Earthquake Magnitude Research Articles

A Comprehensive Case Study on the Historical Earthquakes in Major Fault Zones of Bangladesh

An earthquake is a fatal natural disaster that occurs suddenly without any prior signal. Every year the world faces many earthquakes with magnitudes varying from low to high. The uncertain nature of earthquakes makes it almost impossible to predict the exact time of occurrence. So, it is necessary to take precautionary steps before an earthquake hits. To take preventive measures, a proper study of tectonic plates and faults must be done to gain insight into the study area's susceptibility. Due to the geological characteristics and tec-tonic plate activity, Bangladesh risks facing an Earthquake. Every year, Bangladesh also faces some number of earthquakes of different magnitude. This study analyzes the earth-quake events in different fault zone of Bangladesh in the last 49 years. The study showed that the Tripura Fault and Shillong Plateau are the most active among the five major faults. The frequency of these faults is higher than the other ones. Though the past earthquake magnitudes were small, it is still concerning that the Tripura fault and Shillong plateau show much more frequent seismic activities that may indicate a bigger earthquake incident in the near future. The epicenter depth of most earthquakes is within 40 km from the sur-face, which is a concerning issue because most earthquakes are shallow earthquakes that tend to have more damaging properties than deep ones.

MACHINE LEARNING FOR THE DOCUMENTATION, PREDICTION, AND AUGMENTATION OF HERITAGE STRUCTURE DATA

Abstract. The paper presents an effort to develop learning models based on the massive amounts of data that has been accumulated over the past decades during the process of digital documentation of heritage structures around the globe especially those in disaster zones.The development of an ontology is proposed that describes heritage buildings, their sites, and major hazard events that may cause damage to them. This ontology can serve as a repository for documenting heritage structures and provide highly structured data for developing machine learning systems that can identify patterns of damage from recorded image data. For heritage structures in seismic zones, the first step in ontology development is analyzing available earthquake information about the event and the damage information. The resulting model will create links between information items, for example relating the extent of the damage of an element to the earthquake magnitude and its distance from the epicenter. The ontology may also include collected images from previous earthquake events, with links to the objects in each image. Special tools will focus on selecting sub-models to be included in a machine learning model. For example, if the learning objective is to identify the damage and its extent from an image, then the rules will select the features in the model that relate to structural damage and identify each type of damage. It is hoped that this work will help develop learning systems that speed up processing of large volumes of image damage data collected from heritage sites.

Uncertainties in Intensity-Based Earthquake Magnitude Estimates

Abstract Estimating the magnitude of historical earthquakes is crucial for assessing seismic hazard. Magnitudes of early-instrumental earthquakes can be inferred using a combination of instrumental records, field observations, and the observed distribution of shaking intensity determined from macroseismic observations. For earthquakes before 1900, shaking intensity distributions often provide the only information to constrain earthquake magnitude. Considerable effort has been made to develop methods to estimate the magnitude of moderate-to-large historical earthquakes using shaking intensities derived from macroseismic data. In this study, we consider earthquakes in California with known instrumental magnitudes to explore uncertainties in estimating the magnitude of historical earthquakes from intensity information alone. We use three California-specific intensity prediction equations (IPEs) and an IPE based on a global ground-motion model (GMM) to determine optimum intensity-based magnitudes for 33 moderate-to-large California earthquakes between 1979 and 2021. Intensity-based magnitudes are close to instrumental magnitudes on average. However, intensity-based magnitudes for individual events differ by as much as 2.2 magnitude units from instrumental magnitudes. This result reflects the weak dependence of ground motions and shaking intensities on moment magnitude and their strong dependence on stress drop. Considering the intensity distributions of the 1906 San Francisco and 1989 Loma Prieta earthquakes, we show that information that could constrain rupture length is discarded when considering only the 2D decay of intensity with distance. We also show that ground-motion intensity conversion equations used in a GMM-based approach may cause a systematic overestimation of large historical earthquake magnitudes. This study underscores both the reducible and potentially irreducible uncertainties associated with using intensity data to estimate magnitudes of historical earthquakes using IPEs and highlights the value of using additional information to constrain rupture dimensions. Using intensity observations alone, moment magnitude uncertainties are typically on the order of a full unit.

