Earthquake Probability Research Articles

Mitigation strategies and policies recommendation for the economic impact of the Sunda Strait Megathrust: Seismic risk probability assessment and cost loss estimates

This study investigates seismic risk and potential impacts of future earthquakes in the Sunda Strait region, known for its susceptibility to significant seismic events due to the subduction of the Indo-Australian Plate beneath the Eurasian Plate. The aim is to assess the likelihood of major earthquakes, estimate their impact, and propose strategies to mitigate associated risks. The research uses historical seismic data and probabilistic models to forecast earthquakes with magnitudes ranging from 6.0 to 8.2 Mw. The Gutenberg-Richter model helps project potential earthquake occurrences and their impacts. The findings suggest that the probability of a major earthquake could occur as early as 2026–2027, with a more significant event estimated to likely occur around 2031. Economic estimates for a 7.8–8.2 Mw earthquake suggest potential damage of up to USD 1.255 billion with significant loss of life. The study identifies key vulnerabilities, such as inadequate building foundations and ineffective disaster management infrastructure, which could worsen the impact of future seismic events. In conclusion, the research highlights the urgent need for comprehensive seismic risk mitigation strategies. Recommendations include reinforcing infrastructure to comply with seismic standards, implementing advanced early warning systems, and enhancing public education on earthquake preparedness. Additionally, government policies must address these issues by increasing funding for disaster management, enforcing building regulations, and incorporating traditional knowledge into construction practices. These measures are essential to reducing future earthquake impacts and improving community resilience.

Seismic slip channeling along the East Anatolian Fault illuminates long-term supercycle behavior

The two Mw > 7.5 earthquakes that struck the East Anatolian Fault (EAF), Türkiye, in 2023 caused more slip than expected, indicating that they were potentially part of a supercycle, in which the occurrence probability of a large earthquake is determined by accumulated strain rather than time since the last large earthquake. Here, we show two potential supercycles along the EAF, analyzing earthquakes from the last two millennia. Within each supercycle, seismic ruptures originated in the northeast and progressively spread southwestward with an increasing number of earthquakes until a new supercycle began with another large earthquake in the northeast. To understand the supercycle behavior, we analyze the aftershock sequences of the four most recent Mw≥6.1 mainshocks (2010–2023). This series of earthquakes progressed southwestward, characterized by an increasing diversity of focal mechanisms and a heightened dispersion of epicenters across a branched seismotectonic environment. Earthquakes in the northeast exhibit spatial and kinematic channeling along the master fault surface, effectively transferring slip southwestward and there potentially triggering dispersed and heterogeneous earthquakes. This spatiotemporal pattern seems connected with varying levels of a presumably-innate property of fault sections or regions, ruling the process of seismic slip channeling, which could also explain the behavior of long-term supercycles.

Earthquake Predictability and Forecast Evaluation Using Likelihood-Based Marginal and Conditional Scores

Abstract Earthquake probability forecasts are typically based on simulations of seismicity generated by statistical (point process) models or direct calculation when feasible. To systematically assess various aspects of such forecasts, the Collaborative Studies on Earthquake Predictability testing center has utilized N- (number), M- (magnitude), S- (space), conditional likelihood-, and T- (Student’s t) tests to evaluate earthquake forecasts in a gridded space–time range. This article demonstrates the correct use of point process likelihood to evaluate forecast performance covering marginal and conditional scores, such as numbers, occurrence times, locations, magnitudes, and correlations among space–time–magnitude cells. The results suggest that for models that only rely on the internal history but not on external observation to do simulation, such as the epidemic-type aftershock sequence model, test and scoring can be rigorously implemented via the likelihood function. Specifically, gridding the space domain unnecessarily complicates testing, and evaluating spatial forecasting directly via marginal likelihood might be more promising.

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.

Characteristics of the seismogenic zone in an arc-continent collision belt: insights from seismic b values in Eastern Taiwan

Eastern Taiwan overlies a suture zone between the Eurasian Plate and the Philippine Sea Plate and is characterized by frequent earthquakes, often resulting in significant disasters. Notably, the region exhibits characteristics such as a high frequency of earthquakes and a short recurrence period for intense seismic events. While prior research has explored seismic b values across various periods in Taiwan, detailed investigations of the b value in the eastern region are lacking. This study employs the earthquake catalog compiled by the Taiwan Central Weather Administration to analyze spatial–temporal variations in b values in eastern Taiwan. The analysis encompasses seismic events occurring between January 1996 and June 2019. The seismic catalog is divided into three distinct time periods related to large seismic events: period I, 1996–2003 (the Chengkung earthquake); period II, 2003–2013 (the Ruisui earthquake); and period III, 2013–2019 (the Hualien earthquake). Our results indicate that most seismic events with a magnitude greater than 6 are associated with low b values. The overall b value increases during period II and then decreases substantially during period III. Although the estimated b values changed slightly, but the uncertainty in b values remained stable in this study. The epicenters of large earthquakes often overlap with areas with lower b values, especially in plate suture zones, which means that areas with lower b values usually have a higher probability of larger earthquakes. Given the extremely high potential for a catastrophic earthquake, mitigating measures should be adopted at all times.Graphical

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.

