Earthquake Model Research Articles

Duration Models for Subduction Earthquakes

ABSTRACT An empirical model for the significant duration of acceleration for subduction zone earthquakes is developed based on the normalized Arias intensity. The data set is a subset from the Next Generation Attenuation-Subduction database (Bozorgnia and Stewart, 2020). The functional form of the duration model uses an additive model for the source, path, and site terms with three lognormally distributed random effects for the source, path, and site. The remaining aleatory variability is modeled by a power-normal distribution. There are two parts of the duration model: a model for the D5–75 duration and a model for the D5–X/D5–75 ratio, which can be combined with the D5–75 model to estimate the DY–X for any interval. The D5–75 model includes regionalized coefficients for Japan, New Zealand, South America, and Taiwan. The large-magnitude (M >7) scaling of the D5–75 model is much weaker than the recent duration model by Bahrampouri et al. (2021). The residuals of the D5–75 model are negatively correlated with the lnPGA residuals with a correlation coefficient of −0.50 such that above-average peak ground acceleration values are associated with below-average duration values.

Development of a regionally consistent and fully probabilistic earthquake risk model for Central Asia

Abstract. A fully probabilistic earthquake risk model was developed for five countries in Central Asia, providing updated earthquake loss estimates with a higher level of detail on all components with respect to previous studies in the region. Additionally, a regionally consistent approach that, on the one hand, allows for direct comparisons at different disaggregation levels (e.g., country and oblast) was used. On the other hand, this updated earthquake risk assessment aims to facilitate initiating a policy dialogue regarding national and regional disaster risk financing and insurance applications. This earthquake risk model made use of a regional probabilistic seismic hazard analysis and a comprehensive exposure database on which different types of assets and sectors were included and for which two scenarios (the years 2020 and 2080) were modeled. For each type of asset, a unique vulnerability function was derived and later used for convolution with the hazard data, which allowed for the estimation of the loss exceedance curve, at different disaggregation levels, from where other risk metrics such as the average annual loss (AAL) and specific return period losses were obtained. The regional earthquake AAL for the five countries and for the 2020 exposure scenario has been estimated to be around USD 2 billion, with the Kyrgyz Republic and Tajikistan being the countries with the highest earthquake risk levels in the region. Besides the probabilistic earthquake risk results, as-if scenarios were modeled using a pseudo-deterministic approach to assess the human and economic losses for realistic and representative earthquakes for the main cities within earthquake-prone regions in the five countries of the study area.

Real-time modeling of transient crustal deformation through the quantification of uncertainty deduced from GNSS data

We propose a new method for real-time uncertainty monitoring of earthquake and volcano source models using data from the global navigation satellite system (GNSS) and explore its application concerning observation station placement. The Geospatial Information Authority of Japan operates two main types of GNSS earth observation network system (GEONET) coordinates for crustal deformation monitoring on different time scales: post-processing analysis values and real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD uses the Markov Chain Monte Carlo (MCMC) method termed real-time automatic uncertainty estimation of a coseismic single rectangular model using GNSS data (RUNE) for single rectangular fault model estimation to handle uncertainty. Thus far, no GNSS monitoring system can automatically detect transient crustal deformation events, such as volcanic activity and earthquake swarms, on timescales of a day or less. We extended RUNE and developed a core program for a new monitoring system for earthquake and volcanic source models and their uncertainties. Our program achieved automatic and stable MCMC utilization for rectangular fault, dike, Mogi, and spheroid models by increasing the computational speed, improving search efficiency, and adjusting hyperparameters. The program automatically determines the standard deviation of the likelihood function assuming a normal distribution with weights for each observation station. The calculation time was within 15 s for 1 × 106 samples on a standard 1U server. We assessed the reliability of the developed method using synthetic and observed GNSS data from the 2015 Sakurajima volcanic event. The results were consistent with the assumed model and previous studies and indicated an advantage in automatically quantifying uncertainty in a short computation time. Based on MCMC samples, we developed a new visualization algorithm to indicate areas on a map in which the number of observation stations should be expanded. We assessed the reliability using data from the 2023 Noto Peninsula earthquake [Mj 6.5]. The results indicate that the algorithm is helpful in studying the placement of stations. The above model extensions and their application are essential to achieve a rapid quantitative understanding of disaster events near urban areas and for utilizing this information in emergency response activities.Graphical