An analysis of the spatial distribution of geophysical filds in the source region of the 2013 Ilin-Tas (Aby) earthquake

This article analyzes the powerful Ilin-Tas earthquake that occurred in the Chersky Range system in the territory of the Republic of Sakha (Yakutia) in 2013. The mountain system of the Chersky Range system is confied to the Chersky seismotectonic zone, which is part of the Arctic-Asian seismic belt separating the Eurasian and North American lithospheric plates in Northeast Asia. The earthquake magnitude at the epicenter was 9.0. Geophysical filds were used as a material to clarify the structural-tectonic features of the study area and their relationship with seismicity. Involving the data on spatial distribution of anomalies of magnetic and gravity filds made it possible to detail the parameters of the position of the Ilin-Tas fault, where the epicentre of the Ilin-Tas earthquake originated. This fault traces between the Indigirka and Kolyma rivers along the border of the junction of the Moma Range with the Indigiro-Zyryansk depression, and is traced by gradient transition zones from negative to positive values of ΔTa and Δg. The main seismic shock is confied to thickening of Δg isoanomalies, forming a gravitational step, within which gravity values decrease from south to north. The epicentral fild of this earthquake with aftershocks captures the western slope of the Andrey-Tas Range (northwestern flnk of the Moma Range). A qualitative analysis of the geophysical filds in the epicentral region of the earthquake is carried out. Materials of instrumental and macroseismic data, focal mechanism and seismotectonic setting of the focal zone are given in the article. The data obtained as a result of studying the Ilin-Tas earthquake consequences allows to reveal local seismic activity in details and specify seismodanger degree of north-east Yakutia.

Scenario-based multi-risk assessment from existing single-hazard vulnerability models. An application to consecutive earthquakes and tsunamis in Lima, Peru

Abstract. Multi-hazard risk assessments for building portfolios exposed to earthquake shaking followed by a tsunami are usually based on empirical vulnerability models calibrated on post-event surveys of damaged buildings. The applicability of these models cannot easily be extrapolated to other regions of larger/smaller events. Moreover, the quantitative evaluation of the damages related to each of the hazard types (disaggregation) is impossible. To investigate cumulative damage on extended building portfolios, this study proposes an alternative and modular method to probabilistically integrate sets of single-hazard vulnerability models that are constantly being developed and calibrated by experts from various research fields to be used within a multi-risk context. This method is based on the proposal of state-dependent fragility functions for the triggered hazard to account for the pre-existing damage and the harmonisation of building classes and damage states through their taxonomic characterisation, which is transversal to any hazard-dependent vulnerability. This modular assemblage also allows us to separate the economic losses expected for each scenario on building portfolios subjected to cascading hazards. We demonstrate its application by assessing the economic losses expected for the residential building stock of Lima, Peru, a megacity commonly exposed to consecutive earthquake and tsunami scenarios. We show the importance of accounting for damage accumulation on extended building portfolios while observing a dependency between the earthquake magnitude and the direct economic losses derived for each hazard scenario. For the commonly exposed residential building stock of Lima exposed to both perils, we find that classical tsunami empirical fragility functions lead to underestimations of predicted losses for lower magnitudes (Mw) and large overestimations for larger Mw events in comparison to our state-dependent models and cumulative-damage method.