Conditional earthquake probabilities along the North Anatolian fault zone based on inverse Gaussian against lognormal distribution

Conditional earthquake probabilities along the North Anatolian fault zone based on inverse Gaussian against lognormal distribution

Probability and recurrence interval of large earthquakes (Mw ≥ 6) in the Himalayan seismicity zone

The Himalayan Mountains are one of the world’s most seismically active regions. Due to the active seismic status of the Himalayan belt and ongoing development activities in the region, it is important to keep the seismic hazard estimates updated by incorporating historical and new data. This study uses the Gutenberg-Richter relations for four seismogenic source zones (SSZ) to estimate the probability of earthquakes of a specified magnitude in a given time interval. We used exponential, Weibull, Rayleigh, and log-normal statistical models to assess the probability of earthquakes (Mw ≥ 6). The probable recurrence time of the large earthquake (Mw ≥ 6) can be estimated by maximizing the conditional probability estimates. This recurrence interval is calculated after the elapsed time since the most recent large earthquake in the region. We also apply an autoregressive (AR) forecasting technique to predict earthquake recurrence intervals. The results indicate that the approximate recurrence interval of the large earthquakes (Mw ≥ 6) may be 22.052 ± 5.65 years within the seismicity zones, as per our statistical estimates based on limited and available datasets. The results of this study may be useful for the agencies involved in assessing seismic hazards and developing mitigation and preparedness strategies in the region.

Comments on Earthquake Probabilities Estimated by the Geometrical Mean Likelihood of the Brownian Passage Time Distribution Model for Recurrent Paleoearthquakes

Comments on Earthquake Probabilities Estimated by the Geometrical Mean Likelihood of the Brownian Passage Time Distribution Model for Recurrent Paleoearthquakes

Seismic forecasting by gapped wavelet transform for the Río de la Plata craton and adjacent continental platform

Historical and instrumental seismicity from 1845 to the present was compiled for the Pampean regions of Argentina and Uruguay and the adjacent continental platform. These data were analyzed using the Gapped Wavelet Transform (GWT) with the aim of investigating recurrence patterns in the time series.The GWT analysis reveals the existence of periods of seismic activity and periods of seismic silence within the considered area. A 22-year period was determined as the main pattern of seismic activity, during which earthquakes are registered only in 11 years. Based on these results, a probabilistic earthquake forecasting model was created using the Bayesian Machine Learning method, which suggests that the current seismically active period spans from 2011 to 2023 ± 1, with a cessation until 2034 ± 1, while the next seismically active period would begin in 2034 ± 2 ending in 2046 ± 2.These periods or recurrence patterns are interpreted as intervals during which stress accumulates throughout intracrustal faults in the studied area, being then released through various events within the established temporal interval. The accumulation and release mechanisms are still a subject of debate. However, the earthquakes distribution suggests a sublatitudinal arrangement that tends to confirm some correspondence with the controlling faulting of the Quilmes Trough and others transtensional regional lineaments.

Modeling the Asymptotic Behavior of Higher Order Aftershocks with Deep Learning

Abstract Aftershocks of aftershocks—and their aftershock cascades—substantially contribute to the increased seismicity rate and the associated elevated seismic hazard after the occurrence of a large earthquake. Current state-of-the-art earthquake forecasting models therefore describe earthquake occurrence using self-exciting point processes, where events can recursively trigger more events according to empirical laws. To estimate earthquake probabilities within future time horizons of interest, a large number of possible realizations of a process are simulated, which is typically associated with long computation times that increase with the desired resolution of the forecast in space, time, or magnitude range. We here propose a machine learning approach to estimate the temporal evolution of the rate of higher order aftershocks. For this, we train a deep neural network to predict the mean output of the simulation-based approach, given a parametric description of the rate of direct aftershocks. A comparison of the two approaches to estimate the mean outcome reveals that they perform very similarly in describing synthetic datasets generated with the simulation-based approach. Our method has two major benefits over the traditional approach. It is faster by several orders of magnitude, and it is not biased by ‘extreme’ realizations containing exceptionally high or low numbers of aftershocks and thus enables accurate earthquake forecasting in near-real time.

