Earthquake catalogues commonly integrate heterogeneous magnitude scales whose relationships evolve in time and space, introducing systematic biases in statistical seismology. Here I quantify the impact of magnitude-scale heterogeneity using the GeoNet earthquake catalogue (New Zealand, 2003–2025). I develop a time-dependent magnitude homogenization framework based on moment tensor solutions, deriving a proxy moment magnitude M_w^* through sliding-window regressions. The relationship between catalogue magnitude and moment magnitude is strongly non-stationary, with significant temporal variations in both slope and intercept, as well as spatial differences across tectonic domains. These variations propagate directly into seismicity metrics. When evaluated on a common magnitude scale, the magnitude of completeness M_c is systematically higher than that inferred from the original catalogue, with local discrepancies exceeding one magnitude unit. Similarly, when both estimates are expressed on a common homogenized magnitude scale, residual b-value differences are generally modest but temporally and spatially structured, indicating that a substantial fraction of the apparent discrepancy is explained by the non-stationary magnitude transformation itself. The strongest effects are observed in clustering properties: although aftershock productivity follows an exponential scaling with magnitude in both representations, homogenized magnitudes yield a steeper productivity exponent (α ≈ 1.08 vs. 1.01) and a reduced branching proxy. These results demonstrate that magnitude heterogeneity introduces coherent, scale-dependent distortions in completeness, b-value, and clustering metrics, potentially leading to misinterpretation of seismicity patterns if not explicitly accounted for.
The Philippine Strong Motion Network (PSMNet), established in 1992, is one of the primary seismic monitoring systems in the Philippines and consists of strong-motion accelerographs deployed to record earthquake ground motions. In this study, strong-motion records collected from 2019 to 2023 were analyzed to develop attenuation relations and local magnitude models tailored to Philippine seismic conditions. The dataset consists of 3,176 waveform records from 527 shallow earthquake events ( M_w≥5.0 ) recorded across the PSMNet. Peak ground displacement values were obtained from processed acceleration waveforms, and a two-step stratified regression analysis was applied to determine the geometrical spreading and attenuation coefficients. Three high-pass filter cutoff periods ( T_c= 5, 10, and 20s ) were evaluated, producing corresponding magnitude models with RMSE values of 0.324, 0.305, and 0.297, respectively. Paired bootstrap tests indicate that the reductions in RMSE with increasing cutoff period are statistically significant, with the T_c=20s model showing the best overall performance. Independent validation using independent 2024 strong-motion data also demonstrated comparable predictive capability, with RMSE values ranging from 0.350 to 0.386. The proposed magnitude relations provide a region-specific framework for rapid earthquake magnitude estimation using strong-motion data and may support future improvements in real-time seismic monitoring and earthquake early warning applications in the Philippines.
The M7.7 Mandalay earthquake in Myanmar occurred on 28 March 2025 at 12:50:54 local time, with an epicenter at 22.013°N, 95.922°E and a focal depth of 10 km. The event produced extreme to severe ground shaking affecting more than 7.5 million people. This earthquake ranks among the deadliest events since 2000 highlighting once again the limitations of Probabilistic Seismic Hazard Analysis (PSHA) and the existing global maps, whose estimates of ground shaking at Mandalay City were exceeded by a factor greater than 4. Notably, at the site of the main shock, the 2019 Global GEM Seismic Hazard Map provides peak ground acceleration estimates approximately 20
