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The Ionosphere, a crucial region of Earth's atmosphere extending from approximately 50 to 1000 km above the Earth's surface, plays a pivotal role in global communication, navigation, and satellite-based technologies. Among its various parameters, Total Electron Content (TEC) stands out as a key metric, representing the integrated electron density along a specific path through the ionosphere. This research focuses on analyzing TEC variations during Indonesian earthquakes between 2004 and 2024 using a Recurrent Neural Network (RNN) model. The study investigates the ionospheric response to seismic activity, particularly in Indonesia, a region prone to earthquakes due to its location within the Pacific Ring of Fire and complex tectonic setting involving multiple lithospheric plates. The RNN model, designed to predict TEC variations during earthquakes, utilizes data on solar and geomagnetic activity, including the Kp index, solar wind, and geomagnetic storm activity, obtained from the OMNIWEB data centre, along with TEC data from the IONOLAB data servers for the BAKO station in Cibinong, Indonesia. The earthquakes considered for observation include significant events such as the 2004 Indian Ocean earthquake and tsunami, the 2012 Wharton Basin earthquake, and subsequent seismic events up to 2024. Statistical metrics such as Mean Bias Deviation (MBD), Relative Error (REL_E), Absolute Error (ABS_E), and Root Mean Square Error (RMSE) are used to evaluate the predictive capability of the RNN model and compare it with the International Reference Ionosphere (IRI) 2020 model, a widely accepted empirical model for describing ionospheric parameters. The comparison reveals the performance of the RNN model in capturing TEC variations during seismic events, providing valuable insights for earthquake monitoring and early warning systems. [ABSTRACT FROM AUTHOR]

The Epidemic-Type Aftershock Sequence (ETAS) model is currently the most powerful statistical seismicity model that reproduces the general characteristics of earthquake clustering in space and time. However, its application can be hampered by biased parameter estimations related to earthquake catalogue deficiencies, particularly in regions where the spatial coverage of local recording networks is relatively poor. Here, we systematically investigate the possible influences of the effect introduced by data truncation through the choice of the cut-off magnitude (⁠|${{m}_{\rm cut}})$| and missing events due to heterogeneity of the seismic network on ETAS parameter estimates along the East African Rift System (EARS). After dividing the region into six source zones based on rheological and mechanical behaviours, the ETAS model is fitted to the earthquakes within each zone using the Davidon–Fletcher–Powell optimization algorithm. The fits and variations in parameter estimates are compared for each zone to the others and the seismological implications are discussed. We found that some parameters vary as a function of |${{m}_{\rm cut}}$| primarily driven by changes in catalogue size. Additionally, a systematic regional dependency of ETAS parameters is found across source zones. Furthermore, a median heat flow value for each analysed source zone in the EARS is calculated. In contrast to previous findings in other tectonic settings, the results reveal no significant correlations between the crustal heat flows and the ETAS parameters describing earthquake productivity (⁠|${{K}_0}$|⁠) and the relative efficiency of an earthquake with magnitude M to produce aftershocks (⁠|$\alpha $|⁠). Our findings have significant implications for understanding the mechanisms of earthquake interaction and, therefore, provide tight constraints on the model's parameters that may serve as a testbed for existing earthquake forecasting models in this region where the vulnerability of local buildings and structures exacerbate seismic risk. [ABSTRACT FROM AUTHOR]

With the highly nonlinear relationship between various seismic feature indicators and earthquakes, the researchers can hardly construct an earthquake model. Meanwhile, the lack of samples for destructive earthquakes also leads to inaccurate medium-to-short-term earthquake magnitude predictions. Therefore, this study proposes a novel model for earthquake prediction, named variant detector bagging algorithm (V-detector-bagging). First, we adopt the geological distribution of earthquakes and faults as a criterion to select the appropriate history catalog data area, and the seismic indicators are calculated through the Gutenberg–Richter laws and Panakkat indicators. Then, we propose the V-detector-bagging algorithm, which combines the V-detector algorithm with the bagging method. The proposed algorithm converts the self-tolerance process into a cycle process, during this process, different selves guide samples to spread widely, generate various detectors covering more wide nonself areas, fill holes that are not covered by detectors in nonself areas, and reduce the false negative rate. Thus, the V-detector-bagging algorithm improves the detection performance of the V-detector. Finally, through experimental validation analysis, the proposed algorithm ranked first in the detection rate on Sichuan and Xinjiang catalog data compared to the popular machine learning methods used in predicting earthquakes and the original V-detector algorithm, with Xinjiang yielding the best results. [ABSTRACT FROM AUTHOR]

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips. [ABSTRACT FROM AUTHOR]

This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. We meticulously constructed a comprehensive feature matrix to maximize predictive accuracy. By synthesizing existing research and integrating novel predictive features, we developed a robust subset capable of estimating the maximum potential earthquake magnitude. Our standout achievement is the creation of a feature set that, when applied with the Random Forest machine learning model, achieves a high accuracy in predicting the maximum earthquake category within the next 30 days. Among sixteen evaluated machine learning algorithms, Random Forest proved to be the most effective. Our findings underscore the transformative potential of machine learning and neural networks in enhancing earthquake prediction accuracy, offering significant advancements in seismic risk management and preparedness for Los Angeles. [ABSTRACT FROM AUTHOR]

Earthquakes, representing some of the most devastating natural phenomena, pose a persistent challenge in prediction due to their unpredictable nature. Despite significant insights offered by traditional methods, their lack of precision often leaves communities at risk. This research explores a cutting-edge approach in earthquake prediction using artificial intelligence (AI), with a particular emphasis on the attention encoder–decoder long short-term memory (LSTM) model. Focused on modeling seismic events of varied scales that occurred from 2007 to 2010 in the North Anatolian Fault Zone (NAFZ), Turkey, this study compares the efficacy of various AI models including multi-layer perceptron (MLP) and LSTM. The research reveals that the attention encoder–decoder LSTM model surpasses its counterparts in performance. It demonstrates a remarkable capability in forecasting earthquakes by effectively deciphering complex patterns within the data, underscoring its viability as a powerful tool in seismic prediction. The attention encoder–decoder LSTM model, utilizing AI's latest advancements, offers a nuanced approach by selectively concentrating on relevant data segments, a method particularly beneficial in analyzing complex seismic patterns. This study endeavors to advance the field of earthquake prediction, proposing a model that combines sophisticated AI techniques with in-depth seismic data analysis for more accurate forecasting. [ABSTRACT FROM AUTHOR]

The latest earthquakes (Morrocco, Nepal, Sichuan – China, etc.) have highlighted the critical importance of local-site parameters on the vulnerability of existing building stock. The paper performs the clustering method based on the sub-surface parameters for structural damage prediction. The data set includes the damage status for 44 locations after the 2023 Kahramanmaraş earthquake sequence and local site parameters: Vs30, predominant frequency (f0), horizontal to vertical spectral ratio value (A0), and engineering bedrock depth (VsD760). The Fuzzy C-Means (FCM) and Spectral Clustering (SC) algorithms are carried out on the pre-processed data set, including the sub-surface parameters for each location and the data set clustered into two-clusters within each method. Then, the estimated clusters are compared with the post-earthquake two clusters representing the cluster of damage and no-damage state for considered locations that composed through official damage assessment reports The FCM algorithm yielded a 90% accuracy compared to actual clusters, while the results of the SC algorithm indicated an 86% accuracy. Among the parameters, the VsD760 and f0 demonstrate the ability to establish a discernible demarcation by manifesting distinguishable clustering patterns. Notably, the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) value is calculated at 97% and 85% for FCM and SC algorithms, respectively. The outcomes of this study offer the potential to predict the structural damage status of a location under a crucial seismic hazard in the pre-earthquake condition. This enables the development earthquake-resistant cities prior to earthquakes or implement necessary precautions to mitigate seismic risk in the afterward. [ABSTRACT FROM AUTHOR]