Insights on the role of local site effects on damage distribution in the Izmir metropolitan area induced by the October 30, 2020 Samos earthquake

On the 30th of October 2020, a 6.6 magnitude earthquake occurred 14 km north of Samos Island, causing 119 casualties (117 in Izmir, Türkiye, and 2 in Samos, Greece) and significant damage in the 3rd biggest city of Türkiye, Izmir. Although the city is roughly 70 km far away from the epicenter, the damage was significant and concentrated in the city center settled on alluviums. This paper aims to analyze the distribution of damage in Izmir province, by crosschecking the recorded motions, the subsoil conditions and the evidence of damage as collected by an ad-hoc on-site reconnaissance. The intrinsic behavior of the Samos earthquake was investigated by employing three different ground-motion prediction equations. The results of the analyses revealed that site effects play a significant role in the amplification of ground motions, and valley effects are responsible for the concentration of damage. The damage in buildings was classified in terms of the intensity and structural typologies for the 30 districts of Izmir metropolitan area. In-depth analysis of the distribution of damages revealed that the earthquake caused damage all over the boundaries of Izmir province, and the concentration of damage in Bornova and Karşıyaka districts has a clear correlation with double resonance effects.

Evidence of frictional melting in fault rock drill cuttings from the enhanced geothermal system site in Pohang, South Korea

The 5.5 magnitude (Mw) earthquake in Pohang, South Korea in 2017 was one of the largest triggered earthquakes at an enhanced geothermal system (EGS) site. Faults that ruptured in Pohang were not identified by preliminary geological investigations or geophysical surveys, and the subsequent study of the fault rocks at the Pohang EGS site was limited to depths of 3790–3816 m. In this study, we present new observations of fault rocks from drill cuttings retrieved from the Pohang EGS. The drill cuttings obtained from 3256 to 3911 m contained “mud balls,” which showed a clay matrix with foliation and a cataclastic texture, indicating a typical fault gouge or breccia. Furthermore, the mud ball samples retrieved from depths of 3256 m and 3260 m contained black fragments. Scanning and transmission electron microscopy revealed that the black fragments consisted of glass-like material, which is indicative of frictional melting during coseismic slip. The presence of these black fragments suggests that at least one seismic event had occurred at the Pohang EGS site prior to the hydraulic stimulation test.

Convolutional variational autoencoder for ground motion classification and generation toward efficient seismic fragility assessment

AbstractThis study develops an end‐to‐end deep learning framework to learn and analyze ground motions (GMs) through their latent features, and achieve reliable GM classification, selection, and generation of simulated motions. The framework is composed of an analysis workflow that transforms and reconstructs GMs through short‐time Fourier transform (STFT), encodes and decodes their latent features through convolutional variational autoencoder (CVAE), and classifies and generates GMs by grouping and interpolating latent variables. A benchmark study is established to confirm the minor difference between original GMs and the corresponding reconstructed accelerograms. The encoded latent space reveals that certain latent variables are directly linked to the dominant physical features of GMs. Resultantly, clustering latent variables using the k‐means algorithm successfully classifies GMs into different groups that vary in earthquake magnitude, soil type, field distance, and fault mechanism. By linearly interpolating two parent latent variables, simulated GMs are generated with consistent class information and matching response spectra. Furthermore, seismic fragility models are developed for a steel frame building and a concrete bridge using different sets of GMs. Using five classified, top‐ranked motions, regardless of recorded or simulated accelerograms, can achieve reasonable and efficient fragility estimates compared to the case that adopts 230 GMs. The proposed deep learning framework addresses two compelling questions regarding seismic fragility assessment: How many GMs are sufficient and what types of motions should be selected.

Estimating seismic vulnerability in West Bengal by AHP-WSM and AHP-VIKOR

West Bengal is situated primarily in the Surma Valley at the foothills of the Himalayas and near the western foreland of the Assam-Arakan Orogenic Belt. Several low to moderate-magnitude earthquakes are felt in the region frequently. In this study, we use integrated multi-criteria decision-making (MCDM) models to assess the seismic vulnerability in West Bengal. Twenty-four parameters that were susceptible to seismicity in the region have been used to evaluate geotechnical, structural, social, and physical vulnerability. The analytical hierarchy process (AHP) model was used to estimate the priorities of the parameters, which was then used to estimate seismic vulnerability using the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and the weighted sum method (WSM). The results reveal that approximately ∼17.81% of the total area and ∼65.36% population may fall under a high to very high-vulnerable zone, causing concerns for planning and disaster mitigation. The receiver operating characteristic curve estimated to validate the results, indicate that the AHP-VIKOR performs better for seismic vulnerability estimation. The results of this study may help various mitigation and planning agencies in identifying earthquake-vulnerable zones and preparing in advance for any potential large magnitude earthquakes that may occur in the region.

