Seismicity Patterns Research Articles

A Methodology to Evaluate the Real-Time Stability of Submarine Slopes under Rapid Sedimentation

Rapid sedimentation is widely recognized as a crucial factor in initiating the instability of submarine slopes. Once the slope fails, the subsequent landslide poses a significant threat to the safety of underwater infrastructures and potentially leads to severe damage to seabed pipelines, offshore foundations, and oil and gas exploitation wells. However, there is currently a lack of numerical methods to effectively assess the real-time stability of submarine slopes under rapid sedimentation. This study firstly employs a calibrated finite element (FE) model-change approach to reproduce the rapid sedimentation processes and proposes a concise method to calculate the safety factors for the real-time stability of sedimenting submarine slopes. Further, a parametric analysis is carried out to evaluate the effect of varying sedimentation rates on slope stability, and the critical sedimentation rate is numerically solved. Moreover, the effect of seismic events with different occurring times on the stability of rapidly sedimenting slopes is investigated in depth, and the most critical seismic loading pattern among various acceleration combinations is achieved. The results indicate that the presence of weak layers during sedimentation is a critical factor contributing to slope instability. The introduced rate of decrease in the safety factor proves valuable in assessing slope safety over a specific period. As the occurrence time of seismic events is delayed, the seismic resistance of the slope decreases, increasing the likelihood of shallower sliding surfaces. The findings offer insights into the mechanisms by which rapid sedimentation influences the stability of submarine slopes and provide valuable insights for predicting the potential instability of rapidly sedimenting slopes under specific seismic activity levels.

Fairweather transform boundary Oligocene to present orogenesis: Fairweather Range vertical extrusion and rotation of the Yakutat microplate at ca. 3 Ma

Oblique-slip along transform fault boundaries is often partitioned between a strike-slip system and thrust faults that accommodate contraction. However, topography along the Yakutat-North American transform (Fairweather fault), is asymmetric with low-terrain above active thrusts on the western, Yakutat side of the transform and high topography on the continental side with peaks >4500 m (Mount Fairweather: 4671 m) to the west of the Border Ranges fault, limited recorded earthquakes >M4, and no apparent reverse faults to generate the highest terrain. In this study we compile, for the first time, published U-Pb zircon, 40Ar/39Ar and K-Ar (hornblende, muscovite, and biotite) and U-Th/He and fission-track (zircon and apatite) bedrock ages (109) from 75 samples to investigate the exhumation history of the Fairweather Range region, complemented by a published detrital sample (ZFT and AFT) and 13 new 40Ar/39Ar (hornblende, biotite, and K-feldspar) ages on 9 bedrock samples from both sides of the Fairweather fault. Additionally, we examined published seismicity and geodetic data of the Fairweather region and assessed if plate paleo-vectors correlate with the cooling history of the Fairweather Range. Cooling age, seismic, and block-motion patterns indicate the Fairweather Range has been vertically extruded between the Fairweather and the Border Ranges faults as a coherent block since ca. 25 Ma. The pre-6 Ma Pacific plate motion (N30°W) aligns with the N33°W strike of the Fairweather Fault whereas a hypothetical pre-6 Ma Yakutat microplate paleo-vector of (N39°W) does not: indicating a post-6 Ma timing for Yakutat microplate counter-clockwise rotation (9°). We infer that rotation and impingement of the Yakutat microplate along the Fairweather fault at ca. 3 Ma led to the development of the Fairweather restraining bend and increased cooling rates. The resultant thickened Fairweather welt and the ∼30 km thick southeast end of the Yakutat microplate compounded double-indenter tectonics into Alaska's southeast convergent corner

Coherent Seismic Anisotropy Pattern Across Southern Africa Revealed by Shear Wave Splitting Measurements