The electrical resistivity of subsurface rocks is one of the important sensitive parameters characterizing the internal physics of the Earth. Currently, research on subsurface electrical structures using geomagnetic sounding methods primarily focuses on two approaches: the first is based on observations from a few geomagnetic stations, which have low spatial resolution and cannot effectively describe the distribution of anomalies; the second is based on mobile geomagnetic observations, which have low temporal resolution and cannot promptly reflect anomalies. To address these issues, this study deployed a dense geomagnetic array for long-term observation in the southern segment of the Xiaojiang Fault Zone in the Yuxi area of southern Yunnan. This setup aims to promptly capture seismic magnetic anomalies, providing more data support and fundamental information for short-term earthquake prediction. Based on the long-term observation data from the dense array, the study of the subsurface electrical structure is carried out. The results indicate that during the observation period, which was seismically quiet, the regional subsurface electrical structure remained stable. A large-scale subsurface low-resistivity body was observed in the region, and the electrical structures at the two ends of the southern segment of the Xiaojiang Fault Zone were found to be completely different. [ABSTRACT FROM AUTHOR]

In the present analysis, the homogeneous, updated and complete seismic catalog of the Kohat-Potwar plateau was analyzed through dynamic time dependent statistical models. The study region earthquake interoccurrence times are assumed as a stochastic study variable and it follows one of the renewal models. A rich and updated class of statistical models was used to identify the most parsimonious one. The developed models will serve the purpose of describing, analyzing and forecasting the earthquake occurrence probabilities in the future. The model parameters were estimated through the method of maximum likelihood. Different statistical evaluation criteria were used to prioritize the estimated models according to their fitting performance to the observed data. We have found that the interoccurrence times of the study region can plausibly be described through the log-logistic model. Based on the log-logistic model, it has been found that there is a significantly high probability (>90%) of another earthquake occurrence of Mw ≥ 5.3 in the region in near future (2023‒2028). These probabilities increase with increasing time periods since the last event occurrence. The mean recurrence period of another earthquake of Mw ≥ 5.3, based on the most suitable log-logistic model, is almost three and half years with 1.80 years as standard error, i.e., 3.38 ± 1.80 years. The quantification of earthquake occurrence uncertainties in the study region provides many useful quantitative estimates that have crucial importance in seismic hazard and risk analysis studies of a region. [ABSTRACT FROM AUTHOR]

The article discusses the Neodeterministic (NDSHA) approach to seismic hazard assessment, which aims to prevent seismic disasters rather than simply addressing their consequences. The NDSHA method takes into account the tensor nature of earthquake ground motions and has been proven reliable through experiments conducted over the past 25 years. The article also highlights the organization of the book "Earthquakes and Sustainable Infrastructure- Neodeterministic (NDSHA) Approach Guarantees Prevention Rather Than Cure," which compiles developments in the NDSHA approach and its application in various countries and regions. The book covers topics such as hazard analysis, earthquake forecasting, site response evaluation, and the seismic performance of infrastructure. This book provides a comprehensive exploration of seismic hazard assessment, focusing on the gap between engineering principles and the needs of seismologists, engineering seismologists, and policymakers. It emphasizes the importance of designing infrastructure to withstand future earthquakes and prevent damage. [Extracted from the article]

The observation of geomagnetic field variations is an important approach to studying earthquake precursors. Since 1987, the China Earthquake Administration has explored this seismomagnetic relationship. In particular, they studied local magnetic field anomalies over the Chinese mainland for earthquake prediction. Owing to the years of research on the seismomagnetic relationship, earthquake prediction experts have concluded that the compressive magnetic effect, tectonic magnetic effect, electric magnetic fluid effect, and other factors contribute to preearthquake magnetic anomalies. However, this involves a small magnitude of magnetic field changes. It is difficult to relate them to the abnormal changes of the extremely large magnetic field in regions with extreme earthquakes owing to the high cost of professional geomagnetic equipment, thereby limiting large-scale deployment. Moreover, it is difficult to obtain strong magnetic field changes before an earthquake. The Tianjin Earthquake Agency has developed low-cost geomagnetic field observation equipment through the Beijing–Tianjin–Hebei geomagnetic equipment test project. The new system was used to test the availability of equipment and determine the findings based on big data. [ABSTRACT FROM AUTHOR]

While deterministically predicting the time and location of earthquakes remains impossible, earthquake forecasting models can provide estimates of the probabilities of earthquakes occurring within some region over time. To enable informed decision‐making of civil protection, governmental agencies, or the public, Operational Earthquake Forecasting (OEF) systems aim to provide authoritative earthquake forecasts based on current earthquake activity in near‐real time. Establishing OEF systems involves several nontrivial choices. This review captures the current state of OEF worldwide and analyzes expert recommendations on the development, testing, and communication of earthquake forecasts. An introductory summary of OEF‐related research is followed by a description of OEF systems in Italy, New Zealand, and the United States. Combined, these two parts provide an informative and transparent snapshot of today's OEF landscape. In Section 4, we analyze the results of an expert elicitation that was conducted to seek guidance for the establishment of OEF systems. The elicitation identifies consensus and dissent on OEF issues among a non‐representative group of 20 international earthquake forecasting experts. While the experts agree that communication products should be developed in collaboration with the forecast user groups, they disagree on whether forecasting models and testing methods should be user‐dependent. No recommendations of strict model requirements could be elicited, but benchmark comparisons, prospective testing, reproducibility, and transparency are encouraged. Section 5 gives an outlook on the future of OEF. Besides covering recent research on earthquake forecasting model development and testing, upcoming OEF initiatives are described in the context of the expert elicitation findings. Plain Language Summary: The exact location, time, and magnitude of future earthquakes cannot be predicted. However, based on past earthquake sequences, it is possible to assess probabilities for future earthquakes. This is called earthquake forecasting. Operational Earthquake Forecasting (OEF) systems are designed to provide near‐real‐time authoritative earthquake forecasts, based on current earthquake activity, to aid the decision‐making of various societal stakeholders. Setting up these systems is complex, involving decisions about which model to use, how to best test the model, and how to turn earthquake probability estimates into practical information. This review captures the current state of OEF worldwide and analyzes expert recommendations on the development, testing, and communication of earthquake forecasts. Section 2 provides an overview of OEF‐related research and the background knowledge required to understand the other parts. Section 3 describes existing OEF systems of Italy, New Zealand, and the United States in detail. Section 4 discusses an elicitation of expert views on modeling, testing, and communicating earthquake forecasts (Mizrahi, Dallo, & Kuratle, 2023, https://doi.org/10.3929/ethz‐b‐000637239). Data from the elicitation allow to identify consensus and dissent on OEF issues and provide guidance for future earthquake forecasting efforts. Finally, Section 5 gives an outlook on future OEF‐related research and planned OEF efforts at various institutions. Key Points: We capture the state of earthquake forecasting systems in Italy, New Zealand, and the United States, and future plans in these and other countriesExperts encourage benchmark comparison, prospective testing, reproducibility and transparency, but avoid endorsing specific models or testsExperts stress the need to co‐design forecast communication products with end‐users to ensure their societal relevance and usefulness [ABSTRACT FROM AUTHOR]

