Journal of Seismology最新文献

This study applied an earthquake nowcasting approach to assess the current stage of the seismic cycle along three major right-lateral strike-slip faults: the Nayband, Gowk, and Bam faults, located on the western edge of the Lut Block in southeastern Iran. Seismic catalogs spanning 1 January 1963 to 31 March 2025 were analyzed within 250 km, 300 km, and 350 km radii around each fault, with all magnitude scales converted to moment magnitude and completeness thresholds determined via the maximum‐curvature method. Natural time counts were computed for two upper magnitude thresholds (M_λ = 5.5 and 6.0) and modeled using the Pearson distribution system. Among the candidate distributions, Pearson Type VI consistently provided the best fit across all eighteen scenarios, outperforming Gamma, Weibull, and Exponential models in log-likelihood comparisons. The Earthquake Potential Scores (EPS), calculated from the fitted Type VI cumulative distributions, show that the Gowk fault within a 250 km radius has progressed furthest through its seismic cycle. EPS values exceed 97% for M_λ = 5.5 at the Gowk center (Point 2), indicating that the present natural-time count is higher than in most historical cycles. EPS is a percentile-based indicator of cycle stage and does not constitute a calendar-time forecast of imminence.Nevertheless, elevated EPS motivates continued monitoring and preparedness for nearby population centers such as Kerman, Bam, and Rafsanjan. By comparison, the Nayband fault has lower EPS values, reflecting its past quiet behavior but highlighting the importance of ongoing monitoring. These findings demonstrate the utility of Pearson-based nowcasting for quantitative seismic hazard assessment and regional risk mitigation.

Most existing Ground Motion Models (GMMs) are developed by specifying empirical functional forms derived from expert judgment, with regression techniques used to estimate model coefficients from recorded ground motion data. However, due to the complex and nonlinear nature of earthquake source and path effects, it is difficult to establish a direct analytical relationship between ground motion intensity measures and seismological predictors. This, combined with inherent natural variability, poses significant challenges for accurately characterizing ground motion using predefined functional forms. The emergence of deep learning methods offers a promising avenue for uncovering non-linear correlations among high-dimensional variables. In this study, the deep learning technique, Conditional Generative Adversarial Network (CGAN), is proposed as a novel, data-driven GMM for predicting horizontal-component spectral accelerations over a period range of 0 to 10 s. The model is trained and evaluated using 11675 sets of recorded ground motions from the Engineering Strong motion database (ESM2.0). Model performance is rigorously evaluated through multiple metrics, including residual analysis and comparison with benchmark empirical GMMs. Results demonstrate that the CGAN outperforms traditional models in capturing complex spectral patterns and exhibits superior generalization with reduced prediction bias. Furthermore, a comparative analysis with other deterministic and probabilistic machine learning models developed using the same dataset highlights similarity in aleatory uncertainty but notable differences in epistemic uncertainty estimation, attributed to the fundamentally different uncertainty quantification mechanisms. A key strength of the CGAN approach lies in its ability to generate physically consistent, high-fidelity synthetic ground motion spectra, making it a promising alternative to standard regression-based GMMs and conventional deep learning models.

Seismic traveltime tomography methodologies — commonly categorized into wave-equation, ray-based, and eikonal equation-based approaches — face significant limitations in regional passive-source applications. Wave-equation methods deliver high-resolution imaging but are hindered by prohibitive computational costs and strong sensitivity to the initial velocity model. In contrast, ray-based and eikonal equation-based methods leverage efficient traveltime-velocity inversion schemes, but still exhibit fundamental divergences in their implementation. Among them, eikonal equation-based adjoint-state traveltime tomography (ATT) offers higher computational efficiency and lower memory usage through matrix-free formulations. However, conventional ATT implementations still suffer from two major limitations: (1) local extremes in the gradient, typically manifested as high-amplitude, short-wavelength artifacts caused by uneven ray coverage, and (2) substantial computational burdens caused by the imbalance between the numbers of seismic sources and receivers. To address these issues, we propose a novel ATT methodology that incorporates three key innovations: (1) preconditioned adjoint-state inversion, (2) spatially adaptive regularization to mitigate artifacts induced by non-uniform ray distribution and accelerate convergence, and (3) the application of the reciprocity principle to significantly improve computational efficiency. Synthetic experiments show that the proposed method not only improves geometric fidelity and amplitude recovery, but also achieves a 74-fold speedup per iteration compared to conventional approaches. When applied to the Southern California plate boundary, our approach further proves its robustness by resolving geologically consistent structures and detecting strong variations along the strike of the San Jacinto Fault, features that remain poorly imaged using conventional methods.