AbstractWe report new PKS, SKS, and SKKS splitting measurements for 88 seismic stations in Namibia, Botswana, South Africa, and Mozambique. When combined with measurements from previous studies, the ensemble of measurements shows a fairly uniform NNE to NE (∼41° on average) fast‐polarization direction (ϕ) and delay time (δt) (∼0.7 s on average) across the entire southern African subcontinent. It is difficult to attribute the NNE‐NE ϕ direction to just one source of anisotropy either within the lithospheric or sublithospheric mantle. We instead propose the observed anisotropy pattern could result from a combination of several sources that together give rise to a pervasive NNE‐NE ϕ direction; (a) fossil anisotropy in the lithospheric mantle resulting from the Neoproterozoic collision of the Congo and Kalahari cratons to form the Damara Belt, (b) movement of the African plate over the asthenosphere, and (c) flow in the upper mantle induced by the African Superplume. In addition, a contribution from anisotropy in the lowermost mantle in the vicinity of the African large low shear velocity province cannot be ruled out.

Seismic Constraints on Hydrothermal Circulation and Magmato‐Tectonic Interactions Beneath Lucky Strike Volcano, Mid‐Atlantic Ridge

AbstractThe Lucky Strike volcano is the central edifice of the Lucky Strike segment on the slow spreading Mid‐Atlantic Ridge. The volcano summit hosts one of the largest known deep‐sea hydrothermal fields, and overlies an axial magma chamber (AMC) whose summit reflector lies 3–3.8 km beneath the seafloor. We present a 12‐year microearthquake catalog beneath the volcano, which we constructed using data collected from 2007 to 2019 as part of the EMSO‐Azores observatory. The catalog reveals continuous low magnitude seismicity (85% with ML < 0), focused mainly north‐northwest of the hydrothermal field and 0–2 km above the AMC reflector. Focal mechanisms estimated only from the 2010–2011 deployment show a mixture of thrust, normal and strike/slip faulting mechanisms. We propose that the observed microearthquake activity is due to thermal cracking, tectonic cracking, and possible small dike injections occurring at the base of hydrothermal downflow zones. We document a ∼800 m eastward shift of the seismicity sometime during a catalog gap from June 2013 to April 2015, accompanied by a change in the seismicity pattern from a patch just above the AMC to a more vertically aligned structure. We also identify several higher seismicity periods, often including relatively large magnitude events (ML > 0.8). We interpret the spatio‐temporal variations in seismicity patterns and the correlation between larger magnitude events and higher seismicity periods to indicate that the hydrothermal domain is a dynamic interface, where the variations are caused by changes in permeability and/or temperature induced by tectonic and small episodic melt injections.

Insights into Temporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations from the TXAR Catalog (2009–2016)

Abstract The Texas Seismological Network (TexNet) has enabled real-time monitoring of induced earthquakes since 2017. Before 2017, location uncertainties and temporal gaps in seismic data obscure correlations across Texas between seismicity and saltwater disposal or hydraulic fracturing. Depth biases also complicate linking anthropogenic stress changes to faults. We relocate 73 M 1.5+ earthquakes from the TXAR catalog (2009–2016) relative to the centroid of a calibrated core cluster consisting of 116 earthquakes from the TexNet catalog post-2020, in the southern Delaware basin south of the Grisham fault zone. Hypocentroidal decomposition relocation reduces spatial uncertainties of the TXAR events to <5 km and provides updated depths. The core cluster has uncertainties less than <300 m and depth constrained from near-source stations and S−P differential times. The majority of relocated TXAR events indicate the triggering of northwest-trending faults at a mean depth of 1 km below sea level, suggesting a causal connection with shallow saltwater disposal and consistency with post-2017 seismicity. Spatiotemporal patterns of pre-2017 seismicity and saltwater disposal highlight initial triggering via pore-pressure stress perturbations from nearby low-volume injections and later from southeastward pressure diffusion along permeable anisotropic conduits and fracture zones. The comparison between pre- and post-2017 seismicity indicates shallow fault reactivation through similar triggering mechanisms since 2009.