On the effect of background seismicity in physics-based earthquake simulations

Physics-based simulations have been extensively employed to generate synthetic earthquake catalogs over the past decade. In this kind of simulation, primary known faults are typically modeled, while smaller and intermediate faults are often neglected. As a result, the location of earthquakes in the simulation is confined to the modeled faults. Furthermore, due to the elimination of off-fault seismicity, a complete match of the frequency-magnitude distribution of the synthetic catalog with observed data is not achieved. This paper presents an approach to include off-fault seismicity (or background seismicity) within the simulation. First, background earthquakes are separated from the primary faults’ earthquakes, and a fault section is assigned to each event. By employing a scaling relationship, the dimensions of these fault sections are determined according to the magnitude of the corresponding event. The fault section’s slip rates are estimated based on the observed frequency-magnitude distribution. Combining the background fault model with the primary fault model results in a comprehensive model that accounts for all stress interactions between fault elements within the simulator in actual locations. As a result, a broader range of frequency-magnitude distribution in the synthetic catalog can be matched to the observations. A case study using one of the available simulators, Virtual Quake, on a sample area is presented. The results demonstrate that eliminating off-fault seismicity could lead to an underestimated assessment of the earthquake probabilities for the whole region and a biased estimation of earthquake rates in individual faults.

Agent-based post-earthquake evacuation simulation to enhance early-stage architectural layout and non-structural design

In the indoor design process, architects make crucial decisions regarding architectural layout and the selection of non-structural elements. However, there is a lack of comprehensive consideration for human evacuation behavior, specifically in the event of earthquake evacuation, during the design process. This paper bridges this gap by presenting the application of Agent-Based Building Earthquake Evacuation Simulation (AB2E2S). The paper assesses a post-earthquake evacuation simulation prototype, which integrates an agent-based simulation technique with probabilistic earthquake damage assessment. The model is applied to the case of the engineering building at The University of Auckland, to evaluate the impact of earthquake intensity, design, and behavioral variables on Safe Evacuation Time and number of casualties. Overall, this paper demonstrates the potential of the AB2E2S prototype to inform architects and designers about the effective selection of non-structural elements and architectural layout scenarios for post-earthquake evacuation during the schematic design process.

Prediction Results for the Strongest Earthquakes in Southern Turkey on February 6, 2023

On February 6, 2023, two devastating earthquakes struck southern central Turkey, nin hours apart. The ground shaking from these earthquakes even swept over a significant part of northwestern Syria. In this paper, we consider the locations of the epicenters of these earthquake relative to the prediction of М ≥ 6.5 earthquake epicenters in Anatolia and adjacent regions, which was made in 1973 by I.M. Gelfand, V.I. Keilis-Borok and their colleagues using the Kora-3 pattern recognition algorithm based on morphostructural zoning data. We also present the results of early detection of periods with an increased probability of the strongest earthquakes as determined by the medium-term prediction algorithm for M8 earthquakes. It is found that the epicenters of the February 6, 2023 earthquakes occurred in a knot identified in 1973 as potentially earthquake-prone for М ≥ 6.5, and both events occurred within the spatial and temporal boundaries of the alert area diagnosed by the M8 algorithm in July 2021.

Nowcasting Earthquakes With Stochastic Simulations: Information Entropy of Earthquake Catalogs

AbstractEarthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic methods. Furthermore, analysis of the Shannon information content of the earthquake catalogs has been used to show that there is information contained in the catalogs, and that it can vary in time. So an important question remains, where does the information originate? In this paper, we examine this question using stochastic simulations of earthquake catalogs. Our catalog simulations are computed using an Earthquake Rescaled Aftershock Seismicity (“ERAS”) stochastic model. This model is similar in many ways to other stochastic seismicity simulations, but has the advantage that the model has only 2 free parameters to be set, one for the aftershock (Omori‐Utsu) time decay, and one for the aftershock spatial migration away from the epicenter. Generating a simulation catalog and fitting the two parameters to the observed catalog such as California takes only a few minutes of wall clock time. While clustering can arise from random, Poisson statistics, we show that significant information in the simulation catalogs arises from the “non‐Poisson” power‐law aftershock clustering, implying that the practice of de‐clustering observed catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the ERAS model as it does with observed seismicity.

The earthquake probabilities of the Liupanshan fault zone based on the Coulomb stress and friction constitutive law

As the forefront of the northeastern margin of the Tibetan Plateau, the Liupanshan area is subjected to significant Cenozoic deformation. Considering the background of historical earthquakes and the current seismic activity, the Liupanshan fault poses a risk of strong earthquakes. This paper analyzes the evolution of Coulomb stress induced by the coseismic and postseismic viscoelastic deformation of six historical strong earthquakes that occurred around the Liupanshan area since the twentieth century. Additionally, based on the disturbance in Coulomb stress and the background seismicity rate, the probability of earthquake occurrence is quantitatively evaluated using the frictional constitutive law. The results indicate that northern section of the Liupanshan fault has higher earthquake potential than the southern section, warranting close attention. These findings serve as a reference for medium- and long-term earthquake forecast and risk assessment in the vicinity of the Liupanshan fault.