Limited Preservation of Strike‐Slip Surface Displacement in the Geomorphic Record

AbstractOffset geomorphic markers are commonly used to interpret slip history of strike‐slip faults and have played an important role in forming earthquake recurrence models. These data sets are typically analyzed using cumulative probability methods to interpret average amounts of slip in past earthquakes. However, interpretation of the geomorphic record to infer surface slip history is complicated by slip variability, measurement uncertainty, and modification of offset features in the landscape. To investigate how well geomorphic data record surface slip, we use offset measurements from recent strike‐slip surface ruptures (n = 39), faults with geomorphic evidence of multiple strike‐slip earthquakes (n = 29), and synthetic slip distributions with added noise (n10,000) to examine the constraints of the geomorphic record and the underlying assumptions of the cumulative offset probability distribution analysis method. We find that the geomorphic record is unlikely to resolve more than two paleo‐slip distributions, except in specific cases with low slip variability, high slip‐per‐event, and semiarid climate. In cases where site‐specific conditions allow for interpretation of more than two earthquakes, lateral extrapolation along a fault is not straightforward because on‐fault displacement and distributed deformation may be spatially variable in each earthquake. We also find that average slip in modern earthquakes is adequately recovered by probability methods, but the reported prevalence of strike‐slip faults with characteristic slip history is not supported by geomorphic data. We also propose updated methods to interpret slip history and construct uncertainty bounds for paleo‐slip distributions.

Dynamic Rupture Modeling of Large Earthquake Scenarios at the Hellenic Arc Toward Physics‐Based Seismic and Tsunami Hazard Assessment

AbstractThe Mediterranean Hellenic Arc subduction zone (HASZ) has generated several 8 earthquakes and tsunamis. Seismic‐probabilistic tsunami hazard assessment typically utilizes uniform or stochastic earthquake models, which may not represent dynamic rupture and tsunami generation complexity. We present an ensemble of ten 3D dynamic rupture earthquake scenarios for the HASZ, utilizing a realistic slab geometry. Our simplest models use uniform along‐arc pre‐stresses or a single circular initial stress asperity. We then introduce progressively more complex models varying initial shear stress along‐arc, multiple asperities based on scale‐dependent critical slip weakening distance, and a most complex model blending all aforementioned heterogeneities. Thereby, regional initial conditions are constrained without relying on detailed geodetic locking models. Varying epicentral locations in the simplest, homogeneous model leads to different rupture speeds and moment magnitudes. We observe dynamic fault slip penetrating the shallow slip‐strengthening region and affecting seafloor uplift. Off‐fault plastic deformation can double vertical seafloor uplift. A single‐asperity model generates a 8 scenario resembling the 1303 Crete earthquake. Using along‐strike varying initial stresses results in 8.0–8.5 dynamic rupture scenarios with diverse slip rates and uplift patterns. The model with the most heterogeneous initial conditions yields a 7.5 scenario. Dynamic rupture complexity in prestress and fracture energy tends to lower earthquake magnitude but enhances tsunamigenic displacements. Our results offer insights into the dynamics of potential large Hellenic Arc megathrust earthquakes and may inform future physics‐based joint seismic and tsunami hazard assessments.

Population displacement after earthquakes: benchmarking predictions based on housing damage

In the aftermath of an earthquake, the number of residents whose housing was destroyed is often used to approximate the number of people displaced (i.e., rendered homeless) after the event. While this metric can provide rapid situational awareness regarding potential long-term housing needs, more recent research highlights the importance of additional factors beyond housing damage within the scope of household displacement and return (e.g., utility disruption, tenure, place attachment). This study benchmarks population displacement estimates using this simplified conventional approach (i.e., only considering housing destruction) through three scenario models for recent earthquakes in Haiti, Japan, and Nepal. These model predictions are compared with officially reported values and alternate mobile location data-based estimates from the literature. The results highlight the promise of scenario models to realistically estimate population displacement and potential long-term housing needs after earthquakes, but also highlight a large range of uncertainty in the predicted values. Furthermore, purely basing displacement estimates on housing damage offers no view on how the displaced population counts vary with time as compared to more comprehensive models that include other factors influencing population return or alternative approaches, such as using mobile location data.