Situated at the seismically active junction of the Indian and Eurasian plates, Myanmar faces a persistent threat from large–magnitude earthquakes. The recent March 28, 2025 Mandalay earthquake ((M_w 7.7)) in central Myanmar has reignited the scientific challenges in earthquake hazard estimation. This earthquake, in fact, has divided Myanmar into two broader regions—one significantly impacted by the Mandalay earthquake and another with less devastation. Given the continued seismic activity in Myanmar, estimating the “current level” of earthquake hazards for both types of regions is crucial. In this regard, the present study applies an area-based “earthquake nowcasting” technique to evaluate the contemporary seismic cycle progression in 15 major cities across the entire country. The method utilizes the concept of natural time, the inter–event counts of small earthquakes between consecutive large sized events, to compute the Earthquake Potential Score (EPS) for the target city-regions. To accomplish, we examine several probability distributions and found that the natural-time seismicity statistics follow the exponentiated exponential distribution. As of June 16, 2025, the nowcast scores for (M ge 6.0) events in Myanmar range from 31% to 96%, with the highest scores observed in Pathein (96%), followed by Bago (93%), Yangon (92%), Myitkyina (70%), Mandalay (42%), Sagaing (42%), Monywa (42%), Taunggyi (41%), Magway (41%), Sittwe (40%), Naypyidaw (36%), Lashio (34%), Hakha (32%), Loikaw (32%), and Mawlaik (31%). Higher EPS values generally correspond to cities farther from the epicentral location of the 2025 Mandalay earthquake, while lower EPS values are observed for the cities in central Myanmar, such as Mandalay, Loikaw, Lashio, and Naypyidaw. These nowcast scores indirectly quantify the present state of earthquake hazards in Myanmar and serve as a critical input for several practical uses.

In the traditional method of rapid magnitude estimation based on the predominant period in the earthquake early warning systems, the magnitude of the occurring earthquake is estimated online by the empirical relationship between the magnitude and the maximum predominant period of the P wave (({tau }_{p}^{max})) average of accelerograph stations. This study determines the empirical relationship between the magnitude and ({tau }_{p}^{max}) for each accelerograph station to reduce the local effects in estimating the earthquake magnitude. The final earthquake magnitude will be estimated by averaging the magnitudes obtained at the individual stations, a method we refer to as the mean magnitude method (3M), where “3M” represents the three initial letters of the method’s name. The dataset used in this study consists of more than 25,000 accelerograms (both vertical and horizontal components) from the 1,852 earthquakes with magnitudes ranging from M_JMA 3 to 7.4 recorded. These data were collected eight accelerograph stations in Japan’s KiK-net and K-NET networks. Results demonstrate that the estimated magnitudes using the 3M correlate better with the reported magnitudes than those estimated using the traditional method and provide a more accurate earthquake size, especially for small to moderate events. In contrast to the traditional method, which requires numerous waveforms from many stations with varying local effects to develop a magnitude empirical relationship, the 3M reduces scattering by using station-specific empirical relationships. We also investigated the efficiency of using horizontal components in the first 4 s of the P-wave in the study. After reducing site effects, the horizontal components provided additional useful information alongside the vertical component, improving magnitude estimation accuracy. Although magnitude residuals decrease with increasing number of recording stations in both methods, the 3M achieves greater bias reduction. In addition to the primary analyses conducted using M_JMA, we further evaluated our method with moment magnitude (M_w) values converted from M_JMA using an established empirical relationship. The results revealed notable improvements in residuals across all magnitude ranges, particularly for larger events. The findings indicate that the proposed approach not only performs effectively for Japanese magnitude scales but also shows strong agreement with the internationally recognized M_w scale, highlighting its potential applicability to seismic events worldwide.

This study investigates the ongoing crustal deformation and seismic interactions in the northwestern Himalayan foreland, using the 2019 Mirpur earthquake sequence (Mw 5.8). The aftershock sequence follows the Omori–Utsu decay law, with a decay constant (p-value) of 0.9 observed over a 200-day period. However, seismic activity did not return to background levels after this time, indicating sustained stress perturbations in the region. Following the mainshock, the average seismicity rate in the Mirpur–Kharian region increased by a factor of three. This sustained seismicity suggests permanent activation of subsurface geological structures, likely driven by poroelastic effects associated with coseismic stress redistribution. A decrease in the b-value from 0.69 ± 0.06 to 0.54 ± 0.04 was observed in a declustered aftershock catalog. This reduction likely reflects an increase in differential stress or the reactivation of locked asperities. In December 2024, a moderate earthquake (Mw 5.0) occurred near Kharian, approximately 30 km northeast of the 2019 epicenter, within a zone where Coulomb stress had increased by ~ 0.04 bar. This spatial correlation suggests a causal link through static stress transfer and poroelastic relaxation. Temporal stress evolution was examined using seismicity rate inversion. Results show a stress step of ~ 1.6 bar produced by the mainshock, accompanied by a tenfold increase in the background stress rate, from 0.031 bar/year to 0.3 bar/year. These findings reveal that postseismic deformation is partitioned between two regimes: aseismic slip along a mid-crustal viscous décollement beneath the Salt Range, and continued brittle failure within the overlying seismogenic layer. Together, these processes highlight the complex rheological coupling at the deformation front in this tectonically active region.

