, and VTA (deep volcanic‑tectonic), depth\u0026thinsp;\u0026ge;\u0026thinsp;2.0 km. These thresholds were selected to separate near‑surface fracturing and hydrothermal interactions from deeper magmatic or plumbing‑related sources, consistent with local structural interpretations and the network\u0026rsquo;s vertical resolution (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVelocity layering beneath Mount Sinabung employed in adaptive‑damping Geiger relocation. Each row shows the depth interval, associated \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{P}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\)\u003c/span\u003e\u003c/span\u003e values, vertical resolution (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:z\\)\u003c/span\u003e\u003c/span\u003e), and sensitivity expressed as depth shifts for \u0026plusmn;\u0026thinsp;10% changes in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{P}\\)\u003c/span\u003e\u003c/span\u003e. The model highlights strong constraints in the shallow crust and poor resolution below 15 km.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLayer\u003c/p\u003e\n \u003cp\u003e(top\u0026ndash;bottom km)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{V}\\varvec{p}\\)\u003c/span\u003e\u003c/span\u003e (km s⁻\u0026sup1;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{V}\\varvec{s}\\)\u003c/span\u003e\u003c/span\u003e (km s⁻\u0026sup1;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVertical\u003c/p\u003e\n \u003cp\u003eresolution (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\varDelta\\:}\\varvec{z}\\)\u003c/span\u003e\u003c/span\u003e, km)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003cp\u003e(depth shift for \u0026plusmn;\u0026thinsp;10% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{V}\\varvec{p}\\)\u003c/span\u003e\u003c/span\u003e, km)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u0026ndash;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u0026ndash;2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00\u0026ndash;5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u0026ndash;15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash; (poor constraint)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eMonthly counts and basic summary statistics are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Across the study interval we identify 34 VTA and 27 VTB events. Temporal behaviour shows that VTA events dominated the early part of the record, while VTB proportions increased during months with elevated near‑summit activity; this partitioning suggests alternating periods of deeper source activity and intensified shallow fracturing. We quantified month‑to‑month variability using Poisson confidence intervals on counts and tested for non‑stationarity with a simple \u0026chi;2 test comparing observed monthly counts against a homogeneous Poisson null model; significance and p‑values.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMonthly counts of volcanic earthquakes beneath Mount Sinabung from October 2023 to April 2024. Events are classified into deep and shallow categories, showing temporal variability with alternating phases of deeper source activity and near-summit fracturing.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmounts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTypes of Earthquakes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOctober 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShallow earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNovember 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShallow earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecember 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShallow earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanuary 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShallow earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFebruary 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShallow earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarch 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShallow earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApril 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShallow earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eEach event entry in the catalogue includes event time, geographic coordinates, depth, RMS, number of picks, and formal uncertainty estimates derived from the damped covariance matrix and from station jackknife tests. Classification robustness was assessed by: (1) propagating depth uncertainties to evaluate the fraction of events with depth overlap across the 2.0 km threshold, and (2) performing velocity‑model perturbation tests (\u0026plusmn;\u0026thinsp;10% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e) to bound systematic depth shifts. Where depth uncertainty caused ambiguous classification, events were flagged in the catalogue and treated separately in aggregated statistics. Reporting these metrics ensures that interpretations linking depth classes to physical processes explicitly account for the limits of the local network and velocity model.