Body waves traversing the Earth's interior from a seismic source to receivers on the surface carry rich information about its internal structures. Their travel time measurements have been widely used in seismology to constrain Earth's interior at the global scale by mapping the time anomaly along their ray paths. However, picking the travel time of global seismic waves, suitable for studying Earth's fine‐scale structures, requires highly skilled personnel and is often fairly subjective. Here, we report the development of an automatic picker for PKIKP waves traveling through the inner core (IC), especially nearly along Earth's diameters, based on the latest advances in supervised deep learning. A convolutional neural network (CNN) we develop automatically determines the PKIKP onset on vertical seismograms near its theoretical prediction of cataloged earthquakes. As high‐quality manual onset picks of global seismic phases are limited, we employ a scheme to generate a synthetic supervised training data set containing 300,000 waveforms. The PKIKP onsets picked by our trained CNN automatic picker exhibit a mean absolute error of ∼0.5 s compared to 1,503 manual picks, comparable to the estimated human‐picking error. In an integration test, the automatic picks obtained from an extended waveform data set yield a cylindrically anisotropic IC model that agrees well with the models inferred from manual picks, which illustrates the success of this pilot model. This is a significant step closer to harvesting an unprecedented volume of travel time measurements for studying the IC or other regions of the Earth's deep interior. Plain Language Summary: Seismic body waves traversing the Earth's interior provide critical constraints on structures and dynamics of the Earth's deep interior, including the solid inner core (IC). The onset time of compressional waves from a seismic source passing through the IC, known as PKIKP waves, has been widely used to study the Earth's deepest shell. However, the collection of onset time picks meticulously analyzed by experienced analysts is scarce due to the labor‐intensive nature of the process. At the same time, large collections compiled by multiple data centers, such as the International Seismological Center's PKIKP onset data set, are inhomogeneous and deemed less reliable for inner‐core research than the data collected by individual researchers. Here, we develop an automatic PKIKP onset picker based on recent advancements in machine learning in computer vision, the convolutional neural network. We train the network with synthetic waveforms, mimicking the influence of the Earth's structure on the initial waveform shape. Our comprehensive tests benchmark the consistency between the automatically picked and the researcher‐examined data sets. The automatic picker allows for the further exploration of extensive seismic archives, enabling the study of the Earth's deep interior in greater detail with unprecedentedly large data sets. Key Points: We employed a convolutional neural network to automatically pick the onsets of inner‐core sensitive PKIKP wavesOur automatic picker approaches near human‐level precision and initially reproduces established inner‐core anisotropic modelsAutomatic phase onset measurements would open unprecedented avenues for studying the Earth's deep interior, including the inner core [ABSTRACT FROM AUTHOR]

The rapid and accurate prediction of earthquake Strong-Shaking Zone (SSZ) is crucial for issuing precise early warnings to regions at high risk of strong ground shaking. Generally, the SSZ is derived from the real-time spatial distribution of observed ground motions. However, during the initial stages of large earthquakes, the SSZ is often underestimated and provide alerts without enough lead-time (the time interval between the alert declaration and the S-wave arrival to the target area). In this study, we propose an innovative approach termed Near-epicenter-based Partial Matching Crossover. Leveraging the characteristic that reliable magnitude estimates for large earthquakes are available earlier than accurate predictions of the peak ground velocity (PGV) distribution, this approach utilizes near-epicenter station data to rapidly estimate the SSZ. It achieves this by matching a segment of the fault, defined by a predetermined length, with the predicted PGV map within a 120 km radius centered at the epicenter. Application of our method to strong motion data from China, Japan and Turkey demonstrates its efficacy in quickly anticipating the post-earthquake intensity distributions for large earthquakes. Specifically, it offers a lead time of 5 s or more for 51.5% (39,354 km2), 43.3% (5772 km2), 31%(47,107 km2) and 75.3% (81,966 km2) of the IMM = V region during the M 8 Wenchuan earthquake, the M 7.3 Kumamoto earthquake, the M 7.8 Syria earthquake and M 7.6 Turkey earthquake, respectively. The presented approach introduces a novel methodology to extend the lead time for earthquake early warnings. [ABSTRACT FROM AUTHOR]

b-value analysis obtained using earthquake catalogues has been associated with stress accumulation for a very long time. In this study, the spatial and temporal distribution of the b-value, also known as the frequency magnitude distribution factor, was calculated and examined before and after the February 6, 2023, Kahramanmaraş earthquakes (Mw 7.7 and Mw 7.6). For this, all earthquakes that occurred between 1900 and the earthquakes of February 6, 2023, and all aftershocks that occurred until September 1, 2023, were used. A circular area with a radius of 300 km, including nearby active faults from the center of the first earthquake to the center of the second earthquake, was divided into grids of equal size. The b-value was calculated for each grid using the maximum likelihood method. Very low b values were detected in the area where the first earthquake occurred, and it was observed that the b-value decreased relatively in the area where the second earthquake occurred. From the change in b-value over time, it was determined that the b-value has decreased significantly in recent years. This situation is associated with stress accumulation in the area where both earthquakes occurred, especially the low b values of the region where the first earthquake occurred. The fact that b values obtained using aftershocks are still low suggests that stress in the region is not completely released, has not been completely relieved and aftershocks will continue for a long time. [ABSTRACT FROM AUTHOR]

Earthquake prediction is the holy grail of seismology and one of humanity's greatest dreams. The Earth's magnetic field appears to be one of the best possible precursors of earthquakes, although the topic is controversial. Recent advancements have made it possible to observe magnetic fields from satellites with great accuracy. We utilize magnetic measurements from Swarm satellites to explore the potential identification of anomalous magnetic signals preceding earthquakes. Focusing on 1077 major earthquakes that occurred in 2014–2023 in the Alpine–Himalayan belt, we apply an automatic algorithm to data recorded 10 days before each earthquake. This analysis reveals clear pre-earthquake anomalies in the magnetic field components. Notably, a robust correlation is established between the duration of these anomalies and the earthquake magnitude, indicating that as the earthquake magnitude increases, so does the duration of the anomaly. Here we show that this method has a great ability to make predictions (high accuracy 79%, precision 88%, F1-score and hit rate 84%), thus becoming the basis for an Operational Earthquake Prediction System (OEPS). [ABSTRACT FROM AUTHOR]

Several efforts have been made to understand the complex physical processes involved in a seismic process, but the findings are vague considering prediction capabilities. Nevertheless, recent seismo-ionosphere precursory research has enlightened new pathways toward building an earthquake (EQ) forecasting system. Previously, some conventional mathematical/statistical approaches have been proposed for detecting an anomalous value as a potential precursor. We propose a hybrid Bayesian-based Long Short-Term Memory (B-LSTM) Network model to forecast the Total Electron Content (TEC) data. We applied B-LSTM on different Vertical TEC (VTEC) datasets of two EQs (Mw7.7 Awaran EQ and Mw7.1 Van EQ) by forecasting VTECs with Normalised Root Mean Square Error (NRMSE) scores of 0.15 and 0.10, respectively. We calculated errors and estimated the 99 % confidence interval to extract the VTEC anomalies. The model detects VTEC anomalies successfully but these anomalies may still have some biases and may lead to a false alarm. In order to minimize possible false alarms, we calculated the intensity of each anomaly and found a strong anomaly that occurred 2–3 days before the EQs. To strengthen the relationship of the detected VTEC anomalies with the investigated earthquakes, we examined the state of space weather conditions during and before the event. Our analysis expands the use of deep learning methods in EQ prediction and VTEC forecasting that can be used for various applications e.g. space weather and navigation. [ABSTRACT FROM AUTHOR]

We examine the temporal evolution of sequences of induced seismicity caused by long-term fluid injection using a compilation of over 20 case studies where moderate magnitude (M > 3.0) induced events have been recorded. We compare rates of seismicity with injection rates via the seismogenic index and seismic efficiency parameters, computing both cumulative and time-windowed values. We find that cumulative values tend to accelerate steeply as each seismicity sequence initiates—most cases reach a value that is within 0.5 units of their maximum value within 1–3 years. Time-windowed values tend to increase to maximum values within 25%–35% of the overall sequence, before decreasing as levels of seismicity stabilize. We interpret these observations with respect to the pore pressure changes that will be generated in highly porous, high permeability reservoirs. In such situations, the rate of pore pressure change is highest during the early phases of injection and decreases with time. If induced seismicity scales with the rate of deformation, which in turn is controlled by the rate of pore pressure change, then it is to be expected that induced seismicity is highest during the early phases of injection, and then decreases with time. This article is part of the theme issue 'Induced seismicity in coupled subsurface systems'. [ABSTRACT FROM AUTHOR]