This study evaluates the influence of shear wave velocity (Vs) characterization on seismic site response in Jamshedpur, India. To understand these effects, a one-dimensional equivalent linear ground response analysis has been performed for three distinct shear wave velocity inputs: Vs profiles obtained from the MASW method, Vs profiles obtained from established correlations between Vs and SPT-N values, and the mean shear wave velocity measured for the top 30 m of ground (Vs30). Subsurface conditions at 15 locations were characterized using Multichannel Analysis of Surface Waves (MASW) surveys and borehole investigations. The estimated Vs30 values range from 213 to 493 m/s and were used to categorize and describe these sites according to NEHRP seismic site classification, and the majority of sites fall in seismic site class C, and a few fall in seismic site class D. Four recorded ground motions with PGA values of 0.1 g, 0.16 g, 0.28 g, and 0.41 g have been used to evaluate site behavior under varying shaking intensities. The findings showed that predicted site response is more site-sensitive to the selected Vs model. Surface acceleration increased from 0.20 to 0.35 g, 0.4 to 0.7 g, 0.7 to 1.2 g, and 1 to 1.5 g for 0.10 g, 0.16 g, 0.28 g, and 0.41 g input motion, respectively. Computed amplification ratios varied across the study area, ranging from 1.0 to 5.1. Peak spectral acceleration (PSA) showed substantial spatial variability and increased with shaking intensity, depending on site conditions, input motion, and Vs representation. PSA values ranged from 0.3681 to 9.5938 g under input PGA values of 0.10–0.41 g, indicating significant resonance induced amplification despite moderate earthquake shaking. MASW based profiles tended to predict higher amplification and spectral acceleration values at several locations for moderate to strong shaking, as they were able to represent local subsurface heterogeneity and near-surface variability. Empirical correlation based profiles tend to underestimate PSA values, while Vs30 performs reasonably for low PGA but fails to reflect site-specific heterogeneity at higher PGA. The results can provide essential guidance for microzonation, seismic hazard assessment, and earthquake-resilient urban planning in Jamshedpur.
The Himachal Himalaya, located in the northwestern segment of the Himalayan arc, is one of the most seismically active regions in the Indian Himalaya. This study aims to develop new magnitude scaling relations for this region utilizing a mixed dataset of observed and simulated P-wave onsets. This work employs modified semi empirical technique (MSET) to simulate ground motion records, particularly P-wave. This technique is validated for 3 different instrumentally recorded earthquakes with magnitude (Mw) ranging from 4.2 to 5.1. Further, we utilized this method to simulate 30 future scenario earthquakes ranging from Mw 5.0 to 8.5. The earthquake early warning parameters including average period (τc), peak displacement amplitude (Pd), and cumulative absolute velocity (CAV) are extracted from the P-wave onsets of 61 observed and 450 simulated records. Using this observed and simulated dataset, the developed magnitude scaling relations τc–Mw, Pd–Mw, and CAV–Mw are validated using the available instrumentally recorded event up to Mw 5.1, while their performance at larger magnitudes (Mw 5.0–8.5) are assessed using independently generated synthetic earthquake scenarios from the MSET. This latter validation represents a synthetic self-consistency assessment rather than observational validation. For the synthetic scenarios, the maximum magnitude differences (Mwd) between the cataloged (Mwc) and predicted (Mwp) magnitudes range from 0.18–0.42 for τc–Mw, 0.02–0.38 for Pd–Mw, and 0.01–0.53 for CAV–Mw, respectively. Additionally, the threshold values of various parameters are estimated as 2.0 s for τc, 0.13 cm for Pd, and 0.03 cm/s for CAV, respectively for onsite warnings of earthquakes with magnitude ≥ 6.0. Using the τc × Pd ≥ 1.0 s-cm criterion, several key sites are identified as highly prone to severe damage from large earthquakes. These sites are located at epicentral distance ranging from 50–500 km from the scenario earthquake location and are expected to receive the lead times between 6 and 166 s. This highlights the importance of longer lead times in the Himachal Himalaya for enabling timely early warnings and protective actions.