Teleseismic Indication of Magmatic and Tectonic Activities at Slow- and Ultraslow-Spreading Ridges

Magmatic and tectonic processes in the formation of oceanic lithosphere at slow–ultraslow-spreading mid-ocean ridges (MORs) are more complicated relative to faster-spreading ridges, as their melt flux is overall low, with highly spatial and temporal variations. Here, we use the teleseismic catalog of magnitudes over 4 between 1995 and 2020 from the International Seismological Center to investigate the characteristics of magmatic and tectonic activities at the ultraslow-spreading Southwest Indian Ridge and Arctic Gakkel Ridge and the slow-spreading North Mid-Atlantic Ridge and Carlsberg Ridge (total length of 14,300 km). Using the single-link cluster analysis technique, we identify 78 seismic swarms (≥8 events), 877 sequences (2–7 events), and 3543 single events. Seismic swarms often occur near the volcanic center of second-order segments, presumably relating to relatively robust magmatism. By comparing the patterns of seismicity between ultraslow- and slow-spreading ridges, and between melt-rich and melt-poor regions of the Southwest Indian Ridge with distinct seafloor morphologies, we demonstrate that a lower spreading rate and a lower melt supply correspond to a higher seismicity rate and a higher potential of large volcano-induced seismic swarms, probably due to a thicker and colder lithosphere with a higher degree of along-axis melt focusing there.

Time-dependent magnetic anomaly variations in Turkey and Greece using swarm satellites: A comprehensive precursory multi-track analysis of M≥6 earthquakes from 2017 to 2020

This article presents a comprehensive multi-track analysis aimed at investigating time-dependent magnetic anomaly variations in Turkey and Greece resulting from five M ≥ 6 earthquakes occurring between January 2017 and October 2020. The study utilizes data from the Swarm satellites and employs various analytical techniques, including first-time derivative calculations, long-wavelength component removal, and moving RMS filters, to construct cumulative anomaly count graphs for each magnetic component. By eliminating the influence of the external magnetic field, we focus solely on the magnetic components (X, Y, Z, and F). The changes in magnetic anomalies before, during, and immediately after the earthquakes are carefully interpreted. The research systematically analyses the Plomari (June 12, 2017), Kos (July 20, 2017), Doğanyol (January 24, 2020), Özalp (February 23, 2020), and Karlovasion (October 30, 2020) Earthquakes, considering the mainshock and aftershock responses for each magnetic component. The findings reveal intriguing patterns, most notably the emergence of distinctive S-shaped anomalies in various magnetic components. Moreover, the study highlights the influence of significant earthquakes, such as Plomari and Kos, on cumulative anomalous tracks, providing critical insights into magnetic field behavior. Additionally, the study investigates the potential impact of the Özalp Earthquake on the magnetic anomalies observed in the Doğanyol earthquake and explores the relationship between the occurrence of the Doğanyol Earthquake and the precursory anomalies leading to the mainshock of the Özalp Earthquake. Furthermore, the analysis of the Karlovasion earthquake identifies magnetic anomalies preceding the mainshock. Finally, the research subjectively assesses the lateral and vertical scores of the five earthquakes to capability to represent the S-shaped pattern. The Plomari and Kos earthquakes score third and fourth highest, respectively, while the Doğanyol Earthquake achieves the top score and distinctly displays an S-shaped pattern, indicating its significance in the magnetic field behavior. The Özalp Earthquake scores second-best with a distinct S-shaped pattern, while the Karlovasion Earthquake receives the lowest score with no evident S-shaped pattern. Additionally, vertical scores indicate the Y and Z components with more pronounced S-shaped patterns, while the X and F components receive lower scores. Notably, the onshore Doğanyol Earthquake exhibits the most pronounced S-shaped pattern across all magnetic components, attributable to its shallow strike-slip fault at 10 km depth, while the offshore Karlovasion Earthquake, generated by a normal fault at 21 km depth, displays the most scattered pattern, providing valuable information on the influence of tectonic settings on magnetic field behaviour. In conclusion, this study provides valuable insights into the magnetic anomalies associated with seismic events in the region, significantly contributing to a better understanding of earthquake precursors and seismicity patterns, and enhancing seismic risk assessment and mitigation strategies.