The paper presents the results of retrospective and tectonophysical analysis of the Tokachi-Oki earthquake source area on the September 25, 2003 (Mw = 8.3). Particular attention is paid to the Hidaka tectonic belt, the structures of which were affected by the Tokachi-Oki earthquake. The load–unload response ratio (LURR) method was applied at sufficient spatial (a square with a side of about 400 km) and temporal (continuous 20-year period) intervals to consider the results reliable. The forerunners of the Tokachi-Oki earthquake on the September 25, 2003, were revealed (on average, two years before the seismic event), which strictly determined the earthquake location. The data obtained with The method of cataclastic analysis (MCA), together with LURR data, made it possible to establish the stages of instability development in the Hidaka collision zone. The Hidaka tectonic belt became outlined by destruction zones (mainly horizontal compression) from 1997 to 2003 (up to the Tokachi-Oki earthquake). LURR anomalies were detected along the belt a couple of years before the earthquake. The prediction of the Tokachi-Oki earthquake by the LURR method is one of the most reliable in our practice. [ABSTRACT FROM AUTHOR]

The Xianshuihe fault is a major tectonic boundary between the Sichuan-Yunnan rhombic and Bayanhar blocks in Southwest China. With an average left-lateral strike-slip movement of 10–15 mm/yr, it is a fast-moving strike-slip continental fault. On September 5, 2022, the Ms6.8 Luding earthquake occurred along the Moxi segment of the Xianshuihe fault, reaching a maximum intensity of IX and resulting in a significant number of casualties and severe property damage. This earthquake broke the long-standing seismic quiescence of the Xianshuihe fault, which lasted for more than 40 years, and was followed by a significant number of aftershocks. An outstanding question is how the behavior of the Xianshuihe fault and major earthquakes changed following this mainshock. In this study, we examined the changes in regional seismicity following the Luding earthquake and identified the potential for future strong earthquakes along the Xianshuihe fault. We used a finite element numerical method to simulate the environment of the seismogenic fault and its adjacent areas. In addition, we used the coseismic slip model of the Luding earthquake with the split-node method to calculate how the stress and strain fields in the surrounding area were affected by the 2022 mainshock. Coulomb stress changes were resolved in the main faults, and the seismicity of adjacent faults was analyzed in conjunction with the observed seismic data. The results indicate that regional tectonic movement primarily occurred to the southeast along the Moxi segment. The stress field is approximately north-south in tension and east-west in compression. Variation in the stress field in the epicentral region of the Luding earthquake exceeded 1 MPa. The maximum displacement of the coseismic deformation field was concentrated between Moxi town and Tuanjie village, and the Coulomb stress of the fault zone in this region experienced the largest decrease. However, the b-value of the Gutenberg-Richter magnitude-frequency relationship at the epicenter and the surrounding area exhibited an abnormal pattern of decrease-decrease-increase, indicating that the regional stress may not be fully released. This earthquake increased the Coulomb stress in other segments of the Xianshuihe, Anninghe, and Daliangshan faults, whereas the Coulomb stress in the Longmenshan and Xiaojinhe fault zones decreased. In addition, it triggered a series of normal-fault, moderate-sized earthquakes in nearby areas. The Dagangshan reservoir, located ∼20 km from the epicenter of the Luding earthquake, received an increase of ∼5.3 MPa in the tensile stress along the NWW-SEE direction. The Xiluodu Reservoir, located approximately 225 km from the epicenter, was less affected by this earthquake, and the seismic activity near the reservoir remained relatively unchanged. In this study, post-earthquake seismicity in the vicinity of the Ms6.8 Luding earthquake was analyzed and predicted by numerical simulation, providing a scientific basis for earthquake prediction and disaster reduction. [ABSTRACT FROM AUTHOR]

In 2022, four earthquakes with MS ≥6.0 including the Menyuan MS 6.9 and Luding MS 6.8 earthquakes occurred in the NorthSouth Seismic Zone (NSSZ), which demonstrated high and strong seismicity. Pattern Informatics (PI) method, as an effective long and medium term earthquake forecasting method, has been applied to the strong earthquake forecasting in Chinese mainland and results have shown the positive performance. The earthquake catalog with magnitude above MS 3.0 since 1970 provided by China Earthquake Networks Center was employed in this study and the Receiver Operating Characteristic (ROC) method was applied to test the forecasting efficiency of the PI method in each selected region related to the North-South Seismic Zone systematically. Based on this, we selected the area with the best ROC testing result and analyzed the evolution process of the PI hotspot map reflecting the small seismic activity pattern prior to the Menyuan MS 6.9 and Luding MS 6.8 earthquakes. A “forward” forecast for the area was carried out to assess seismic risk. The study shows the following. 1) PI forecasting has higher forecasting efficiency in the selected study region where the difference of seismicity in any place of the region is smaller. 2) In areas with smaller differences of seismicity, the activity pattern of small earthquakes prior to the Menyuan MS 6.9 and Luding MS 6.8 earthquakes can be obtained by analyzing the spatio-temporal evolution process of the PI hotspot map. 3) The hotspot evolution in and around the southern Tazang fault in the study area is similar to that prior to the strong earthquakes, which suggests the possible seismic hazard in the future. This study could provide some ideas to the seismic hazard assessment in other regions with high seismicity, such as Japan, California, Turkey, and Indonesia. [ABSTRACT FROM AUTHOR]

Earthquake cycle simulations based on the rate-and-state friction formulation are evolutions of nonlinear dynamical systems (NDS). The term "cycle" implies an overall stable structure that is a phase-space attractor naturally traced out by trajectories of NDS as it evolves. Quantitatively characterizing these attractors should be a basis for measuring complexities of the simulated earthquake cycles, i.e. to determine if and how regular or chaotic they are. I first revisit the textbook-standard quasi-dynamic spring-slider system from an NDS perspective, explicitly showing the attractors, their relationship with the parameters of the NDS, and how they can be characterized taken advantage of their low-dimensionality while aiming to extend the analysis to high-dimensionality. I evaluate two approaches, computing the Lyapunov exponents (LEs) and measuring correlation dimensions, with the simple spring-slider and earthquake-cycle examples whose phase-space attractors can be visually verified. I conclude LEs are too inconvenient and computationally expensive to use whereas measuring correlation dimensions is an easy and effective approach even with highly non-uniform time sampling present in all simulations. For earthquake-cycle simulations, an attractor reconstruction is performed based on Taken's theorem to corroborate my correlation-dimension results. The current method is limited in its ability to detect chaos in a dichotomous manner, which illuminates the direction for future study. An improving ability to quantitatively characterize earthquake-cycle simulations as an overall stable structure offers new opportunities to understand exotic seismic observations such as slow-slip events and enables more informative comparison with real data, particularly from paleoseismology, which could have far-reaching implications in earthquake forecasting. [ABSTRACT FROM AUTHOR]

Earthquakes are one of the most dangerous natural disasters that have constantly threatened humanity in the last decade. Therefore, it is extremely important to take preventive measures against earthquakes. Time estimation in these dangerous events is becoming more specific, especially in order to minimize the damage caused by earthquakes. In this study, a hybrid deep learning model is proposed to predict the time of the next earthquake to potentially occur. The developed CNN+GRU model was compared with RF, ARIMA, CNN and GRU. These models were tested using an earthquake dataset. Experimental results show that the CNN+GRU model performs better than others according to MSE, RMSE, MAE and MAPE metrics. This study highlights the importance of predicting earthquakes, providing a way to help take more effective precautions against earthquakes and potentially minimize loss of life and material damage. This study should be considered an important step in the methods used to predict future earthquakes and supports efforts to reduce earthquake risks. [ABSTRACT FROM AUTHOR]