Assessing the Adequacy of Earthquake Catalog Sampling for Long-Term Seismicity in Low-to-Moderate Seismic Regions: A Geodetic Perspective

Abstract Seismic hazard assessment in low-to-moderate seismicity regions can benefit from the knowledge of surface deformation rates to better constrain earthquake recurrence models. This, however, amounts to assuming that the known seismicity rate, generally observed over historical times (i.e., up to a few centuries in Europe), provides a representative sample of the underlying long-term activity. We here investigate how this limited sampling can affect the estimated seismic hazard and whether it can explain the disagreement between the seismic moment loading rate as seen by nowadays Global Navigation Satellite Systems (GNSS) measurements and the seismic moment release rate by past earthquakes, as is sometimes observed in regions with limited activity. We approach this issue by running simulations of earthquake time series over very long timescales that account for temporal clustering and the known magnitude–frequency distribution in such regions, and that those are constrained to a seismic moment rate balance between geodetic and seismicity estimates at very long timescales. We show that, in the example of southeastern Switzerland, taken here as a case study, this sampling issue can indeed explain this disagreement, although it is likely that other phenomena, including aseismic deformation and changes in strain rate due to erosional and/or glacial rebound, may also play a significant role in this mismatch.

A brief overview of the past, present and future of the Global Earthquake Model (GEM) Foundation

The Global Earthquake Model (GEM) is an initiative that originally emerged from discussions and proposals made by the Global Science Forum of the Organization for Economic Co-operation and Development (OECD) in the early 2000’s. In 2009, GEM established itself as a non-profit legal entity, the GEM Foundation, and 15 years later, it continues to operate globally with a mission to provide transparent resources for seismic hazard and risk assessment, as well as to support disaster risk reduction for a wide range of natural hazards. This paper highlights the main milestones from the past 15 years and the vision of the foundation to 2030. An overview is also provided of the status of GEM products and services, all of which are made available for the benefit of society, under a strategy that aims to assure the continued financial sustainability of the organization.

A Stochastic Model for Induced Seismicity at the Geothermal Systems: A Case of the Geysers

Abstract Induced seismicity has emerged as a source of a significant earthquake hazard associated with recent development of unconventional energy resources. Therefore, it is imperative to develop stochastic models that can accurately describe the observed seismicity rate and forecast its evolution. In this study, a mechanism suggested by linear response theory is incorporated into a stochastic earthquake model to account for changes in the seismicity rate. It is derived that the induced rate can be modeled as a convolution of the forcing, related to fluid injection operations, and a specific response kernel. The model is incorporated into a Bayesian framework to compute the probabilities for the occurrence of the largest expected events during future time intervals. The applicability of the model is illustrated by analyzing the injection and seismicity data at the Geysers geothermal field in California. The suggested approach provides further insight into the probabilistic assessment of earthquake hazard associated with fluid injection operations. It also can be used for probing the rheological properties of the subsurface by analyzing the inherent characteristic timescales associated with the subsurface response to external forcing.

New Features in the pyCSEP Toolkit for Earthquake Forecast Development and Evaluation

Abstract The Collaboratory for the Study of Earthquake Predictability (CSEP) is a global community dedicated to advancing earthquake predictability research by rigorously testing probabilistic earthquake forecast models and prediction algorithms. At the heart of this mission is the recent introduction of pyCSEP, an open-source software tool designed to evaluate earthquake forecasts. pyCSEP integrates modules to access earthquake catalogs, visualize forecast models, and perform statistical tests. Contributions from the CSEP community have reinforced the role of pyCSEP in offering a comprehensive suite of tools to test earthquake forecast models. This article builds on Savran, Bayona, et al. (2022), in which pyCSEP was originally introduced, by describing new tests and recent updates that have significantly enhanced the functionality and user experience of pyCSEP. It showcases the integration of new features, including access to authoritative earthquake catalogs from Italy (Bolletino Seismico Italiano), New Zealand (GeoNet), and the world (Global Centroid Moment Tensor), the creation of multiresolution spatial forecast grids, the adoption of non-Poissonian testing methods, applying a global seismicity model to specific regions for benchmarking regional models and evaluating alarm-based models. We highlight the application of these recent advances in regional studies, specifically through the New Zealand case study, which showcases the ability of pyCSEP to evaluate detailed, region-specific seismic forecasts using statistical functions. The enhancements in pyCSEP also facilitate the standardization of how the CSEP forecast experiments are conducted, improving the reliability, and comparability of the earthquake forecasting models. As such, pyCSEP exemplifies collaborative research and innovation in earthquake predictability, supporting transparent scientific practices, and community-driven development approaches.