Peak Acceleration Distribution on the Surface of Reef Islands under the Action of Vertical Ground Motion

This paper adopts the fluid–structure coupling algorithm based on the acoustic fluid element, the fluid dynamic artificial boundary, and the consistent viscoelastic artificial boundary of solid media to establish a finite element model of the dynamic interaction of the reef-island–seawater system. Then, a numerical simulation of the seismic response of the reef-island site is carried out to study the seismic ground motion distribution patterns of the reef–seawater site and the reef-island–lagoon site. The innovation of this article is that the influence of reef–island topography and fluid–structure coupling is considered in the analysis when vertical ground motion is input. The results show that the slope angle of the bottom layer has a significant influence on the peak ground acceleration distribution and peak size on the island slope surface and the reef platform. For high-frequency input motion, a smaller reef platform width will induce a larger peak acceleration response on the reef platform. Seawater has a significant suppressive effect on vertical ground acceleration. The more high-frequency components of the input bedrock motion, the more obvious this suppression effect will be. The existence of the lagoon will amplify the maximum peak acceleration on the reef platform. According to the calculation results, lagoon terrain can amplify the maximum horizontal and vertical peak accelerations on the reef platform by about 19 and 6 times relative to the free-field results, respectively.

Substructure-based design method for vertically irregular steel buckling-restrained braced frame structures

Buckling-restrained braces (BRBs) can mitigate the uneven inter-story drifts of vertically irregular steel frame structures during earthquakes, preventing unfavorable seismic forces and failure patterns. However, the structural lateral displacement profile of vertically irregular steel buckling-restrained braced frame structures (VSBFSs) can hardly be predicted; hence, parameter tuning and iterative designing are commonly required in conventional direct displacement-based design (DDBD) methods, which strongly rely on the experience of designers and consumes a considerable amount of time. This study proposes a substructure-based design (SBD) method developed explicitly for VSBFSs. In this method, the substructures of every story of a VSBFS are separated from the entire structure and individually analyzed in the subsequent procedures. The boundary conditions of the substructures are initially simplified as hinges, and an explicit equation of BRB stiffness and inter-story drift for the substructures is established. An iterative calculation procedure is consequently applied to correct the equation by considering the influence of the constraints of adjacent stories. In this manner, the inter-story drifts of all the stories of the entire structure uniformly converge to the predetermined targets, in which the demand of BRBs for each story is also determined. Compared with conventional DDBD methods, the proposed method does not assume the structural lateral displacement profile and is not based on experience, yielding relatively economical and safe design outcomes in a timely manner. The proposed SBD method was first validated by comparing it with existing elastic and elastoplastic design methods through a 15-story VSBFS example. Then, the applicability of the SBD method was further explored through four 6-story VSBFSs with different plan layout reductions and two VSBFSs with heights of 20 stories and 30 stories. Finally, the SBD method was applied to redesign the BRBs of a vertically irregular thermal power plant model (an example of a VSBFS) in a shaking table test.

The 2022 Goesan earthquake of the moment magnitude 3.8 along the buried fault in the central Korean Peninsula

AbstractOn October 28, 2022, a moment magnitude (Mw) 3.8 earthquake occurred in Goesan, South Korea, typically characterized as a stable continental region. Herein, we analyze 42 earthquakes, including the Mw 3.8 earthquake, the largest foreshock (Mw 3.3), which preceded the mainshock by 17 s, and the largest aftershock (Mw 2.9). The primary aim of this study is to identify interactions among the seismic events. To this end, we utilized the permanent seismic networks with the closest station at 8.3 km from the epicenter, and the temporary network deployed eight hours after the mainshock’s occurrence. Relocation results delineate that the mainshock occurred at the southeastern tip of the hypocenter distribution of three foreshocks, trending west-northwest–east-southeast. The aftershocks form an overall spatially diffused seismic pattern that propagates toward both ends of the inferred lineament in the downdip direction. The rupture directivity of the mainshock, along with waveform similarity across the mainshock and foreshocks, confirms the inferred geometry, corresponding well with the focal mechanisms of the mainshock and the largest foreshock. We demonstrate that the change in Coulomb failure stress (ΔCFS) by the largest foreshock was positive where the mainshock occurred and that the mainshock generated ΔCFS capable of triggering the propagation of the aftershocks.