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Event catalogue and location quality\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe complete relocated event catalogue is provided in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and contains UTC origin time, latitude, longitude, depth (km), RMS residual (s), number of picks, pick‑quality summary, and formal uncertainty estimates for each entry. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e also lists diagnostic fields used in quality assessment, including azimuthal gap, minimum and maximum station distances, and the per‑event covariance trace so readers can reproduce selection thresholds and perform independent filtering.\u003c/p\u003e\n \u003cp\u003eAccepted locations satisfy a conservative quality threshold of RMS\u0026thinsp;\u0026le;\u0026thinsp;0.20 s. Events failing to meet this threshold after repicking were excluded from the final catalogue. The typical number of picks per retained event ranged from 4 to 12; events recorded on larger station subsets systematically display lower RMS, reduced formal covariance, and smaller confidence ellipses in horizontal position. To preserve transparency we report both the unweighted and weighted RMS values and provide per‑pick weights in the Supplementary files so users may reweight or rerun inversions as required.\u003c/p\u003e\n \u003cp\u003eDepth sensitivity varies systematically with focal depth and network geometry. Shallow events (\u0026thinsp;≲\u0026thinsp;2 km) show relatively stable depth estimates with smaller formal vertical uncertainties under the adopted layered 1‑D model, while deeper events exhibit larger vertical shifts under modest velocity perturbations and larger covariance in z. We quantify this behavior in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e by reporting the vertical variance and by providing results from \u0026plusmn;\u0026thinsp;10% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e perturbation tests and station jackknife runs for each event. For events with depth uncertainties that overlap the VTB/VTA classification threshold, entries are flagged and users are cautioned when attributing physical processes to those particular depths.\u003c/p\u003e\n \u003cp\u003ePost‑processing diagnostics accompany each catalogue entry. These include residual histograms, per‑station residual summaries, and azimuthal‑coverage metrics that identify events with potentially biased solutions (for example, high azimuthal gap or strongly unequal station distribution). We include guidance in the README on how to apply additional selection criteria (e.g., minimum number of high‑quality picks, maximum azimuthal gap) to produce subsets tailored to specific analyses, such as focal‑mechanism studies or fine‑scale migration mapping.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelocated volcanic earthquake events beneath Mount Sinabung (Oct 2023\u0026ndash;Feb 2024). Each entry lists origin time, location, depth, RMS residual, number of picks, pick quality, covariance trace, and azimuthal gap, documenting location accuracy and classification.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvent ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUTC origin time\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLatitude (\u0026deg;N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLongitude (\u0026deg;E)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDepth (km)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMS (s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of picks\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePick‑quality summary\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCovariance trace (km\u0026sup2;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAzimuthal gap (\u0026deg;)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVT_0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023-10-03T02:14:12.345Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6H,2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVT_0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023-10-07T11:05:47.120Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4H,2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVT_0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023-10-15T18:23:09.987Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8H,2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVT_0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023-11-01T04:56:33.210Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5H,2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVT_0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023-11-20T21:42:02.004Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7H,2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVT_0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023-12-05T13:09:58.460Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3H,2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVT_0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023-12-18T07:34:21.889Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10H,2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVT_0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024-01-09T00:11:44.512Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.4001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4H,2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVT_0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024-02-02T15:28:30.333Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6H,2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVT_0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024-02-25T09:02:11.001Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5H,2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eNotes:\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026bull; Pick‑quality summary: H = high quality pick; M = medium quality pick; L = low quality pick.