Earthquake alert-level classification can support rapid decision-making by transforming available seismic descriptors into actionable warning categories. However, many machine learning studies in earthquake-related applications report high predictive performance on limited datasets without sufficient statistical validation, which may lead to over-optimistic conclusions. This study presents an explainable and statistically validated machine learning framework for multi-class earthquake alert-level classification using a seismic dataset consisting of 1,300 records and five input variables: magnitude, focal depth, Community Decimal Intensity (CDI), Modified Mercalli Intensity (MMI), and significance (SIG). An Optuna-tuned XGBoost model was developed and compared with conventional machine learning classifiers, including Random Forest, standard XGBoost, K-Nearest Neighbors, Logistic Regression, and Support Vector Machine. To improve the reliability of the evaluation, the experimental protocol was strengthened using stratified repeated cross-validation, macro-averaged performance metrics, bootstrap-based confidence intervals, ablation analysis, and feature-set sensitivity experiments. The optimized XGBoost model achieved strong classification performance; however, the results also indicate that intensity-related variables, particularly MMI, CDI, and SIG, contribute substantially to the predictive outcome. Therefore, the proposed framework should be interpreted as an alert-level classification and decision-support model based on available seismic descriptors rather than as a prospective earthquake prediction system. SHAP-based explainability analysis confirmed that the model decisions were mainly driven by physically meaningful intensity-related features. Overall, the study provides a transparent benchmark framework for earthquake alert classification while explicitly addressing the limitations associated with small datasets, synthetic oversampling, and possible target-related feature dependence.
The earthquake swarm along the Lesotho-South Africa border began in late December 2023 and persisted into 2025, with activity declining during the latter part of the sequence. Earthquake swarms are of growing interest as they do not exhibit the typical mainshock-aftershock decay described by the Omori-Utsu law; instead, they reflect complex and often externally driven processes. In intraplate regions such as southern Africa, where tectonic earthquakes are relatively infrequent, such sequences provide a rare opportunity to investigate subsurface processes within a stable continental setting. This study aims to constrain the dominant driving mechanisms of the swarm using migration analysis, velocity-duration scaling, and non-stationary Epidemic-Type Aftershock Sequence (ETAS) modelling. The swarm evolves from a radial cluster into a linear NE-SW trend, propagating at 1.43 ± 0.13 km/day with diffusivity values > 30 m2/s, suggesting that fluid diffusion alone is unlikely to explain the observed migration. The velocity-duration relationship places the sequence near the global Slow Slip-Driven (SSD) trend, indicating a transient aseismic slip phase early in the sequence. Non-stationary ETAS modelling was applied to separate background seismicity from earthquake triggering. Results show elevated background rates early in the sequence, followed by increased aftershock productivity, suggesting a transition from externally driven to internally triggered seismicity. Overall, the swarm was likely initiated by fluid circulation at depth that triggered a transient aseismic slip phase and was later sustained by earthquake interactions.
We have detected and located 1,015 seismic events observed at 4 or more stations of the 17-station Dongbei Broadband Seismographic Network (DBN), which operated from June 2004 to September 2010 in Northeast China near the border with the Democratic People’s Republic of Korea (DPRK). We chose 447 master seismic events reported in catalogs published by the International Seismological Centre (ISC), the Korean Meteorological Agency (KMA), the Earthquake Administration of the Jilin Province of China. Using Lg wave-trains of the master events we searched the continuous archive recorded by DBN to detect (via cross-correlation) events similar to the master events. The relative locations of this event-catalog have 95
The 2025 Myanmar earthquake Mw 7.7, one of the most devastating earthquakes and widely recorded the ground motion shaking in Indian subcontinent. The event caused severe local devastation and also provided valuable insights about the ground motion datasets. In this study, the recorded ground motions are analyzed and physics-based ground-motion simulations are carried out to investigate the sensitivity of the simulations to velocity uncertainties. Strong motion data from Indian seismic networks, together with five near-epicentral IRIS stations, are analyzed using evolutionary power spectral density (EPSD) and response spectra parameters to investigate source, path, and site effects. The analysis revealed a broad attenuation pattern across the Indian subcontinent, with pronounced amplification observed in regions underlain by alluvial deposits, such as the Indo-Gangetic basin, underscoring the role of site effects in modifying ground motion propagation. To complement these observations, physics-based simulations are conducted at the same set of stations using the spectral element method, incorporating seven different velocity models: a default global model, six randomized velocity models with uncertainties in shear wave velocity Vs. A strong agreement between simulated and recorded time histories in the near-field validated both the reported finite-fault rupture model and the adopted simulation framework. In contrast, systematic mismatches in the far-field synthetics highlight the sensitivity of the simulations to regional velocity model uncertainty. Qualitative and quantitative evaluation is performed based on waveform comparisons, peak ground velocity (PGV) residuals, Anderson Goodness-of-Fit (GOF) scores and comparison with existing ground motion models. The results from the randomized models are observed that the better agreement when compared to recordings at several stations. These findings emphasize the importance of detailed soil microzonation, heterogeneity of region velocity structure and improved velocity profiles for India, crucial for advancing regional ground motion prediction and seismic hazard assessment.