On the role of poroelastic stressing and pore pressure diffusion in discrete fracture and fault system in triggering post-injection seismicity in enhanced geothermal systems

Injection-induced seismicity has become one of the most critical challenges for the widespread deployment of Enhanced Geothermal Systems (EGS). In particular, some EGS development projects have led to large, damaging earthquakes that unexpectedly occurred far off the stimulated reservoir region and, in particular, after stopping fluid injection. Yet, the causative mechanisms of these seismicity patterns remain highly elusive. Here, we identify a combination of mechanisms that could explain delayed seismicity in EGS sites by conducting fully-coupled hydromechanical simulations of the hydraulic stimulation of a naturally-fractured granitic reservoir. The model comprises a sparse network of long, variably-oriented fractures interacting with a nearby, critically-oriented fault. The results show that the presence of fractures introduces notable nonlinearities in the flow field and rock deformation and significantly expands the rock volume affected by fluid injection. First, the stimulated fracture network provides highly-permeable conduits for communicating elevated pore pressure over long distances. Second, the anisotropic expansion of fractures generates shear stress that is transmitted almost instantaneously across the reservoir. The pore pressure and stress perturbations can not only cause slip along fractures, inducing (micro)seismicity during injection, but also affect the stability of nearby faults, which may not necessarily be pressurized during injection. The transferred poroelastic stresses can increase or decrease the slip tendency along different fault segments. However, the fault may reactivate only after several months following injection when a progressive pore pressure diffusion modulated by the transient fault permeability evolution brings a critically-stressed fault segment to failure conditions. We also find that the spatiotemporal evolution of seismicity depends largely on the nearby fault orientation, hydromechanical properties, and hydraulic connection with the fracture network, as well as the initial state of stress. We conclude that accurate subsurface characterization and continuous monitoring during and after injection should allow for managing the risks posed by injection-induced seismicity and safely unlocking the immense potential for clean and sustainable geothermal energy.

A Practical Approach to Automatic Earthquake Catalog Compilation in Local OBS Networks Using Deep-Learning and Network-Based Algorithms

Abstract In land-based seismology, modern automatic earthquake detection and phase picking algorithms have already proven to outperform classic approaches, resulting in more complete catalogs when only taking a fraction of the time needed for classic methods. For marine-based seismology, similar advances have not been made yet. For ocean-bottom seismometer (OBS) data, additional challenges arise, such as a lower signal-to-noise ratio and fewer labeled data sets available for training deep-learning models. However, the performance of available deep-learning models has not yet been extensively tested on marine-based data sets. Here, we apply three different modern event detection and phase picking approaches to an ∼12 month local OBS data set and compare the resulting earthquake catalogs and location results. In addition, we evaluate their performance by comparing different subcatalogs of manually detected events and visually revised picks to their automatic counterparts. The results show that seismicity patterns from automatically compiled catalogs are comparable to a manually revised catalog after applying strict location quality control criteria. However, the number of such well-constrained events varies between the approaches and catalog completeness cannot be reliably determined. We find that PhaseNet is more suitable for local OBS networks compared with EQTransformer and propose a pick-independent event detection approach, such as Lassie, as the preferred choice for an initial event catalog compilation. Depending on the aim of the study, different schemes of manual repicking should be applied because the automatic picks are not yet reliable enough for developing a velocity model or interpreting small-scale seismicity patterns.