Rock physics and machine learning comparison: elastic properties prediction and scale dependency

Rock physics diagnostics (RPD) established based upon the well data are used to deterministically predict elastic properties of rocks from measured petrophysical rock parameters. However, with the recent advances in statistical methods, machine learning (ML) can help to build a shortcut between raw well data and rock properties of interest. Several studies have reported the comparison of rock physics and machine learning methods for the prediction of rock properties, but the scale dependence of the ML models was never investigated. This study aims at comparing the results from rock physics and machine learning models for predicting elastic properties such as bulk density (ρb), P-wave velocity (Vp), S-wave velocity (Vs), as well as Poisson’s ratio (v) and acoustic impedance (Ip) in a well from the Gulf of Mexico (GOM) in two different scale scenarios: the well log and seismic scales. The well data under examination was split into training and testing subsets to optimize and test the developed ML models. The RPD approach was also used to validate and compare the accuracy of predicted elastic properties. Backus averaging was later applied to upscale the well data to the seismic scale to examine the scale dependence and prediction accuracy of aforementioned physics-driven and data-driven approaches. Results show that RPD and ML methods provided consistent results at both well log and seismic scales, suggesting the scale independence of both approaches. Moreover, ML models showed better estimation of rock properties due to their “apparent” match with measured data at both scales compared to the RPD approach where a significant mismatch between measured and predicted rock properties was found in the reservoir section of the well. However, by conducting further quality control of the sonic data, it was found that the measured Poisson’s ratio was extremely high in the gas-saturated interval. Hence, the prediction from ML models in this particular case cannot be trusted as ML models were trained based on poor-quality well data with non-realistic Vs and v values. Such an issue, however, could be identified and corrected using RPD as presented in this study. We demonstrate the importance of incorporating domain knowledge, i.e., rock physics, to check data quality and validate results from data-driven models.

Seismogenic potential and tsunami threat of the strike-slip Carboneras fault in the western Mediterranean from physics-based earthquake simulations

Abstract. Strike-slip fault ruptures have a limited capacity to generate vertical deformation, and for this reason they are usually dismissed as potential destructive tsunami sources. At the western tip of the western Mediterranean, in the Alboran Sea, tectonics is characterized by the presence of large transcurrent fault systems and minor reverse and normal faults in a zone of diffuse deformation. The strike-slip Carboneras fault is one of the largest sources in the Alboran Sea and therefore with the greatest seismogenic capacity. It is also one of the active structures with higher slip rates in the eastern Betic fault zone and has been proposed as the source of the damaging 1522 (M 6.5; Int. VIII–IX) Almeria earthquake. The dimensions and location of the Carboneras fault imply a high seismic and tsunami threat. In this paper we present tsunami simulations from seismic sources generated with physics-based earthquake simulators. We have generated a 1 Myr synthetic seismic catalogue consistent on 773 893 events, with magnitudes ranging between Mw 3.3 and 7.6. From these events we have selected those sources producing a potential energy capable of generating a noticeable tsunami, those sources being earthquakes with magnitudes ranging from 6.71 to 7.62. The Carboneras fault has the capacity to generate locally damaging tsunamis; however, on a regional scale its tsunami threat is limited. The frequency–magnitude distribution of the generated seismic catalogue reflects the variability of magnitudes associated with the rupture of the entire fault, departing the upper limit from the classical Gutenberg–Richter potential relation. The inter-event time for the maximum earthquake magnitudes is usually between 2000 and 6000 years. The use of physics-based earthquake simulations for tsunamigenic sources allows an in-depth characterization of the scenarios, allowing a qualitative leap in their parametrization.