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Covariance trace reports the trace of the parameter covariance submatrix for spatial coordinates (proxy for combined location uncertainty); units in km\u0026sup2;.\u003c/p\u003e\n \u003cp\u003e\u0026bull; Azimuthal gap is the largest back‑azimuthal gap in degrees for stations used in the solution.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Spatial distribution and Cross‑sections\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eEpicentres are tightly clustered around the volcanic edifice, with the majority of events occurring within a radial distance of approximately 0\u0026ndash;5 km from the summit (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The plan-view pattern shows a primary concentration on the upper flank and a secondary, more diffuse cluster extending downslope to the north‑west, consistent with shallow fracturing and conduit‑proximal stress release. Station coverage and topographic shadowing are reported in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and were considered when interpreting lateral density variations to avoid over‑interpreting apparent gaps caused by network geometry.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eOrthogonal cross‑sections oriented west\u0026ndash;east and south\u0026ndash;north (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) reveal vertically distinct hypocentre clusters. Shallow VTB events are concentrated between ~\u0026thinsp;0.3 and 2.0 km depth and form tight, semi‑continuous swarms beneath the summit and upper flanks, consistent with near‑surface brittle failure or hydrothermal cracking. Deeper VTA events define a separate population distributed between ~\u0026thinsp;2.5 and 14 km depth and are spatially coherent with deeper conduit or mid‑crustal magma‑transport pathways. The deep population is resolved as several aligned clusters rather than a single point source, suggesting vertical segmentation of the plumbing system or multiple focal zones.\u003c/p\u003e\n \u003cp\u003eThe deepest relocated event in the catalogue occurred in February 2024 at an estimated depth of ~\u0026thinsp;14 km; this event is reported with its full uncertainty metrics in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and was robust to \u0026plusmn;\u0026thinsp;10% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e perturbation tests and station jackknife runs. Although deeper events exhibit larger formal vertical uncertainties, the February event remained classified as VTA under all sensitivity tests and indicates magmatic or deep crustal processes active beneath the edifice during the study period.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe depth histogram (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) exhibits a clear bimodal distribution that matches the operational VTB (shallow) and VTA (deep) classifications. This bimodality supports a two‑tier source model in which shallow, summit‑proximal fracturing and deeper, plumbing‑related processes operate concurrently but at distinct depths. We quantify the separation by reporting the kernel‑density estimate and the fraction of events with overlapping 2 km threshold uncertainty in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; these metrics indicate that the bimodal signature is robust to plausible velocity‑model and pick‑uncertainty perturbations. Together, plan‑view clustering and cross‑sectional segmentation provide a consistent structural framework for interpreting magma and fluid migration beneath the volcano.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Sensitivity test outcomes\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePerturbing the adopted \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e model by \u0026plusmn;\u0026thinsp;10% yields depth shifts that scale with focal depth: shallow VTB events show shifts typically\u0026thinsp;\u0026lt;\u0026thinsp;1 km, whereas the deepest VTA events can shift by up to ~\u0026thinsp;1\u0026ndash;3 km under these perturbations. Jackknife tests that systematically remove single stations reveal that horizontal location sensitivity increases markedly for events with poor azimuthal coverage; events recorded by six or more stations remain robust, exhibiting only minor positional changes in both horizontal and vertical components. Complementary synthetic experiments and interval‑velocity sensitivity analyses indicate that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp/Vs\\)\u003c/span\u003e\u003c/span\u003e and layer thickness trade‑offs primarily control vertical resolution and that shallow layers are generally better constrained by the local network than deeper crustal layers.