Compelling evidence has shown that geomagnetic disturbances in vertical intensity polarization before great earthquakes are promising precursors across diverse rupture conditions. However, the geomagnetic vertical intensity polarization method uses the spectrum of smooth signals, and the anomalous waveforms of seismic electromagnetic radiation, which are basically nonstationary, have not been adequately considered. By combining pulse amplitude analysis and an experimental study of the cumulative frequency of anomalies, we found that the pulse amplitudes before the 2022 Luding M6.8 earthquake show characteristics of multiple synchronous anomalies, with the highest (or higher) values occurring during the analyzed period. Similar synchronous anomalies were observed before the 2021 Yangbi M6.4 earthquake, the 2022 Lushan M6.1 earthquake and the 2022 Malcolm M6.0 earthquake, and these anomalies indicate migration from the periphery toward the epicenters over time. The synchronous changes are in line with the recognition of previous geomagnetic anomalies with characteristics of high values before an earthquake and gradual recovery after the earthquake. Our study suggests that the pulse amplitude is effective for extracting anomalies in geomagnetic vertical intensity polarization, especially in the presence of nonstationary signals when utilizing observations from multiple station arrays. Our findings highlight the importance of incorporating pulse amplitude analysis into earthquake prediction research on geomagnetic disturbances. [ABSTRACT FROM AUTHOR]

Prehistoric records of subduction earthquakes are often distinguished by evidence of synchronous widespread coastal deformation, the extent of which negates the plausibility of alternative source faults. At the Hi-kurangi subduction margin in New Zealand, untangling the record of subduction interface ruptures is complicated. Large earthquake age uncertainties inhibit unique solutions of along-strike correlations, and complex patterns of coastal deformation caused by upper-plate faulting prevent reliable indication of source faults. In this work, we improved paleoearthquake reconstructions on the central Hikurangi margin with a new, well-constrained 5000 yr earthquake record from Pakuratahi Valley near Napier, North Island, New Zealand. Evidence of laterally extensive paleoenvironmental changes is consistent with coseismic subsidence and coseismic uplift in large earthquakes. Radiocarbon dates on fragile terrestrial macrofossils and tephra isochrons were used to construct robust age models that yielded earthquake ages of 4839-4601 calibrated (cal.) yr B.P., 3630-3564 cal. yr B.P., 2687-2439 cal. yr B.P., and 1228-823 cal. yr B.P. Integration of these ages with refined earthquake chronology from nearby Ahuriri Lagoon indicated that the next large earthquake impacting the Napier area is more likely to cause coastal subsidence than uplift. Drawing on correlations with cotemporal evidence elsewhere on the central margin, we infer that the overall patterns of coseismic deformation could be generated by either rupture of the subduction interface or upper-plate faults, or both. This inability to separate source faults for past earthquakes limits the efficiency of forecasting future earthquakes. Similar problems of intertwined paleoearthquake signatures likely apply to other plate boundaries, where we recommend cautious interpretation of coastal deformation to accurately address the hazard from both types of source faults. [ABSTRACT FROM AUTHOR]

The detection of preslip, occurring hours to days before a large earthquake, using geodetic measurements has been a major focus in earthquake prediction research. A recent study claims to have detected a preseismic signal interpreted as accelerating slip near the hypocenter of the 2011 great Tohoku‐oki earthquake, starting approximately 2 hr before the mainshock. This claim is based on a stacking procedure using GNSS (Global Navigation Satellite System) data. However, a follow‐up study demonstrated that the signal disappeared when specific GNSS noise was corrected. Here we utilize tiltmeter records, independent on GNSS, to check whether the claimed preseismic signal is detected using a similar stacking procedure. Our results show no acceleration‐like deformation from 2 hr before the mainshock. This indicates that no precursory slip exceeded the noise level of the tilt data, and if any preslip occurred, it was less than 5.0 × 1018 Nm in seismic moment. Plain Language Summary: The ability to detect large earthquakes before they occur would be invaluable for mitigating damage. Researchers have been searching for precursory signals in crustal deformation data, believing that if precursory slip (fault movement before a major earthquake) is large enough, it could be detected by GNSS or tiltmeters, enabling earthquake prediction. The 2011 Tohoku‐oki earthquake provided a valuable opportunity to study this phenomenon. Extensive data was collected, but previous studies found no significant pre‐earthquake signals on timescales of hours to days. A recent GNSS study claimed an acceleration‐like change 2 hr before the main shock, but another study using the same data disputed this. While reanalysis using the same GNSS data is important, it is also susceptible to the influence of the same noise sources. To avoid this, this study analyzed tiltmeter data, finding no evidence for the precursory deformation suggested earlier. This result indicates no significant precursory slip before the Tohoku‐oki earthquake that causes deformation larger than a noise level of the tiltmeter data. This result can provide constraints on the magnitude of the precursory slip immediately before the mainshock. Key Points: Tiltmeter records are used to determine whether a preseismic signal of the 2011 great Tohoku‐oki earthquake is detectedNo acceleration‐like tilt deformation from about 2 hr before the mainshock is recordedAn upper bound on the size of the preslip immediately before the mainshock can be estimated from the noise level of the observation data [ABSTRACT FROM AUTHOR]

In this paper, the new dynamic geophysical and geochemical MUltiparametric DAtabase (MUDA) is presented. MUDA is a new infrastructure of the National Institute of Geophysics and Volcanology (INGV), published on-line in December 2023, with the aim of archiving and disseminating multiparametric data collected by multidisciplinary monitoring networks. MUDA is a MySQL relational database with a web interface developed in php, aimed at investigating in quasi real time possible correlations between seismic phenomena and variations in endogenous and environmental parameters. At present, MUDA collects data from different types of sensors such as hydrogeochemical probes for physical-chemical parameters in waters, meteorological stations, detectors of air Radon concentration, diffusive flux of carbon dioxide (CO2) and seismometers belonging both to the National Seismic Network of INGV and to temporary networks installed in the framework of multidisciplinary research projects. MUDA daily publishes data updated to the previous day and offers the chance to view and download multiparametric time series selected for different time periods. The resultant dataset provides broad perspectives in the framework of future high frequency and continuous multiparametric monitorings as a starting point to identify possible seismic precursors for short-term earthquake forecasting. MUDA is now quoted with the Digital Object Identifier https://doi.org/10.13127/muda (Massa et al., 2023). [ABSTRACT FROM AUTHOR]

Electromagnetic indices play a potential role in the forecast of short-term to imminent M ≥ 5.5 earthquakes and have good application prospects. However, despite possible progress in earthquake forecasting, concerns remain because it is difficult to obtain accurate epicenter forecasts based on different forecast indices, and the forecast time span is as large as months in areas with multiple earthquakes. In this study, based on the actual demand for short-term earthquake forecasts in the Gansu–Qinghai–Sichuan region of western China, we refined the construction of earthquake forecast indicators in view of the abundant electromagnetic anomalies before moderate and strong earthquakes. We revealed the advantageous forecast indicators of each method for the three primary earthquake elements (time, epicenter, magnitude) and the spatiotemporal evolution characteristics of the anomalies. The correlations between the magnitude, time, intensity, and electromagnetic anomalies of different M ≥ 5.5 earthquakes indicate that the combination of short-term electromagnetic indices is pivotal in earthquake forecasting. [ABSTRACT FROM AUTHOR]

A rapid and accurate estimate of earthquake damage is a key component in a successful earthquake early warning (EEW) system. The cumulative absolute velocity (CAV) is an important and widely used parameter to measure ground motion intensity, but it cannot be correctly estimated via the traditional approach with the limited information available in typical EEW systems. Therefore, current EEW systems cannot effectively use CAV to predict earthquake damage. Herein, a CAV prediction model (DLcav) based on convolutional neural networks was proposed for EEW systems. DLcav is an end‐to‐end solution to continuously predict CAV using arriving seismic waves of increasing length and supplemented with additional auxiliary information. The effectiveness of DLcav to predict CAV was tested based on Japanese ground motion records, and the generalization ability of DLcav was assessed using the ground motion records from Chile. The results demonstrate that DLcav can rapidly predict CAV with good accuracy, which will help better estimate earthquake damage in EEW systems. [ABSTRACT FROM AUTHOR]