Seismicity parameters of Nepal by cumulative slope point change method

Nepal is one of the most seismically active countries with several seismic sources that are capable of generating moderate to high magnitude earthquakes. In this study, earthquake catalogue was developed by considering six major source zones. The completeness time period was determined by performing sensitivity analysis for various magnitude bin widths and time intervals. The seismicity parameters were computed by the least square method (LSM) and maximum likelihood method (MLM). LSM was based on Gutenberg Richter’s scale relationship, whereas for the maximum likelihood method, it was performed by a new method called cumulative slope point change method (CSPCM). The CSPCM was introduced to account the drawbacks of maximum curvature method (MCM), especially for bulk number of data. Furthermore, paper compares the LSM and the MLM for the calculation of seismicity parameters. The comparison shows that the proposed CSPCM based on MLM is reliable in terms of theoretical and analytical way as compared to LSM and MCM.

Implementation of Bayesian Simulation for Earthquake Disaster Risk Analysis in Indonesia based on Gutenberg Richter Model and Copula Method

Indonesia is a country prone to earthquakes because it is located in the Pasific ring of fire area. The earthquakes caused a lot of damages and casualties. In this paper, we use Bayesian Simulation on Gutenberg Richter model and Copula method to estimate the risk parameters of earthquake, specifically the probability and the recurrence (return) period of an earthquake occurrence in Indonesia. Those risk parameters are estimated from dependence structure of frequency and magnitude of earthquakes. The dependence structure can be determined by using Gutenberg Richter model and Copula method. The Gutenberg Richter model is a model based on linear regression used to determine dependence structure, while the Copula method is a statistical method used to determine dependence structure that ignores linearity and normality assumptions of data. Bayesian Simulation is a method used to estimate parameters based on simulation. The data used is an annual data of frequency and magnitude (magnitude ≥ 4 Richter Scale) of earthquakes occur in Indonesia for 4 years from Meteorological, Climatological, and Geophysical Agency of Indonesia. There are several steps of analysis to be performed: firstly, we perform regression analysis of frequency and magnitude of the earthquakes to determine Gutenberg Richter Model; secondly, we perform Copula analysis; thirdly, we estimate probability and the recurrence (return) period of an earthquake occurrence using Bayesian Simulation based on the result of step one and two. The result indicates Bayesian Simulation can estimate risk parameters very well.

Observed hydrological changes associated with active tectonic blocks before three consecutive earthquakes in Qinghai, China

In the past 2 years, three earthquakes of magnitude 6.0 and above occurred consecutively in Qinghai province, China, i.e., the 22 May 2021 Ms7.4 Maduo, 8 January 2022 Ms6.9 Menyuan, and 26 March 2022 Ms6.0 Delingha earthquakes. The hydrological observation instruments set up by the China Earthquake Administration allow us to study the dynamic processes in the well-aquifer systems during the establishment of criticality. Particularly, the observations played an important role in the prediction of the 8 January 2022 Ms6.9 Menyuan earthquake that was approved by the People’s Government of Qinghai province. This work presents the hydrological data recorded by 7 stations to show the short-term anomalies before these earthquakes. To explore the performance of the hydrological observations in detecting earthquakes that occurred on different active tectonic blocks, we calculate the relative amplitudes of the pre-seismic changes. Results indicate that markedly pre-seismic change is found if the observation station and the detection earthquake are on the same block, and moderate change is found if they are on the adjacent blocks, while the precursor is hard to be identified if they are on the separated blocks. The variations in the hydrological responses may be caused by the strength weakening (or dilatancy) of source media. And the increased volumes in the crust can be evidenced by the changes in the geodetic time series in the same neighborhoods and during the same period, augmenting stress loading between the blocks.