\u003c/p\u003e\n \u003cp\u003eThese outcomes justify the operational depth classification and the conservative quality thresholds applied to the catalogue: events with depth shifts or jackknife‑induced relocations that cross the 2.0 km classification boundary are flagged and excluded from depth‑sensitive process interpretations. We report per‑event sensitivity metrics (\u0026plusmn;\u0026thinsp;10% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e depth shifts, jackknife displacement statistics, and formal covariance traces) in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e so that readers can independently assess which events are robust for plumbing‑system inference and which require cautious interpretation.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Seismotectonic interpretation and implications\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHypocenter clustering beneath the summit and vertically coherent alignments indicate a vertically segmented magmatic plumbing system beneath Sinabung. Shallow VTB events (\u0026le;\u0026thinsp;2 km) record brittle failure in a pressurized near‑surface hydrothermal or conduit environment. Deeper VTA events (to ~\u0026thinsp;14 km) mark fracture networks that accommodate magma ascent, volatile release, and depth‑dependent stress transfer. Episodic deep VTA activity can transfer stress or mass upward, producing transient overpressure pulses, dike propagation, or fluid migration that trigger shallow VTB swarms and generate hybrid or low‑frequency signals. The observed bimodal depth distribution therefore reflects multi‑stage storage and intermittent transfer rather than a single continuous conduit, and periods dominated by deep activity can presage increased shallow fracturing, enhanced degassing, and elevated eruption probability if pathways become connected.\u003c/p\u003e\u003cp\u003eLocal network geometry and velocity‑model trade‑offs limit vertical resolution and impart systematic depth uncertainty. Events that cross classification thresholds under \u0026plusmn;\u0026thinsp;10% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e perturbations or jackknife relocations are flagged and interpreted cautiously. Strengthening the seismotectonic model requires dense upper‑flank arrays, joint seismic\u0026ndash;geodetic inversions, finite‑frequency waveform depth refinement, and continuous spectral monitoring to discriminate brittle failure, fluid‑driven resonance, and true magmatic intrusion.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Comparison with previous studies\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eOur relocated hypocenter distribution and depth ranges align with prior investigations (2016, 2021) that documented dominant shallow\u0026ndash;to\u0026ndash;mid‑crustal seismicity beneath the summit. The clear bimodal signature we find \u0026mdash; a shallow VTB population concentrated\u0026thinsp;\u0026le;\u0026thinsp;2 km and a deeper VTA population extending several kilometres into the mid‑crust \u0026mdash; reproduces the principal depth bands reported earlier while improving spatial coherence through denser picking and rigorous relocation criteria.\u003c/p\u003e\u003cp\u003eThe emergence of deeper VTA events in February 2024 signals episodic deep activity or transient stress‑state changes that were not evident in earlier periods. Absolute depths remain sensitive to the adopted 1‑D velocity model and to station geometry; \u0026plusmn;10% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e perturbations and jackknife tests show systematic depth shifts up to kilometres for the deepest events. We therefore treat the deepest locations as robust indicators of deep‑seated activity but emphasize caution in attributing precise depth values; targeted dense‑array deployments and joint seismic\u0026ndash;geodetic inversions are needed to confirm and refine these deep event depths.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Method limitations and monitoring implications\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe principal limitations are the adopted 1‑D velocity model and incomplete azimuthal station coverage. Geiger linearization presumes small perturbations and depends on initial location guesses and travel‑time accuracy; adaptive damping stabilizes inversions but cannot remove systematic biases from an incorrect velocity structure. As a result, depth estimates\u0026mdash;especially for the deepest VTA events\u0026mdash;carry systematic uncertainties that can reach kilometres under plausible \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e perturbations. Formal covariance and jackknife diagnostics identify which events are robust and which are model‑sensitive. To reduce bias and improve depth fidelity we recommend joint tomographic or full‑waveform inversion using dense local data and incorporation of surface‑wave and receiver‑function constraints.