We detected, located, analyzed and identified new seismic events from continuous waveforms of the 17-station Dongbei network (DBN) from June 2004 to September 2010 in Northeast China near the border with North Korea. We used 447 master events from the International Seismological Centre (ISC), the Korean Meteorological Agency (KMA), and Jilin Province of China catalogs to detect more events using Lg waves. 1,015 located events were comprised of 140 similar event clusters. About 40
A homogeneous, completeness-controlled, and declustered moment-magnitude earthquake catalog is fundamental for reliable seismicity modelling and Probabilistic Seismic Hazard Assessment (PSHA). This study introduces PECAAR v2.0, a substantially updated MW-referenced catalog for Algeria and adjacent regions, covering the period 1658–2022. This update incorporates previously unused historical and instrumental datasets, adds 1554 new events, and expands the multi-agency database to improve both temporal and spatial coverage. A large multi-agency waveform-based MW reference dataset from the Western Mediterranean region supports the derivation of ten new regional magnitude-conversion relations for mb, MS, ML, mbLg, and related scales after inter-agency bias correction and robust outlier filtering. The new relations reduce residual scatter, enhance magnitude stability, and reveal significant scale-dependent biases in commonly used global conversion equations when applied to Northern Algeria. The study presents the first dedicated empirical conversion for European-Mediterranean Seismological Centre (EMSC) mb magnitudes in the Euro-Mediterranean region and introduces a new calibrated relation for local magnitude (ML) values from the “Centre National pour la Recherche Scientifique et Technique (CNRST) facilitating more consistent cross-border integration of regional seismic data. Catalogue completeness is extended to 2022 using the framework established in previous studies, resulting in six completeness-consistent intervals and demonstrating that the instrumental period is complete to MW 3.0 since 2000. Declustering is conducted using a nearest-neighbour space–time–magnitude metric, resulting a physically consistent mainshock catalog. PECAAR v2.0 includes 12,719 homogenized earthquakes and 8449 mainshocks from 11,013 events with MW ≥ 3.0. This dataset strengthens recurrence modelling, background seismicity estimation, and hazard calculations, providing a modern and reproducible foundation for PSHA and seismotectonic studies in Northern Algeria.
Understanding the interaction between stress redistribution and seismicity is crucial for assessing seismic hazards in tectonically complex regions like the northern Banda Arc. The Ambon–Seram region of eastern Indonesia features a complex regime of strike-slip, normal, and reverse faulting driven by the high-curvature Banda plate boundary. This study investigates the spatiotemporal evolution of Coulomb Failure Stress changes and b-values across three distinct tectonic domains: Ambon–West Seram, the Kawa Shear Zone, and East Seram. We quantified ΔCFS on specific receiver fault segments at depths of 5, 10, and 15 km across four temporal intervals from 1980 to 2025. Seismicity characteristics were evaluated using an adaptive Voronoi-based approach, incorporating median b-values, Median Absolute Deviation, and the number of valid models to ensure statistical stability. The results reveal pronounced spatial and temporal heterogeneity in stress evolution throughout the study area. The Ambon–West Seram domain exhibits the most persistent stress concentration, particularly along the AMB1, AMB2, and AMB19 fault systems. Notably, the AMB19 segment, associated with the 2019 Mw 6.5 Ambon earthquake, experienced positive Coulomb loading prior to the event, with local ΔCFS values reaching approximately + 0.1 bar during the 2001–2018 period. Following the 2019 earthquake, AMB19 displayed a depth-dependent mechanical response, transitioning into a stress shadow at shallow depths (5 km) while maintaining positive loading at 10 and 15 km. This domain also retains a persistent low b-value anomaly, with median values ranging from 0.65–0.66 before 2019 to 0.62–0.63 thereafter. In contrast, the Kawa Shear Zone exhibits segmented unloading–reloading behavior, with stress loading recurring on the MSL, KWA, ALU, and TLT segments following significant seismic events. East Seram displays highly localized interactions characterized by segment-specific combinations of loading and shadowing on individual faults rather than a uniform regional pattern. Overall, the integration of ΔCFS and adaptive b-values demonstrates that seismic hazard in the northern Banda Arc is strongly controlled by fault segmentation, source mechanisms, and depth-dependent stress transfer. These findings provide a high-resolution, segment-based framework for evaluating stress heterogeneity and future seismic potential in the region.