Seismic reliability analysis using Subset Simulation enhanced with an explorative adaptive conditional sampling algorithm

Reliability analysis of structures under earthquake loading represents a significant engineering challenge. This is due to the required and rather numerically involving non-linear dynamic analysis, the large computational burden when targeting small failure probabilities, and the synthetic earthquake model representation that may contain thousands of random variables. Subset Simulation is an efficient reliability analysis technique that can handle the challenge of a high-dimensional space with a reduced number of structural analysis calls compared to crude Monte Carlo Simulation. In this contribution, firstly, we investigate the conditions for which Subset Simulation performs efficiently. Thereafter we propose an enhancement to the existing Subset Simulation schemes that shows significant potentials for enhancing the strategy for the starting of the Markov Chain Monte Carlo simulations whenever a new level is reached in the Subset Simulation. Finally, the information gathered from the simulations is investigated to verify that Subset Simulation provides meaningful results from a physical point of view.

Reduced Injection Rates and Shallower Depths Mitigated Induced Seismicity in Oklahoma

Abstract The proximity of wastewater disposal to the Precambrian basement is a critical factor influencing induced earthquake rates in the Central United States, but the impact of reducing injection depths has not been widely demonstrated. Beginning in 2015, state regulatory efforts in Oklahoma and Kansas mandated that wells injecting into the lower Arbuckle Group, a basal sedimentary unit, be backfilled with cement (i.e., “plugged back”) so that they inject into shallower formations. This plug back activity gives us a unique opportunity to investigate the relationship between injection depth and induced seismicity rate. To evaluate the impact that decreased injection rates and plug backs had on the seismicity rates, we created a suite of rate–state earthquake models. Observed seismicity rates are best fit when only lower Arbuckle volumes are considered, suggesting the lower Arbuckle injectors were primarily responsible for the seismicity and that plug backs were effective at isolating the injected volumes to shallower formations. Our models demonstrate that if these wells had not been plugged back, seismicity rates would be multiple times larger than they are today. We find that the combination of well plug backs and injection volume decreases can be an effective strategy for reducing induced seismicity rates.

Declustering characteristics of the North China Plain seismic belt and its effect on probabilistic seismic hazard analysis

The accurate and impartial identification of background seismic activity is an important foundation for earthquake model construction and probabilistic seismic hazard analysis (PSHA). We use the Gardner and Knopoff, Reasenberg, nearest-neighbor and stochastic declustering methods to decluster in the North China Plain seismic belt, analyzing the effect of declustering. We optimize the input parameters of the algorithms through Kolmogorov-Smirnov Poisson distribution tests (e.g., the clustering threshold of the nearest-neighbor method is set to 0.6). Four algorithms demonstrate good declustering effects, removing the impact of the strong seismicity in Tangshan while retaining similar trends to all events. The occurrence of major earthquake events can significantly impact the declustering characteristics in terms of time, space and magnitude. There is a difference in the overall hazard between the four declustering algorithms and the Fifth Generation Seismic Ground Motion Parameters Zonation Map of China. The difference in seismic hazard curves is mainly influenced by the annual average occurrence rate (background rates) and the Gutenberg-Richter b value, with the effect of the b value being more pronounced. The analysis of the effect of algorithms and their impact on PSHA can provide reference for earthquake risk assessment, engineering seismic design and disaster research.

Cross-fade sampling: extremely efficient Bayesian inversion for a variety of geophysical problems

SUMMARY This paper introduces cross-fade sampling, a computationally efficient Markov Chain Monte Carlo simulation method that uses a semi-analytical approach to quickly solve Bayesian inverse problems that do not themselves have an analytical solution. Cross-fading is efficient in two ways. First, it requires fewer samples to obtain the same quality simulation of the target probability density function (PDF). Secondly, it is much faster to evaluate the posterior probability of each sample than conventional sampling methods for simulating Bayesian posterior PDFs. Conventional methods require evaluating the prior probability (which describes your a priori constraints) and data likelihood (which describes the fit between the observations and the predictions of the model) for each sample model. However, cross-fading does not require evaluating the data likelihood, meaning that ‘big data’ can be fit with zero additional computational cost. Further, the cross-fading approach can be used to calculate the marginal likelihood associated with a model design, facilitating model comparison and Bayesian model averaging. Topics covered in this paper include derivation of the cross-fade approach and how it can be used to simulate Bayesian posterior PDFs and compute the marginal likelihood, discussion of the class of problems to which cross-fading can be applied (with examples from earthquake statistics, earthquake ground motion modelling, volcanic eruption forecasting, and finite fault slip modelling), demonstration of efficiency relative to existing sampling methods and discussion of how cross-fading can be used to account for prediction errors (i.e. epistemic errors) as part of the geophysical inverse problem.