\u003c/p\u003e\u003cp\u003eThe relocated hypocentre patterns and monthly event rates have direct operational value for PVMBG. Strategic station densification along NW\u0026ndash;SE and NE\u0026ndash;SW transects will reduce azimuthal gaps and halve horizontal uncertainties for many events. Implement routine, automated relocations using updated velocity models and publish per‑event sensitivity metrics (\u0026plusmn;\u0026thinsp;10% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e shifts, jackknife displacements) alongside catalogues. Integrate seismic relocations with continuous deformation, gas‑flux, and visual observations to detect coupled signals that precede transitions from deep activity to shallow unrest. These measures will tighten eruption forecasting capability and provide clearer, evidence‑based triggers for alert‑level decisions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSixty‑one volcanic earthquakes were located beneath Mount Sinabung (Oct 2023\u0026ndash;Apr 2024): 34 deep VTA events at 2.5\u0026ndash;14 km and 27 shallow VTB events at 0.3\u0026ndash;2 km.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEpicenters cluster within ~\u0026thinsp;0\u0026ndash;5 km of the summit; cross‑sections reveal vertically continuous hypocentre pathways consistent with magma‑related processes.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLocation quality was controlled by RMS thresholding and repicking; sensitivity tests show greater depth uncertainty for deep events due to velocity‑model trade‑offs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRecommendations: densify the seismic network to improve azimuthal coverage; refine velocity models via tomography or full‑waveform inversion; maintain routine hypocentre relocations to strengthen monitoring and hazard mitigation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003cp\u003eEthics, Consent to Participate, and Consent to Publish declarations: not applicable.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eE. Minarto: conceptualization; data processing and Geiger relocations; analysis and visualization; writing \u0026mdash; original draft, review \u0026amp; editing. A. Ditaningrum: manual picking and QC; figure and table preparation; writing \u0026mdash; review \u0026amp; editing. Kristianto: data provision (PVMBG liaison); methodological advice; manuscript review. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe thank PVMBG Bandung for providing seismic data and station metadata. We acknowledge technical assistance with picking and processing from colleagues in the Department of Physics, Sepuluh Nopember Institute of Technology. This research received no external funding.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eArrival picks (Arrival.dat), station metadata (station.dat), velocity model (Table 2), GAD outputs (Result.dat), and plotting scripts are provided as Supplementary Material or are available from PVMBG Bandung upon reasonable request subject to their data-sharing policy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCharlton TR. Tertiary evolution of the Eastern Indonesia Collision Complex. J Asian Earth Sci. Apr. 2000;18(5):603\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1367-9120(99)00049-8\u003c/span\u003e\u003cspan address=\"10.1016/S1367-9120(99)00049-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHariyono E. and L. S, The Characteristics of Volcanic Eruption in Indonesia, in \u003cem\u003eVolcanoes - Geological and Geophysical Setting, Theoretical Aspects and Numerical Modeling, Applications to Industry and Their Impact on the Human Health\u003c/em\u003e, G. Aiello, Ed., InTech, 2018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5772/intechopen.71449\u003c/span\u003e\u003cspan address=\"10.5772/intechopen.71449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnnisa Y, Astriyan GC, Wahyunia S, Indrastuti N, Massinai MFI. Determination of Hypocenter Using Geiger Method in Sinabung Volcano, April-July 2016 Period. IOP Conf Ser : Earth Environ Sci. Oct. 2021;873(1):012007. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1088/1755-1315/873/1/012007\u003c/span\u003e\u003cspan address=\"10.1088/1755-1315/873/1/012007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Hara D, Karlstrom L. The arc-scale spatial distribution of volcano erosion implies coupled magmatism and regional climate in the Cascades arc, United States. Front Earth Sci. June 2023;11:1150760. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/feart.2023.1150760\u003c/span\u003e\u003cspan address=\"10.3389/feart.2023.1150760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGunawan H, et al. Overview of the eruptions of Sinabung Volcano, 2010 and 2013\u0026ndash;present and details of the 2013 phreatomagmatic phase. J Volcanol Geoth Res. Sept. 2019;382:103\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jvolgeores.2017.08.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jvolgeores.2017.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNakada S et al. Sept., Growth process of the lava dome/flow complex at Sinabung Volcano during 2013\u0026ndash;2016, \u003cem\u003eJournal of Volcanology and Geothermal Research\u003c/em\u003e, vol. 382, pp. 120\u0026ndash;136, 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jvolgeores.2017.06.