A homogeneous moment magnitude (MW) catalog is essential for robust seismological analysis and seismic hazard assessment, as MW provides a physically based measure of earthquake size, in contrast to amplitude-based magnitude scales. In Iran, operational earthquake catalogs predominantly report Nuttli (MN) and local (ML) magnitudes, whereas the principal large-scale MW source, the GCMT catalog, has a magnitude of completeness of 5.3 since 1976. This results in a substantial gap in MW coverage for small-to-moderate earthquakes. Addressing this gap requires region-specific empirical magnitude-conversion relationships; however, most existing models are reliable only for magnitudes larger than about 3.5. In this study, we develop and validate new regional scaling relationships to convert MN and ML to MW for earthquakes with 1.4 ≤ ML ≤ 5.0 in northeastern and eastern Iran. For the ML–MW conversion, we propose both a piecewise linear model with a breakpoint at ML = 2.9 and a quadratic model. For ML ≥ 2.9, the two formulations yield closely consistent estimates, differing by no more than 0.1 magnitude units. At lower magnitudes, systematic divergence emerges; for example, an earthquake with ML = 1.4 corresponds to MW ≈ 1.9, indicating a difference of approximately 0.5 units. Our proposed linear MN–MW relation, valid over the MN range of 2.0–4.8, exhibits close agreement with the line MN = MW. These empirically robust relationships enable the construction of a consistent MW catalog by correcting magnitude-dependent biases, thereby providing a reliable foundation for improved seismotectonic interpretation and seismic hazard assessment within the study region. Outside this region, these relationships should be applied only provisionally and with caution until local calibrations are developed.
Within the fractured Deccan Volcanic Province of western India, the Palghar earthquake swarm region has experienced repeated seismic activity since 2018 and provides an opportunity to investigate seasonal variations in site-response characteristics and shallow subsurface properties. In this study, we applied the diffused-regime earthquake-derived Horizontal-to-Vertical Spectral Ratio (EHVSR), Site-to-Reference Spectral Ratio (SSR), and EHVSR inversion techniques to earthquake recordings from six broadband seismic stations to evaluate seasonal changes in site response and shallow shear-wave velocity (Vs) structure within the upper 250 m. The analyses were performed separately for pre-monsoon (February-June) and monsoon/post-monsoon (July-December) periods. For robustness, bootstrap analysis was performed to evaluate the stability and uncertainty of the EHVSR estimates, with 95 K_g ), inversion-derived Vs models, and VP/VS ratios exhibit pronounced temporal variability. SSR analyses produce resonance frequencies broadly comparable to those obtained from EHVSR, but with stronger and sharper amplification peaks. SSR-derived amplification factors range from approximately 2.6 to 6.3 and generally exceed the corresponding EHVSR amplitudes. The fair agreement between EHVSR and SSR-derived resonance frequencies supports the robustness of the identified site-response characteristics (mainly f0). The inversion results indicate subtle reductions in shallow Vs and increases in VP/VS ratios at several stations during the monsoon/post-monsoon period, with some of the most pronounced variations observed at KAWA, located closest to the swarm nucleation zone. This study supports the hypothesis of possible hydrological influences associated with monsoonal recharge and fluid circulation within fractured basaltic formations, as similarly suggested by regional studies. The results provide new constraints on the seasonal sub-surface characterization in the Palghar swarm region and demonstrate the usefulness of integrated EHVSR, SSR, and inversion approaches for, seismic microzonation, and long-term monitoring of fluid-driven earthquake sequences in western India.