A combining earthquake forecasting model between deep learning and epidemic-type aftershock sequence (ETAS) model

SUMMARY The scientific process of earthquake forecasting involves estimating the probability and intensity of earthquakes in a specific area within a certain timeframe, based on seismic activity features and observational data. Among the various methodologies, epidemic-type aftershock sequence (ETAS) models, rooted in seismic empirical laws, stand as widely used tools for earthquake forecasting. In this study, we introduce the CL-ETAS model, a novel approach that integrates convolutional long short-term memory (ConvLSTM), a deep learning model, with the ETAS model. Specifically, we leverage the forecasting outputs of ETAS to enhance both the training and forecasting processes within the ConvLSTM framework. Through forecasting tests, our findings illustrate the effectiveness of the CL-ETAS model in capturing the trends observed in earthquake numbers ($M \ge 3$) in Southern California following three main shocks. Overall, our model outperforms both a simple ETAS model and ConvLSTM in this context.

Generative Adversarial Networks-Based Ground-Motion Model for Crustal Earthquakes in Japan Considering Detailed Site Conditions

ABSTRACT We develop a ground-motion model (GMM) for crustal earthquakes in Japan that can directly model the probability distribution of ground-motion acceleration time histories based on generative adversarial networks (GANs). The proposed model can generate ground motions conditioned on moment magnitude, rupture distance, and detailed site conditions defined by the average shear-wave velocity in the top 5, 10, and 20 m (VS5, VS10, and VS20) and the depth to shear-wave velocities of 1.0 km/s and 1.4 km/s (Z1.0 and Z1.4). We construct the neural networks based on styleGAN2 and introduce a novel neural network architecture to generate ground motions considering the effect of source, path, and such detailed site conditions. The resulting 5% damped spectral acceleration from the proposed GMM is consistent with empirical GMMs in terms of magnitude and distance scaling. The proposed GMM can also generate ground motions accounting for the shear-wave velocity profiles of surface soil with different magnitudes and distances and represent characteristics that are not explained solely byVS30.

Adjoint-based inversion for stress and frictional parameters in earthquake modeling

We present an adjoint-based optimization method to invert for stress and frictional parameters used in earthquake modeling. The forward problem is linear elastodynamics with nonlinear rate-and-state frictional faults. The misfit functional quantifies the difference between simulated and measured particle displacements or velocities at receiver locations. The misfit may include windowing or filtering operators. We derive the corresponding adjoint problem, which is linear elasticity with linearized rate-and-state friction and, for forward problems involving fault normal stress changes, nonzero fault opening, with time-dependent coefficients derived from the forward solution. The gradient of the misfit is efficiently computed by convolving forward and adjoint variables on the fault. The method thus extends the framework of full-waveform inversion to include frictional faults with rate-and-state friction. In addition, we present a space-time dual-consistent discretization of a dynamic rupture problem with a rough fault in antiplane shear, using high-order accurate summation-by-parts finite differences in combination with explicit Runge–Kutta time integration. The dual consistency of the discretization ensures that the discrete adjoint-based gradient is the exact gradient of the discrete misfit functional as well as a consistent approximation of the continuous gradient. Our theoretical results are corroborated by inversions with synthetic data. We anticipate that adjoint-based inversion of seismic and/or geodetic data will be a powerful tool for studying earthquake source processes; it can also be used to interpret laboratory friction experiments.