This paper presents a multi-parameter ionospheric disturbance analysis of the total electron content (TEC), density (Ne), temperature (Te), and critical frequency foF2 variations preceding two significant earthquake events (2015 Mw 7.5 and 2023 Mw 6.3) that occurred in Afghanistan. The analysis from various ground stations and low-Earth-orbit satellite measurements involved employing the sliding interquartile method to process TEC data of Global Ionospheric Maps (GIMs), comparing revisit trajectories to identify anomalies in Ne and Te from Swarm satellites, applying machine learning-based envelope estimation for GPS-derived TEC measurements, utilizing the least square method for foF2 data and ionograms obtained from available base stations in the Global Ionosphere Radio Observatory (GIRO). After excluding potential influences caused by solar and geomagnetic activities, the following phenomena were revealed: (1) The GIM-TEC variations displayed positive anomalies one day before the 2015 Mw 7.5 earthquake, while significant positive anomalies occurred on the shock days (7, 11, and 15) of the 2023 Mw 6.3 earthquake; (2) the Swarm satellite observations (Ne and Te) for the two earthquakes followed almost the same appearance rates as GIM-TEC, and a negative correlation between the Ne and Te values was found, with clearer appearance at night; (3) there were prominent positive TEC anomalies 8 days and almost 3 h before the earthquakes at selected GPS stations, which were nearest to the earthquake preparation area. The anomalous variations in TEC height and plasma density were verified by analyzing the foF2, which confirmed the ionospheric perturbations. Unusual ionospheric disturbances indicate imminent pre-seismic events, which provides the potential opportunity to provide aid for earthquake prediction and natural hazard risk management in Afghanistan and nearby regions. [ABSTRACT FROM AUTHOR]

Abstract—On February 6, 2023, two devastating earthquakes struck southern central Turkey, nine 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 earthquakes against the prediction of the 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 the periods with an increased probability of the strongest earthquakes as determined by the medium-term prediction algorithm for the M8 earthquakes. It is found that the epicenters of the February 6, 2023 earthquakes occurred in a node identified in 1973 as potentially earthquake-prone for М ≥ 6.5, and both events occurred within the spatial and temporal coverage of the warning area diagnosed by the M8 algorithm in July 2021. [ABSTRACT FROM AUTHOR]

Studying the temporal characteristics of earthquake activity contributes to enhancing earthquake prediction capabilities. The seismic b-value is a key indicator describing the relationship between seismic frequency and magnitude. This study investigates the correlation between the occurrence of major earthquakes and seismic b-values using earthquake activity records in Japan from 1990 to 2023. Local singularity analysis and wavelet analysis of earthquake frequency and b-value time series reveal significant 5-year periodic features in seismic activity in Japan. Furthermore, our research identifies that this periodicity is also prominent in major earthquakes with magnitudes of 7 and above. Additionally, through a detailed analysis of the cross-correlation between seismic b-values and the occurrence time of major earthquakes, we uncover a notable pattern: major earthquakes often occur approximately two years after the peak of seismic b-values. This discovery offers a new perspective on earthquake prediction and may play a crucial role in future earthquake early warning systems. [ABSTRACT FROM AUTHOR]

The seismic b -value in Gutenberg–Richter law is an important parameter in earthquake science research and earthquake risk assessment. People have tried to use b -values for short-term earthquake forecasts, and this requires the premise of estimating reliable b -values for real-time seismic catalogues. However, estimating b -values for real-time catalogues, which are usually of poor qualities, is usually faced with many difficulties and problems. In this study, through a series of numerical tests, we investigate the performance of three methods, including the commonly used maximum-likelihood estimation method and two relatively new b -value estimation methods, namely the b -positive and K–M slope methods, on calculating b -values for real-time seismic catalogues. We also apply these three methods to both observed seismic catalogues (the seismic sequence in the Costa Marchigiana, Italy, and a high-resolution early aftershock sequence of the 2023 two M w ∼7.8 earthquakes in Türkiye) and synthetic real-time seismic catalogues. The results in this study show that it seems difficult to obtain accurate b -values for real-time earthquake catalogues by each of these three methods, but the combination of these methods may give a better judgment—if all three methods suggest that the change in b -value is significant, the probability of making a correct decision is very high. Facing the uncertainty of b -value estimation that always exists, we advocate exploring the effectiveness of standard b -value estimation strategies in practical applications. [ABSTRACT FROM AUTHOR]

In this study, accelerometric data from seven Japanese buildings under long-term monitoring were analysed to explore the variability of the buildings' co-seismic response over time and its within- and between-building components, using co-seismic capacity curves developed in acceleration-displacement-response-spectrum format. The data include the 2011 Tohoku Mw9.1 earthquake, which caused building damage of different levels of severity, and the time-varying actual capacity curves were analysed considering earthquakes before and after 2011. Result showed that the initial slope of the capacity curves reflects the amount of damage. The between-building and within-building components of the variability are discussed by comparing a single building and several buildings in the same class for several earthquakes. Finally, the epistemic uncertainty of seismic risk assessment studies is discussed in relation to the selection of a generic capacity model for all buildings in a single class. [ABSTRACT FROM AUTHOR]

Although earthquake early warning (EEW) systems have advanced significantly, accurately determining earthquake parameters from the initial 3 s of P-wave motion remains challenging. Factors such as the complexity of the earthquake source and variability of ground motion due to site conditions contribute to this difficulty. This article aims to investigate how local site conditions impact the correlation between EEW parameters and earthquake magnitude, to better understand the influence of site conditions on the accuracy of EEW systems. Specifically, the study examines the effect of variation site conditions on commonly used EEW parameters, such as average characteristic period (τc) and peak displacement amplitude (Pd), for different site classes. A dataset of 432 strong-motion records with magnitude ranging from 5 to 7.3 was analyzed and site characterization information from the Kiban Kyoshin Network (KiK-net) in Japan was used. A linear relationship between EEW parameters (τc, Pd) and magnitude for the combined dataset (all data), as well as separate datasets based on site classes C (very dense soil and soft rock) and D (stiff soil site), was developed, and then the statistical parameters, correlation coefficient value (R), and standard deviation error (SD) in the linear regression analysis were compared. The study finds that τc and Pd have a significant correlation with magnitude when separate correlations are developed for site classes C and D. Absolute residual error and percentage error analyses were carried out. It was found that magnitude prediction errors were reduced particularly for class D sites. Overall, the study suggests need for use of site class based magnitude prediction equations in earthquake early warning, especially for softer soil sites. [ABSTRACT FROM AUTHOR]

Twenty-seven earthquakes in Taiwan with local magnitudes M L = 5. 6 − 7. 3 and focal depths d = 8.0−215.8 km were preceded by thermal infrared radiation (TIR) anomalies. The data are taken as an example to investigate the mechanisms of generating the TIR and to examine whether the TIR anomalies can be considered as a precursor or not. Let T D and T N be the largest number of days before forthcoming earthquakes when the anomalies are observed in the day-time and the night-time, respectively. The precursor time, T, for an event, is the larger value of T D and T N . The plots of both T vs. M L and T D vs. M L are scattered. Regardless of two data points with abnormally large T N values, it seems able to recognize a positive correlation between T N and M L . Among the 27 earthquakes, 8 events were proceeded by Rn concentration changes whose precursor times are T Rn = 7. 4 − 1 7 1 days. For most of the eight events, T Rn is much longer than either T D or T N . Hence, the model of outflow of gases from the depths to the ground surface for describing the Rn concentration changes cannot interpret the TIR anomalies. Based on rock mechanics, the temperature rise, Δ T , is generated by frictional heating due to preseismic slip. That the TIR anomalies discontinuously appeared before earthquakes and the occurrence times vary for different events is inconsistent with the processes of generation of frictional heating caused by preseismic slip on a few shallow faults in the source areas before forthcoming earthquakes. Since theoretical results and observations are not consistent, the answer to the title of this study is negative. [ABSTRACT FROM AUTHOR]