Rupture Jumping and Seismic Complexity in Models of Earthquake Cycles for Fault Stepovers with Off-Fault Plasticity

ABSTRACT Fault stepovers are prime examples of geometric complexity in natural fault zones that may affect seismic hazard by determining whether an earthquake rupture continues propagating or abruptly stops. However, the long-term pattern of seismicity near-fault stepovers and underlying mechanisms of rupture jumping in the context of earthquake cycles are rarely studied. Leveraging a hybrid numerical scheme combining the finite element and the spectral boundary integral methods, FEBE, we carry out fully dynamic simulations of sequences of earthquakes and aseismic slip for both compressive and tensile stepovers with off-fault plasticity. We consider a rate-and-state friction law for the fault friction and pressure-sensitive Drucker–Prager plasticity for the off-fault bulk response. We observe that the accumulation of plastic deformation, an indication of off-fault damage, is significantly different in the two cases, with more plastic deformation projected in the overlapping region for the tensile stepover. The seismic pattern for a tensile stepover is more complex than for a compressive stepover, and incorporating plasticity also increases complexity, relative to the elastic case. A tensile stepover with off-fault plasticity shows rupture segmentation, temporal clustering, and frequent rupture jumping from one fault to another. These results shed light on possible mechanisms of rupture jumping in fault stepovers as well as the long-term evolution of the fault zone.

Spatio-Temporal Variation Seismicity Pattern in East Java Between 2002 and 2022 Based on the b-value and Seismic Quiescence z-value

East Java is one of the seismically active regions in part of the Sunda Arc whose seismicity pattern must be studied. Therefore, a spatio-temporal analysis of seismicity between 2002 - 2022 in East Java has been conducted. This analysis aims to investigate the seismotectonic conditions in the East Java region over the last twenty years, especially the seismicity patterns associated with significant earthquakes in East Java. The analysis was carried out by quantifying the spatial and temporal variations of seismicity parameters using b-value and z-value. The used data used are earthquake data from the USGS earthquake catalog during 2002 - 2022 in the East Java region. The analysis was performed using the ZMAP 6.0 program code using the maximum likelihood method. The results of the temporal analysis of b-values show that almost all significant earthquakes in East Java were characterized by a decrease in b-values of up to 0.2. Spatially, the significant earthquake on 10 April 2021 was associated with a low b-value (0.9). Temporal z-value analysis shows that the significant East Java earthquake was preceded by a quiet period (positive z-value) between 4 - 5 years (2003 - 2008 and 2012 - 2016). Spatial analysis of the z-value in the last 7 shows an increase in the z-value before the significant earthquakes occurred on 16 July 2019, 18 March 2020, and 10 April 2021. Therefore, the results of this study indicate that the b-value and z-value potentially can be used as precursors of the significant East Java earthquake. HIGHLIGHTS This is the first time a correlation of b-value and z-value as seismicity parameters has been carried out in East Java, Indonesia, as an attempt to determine the significant earthquake precursors The significant earthquakes in East Java during 2002 - 2022 were marked by a decrease in b-value of up to 0.2 The quiet period in East Java lasted between 4 - 7 years, i.e. 2002 - 2009 and 2012 - 2016, characterized by a positive z-value peaked until +3.5 The z-value at the location of a significant earthquake epicenters show a positive value 2 - 3 years prior to the main quake GRAPHICAL ABSTRACT

Applying Geostatistics to Understand Seismic Activity Patterns in the Northern Red Sea Boundary Zone