A Bayesian Approach for Forecasting the Probability of Large Earthquakes Using Thermal Anomalies from Satellite Observations

Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies.

Evaluating seismic risk by MCDM and machine learning for the eastern coast of India.

Natural disasters such as earthquakes endanger human lives and infrastructure, particularly in urban areas. With the advancements in science and technology in understanding natural hazards, recent studies have attempted to mitigate them by mapping the risks using geospatial technology. In this paper, we attempt to integrate the multi-criteria decision-making (MCDM) models, namely the Analytical Hierarchy Process (AHP) and the Criteria Importance Through Inter-criteria Correlation (CRITIC), besides using the artificial neural network (ANN) to assess the seismic risk in the eastern coast of India. The AHP-CRITIC technique is used to evaluate the earthquake coping capacity and vulnerability and has been further used to generate a training base for earthquake probability mapping by ANN. The earthquake probability and spatial intensity information are used to develop the hazard map. Following that, integrating vulnerability, hazard and coping capacity spatial information assessed earthquake risk. Our results indicate that approximately 5% of the study area is at high risk, whilst more than 11% of the population is at high risk due to seismic inducedhazards. The area under the curve of the receiver operating characteristic curve is 0.85, which indicates reliable results. The results of this study may help various agencies involved in planning, development and disaster mitigation to develop seismic hazard mitigation methods by better understanding their impacts on the eastern coastal region of India.

Adaptive estimation of the Gutenberg–Richter b value using a state space model and particle filtering

Earthquakes follow an exponential distribution referred to as the Gutenberg–Richter law, which is characterized by the b value that represents a ratio of the number of large earthquakes to that of small earthquakes. Spatial and temporal variation in the b value is important for assessing the probability of a larger earthquake. Conventionally, the b value is obtained by a maximum-likelihood estimation based on past earthquakes with a certain sample size. To properly assess the occurrence of earthquakes and understand their dynamics, determining this parameter with a statistically optimal method is important. Here, we discuss a method that uses a state space model and a particle filter, as a framework for time-series data, to estimate temporal variation in the b value. We then compared our output with that of a conventional method using data of earthquakes that occurred in Tohoku and Kumamoto regions in Japan. Our results indicate that the proposed method has the advantage of estimating temporal variation of the b value and forecasting magnitude. Moreover, our research suggests no heightened probability of a large earthquake in the Tohoku region, in contrast to previous studies. Simultaneously, there is the potential of a large earthquake in the Kumamoto region, emphasizing the need for enhanced monitoring.

The analysis and mapping of an urban planning area in risk and hazard dimensions using earthquake-MASW-VES data: the case of Yenişehir (Bursa), Turkey

The study area was defined by geophysical level maps and earthquake parameters. Geophysical measurements were collected from the Mesudiye formation and alluvial units. The seismic Vs–Vp–Vs30, dynamic parameter and electrical resistivity values of the layers were calculated. Vs–Vp–Gmax–σ–Ak and ρ level maps were prepared for depths of 0–5, 5–10, and > 10 m. Seismic Vs30 and groundwater maps were prepared for risk/hazard and liquefaction interpretations, and a risk map was drawn from Ak–Vs30–groundwater maps. In the Mesudiye formation, the average ground amplification value is Ak = 2.21 and according to this value, the danger level is class A and low (Ansal et. all 2004). In alluviums, the average Ak was calculated as 2.51. According to this value, its class is B (medium) and the danger level is medium level. According to the level maps, it was observed that seismic velocities and electrical resistivities increased, and they were compatible with the geological units when passing to the more tight-ground Mesudiye formation from loose-porous-water-saturated alluvial units towards the north and deeper. It was determined that the alluvial grounds with low resistivity (8–30 Ohm m) are moderately corrosive–corrosive; groundwater levels are at ~ 5 m and contain different geological gradations. Therefore, the alluviums were interpreted as a risky area. This interpretation was also proved and supported with the results of the earthquake parameters. It was determined that as the magnitude of the earthquake increased, the probability of a larger earthquake occurred with a longer recurrence year. It was found that the frequency of recurrence of earthquakes with M ≤ 5.5 within 10–50–75–100 years was higher, and M ≥ 6 decreased. For M = 7.4, the average amax = 0.31 g was calculated, and it was determined the liquefaction risk is high hazard, especially in fine-gravel-sandy alluviums (in the south). Therefore, these areas may be more affected by the earthquake.