Seismoacoustic Signatures Observed During a Long-Term Deployment of Infrasound Sensors at the Nevada National Security Site

ABSTRACT Earthquakes have repeatedly been shown to produce inaudible acoustic signals (<20 Hz), otherwise known as infrasound. These signals can propagate hundreds to thousands of kilometers and still be detected by ground-based infrasound arrays depending on the source strength, distance between source and receiver, and atmospheric conditions. Another type of signal arrival at infrasound arrays is the seismic induced motion of the sensor itself, or ground-motion-induced sensor noise. Measured acoustic and seismic waves produced by earthquakes can provide insight into properties of the earthquake such as magnitude, depth, and focal mechanism, as well as information about the local lithology and atmospheric conditions. Large earthquakes that produce strong acoustic signals detected at distances greater than 100 km are the most commonly studied; however, more recent studies have found that smaller magnitude earthquakes (Mw<2.0) can be detected at short ranges. In that vein, this study will investigate the ability for a long-term deployment of infrasound sensors (deployed as part of the Source Physics Experiments [SPE] from 2014 to 2020) to detect both seismic and infrasonic signals from earthquakes at local ranges (<50 km). Methods used include a combination of spectral analysis and automated array processing, supported by U.S. Geological Survey earthquake bulletins. This investigation revealed no clear acoustic detections for short range earthquakes. However, secondary infrasound from an Mw 7.1 earthquake over 200 km away was detected. Important insights were also made regarding the performance of the SPE networks including detections of other acoustic sources such as bolides and rocket launches. Finally, evaluation of the infrasound arrays is performed to provide insight into optimal deployments for targeting earthquake infrasound.

An Integrated Uncertainty Quantification Framework for Probabilistic Seismic Hazard Analysis

Probabilistic seismic hazard analysis (PSHA) is a tool to quantify the annual rate of exceedance of seismic intensity measure at a location of interest. The present study investigates the uncertainty embedded in three PSHA parameters: magnitude of earthquake, source-to-site distance, and corresponding ground shaking. Although previous researchers have worked to quantify the uncertainty in these hazard parameters, the source-to-site distance uncertainty in PSHA has not been explicitly addressed to acquire more accurate and reliable seismic hazard estimates. This study develops an uncertainty quantification framework for PSHA considering source-to-site distance uncertainty, including interdependency between hazard parameters. To capture this uncertainty, an approach of considering grids in the area seismic source zone is adopted using a geographic information system (GIS) and implementing Latin hypercube sampling (LHS) to quantify the uncertainty of the occurrence of an earthquake at any distance from the site. The interaction of magnitude and source-to-site distance has been investigated using copula theory. As a part of practical implementation, PSHA of Silchar city in Northeast India is taken into consideration due to its tectonic features and high seismicity. This work applies two processes to quantify uncertainty: (1) considering the center point of a grid, and (2) stratified sampling of points within a grid by LHS. The results reveal that on applying LHS (with uncorrelated random events), the hazard increases due to discretization of the area sources, and robust consideration of uncertainty. While, in case of correlated random events, the hazard levels are relatively low. The results are being subjected to verification and are compared with the previous studies to ensure fulfilling specifications and its intended purpose. Structural designers and decision makers can utilize the developed PSHA framework to plan and build new projects and to evaluate the seismic risk of existing infrastructure in a city.

Liquefaction severity assessment for the Anutapura Medical Center Area of Palu-Indonesia

A liquefaction hazard assessment for a particular location is important to reduce disasters that may occur in the future. The liquefaction severity assessment for the Anutapura Medical Centre region of Palu-Indonesia was carried out on various pairs between peak ground surface acceleration and earthquake magnitude. It was conducted by first determining the factor of safety for each existing soil layer and applying the formulation prepared by Iwasaki to measure Liquefaction Potential for the entire soil column. The possible severity of liquefaction at these locations was compared based on the limits set by the respective theories of Iwasaki and Papathanassiou. The results showed that the Anutapura Medical Centre location was very susceptible to liquefaction events with a very high potential level of ground surface damage based on the Iwasaki boundary line. The above-mentioned severity by Papathanassiou limits ranges from high to very high.