A comprehensive geostatistical analysis was conducted on a dataset comprising 24,321 seismic events in the Red Sea region, spanning from 1997 to 2020. This analysis involved the creation of a new seismic activity database, incorporating data from both Egyptian and Saudi Seismic Networks. This enriched database provided a robust foundation for a detailed examination of the seismic patterns and activities in the region. Utilizing geographic information systems and various spatial analytic methods, it identifies seismic patterns and tectonic influences. The findings reveal significant seismic clustering along the Central Red Sea axis, indicative of active rifting between the Nubian and Arabian plates. The study demonstrates spatial autocorrelation in seismic activities, with high-high clusters marking zones of elevated seismicity. Kernel Density Estimator analyses highlight concentrated seismic activity in the Gulfs of Aqaba and Suez. Higher magnitude events are shown to localize in areas of greater tectonic stress, aligning with known geological features. This research provides critical insights into the seismic dynamics of the Red Sea, showcasing the effectiveness of geostatistical techniques in analyzing seismic data in tectonically active regions.

Monitoring the Workflow of Earthquake by Using Machine Learning

This study explores the innovative application of machine learning techniques in monitoring and analyzing the workflow associated with earthquakes. The primary objective is to enhance the accuracy and efficiency of earthquake detection, prediction, and response mechanisms the development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. This paper proposes a machine learning-based approach to monitor seismic workflows efficiently. We apply various Machine Learning algorithms, including deep learning neural networks and decision trees, to process and interpret vast amounts of seismic data. These algorithms were trained on historical earthquake records to identify patterns and anomalies that precede seismic events. Our approach not only focused on the detection of seismic activities but also on the prediction of potential aftershock locations and magnitudes. Earthquake monitoring and response systems play a crucial role in mitigating potential damages and ensuring rapid response to seismic events. Traditional methods rely on sensor networks and manual analysis, often leading to delays in detection and response. The system leverages real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. By employing deep learning models, such as convolution neural networks (CNNs) and recurrent neural networks (RNNs), the system can detect and classify seismic activities accurately. Additionally, anomaly detection techniques enable the identification of unusual seismic patterns that might indicate impending earthquakes or aftershocks. The system integrates geographical information systems (GIS) to map the seismic activities spatially, aiding in the visualization and understanding of seismic patterns across regions. The machine learning models are trained on historical seismic data to enhance their predictive capabilities and adaptability to varying seismic behaviors. This proposed system offers a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. Its ability to continuously learn from incoming data ensures adaptability to changing seismic behaviors, thereby improving overall efficiency and reliability in earthquake monitoring workflows.

Are Foreshocks Fore‐Shocks?

AbstractForeshocks are spatially clustered seismic events preceding large earthquakes. Since the dawn of seismology, their occurrence has been identified as a possible mechanism leading to further crustal destabilization, hence, to major failures. However, several cases occurred without any previous anomalous seismic activity, so that the hypothesis of foreshocks as reliable seismic precursors fails to pass statistical tests. Here, we perform an all‐round statistical comparative analysis of seismicity in Southern California to assess whether any differences can be identified between swarms and foreshocks. Our results suggest that extremely variable seismic patterns can forerun mainshocks, even though they tend to be preceded by clusters with more numerous events spread over larger areas than swarms and with a wider range of magnitudes. We provide a physical explanation of such dissimilarity and conclude, despite it, that foreshocks can hardly be reliable short‐term precursors of large earthquakes in California.

Advanced Seismic Data Analysis: Comparative study of Machine Learning and Deep Learning for Data Prediction and Understanding

This study delves into the application of machine learning (ML) and deep learning (DL) techniques for the analysis of seismic data, aiming to identify and categorize patterns and anomalies within seismic events. Using a robust dataset, we applied three distinct clustering approaches: K-Means, DBSCAN, and an Autoencoder-based method, each offering unique perspectives on the data. K-Means clustering provided a fundamental partitioning of the data into five predefined clusters, facilitating the identification of broad seismic patterns. DBSCAN, a density-based clustering algorithm, offered insights into the spatial distribution and density of seismic events, adeptly pinpointing anomalies and outliers that signify unusual seismic activity. The Autoencoder, leveraging deep learning, excelled in capturing complex and non-linear relationships within the data, revealing subtle patterns not immediately apparent through traditional methods. The effectiveness of these clustering techniques was quantitatively evaluated using the Silhouette Score and the Davies-Bouldin Score, alongside visual assessments through PCA and t-SNE for dimensionality reduction. The results indicated that while K-Means provided clear partitioning, DBSCAN excelled in outlier detection, and the Autoencoder offered a balanced approach with its nuanced analysis capabilities. Our comprehensive analysis underscores the significance of employing a multi-methodological approach in seismic data analysis, as each method contributes uniquely to the understanding of seismic events. The insights gained from this study are valuable for enhancing predictive models and improving disaster risk management strategies in seismology. Future research directions include the integration of additional seismic features, validation against larger datasets, and the development of hybrid models to further refine the predictive accuracy of seismic event analysis.