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jvolgeores.2017.06.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKusumo AW, Azuma H, Watanabe T, Oda Y. Seismic tomography for subsurface structures imaging beneath Hachijojima Volcanic Island, Izu-Bonin Arc, Japan. J Seismol. Aug. 2025;29(4):855\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10950-025-10309-9\u003c/span\u003e\u003cspan address=\"10.1007/s10950-025-10309-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaras\u0026ouml;zen E, Karas\u0026ouml;zen B. Earthquake location methods. Int J Geomath. Dec. 2020;11(1):13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13137-020-00149-9\u003c/span\u003e\u003cspan address=\"10.1007/s13137-020-00149-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSutawidjaja IS, Prambada O, Siregar DA. The August 2010 Phreatic Eruption of Mount Sinabung, North Sumatra. Indonesian J Geosci. Mar. 2013;8(1):55\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17014/ijog.8.1.55-61\u003c/span\u003e\u003cspan address=\"10.17014/ijog.8.1.55-61\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSenathirajah K, Bonner M, Schuyler Q, Palanisami T. A disaster risk reduction framework for the new global instrument to end plastic pollution. J Hazard Mater. May 2023;449:131020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhazmat.2023.131020\u003c/span\u003e\u003cspan address=\"10.1016/j.jhazmat.2023.131020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBakhshian E, Martinez-Pastor B. Evaluating human behaviour during a disaster evacuation process: A literature review, \u003cem\u003eJournal of Traffic and Transportation Engineering (English Edition)\u003c/em\u003e, vol. 10, no. 4, pp. 485\u0026ndash;507, Aug. 2023, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jtte.2023.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jtte.2023.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKatoh S, Iio Y, Nagao H, Katao H, Sawada M, Tomisaka K. SegPhase: development of arrival time picking models for Japan\u0026rsquo;s seismic network using the hierarchical vision transformer. Earth Planet Space. July 2025;77(1):118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40623-025-02249-y\u003c/span\u003e\u003cspan address=\"10.1186/s40623-025-02249-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBourne SJ, Oates SJ, Van Elk J, Doornhof D. A seismological model for earthquakes induced by fluid extraction from a subsurface reservoir. JGR Solid Earth. Dec. 2014;119(12):8991\u0026ndash;9015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/2014JB011663\u003c/span\u003e\u003cspan address=\"10.1002/2014JB011663\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNakamichi H, Ukawa M, Sakai S. Precise hypocenter locations of midcrustal low-frequency earthquakes beneath Mt. Fuji, Japan. Earth Planet Sp. June 2014;56(11):e37\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/BF03352542\u003c/span\u003e\u003cspan address=\"10.1186/BF03352542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang W. From Elastic Waves to Seismic Waves. in Reflection Seismology. Elsevier; 2014. pp. 47\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-0-12-409538-0.00003-8\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-409538-0.00003-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z-X. Stress Waves. in Rock Fracture and Blasting. Elsevier; 2016. pp. 1\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-0-12-802688-5.00001-4\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-802688-5.00001-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShayakhmetov SB, Kalpenova ZD, Lesov KS, Umarov KK. Rayleigh and love surface waves with regard to seismic stress state of earth bed, \u003cem\u003eE3S Web of Conf.\u003c/em\u003e, vol. 401, p. 01077, 2023, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1051/e3sconf/202340101077\u003c/span\u003e\u003cspan address=\"10.1051/e3sconf/202340101077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBowden DC, Tsai VC. Earthquake ground motion amplification for surface waves. Geophys Res Lett. Jan. 2017;44(1):121\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/2016GL071885\u003c/span\u003e\u003cspan address=\"10.1002/2016GL071885\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarghany M. Wavelet transform and particle swarm optimization algorithms for automatic detection of internal wave from synthetic aperture radar. in Nonlinear Ocean Dynamics. Elsevier; 2021. pp. 247\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-0-12-820785-7.00005-8\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-820785-7.00005-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Wang T, Bian Y, Yang Q. Features of different types of seismic events in China\u0026rsquo;s Capital Region, \u003cem\u003eEarthquake Science\u003c/em\u003e, vol. 34, no. 6, pp. 489\u0026ndash;506, Dec. 2021, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.29382/eqs-2021-0035\u003c/span\u003e\u003cspan address=\"10.29382/eqs-2021-0035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Der