On 6 February, 2023, two sequential earthquakes hit southeastern Türkiye, causing catastrophic damage and loss of life. The first severe earthquake, M_w 7.7, occurred at 04:17 local time in the Pazarcık segment of the Eastern Anatolian Fault Zone, in Kahramanmaras. The second earthquake, M_w 7.6, occurred approximately 9 h later at 13:24 local time on the Çardak fault's north branch of the East Anatolian Fault Zone in Ekinozu-Elbistan (Kahramanmaraş). The first earthquake specifically ruptured more than a 300-km-long lateral strike slip fault and generating extreme ground motions with horizontal accelerations exceeding 2 g, vertical accelerations surpassing 1 g, and peak ground velocities reaching 216 cm/s in the horizontal direction. This study presents a comprehensive analysis of strong ground motions recorded in the region heavily impacted by the successive Kahramanmaraş earthquakes, aiming to assess the relationship between these parameters and structural damage. To achieve this, the study focuses on multiple ground-motion intensity measures, including peak ground acceleration (PGA), peak ground velocity (PGV), Arias intensity (I_A), Housner intensity (HI), horizontal acceleration response spectra (S_a: T = 0.2 s and T = 1.0 s), acceleration spectrum intensity (ASI), velocity spectrum intensity (VSI), and power spectral density (PSD). Unlike previous studies that typically examined individual parameters or limited areas, this work systematically integrates these advanced measures with observed structural damage data across the most severely affected regions. This integrated approach has provided new insights into which parameters are most indicative of severe damage and offers valuable implications for seismic risk assessment and future code development in Türkiye.

The significant Mw 6.1 Kopili Fault earthquake that struck on April 28, 2021, profoundly impacted causing substantial structural damage, ground cracks, liquefaction, provides an important instance to examine seismic processes in the Kopili Fault region. Considering an integrated approach, the study aims to quantify ground deformation, hydrological changes, seismicity and strong ground motion parameters, to evaluate its implications for regional seismic hazard. DInSAR analysis indicates no significant coseismic ground deformation in the epicentral region, with a mean deformation of about -0.71 (pm) 9.71mm while NDWI analysis indicate increase in humid surface from 0.41% to 0.62% in Missamari area characterized by liquefaction, suggesting the development of new pits signifying subsidence. Intense seismic activity, observed from database of 6336 events during 1964 to 2022, highlight clusters of aftershocks, suggesting continued stress transfer and fault reactivation mainly at depth of 10-30km. Evidently, the event is characterised by intersection of Kopili Fault to the HFT in lower Himalaya imparting stress to the rupture area of the fault. Stress tensor inversion of fault plane solutions indicates NNE directed principal stress, reflecting the prevailing stress conditions of the Kopili valley, aligned with regional pattern. Notably, accelerograms recorded at five SMA stations showed high shaking in the epicentral region, by strong site-to-site variability influenced by local geology and basin effects identifying stiff rock at Agia and Tura; and softer rock at Jorhat, Guwahati and Golaghat. Detail analysis of ground motion parameters portray peak ground acceleration during the main event is highest at Guwahati (0.05g), indicating MMI IV shaking intensity. The parameters investigated under this study contribute to a better understanding of the regional tectonics and fault behaviour, providing valuable insights into the consequences of the 2021 earthquake and its implications for future events.

Seismic anisotropy is a robust mechanism to infer the strength and direction of deformation in the crust, lithosphere, and sub-lithospheric mantle. This study presents new shear wave splitting (SWS) measurements from the Sikkim Himalaya region utilizing the core refracted (SKS/SKKS/PKS) and crustal (direct-S) seismic phases to understand the mantle and crustal-scale deformation patterns, respectively. Significant time delays ((delta t)) and consistent NW-SE oriented fast polarization directions ((phi )) at all seismic station locations emphasize the dominance of Indo-Eurasian collisional tectonics in governing the deformation patterns beneath Sikkim. The boundary conditions implied by collisional tectonics require deformation with a large amount of shortening of the lithosphere beneath this Sikkim region. A similar crustal deformation pattern (NE-SW) led by the alignment of maximum shear stress is observed throughout this Himalayan region, suggesting that the huge collisional tectonic force influences the coupled crust-mantle dynamics. The deformation in the proximity of regional crustal-scale structures is controlled by the shape-preferred orientation of cracks/voids. Throughout the Sikkim Himalaya, the majority of crustal anisotropy parameters seem to be dominated by the maximum shear (arc-parallel) of convergence tectonics.