Monitoring the workflow of Earthquake by using Machine Learning

The main goal is to improve the accuracy and efficiency of earthquake detection, prediction and response through automation and, over time, to improve machine learning techniques for earthquake monitoring. However, the application of machine learning to earthquake location problems faces challenges in areas with limited data. This article presents an engine-based approach to monitor earthquake performance. We use a variety of Machine Learning algorithms, including deep learning neural networks and decision trees, for processing and interpret large amounts of seismic data. These algorithms are trained on historical earthquake records to identify patterns and anomalies prior to seismic events. Our approach focuses not only on detecting seismic events, but also on predicting the location and magnitude of earthquakes. Earthquake monitoring and response systems play an important role in reducing potential losses and providing rapid response to seismic events. Conventional methods rely on sensor networks and manual analysis, often with delays in detection and response. The system receives real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. The system can detect and describe seismic events using deep learning models such as convolutional neural networks (CNN) and random neural networks (RNN).In addition, anomaly detection techniques allow the identification of unusual seismic patterns that may indicate impending earthquakes or earthquakes. The system integrates geographic information systems (GIS) to comprehensively map seismic activity, helping to visualize and understand seismic patterns in the region. Machine learning models are trained on historical seismic data to improve their predictive capabilities and adapt to different seismic behaviors. This proposed system provides a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. The ability to continuously learn from incoming data enables adaptation to change in seismic activity, thereby increasing the efficiency and reliability of earthquake monitoring operations.

Tectonic Duets: Self-Similar Approach to Investigate Eastern Anatolian Fault’s Recent Seismicity, with Special Emphasis on the 6 February 2023 Earthquake Doublet

Abstract Earthquake clusters possess profound potential for discerning antecedent seismic cues. This study examines the self-similarity of earthquakes to characterize recent seismic patterns in the prolonged quiescent Eastern Anatolian fault zone (EAFZ). We thoroughly investigate the correlation fractal dimension (Dc) formulated upon the scale-invariant relative clustering in earthquake pattern identification. We provide a comprehensive examination of pre- and postseismicity patterns of the Mw 7.7 Nurdağı-Pazarcık, Mw 7.6 Ekinözü, and Mw 6.7 Elazığ earthquakes, each shattering different segments of the EAFZ. The spatiotemporal fluctuations of Dc suggest the commencement of the preparatory process observed around October 2021 to February 2022 for the 2023 dual catastrophe, whereas in the case of the Mw 6.7 Elazığ, this was witnessed from November 2018. Prior to the 2023 events, low-moderate Dc regions predominated on the Pazarcık segment and Cardak fault. The Pürtürge segment that ruptured with an Mw 6.7 event was within a low Dc area. We identified a consistent relationship between stress levels and Dc for the 2023 twin events and the Mw 6.7 earthquake, with low Dc indicative of high stress. Intriguingly, mainshocks and a substantial proportion of their aftershocks have occurred within areas characterized by low to moderate Dc. Various fault zones like Malatya, Amanos, and Adiyaman are situated in areas with low Dc. The southwestern area of the Amanos segment exhibits clustering, elevated stress levels, and low Dc, followed by the Mw 7.7. Therefore, it is imperative to maintain vigilant monitoring of this region to prevent another disaster.