Starting in the early 1950s, Lamont Geological Observatory of Columbia University operated long-period seismographs at multiple locations around the world. At the time of the 1957–1958 International Geophysical Year (IGY), seventeen stations were operating. The seismographs were of a similar design to that later adopted for the World Wide Standardized Seismograph Network (WWSSN), and the Lamont-IGY network can be considered an experimental prelude to that network. Most of the original seismograms from the Lamont-IGY network have been located in storage. Here we describe the network, the instrumentation, and seismogram availability. The heterogeneous instrument responses that are characteristic of the early years of the Lamont-IGY network pose a challenge for modern quantitative waveform analysis. We use detailed calibration data collected in 1962 to estimate instrument parameters at thirteen of the stations. The new instrument transfer functions are converted to a modern poles-zeros description of the seismograph response.
Local site effects represent a major source of ground-motion variability; however, the direct prediction of horizontal-to-vertical (H/V) spectral ratios remains relatively underexplored in seismic hazard modeling. This study introduces a data-driven framework for estimating period-dependent PSA-based H/V ratios using a multivariate ensemble of machine learning models. A comprehensive dataset comprising 38,504 strong-motion records from the Türkiye Strong-Motion Database (1976–2023) was utilized to capture the influence of seismic source (moment magnitude, Mw), propagation path (hypocentral distance, Rₕᵧₚ, and focal depth), and site conditions (Vs₃₀ and station elevation) across 946 stations. Three regression algorithms Gaussian Process Regression (GPR), Gradient Boosted Regression Trees (GBRT), and Random Forest (RF) were combined within an ensemble framework, with optimal period-dependent weights determined using a Genetic Algorithm (GA). The optimized ensemble achieved a mean root mean square error (RMSE) of 0.68 over the spectral period range of 0.01–4 s, corresponding to a 5.6
We present FW-ANSI2D, an open-source package for full waveform forward and inverse modelling of seismic ambient noise cross-correlations in two dimensions. It belongs to the so-called ‘interferometry without Green’s function retrieval’ class of methods, wherein noise cross-correlations are modelled numerically, for arbitrary spatio-spectral distributions of noise source power spectral density (PSD). A multifrequency inversion for the source PSD is achieved via a nonlinear finite-frequency waveform inversion technique, implemented under the assumption of a fixed Earth structure model. FW-ANSI2D seamlessly integrates with other open-source Python packages for seismic wave propagation modelling, most notably a C-based numerical solver for acoustic modelling in 2-D media. Whilst the package is currently limited to the acoustic modelling regime and flat geometries (Earth’s sphericity is unaccounted for), it is a powerful tool for ambient noise analysis at local scales. It exhibits certain advantages over other waveform inversion techniques for ambient noise sources, such as an enhanced ability to resolve sources outside the receiver network, and a relatively high tolerance for velocity model inaccuracies. Moreover, it is amenable to Hessian-based optimization, which ensures speedy convergence ( ≈ 10 iterations) of the nonlinear inverse problem, compared to purely gradient-based methods. We introduce the package in detail, describing both the serial and parallel versions of the code, and present synthetic tests designed to assess the efficacy of the inversion technique in realistic field scenarios. These tests show that reasonably good inversions are achievable even with relatively sparse receiver networks, and with sources lying outside the modelling domain.