Pakistan is situated in an earthquake-prone area, and in the past, many destructive earthquakes have occurred including the most destructive 1935 Quetta, and 2005 Kashmir, earthquakes. Therefore, it is of vital importance to investigate the statistical properties of the earthquake temporal data of Pakistan. The present study brings novelty through the vast extension of the linear and nonlinear autoregressive conditional duration models in the field of seismology. A simple duration model capable of describing and forecasting the earthquake elapsed times data has been developed. Different specifications of the duration models were considered to completely extract the time-dependent structure in earthquake data. This study aims to identify the most suitable in-sample fitted and out-of-sample forecasting models for the earthquake elapsed times data of Pakistan. A variety of autoregressive conditional duration models were applied to the complete and updated earthquake catalog of Pakistan. The most suitable model was chosen through statistical model evaluation techniques. The method of maximum likelihood was used to estimate the model parameters. The adequacy of fitted models is assessed through residual analysis. The obtained results suggest that the Logarithmic Autoregressive Conditional Duration model of type 2 (LACD2) appeared as the most suitable in-sample fitted model for describing the earthquake temporal data of the different zones of Pakistan. Further, the simple autoregressive conditional duration (ACD) model outperforms the remaining considered models regarding 1-through-4 steps-ahead out-of-sample forecasting performance for both India–Eurasia collision (IEC) and Makran subduction (MS) zones. Autoregressive models with Burr and exponential distributions as assumed error distributions have appeared as the most suitable fitted models for IEC and MS zones, respectively. The residuals analysis results show that the most suitable fitted models are correctly identified. The results show that the earthquake short-term (h = 1) forecasting with duration models is more accurate in comparison with earthquake long-term (h = 1 , 2 , 3) forecasting. The forecasted elapsed times for IEC and MS zones are 0.60 and 2.94 years, respectively. The obtained results show that the autoregressive conditional models are a more useful tool for forecasting the earthquake elapsed times for short term in comparison with the long-term forecasting. Hence, autoregressive conditional models are capable of modeling and forecasting the earthquake temporal data of the different regions of Pakistan. Among the competing models, the best fit model can serve the purpose of data description, missing value estimation in earthquake catalogs and uncertainty quantification in the earthquake occurrence process. The most suitable forecasted model yields the future earthquake occurrence trend in the study region. [ABSTRACT FROM AUTHOR]

The meta-instability experiment provides a theoretical basis for the extraction of precursor information and the study of earthquake prediction. To study the precursor information of earthquakes, the apparent wave velocity ratio of earthquakes with a longitude of E98°–E102° and latitude of N24°–N28° before the Yangbi Ms6.4 earthquake on May 1 to 21, 2021, was calculated based on experiment results of the relationship between seismic wave velocity and stress. Regional gridding was achieved by interpolation, and the gradient of the apparent wave velocity ratio field and its divergence were analyzed. Energy was found to have been released at points (N26.10°, E99.59°) and (N25.73°, E99.87°) as the center, with a connecting line strike of 145°. Energy was absorbed at multiple points along the axis of the line connecting points (N25.81°, E99.59°) and (N26.58°, E100.26°), with a strike of 38°. Precursor information is a characteristic of meta-instability. According to the meta-instable theory, an earthquake will occur near points (N26.10°, E99.59°) or (N25.73°, E99.87°) or on the line connecting them within 0 to 5 days. The rupture length of the fault was about 49.8 km, which, when converted to Ms6.8, is ideal for comparison with the measured Ms6.4 earthquake that had occurred at point (N25.67°, E99.87°). It is believed that this method can provide a reference for the study of precursors of shallow strike-slip earthquakes with a certain number of foreshocks. [ABSTRACT FROM AUTHOR]

Various anomalies occurring before earthquakes are currently being studied to predict seismic events, with one of them being the radioactive element radon (222Rn). Radon concentrations in the soil, water, and atmosphere fluctuate in response to crustal movement. Recent research has statistically detected anomalies by analyzing the fluctuations in radon concentrations before earthquakes and conducting quantitative evaluations of radon. However, the method used to determine the parameters in the analysis was problematic. Therefore, in this study, we compared observed atmospheric radon concentration data with predicted values based on typical annual patterns using Random Forest analysis. We conducted a more objective analysis by employing this method and statistically determining anomalies using thresholds. This analysis was conducted using atmospheric radon concentration observation data obtained at Kobe Pharmaceutical University (KPU) before the 1995 Kobe Earthquake, and ionization currents emitted when radon decays were obtained at Fukushima Medical University (FMU) before the 2011 Tohoku-oki Earthquake. Consequently, before the major earthquakes occurred at both locations, the difference between the predicted and observed values exceeded the standard deviation by a factor of three. These results indicate the potential of Random Forest analysis to identify anomalies in atmospheric radon concentrations before earthquakes occur. [ABSTRACT FROM AUTHOR]

The epidemic type aftershock sequence (ETAS) model is used as a baseline model both for earthquake clustering and earthquake prediction. In most forecast experiments, the ETAS parameters are estimated based on a short and local catalog, therefore the model parameter optimization carried out by means of a maximum likelihood estimation may be not as robust as expected. We use Bayesian forecast techniques to solve this problem, where non-informative flat prior distributions of the parameters is adopted to perform forecast experiments on 3 mainshocks occurred in Southern California. A Metropolis–Hastings algorithm is employed to sample the model parameters and earthquake events. We also show, through forecast experiments, how the Bayesian inference allows to obtain a probabilistic forecast, differently from one obtained via MLE. [ABSTRACT FROM AUTHOR]

Estimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield‐based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES' internal activations, which we call "seismic vectors", correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake. Plain Language Summary: Have you ever heard something through the grapevine? It often takes you by surprise to hear a message from someone other than the original source. You might have felt an earthquake in a similar way: experiencing shaking (the message) at your location rather than movement along a fault (the source). We apply grapevine‐style communication to earthquake early warning (EEW). The goal of EEW is to warn people to prepare for earthquake shaking before damaging seismic waves arrive at their location. We build on recent work that used deep learning and large earthquake data sets to predict earthquake shaking. We developed a deep learning algorithm called GRAPES which predicts shaking in a manner similar to a game of seismic telephone: when a seismic sensor detects shaking, it sends a message to its neighboring sensors, warning them to expect shaking soon. These sensors then pass on the message to their more distant neighbors along the grapevine. We show that the messages GRAPES learned to send between sensors are like seismic status updates: "I'm seeing this type of seismic wave right now". We applied GRAPES to the 2019 M7.1 Ridgecrest, CA earthquake and it predicted shaking accurately and quickly. Key Points: A deep learning network trained to predict ground motion learned an internal representation of the seismic wavefieldIndividual neurons within the network activate with the arrival of P waves, S waves, surface waves, coda waves, and ambient noiseWhile trained on earthquakes in Japan, the model generalizes well to predicting ground motions for the 2019 Ridgecrest, CA earthquake [ABSTRACT FROM AUTHOR]

In this paper, the Integrated Nested Laplace Algorithm (INLA) is applied to the Epidemic Type Aftershock Sequence (ETAS) model, and the parameters of the ETAS model are obtained for the earthquake sequences active in different regions of Xinjiang. By analyzing the characteristics of the model parameters over time, the changes in each earthquake sequence are studied in more detail. The estimated values of the ETAS model parameters are used as inputs to forecast strong aftershocks in the next period. We find that there are significant differences in the aftershock triggering capacity and aftershock attenuation capacity of earthquake sequences in different seismic regions of Xinjiang. With different cutoff dates set, we observe the characteristics of the earthquake sequence parameters changing with time after the mainshock occurs, and the model parameters of the Ms7.3 earthquake sequence in Hotan region change significantly with time within 15 days after the earthquake. Compared with the MCMC algorithm, the ETAS model fitted with the INLA algorithm can forecast the number of earthquakes in the early period after the occurrence of strong aftershocks more effectively and can forecast the sudden occurrence time of earthquakes more accurately. [ABSTRACT FROM AUTHOR]