Laat L, Mora MM, Fco. Pacheco J, Lesage P, Meneses E. Seismicity during the recent activity (2009\u0026ndash;2020) of Turrialba volcano, Costa Rica, \u003cem\u003eJournal of Volcanology and Geothermal Research\u003c/em\u003e, vol. 431, p. 107651, Nov. 2022, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jvolgeores.2022.107651\u003c/span\u003e\u003cspan address=\"10.1016/j.jvolgeores.2022.107651\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManzo R, Cesca S, Galluzzo D, La Rocca M, Picozzi M, Di Maio R. Source analysis of low frequency seismicity at Mt. Vesuvius by a hybrid moment tensor inversion, \u003cem\u003eJournal of Volcanology and Geothermal Research\u003c/em\u003e, vol. 454, p. 108173, Oct. 2024, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jvolgeores.2024.108173\u003c/span\u003e\u003cspan address=\"10.1016/j.jvolgeores.2024.108173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKulh\u0026aacute;nek O. 21 The structure and interpretation of seismograms. in International Geophysics. Volume 81. Elsevier; 2002. pp. 333\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0074-6142(02)80224-8\u003c/span\u003e\u003cspan address=\"10.1016/S0074-6142(02)80224-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJain S. Earthquakes. In: Geology S, editor. Fundamentals of Physical Geology. New Delhi: Springer India; 2014. pp. 337\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-81-322-1539-4_15\u003c/span\u003e\u003cspan address=\"10.1007/978-81-322-1539-4_15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLomax A, Michelini A, Curtis A. Earthquake Location, Direct, Global-Search Methods. In: Meyers RA, editor. in Encyclopedia of Complexity and Systems Science. New York, NY: Springer New York; 2009. pp. 1\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-642-27737-5_150-2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-642-27737-5_150-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo Z, Shang X, Wang Y, Li X, Liu I-H, Tai Y. P- and S-wave arrival time combined Bayesian location method for a microseismic event, \u003cem\u003eJ. Cent. South Univ.\u003c/em\u003e, vol. 30, no. 11, pp. 3808\u0026ndash;3820, Nov. 2023, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11771-023-5459-5\u003c/span\u003e\u003cspan address=\"10.1007/s11771-023-5459-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrocher TM. Key elements of regional seismic velocity models for long period ground motion simulations. J Seismol. Apr. 2008;12(2):217\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10950-007-9061-3\u003c/span\u003e\u003cspan address=\"10.1007/s10950-007-9061-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLienert BR, Berg E, Frazer LN. An earthquake location method using centered, scaled, and adaptively damped least squares. Bull Seismol Soc Am. June 1986;76(3):771\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1785/BSSA0760030771\u003c/span\u003e\u003cspan address=\"10.1785/BSSA0760030771\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eD\u0026iacute;az J. On the origin of the signals observed across the seismic spectrum. Earth Sci Rev. Oct. 2016;161:224\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.earscirev.2016.07.006\u003c/span\u003e\u003cspan address=\"10.1016/j.earscirev.2016.07.006\" 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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mount Sinabung, volcanic earthquake, hypocenter, Geiger method, seismic monitoring","lastPublishedDoi":"10.21203/rs.3.rs-8151068/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8151068/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe relocated 61 volcanic earthquakes recorded around Mount Sinabung between October 2023 and April 2024 using a Geiger least-squares algorithm with adaptive damping to produce a rigorously vetted hypocenter catalogue; the dataset contains 34 deep events (2.5\u0026ndash;14 km) and 27 shallow events (0.3\u0026ndash;2.0 km), with epicenters concentrated within 0\u0026ndash;5 km of the summit and cross-sections revealing vertically continuous, segmented pathways consistent with multi-stage magma transport, while per-event\u0026thinsp;\u0026plusmn;\u0026thinsp;10% \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vp\\)\u003c/span\u003e\u003c/span\u003e sensitivity tests and station jackknife analyses confirm robust classifications for the majority of events and flag model-sensitive cases for cautious interpretation.\u003c/p\u003e","manuscriptTitle":"Hypocenter distribution of volcanic earthquakes beneath Mount Sinabung (Oct 2023–Apr 2024) using an adaptive-damping Geiger relocation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 14:25:53","doi":"10.21203/rs.3.rs-8151068/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"461097a6-289a-47d9-bab4-a6ebe048717e","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-14T05:24:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 14:25:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8151068","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8151068","identity":"rs-8151068","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}