Many earthquakes in global moment tensor catalogs contain non-double-couple (NDC) components, but it is often unclear whether they reflect source complexity or artifacts of the inversion. Global moment tensor catalogs constrain the isotropic component during the inversion and thus report deviatoric solutions. Therefore, this study analyzes the reported compensated linear vector dipole (CLVD) components in global catalogs. I test whether intraplate earthquakes with centroid depths of 60 km or less have larger NDC components than other earthquakes using the Global Centroid Moment Tensor (GCMT) catalog and the CMT3D catalog. In the GCMT catalog, 663 intraplate earthquakes have a mean |2ε | of 26.35 p=2.0× 10^-4 ). The difference remains when continental and oceanic domains are analyzed separately, when different depth and distance thresholds are used, after excluding earthquakes near active volcanoes, and after accounting for differences in magnitude, centroid depth, mechanism, and geologic environment. Comparing the distributions of NDC components, I find that the difference in mean NDC components is not caused by a few events with very large NDC components, but reflects a broader shift toward larger |2ε | . Earthquakes that appear in both the GCMT and CMT3D catalogs also show larger NDC components for intraplate events than for non-intraplate events, indicating that the difference is not specific to one catalog. The effect is small in absolute magnitude, but it is detected repeatedly when intraplate earthquakes are compared with non-intraplate earthquakes in global moment tensor catalogs. It is therefore consistent with greater source complexity of intraplate earthquakes, possibly due to rupture on immature or unfavorably oriented faults and to heterogeneous stress fields. However, the difference may also include artifacts of the moment tensor inversion caused by Earth-model uncertainty in intraplate regions. These results show that moment tensor inversions of intraplate earthquakes are expected to yield larger NDC components, which may reflect greater source complexity, inversion-related artifacts, or both.
On February 6, 2023, consecutive M7.8 and M7.5 earthquakes struck southeastern Türkiye, which caused extensive damage to the local buildings and huge casualties. Some unique engineering characteristics of strong motion were observed during the processing of strong ground motion records. These characteristics provide suggestions for the seismic design of structures in the future. The paper analyzes the engineering characteristics of strong ground motion obtained in these earthquakes. This study systematically reveals the spectral acceleration amplification effect of damaged faults in the earthquake sequence, elucidates the impact characteristics of supershear ground motions, explicitly identifies the significant amplification feature of response spectrum values caused by acceleration pulses associated with the forward directivity of subshear ruptures, and simultaneously uncovers the nonlinear characteristics and liquefaction evolution of typical sites. Systematic comparisons reveal that supershear ruptures exhibit characteristics such as broad spectral width, slow attenuation, and significant low-frequency components in the far field.
Northeastern India is a tectonically complex and seismically active region that has experienced several damaging earthquakes. Statistical analysis of earthquake recurrence can provide supporting insights into regional seismic behaviour.Despite its seismic significance, time-dependent statistical characterization of earthquake recurrence in this region remains limited, particularly using established renewal models applied in a comparative framework. In this study, cumulative probabilities and conditional probabilities (hazard rates) were estimated for earthquakes with magnitudes Mw > 3.6, 4, 5, and 6 to examine which renewal model best describes earthquake recurrence in Northeastern India. Weibull, Gamma, and Lognormal distributions were applied to earthquake recurrence intervals. All three models provide a reasonable first-order description of recurrence behavior, with the Weibull distribution yielding a comparatively better statistical fit, followed by the Gamma distribution, while the Lognormal distribution shows weaker performance. Based on the Weibull model, characteristic recurrence intervals are estimated to be approximately 46, 58, 180, and 540 years for Mw > 3.6, 4, 5, and 6 earthquakes, respectively. Over a 70-year period, the estimated conditional probabilities increase within lower and upper time bounds for each magnitude class, indicating higher likelihoods with increasing elapsed time since the last event. In conclusion, the results suggest that renewal-based statistical models, particularly the Weibull distribution, can provide a preliminary characterization of earthquake recurrence behavior in Northeastern India, though the estimates are subject to the assumptions and limitations inherent in such models. The present study is intended as a preliminary investigation. Future work incorporating physically based models (e.g., Brownian Passage Time), higher magnitude thresholds, and more rigorous treatment of dependent seismicity is required to improve the robustness and hazard relevance of time-dependent seismic assessments in the region.