Geomagnetic vertical intensity polarization is a method with a clear mechanism, mature processing methods, and a strong ability to extract anomalous information in the quantitative analysis of seismogenic geomagnetic disturbances. The existing analyses of geomagnetic vertical intensity polarization are all based on the 5~100 s frequency band without refinement of the partitioning process. Although many successful results have been obtained, there are still two problems in the process of extracting anomalies: the geomagnetic anomalies that satisfy the determination criteria are still high in occurrence frequency; and the anomalies are distributed over too large an area in space, which leads to difficulties in determining the location of the epicenter. In this study, based on observations from western China, where fluxgate observation points are positioned in areas with frequent, densely distributed medium-strength earthquakes, we refined the frequency bands of geomagnetic vertical intensity polarization, recalculated the spatial and temporal evolution characteristics of geomagnetic disturbances before earthquakes, and improved the crossover frequency anomaly prediction index while promoting the application of the method in earthquake forecasting. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aftershocks can compound the impacts of a major earthquake, disrupting recovery efforts and potentially further damaging weakened buildings and infrastructure. Forecasts of the probability of aftershocks can therefore aid decision-making during earthquake response and recovery. Several countries issue authoritative aftershock forecasts. Most aftershock forecasts are based on simple statistical models that were first developed in the 1980s and remain the best available models. We review these statistical models and the wide-ranging research to advance aftershock forecasting through better statistical, physical, and machine-learning methods. Physics-based forecasts based on mainshock stress changes can sometimes match the statistical models in testing but do not yet outperform them. Physical models are also hampered by unsolved problems such as the mechanics of dynamic triggering and the influence of background conditions. Initial work on machine-learning forecasts shows promise, and new machine-learning earthquake catalogs provide an opportunity to advance all types of aftershock forecasts. Several countries issue real-time aftershock forecasts following significant earthquakes, providing information to aid response and recovery. Statistical models based on past aftershocks are used to compute aftershock probability as a function of space, time, and magnitude. Aftershock forecasting is advancing through better statistical models, constraints on physical triggering mechanisms, and machine learning. Large high-resolution earthquake catalogs provide an opportunity to advance physical, statistical, and machine-learning aftershock models. [ABSTRACT FROM AUTHOR]

In our exploration, we aimed at identifying seismic anomalies using limited ionospheric data for earthquake forecasting and we meticulously compiled datasets under conditions of minimal geomagnetic disturbance. Our systematic evaluation affirmed the ITransformer as a potent tool for the feature extraction of ionospheric data, standing out within the domain of transformer-based time series prediction models. We integrated the maximum entropy principle to fully leverage the available information, while minimizing the influence of presuppositions on our predictions. This led to the creation of the MaxEnt SeismoSense Model, a novel composite model that combines the strengths of the transformer architecture with the maximum entropy principle to improve prediction accuracy. The application of this model demonstrated a proficient capability to detect seismic disturbances in the ionosphere, showcasing an improvement in both recall rate and accuracy to 71% and 69%, respectively, when compared to conventional baseline models. This indicates that the combined use of transformer technology and the maximum entropy principle could allow pre-seismic anomalies in the ionosphere to be sensed more efficiently and could offer a more reliable and precise approach to earthquake prediction. [ABSTRACT FROM AUTHOR]

The Gumbel max distribution is one of the most important statistical distributions because it has many applications, especially in the field of water research and the prediction of earthquakes and volcanoes. This work includes a comparison of several Shrinkage Bayesian methods, namely the Shrinkage Bayesian Square Loss Function, Shrinkage Bayesian Lenix Function, and Shrinkage Bayesian Unix Function to estimate the distribution parameter. The research also includes several simulated experiments according to the sample size change and the real value of the distribution parameter. The number of experiments is 15 simulated experiments based on the difference sample size and true distribution parameter values. The results of the simulation experiments are compared depending on each of the absolute least difference criteria (ALDC) and the mean squared error (Mse). The comparison results show that the estimation method is affected by the sample size, and the real value of the distribution parameter. The best estimation method is the Shrinkage Bayesian Lenix Function. The Bayesian methods can be applied to other statistical distributions such that the Lindley Weibull distribution and logistics distribution. [ABSTRACT FROM AUTHOR]

Ultra-low frequency (ULF) emissions and total electron content (TEC) anomalies are potential earthquake precursors for short-term earthquake prediction. This study comprehensively examines ULF emissions and TEC anomalies associated with the Sumatra earthquakes spanning 2019–2020. Precursor ULF emissions are identified through the polarization power ratio calculations involving Z-component and H-component geomagnetic data, meticulously recorded by the Magnetik Acquisition Data System (MAGDAS). Employing the Single Station Function Transfer technique, ULF anomalies directed toward the epicenter of earthquakes are recognized as reliable precursors. Among the 29 earthquakes scrutinized, 25 were heralded by ULF emissions acting as precursors. Among these, 15 instances solely exhibited ULF emissions, unaccompanied by TEC anomalies, while the remaining ten were linked to TEC anomalies. The investigation of TEC anomalies employs advanced autocorrelation techniques rooted in the analysis of correlation coefficient deviations. TEC anomalies are meticulously acquired from the observations of the Sumatran GPS Array (SuGAr) stations. Furthermore, this study illuminates a positive correlation (R = 0.65) between the lead time of ULF emissions and TEC anomalies. Moreover, the frequency of ULF emissions and TEC anomalies also showcases a direct relationship with earthquake magnitude. Heightened frequencies of ULF emissions and TEC anomalies as earthquake precursors correspond to more significant magnitudes in ensuing earthquakes. The outcomes of this research significantly augment the understanding of employing ULF emissions and TEC anomalies as effective earthquake precursors in the context of Sumatra. [ABSTRACT FROM AUTHOR]

A new cellular automaton model is developed to examine the nature of temporal sequences of earthquakes. The model takes the space dependence of fault strength into account and assumes that an earthquake produces a continuous area with the stress dropped to zero. In the model, viscous slip is also introduced on the fault and earthquakes are realizable only when the stress accumulates faster than the viscous relaxation. The analysis reveals that the sequences of earthquakes generally satisfy the power law relation between the intensity and frequency of earthquakes so that earthquakes may be in the state of self-organized criticality. On the other hand, periodicity appears in some sequences that consist of the groups of high seismic activity repeated between calm intervals with an almost constant period. Therefore, self-organized criticality and periodicity coexist in these sequences and the claim that earthquakes are unpredictable because of self-organized criticality may be inadequate. [ABSTRACT FROM AUTHOR]

Early Earthquake Warning (EEW) systems alarm about ongoing earthquakes to reduce their devastating human and financial damages. In complicated tasks like earthquake forecasting, Artificial Intelligence (AI) solutions show promising results. The goal of this review is to investigate the AI-based EEW systems. Web of Science, Scopus, Embase, and PubMed databases were systematically searched from its beginning until April 18, 2023. Studies that used AI algorithms to develop EEWs and forecast earthquake magnitude were qualified. The quality assessment was conducted using the Mixed Methods Assessment Tool version 2018. Detailed analysis was performed on 26 of 2604 retrieved articles. Researchers predict earthquakes most often using neural network family models (21 studies). Among eight categorized groups of parameters for earthquake forecasting, it was often predicted utilizing seismic wave characteristics (65.38%) and seismic activity data (61.54%). AI models most often predicted earthquake magnitude (32.69%) and depth (15.38%). Logistic Model Tree and Bayesian Network had the highest sensitivity, accuracy, and F-measure efficiency (99.9%). Findings showed that AI algorithms can forecast earthquakes. However, additional study is needed to determine the efficacy of more data-driven AI algorithms in mining seismic data using more input variables. This review is helpful for seismologists and researchers developing EEW systems using AI. [ABSTRACT FROM AUTHOR]