A 3D Seismotectonic Model and the Spatiotemporal Relationship of Two Historical Large Earthquakes in the Linfen Basin, North China

The Shanxi Graben is a transitional zone between the Ordos Block and North China Plain with complex structures and frequent earthquakes. Six earthquakes with M ≥ 7.0 have been recorded in the area, including the 1303 Hongtong M 8 and 1695 Linfen M 7.8 earthquakes in the Linfen Basin. Research on these two large earthquakes, closely related in time and space, is lacking. Our objective was to use deep seismic reflection profiles and 3D velocity structure data from previous research, along with seismological observation results, to interpret the geological structure near the source of the two earthquakes. A 3D geometric model of the seismogenic fault was constructed, and the relationships among the deep and shallow structures, deep seismogenic environment, and two large earthquakes were explored. Differences in seismogenic environment between the southern and northern Linfen Basin were identified. The distribution of small earthquakes in the southern Linfen Basin was scattered, and the overall distribution was at depths <25 km. The small earthquakes in the northern part of the basin were dense and concentrated at depths of 25–35 km. Low-velocity layers at an approximate depth of 15–20 km in the southern basin led to differences in seismogenesis between the two regions. Based on the area of the 3D geometric model of the Huoshan Fault, the maximum magnitude of an earthquake caused by fault rupture is Mw 7.7, so the magnitude of the 1303 Hongtong earthquake might be overestimated. Numerical simulation results of Coulomb stress showed that the 1303 Hongtong earthquake had a stress-loading effect on the 1695 Linfen earthquake. The change in Coulomb rupture stress was 1.008–2.543 bar, which is higher than the generally considered earthquake trigger threshold (0.1 bar). We created a new 3D source model of large earthquakes in the Linfen Basin, Shanxi Province, providing a reference and typical cases for risk assessment of large earthquakes in different regions of the Shanxi Graben.

Tsunami propagation and flooding maps: An application for the Island of Lampedusa, Sicily Channel, Italy

AbstractThe Mediterranean coastlines are densely populated zones which host key socio‐economic and commercial activities. For this reason, coastal areas are vulnerable sites in case of natural disasters as tsunamis that can strike coasts causing widespread damage to the population and facilities. For these reasons, several studies were performed over the last decade to study the impact of tsunami waves on the coasts. This research assessed the inundation risk due to a tsunami wave which can hit the southeastern coast of Lampedusa Island. The coastal low‐lying geomorphological setting of the southeastern part of the island led to significant socio‐economic growth, but Lampedusa falls within the Mediterranean Sea, a high‐tsunamigenic area, therefore, the need to investigate tsunami propagation and coastal flooding of this sensitive site emerged. For this scope, a calculation chain model was implemented incorporating three steps: the DELFT‐3D software for earthquake effects modelling, MIKE 21 Flow Model FM for nearshore propagation and HEC‐RAS for onshore tsunami inundation modelling. The simulations illustrate the impact of three tsunami scenarios with different magnitudes (Mw 8.5, 7.5, 6.5) generated by hypothetical earthquakes in the Hellenic Arc. In the Mw 8.5 magnitude scenario, significant flooding occurs in the harbour region, with maximum water depths reaching approximately 3.5 m. The maximum water velocity in this scenario reaches about 15 m/s in the eastern portion, adjacent to cliffs impacted by the tsunami wave. In contrast, the Mw 7.5 magnitude scenario demonstrates reduced flooded areas, with the cliffs containing the waves and preventing further flooding. Water depths and velocities in the Mw 7.5 scenario remain minimal. Changes in both propagation and flooding are not significant between scenarios Mw 7.5 and Mw 6.5. This methodology can be employed for more accurate tsunami wave simulations not only in the Mediterranean region but also in various case studies.

Using RSQSim to Determine Seismic Sequence in Eastern Taiwan Fault System

Abstract Understanding the spatial and temporal patterns of seismic activity, along with fault interactions in Taiwan, is essential for earthquake hazard assessment and advancing knowledge of regional tectonics. This study employs the Rate and State Earthquake Simulator (RSQSim) to simulate the eastern Taiwan fault system, integrating fault geometry from the multidisciplinary Taiwan Earthquake Model. We applied long-term simulations spanning 400,000 yr to conduct earthquake sequences and recurrence intervals on five distinct faults in eastern Taiwan: Milun fault, Longitudinal Valley fault, Central Range structure, Luyeh fault, and Taimali Coastline structure. The simulated earthquake catalogs are compared against the historical record in terms of seismicity, frequency–magnitude statistics, and recurrence patterns. The model reasonably reproduces key observational constraints, including spatial patterns, magnitude probabilities fitting a gamma distribution, and periods of quiescence resulting from fault interactions. Overall, the results demonstrate RSQSim’s potential for physics-based seismic hazard modeling and provide insights into regional seismotectonic processes in eastern Taiwan for constructing sustainable cities and communities.