Preliminary System for the Estimation of Peak Ground Acceleration Distribution in Metropolitan Lima and Callao: Application in Recent Seismic Events

The conjunction of seismic records and site effect parameters can lead to the adequate estimation of seismic indexes in urban areas. In this regard, this study uses the map of soil amplification factors obtained in previous studies and the availability of time history waveforms at different locations throughout Metropolitan Lima and Callao to estimate the geospatial distribution of maximum values of horizontal acceleration after the occurrence of earthquakes. Results for three earthquakes of intermediate magnitude and distinct epicenter locations are publicly available in an online system created within the framework of this study (Amaru Peru) and showed that amplified motions could be mainly observed in the lowlands of populated slopes as well as in the eolian sandy deposits.

Earthquake spatial probability and hazard estimation using various explainable AI (XAI) models at the Arabian peninsula

Earthquakes are the most destructive natural hazards because of their adversely severe impacts on urban areas. Earthquakes affect people's lives and properties, thus captivating the extensive attention of seismologists. Carrying out probability and hazard assessment for the prevention, and reduction of mega-events and recovery will be of great significance in affected areas. Given that limited studies have attempted to estimate earthquake Spatial Probability Assessment (SPA) in the Arabian Peninsula, this study aims to evaluate the SPA and Earthquake Hazard Assessment (EHA). This study implements and evaluates various machine learning and explainable-AI (XAI) techniques for the estimation of SPA and EHA in the Arabian Peninsula, explores the contribution and highlights the importance of different factors in the development of AI-based models. A total of twelve factors ranging from seismological to geophysical factors were evaluated. Two machine learning models namely Light Gradient Boosting Machine (LightGBM) and deep Recurrent Neural Networks (RNN) along with three XAI approaches (i.e, Smart predictor, Smart Explainer and Local Interpretable Model-Agnostic Explanation (LIME) model) were investigated. Results of the comparative earthquake SPA estimation demonstrated that the accuracy of 89% and 87% were achieved by LightGBM and RNN models. Moreover, the results of the XAI models show that the Smart Predictor provides better spatial outputs than the other evaluated XAI models. The stable factors identified by Smart Predictor were magnitude variation and earthquake frequency whereas the important factors were magnitude variation, earthquake frequency, depth variation, and seismic gap. Collectively, results of SPA show that, the Gulf of Aden, Red Sea, Iran, and Turkey are falling under a very-high SPA index (0.991–1). Correspondingly, Gulf areas, coastal areas of Saudi Arabia, and areas in the Zagros fault and Anatolian fault zone fall under a very-high hazard zone. This research could support planners, and decision-makers for emergency planning, infrastructure development, and reconstruction projects.

Seismicity Based Maximum Magnitude Estimation of Subduction Earthquakes in Peru

In seismic design, intensity parameters that represent seismic demand are commonly used. A probabilistic seismic hazard assessment is an accurate way of assessing seismic demand, based on a set of parameters that represent the seismicity of a region. However, because some regions lack sufficient information, the selection of these parameters can be controversial. In Peru, selecting a maximum earthquake magnitude (Mmax) for regional seismic hazard assessments has proven to be a challenging task due to the limited available information concerning of large-magnitude events. This study evaluated the Mmax for subduction earthquakes using scaling relationships, empirical evidence, and the extreme value statistics (Kijko and Bayesian) approach. The seismic catalog was updated to February 2022 and divided into 19 subduction seismic sources (5 interface and 14 intraslab). The results showed that the obtained Mmax are within the range of Mw 8.7–9.0 for the interface and Mw 7.6–8.1 for the intraslab sources, which unlike the Mmax values established in previous regional seismic hazard assessments, are more consistent with the historical and instrumental seismicity and rupture models.