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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]

In this study, the precursory seismic activity before a major earthquake was investigated by using the Pattern Informatics (PI) algorithm along the Sagaing fault zone (SFZ), Central Myanmar. After improving the earthquake catalog, the completeness of seismicity data with Mw ≥ 3.6 reported during 1980–2020 was used in retrospective testing to find the suitable parameters of the PI algorithm. According to the retrospective test with 6 cases of different forecast period times related to Mw ≥ 5.0 earthquakes, including verification using the relative operating characteristics (ROC) diagram, the characteristic parameters of both time intervals (change time and forecast time window) = 10 years and target forecast earthquake magnitude Mw ≥ 5.0 are suitable parameters for PI investigation along the SFZ. Therefore, these parameters were applied with the most up-to-date seismic dataset to evaluate the prospective areas of upcoming major earthquakes. The results reveal that the Myitkyina and the vicinity of Naypyidaw might be at risk of a major earthquake in the future. Therefore, effective earthquake mitigation plans should be urgently arranged. [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]

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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]

The Sichuan basin (SCB) is situated at the southeastern edge of the Tibetan Plateau where widespread seismicity has occurred. In the past decades, seismic events occurred in and around SCB have been responsible for more than 100 thousand casualties. To quantify the present-day seismic hazard of this region, especially the densely populated Chengdu-Chongqing economic zone (CCEZ), we develop a probabilistic earthquake forecast model using strain rates derived from 187 Global Navigation Satellite System (GNSS) horizontal velocities, of which 102 velocity vectors are first released. The second invariant of the strain rate tensor suggests that the Shimian County and its surroundings are exposed to the highest seismic hazard in and around SCB. The second most dangerous area is located between 103-105°E. The Chongqing area is the least dangerous. The principal strain rate axes interior of the Sichuan basin suggest that this region is experiencing broad-scale extension, which according to our knowledge, is first revealed by our dense GNSS network. The comparison between the cumulative histograms of the second invariant of geodetic strain rate and earthquake count indicates that the geodetic strain rates in this region can serve as a reliable predictor of M≥6 earthquake locations. Thus, we proceed to calculate the total seismic moment anticipated for the entire area within the next 30 years. [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]

Earthquake misinformation has not only a significant impact on crisis management, but also on trust in scientific information and institutions. As a global center for seismic information, the Euro-Mediterranean Seismological Center (EMSC) has developed a strategy to combat false information related to earthquakes, particularly on Twitter (now known as X). This strategy includes the automation of prebunking tweets to prevent the spread of false information and, particularly unreliable claims of earthquake predictions. In the field of false information, predictions of earthquakes have a particular characteristic: their appearance follows a specific dynamic and the attention paid to them is predictable, which makes the automation of countermeasures (prebunking and debunking) both possible and relevant. Therefore, the objectives of the EMSC's automatic tweets are to warn people in advance, particularly those who may be vulnerable to earthquake misinformation, while filling the information void with verified, scientifically-based information in the meantime. This paper seeks to examine the usefulness of such a tool. It does so by analyzing users' engagement with these tweets. In addition, it focuses on two case studies, the seismic sequence in Turkey following the M7.8 earthquake on February 6, 2023 and the M6.8 Morocco earthquake on September 8, 2023. The results show engagement with the automatically generated tweets but an even greater interaction and engagement with manual tweets, highlighting the importance of a holistic approach. Recommendations include optimizing the visibility of the tweets, extending efforts beyond social media, and exploring alternative platforms. The paper advocates for a comprehensive strategy, combining automated tools with manual engagement and leveraging diverse communication channels to take into account cultural differences. [ABSTRACT FROM AUTHOR]

On April 6, 2009, a magnitude 6.3 earthquake struck L'Aquila, Italy, causing extensive damage and loss of life, and raising significant issues around the communication of scientific risk. In the preceding weeks, increased seismic activity had alarmed the population, prompting authorities to seek expert advice. Public authorities reassured the population that the chances of a dangerous shock were slim. These assurances given by officials led many to remain in their homes when the earthquake struck. The subsequent legal actions against the scientists involved ignited a global debate on the responsibilities and challenges in scientific communication. This paper explores the complexities of conveying probabilistic risk information to the public and decision-makers. It highlights how different formats for presenting probabilistic data can significantly influence understanding and decision-making. In particular, it canvasses how the use of natural frequencies to convey probabilistic information makes it cognitively easier to understand and manipulate them, given how they make more salient and transparent the so-called base rate. However, the benefits of using natural frequencies decrease when dealing with low-probability, high-consequence (LPHC) events like major earthquakes, where even significant increases in relative probability remain small in absolute terms. Moreover, the paper investigates the social dimensions of earth science, examining the multifaceted role of scientists as both technical experts and social actors. The L'Aquila case exemplifies the need for integrating scientific accuracy with an understanding of its social implications. Effective risk communication must address cognitive limitations and the presence of social context to reach appropriate public behavioral responses. In order to achieve that, communication should be handled by actors that have specific expertise in its complexity. [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]

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 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]

The assessment of losses from strong earthquakes and the reduction in earthquake consequences are of great importance in maintaining seismic safety. Special attention is given to evaluating the magnitude of economic losses caused by earthquakes, particularly the assessment of different levels of seismic risk, in order to protect the population and territories located in seismically active areas. To ensure sustainable development of countries, it is essential to estimate the economic losses that will occur in regions due to strong earthquakes and forecast them within the specified return periods at given probabilities. Measures can then be implemented to mitigate the consequences of earthquakes. For the basis of seismic risk assessment, maps of seismic intensity increment and an improved map of seismic hazard have been developed, taking into account the engineering-geological conditions of the territory of Uzbekistan and the seismic characteristics of soils. For seismic risk map development, databases were created based on GIS platforms, allowing us to systematize and evaluate the regional distribution of information on seismic hazards, the number of buildings and construction types, the coefficient of the seismic vulnerability of buildings, the cadastral value of buildings, etc. [ABSTRACT FROM AUTHOR]

In this study, a broad range of supervised machine learning and parametric statistical, geospatial, and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derived geolocated building damage data for earthquakes, via regression- and classification-based models, respectively. For the aggregated observational data, models were ranked via predictive performance of mortality, population displacement, building damage, and building destruction for 375 observations across 161 earthquakes in 61 countries. For the satellite image-derived data, models were ranked via classification performance (damaged/unaffected) of 369,813 geolocated buildings for 26 earthquakes in 15 countries. Grouped k-fold, 3-repeat cross validation was used to ensure out-of-sample predictive performance. Feature importance of several variables used as proxies for vulnerability to disasters indicates covariate utility. The 2023 Türkiye–Syria earthquake event was used to explore model limitations for extreme events. However, applying the AdaBoost model on the 27,032 held-out buildings of the 2023 Türkiye–Syria earthquake event, predictions had an AUC of 0.93. Therefore, without any geospatial, building-specific, or direct satellite image information, this model accurately classified building damage, with significantly improved performance over satellite image trained models found in the literature. [ABSTRACT FROM AUTHOR]

This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. Six hot springs, located within an active fault zone along the southeastern coast of China, were carefully chosen as hydrochemical monitoring sites for an extended period of two and a half years. Using this data, a prediction model integrating six algorithms was developed to forecast M ≥ 5 earthquakes in Taiwan. The model's performance was validated against recorded earthquake events, and the factors influencing its predictive capability were analyzed. Our comprehensive analysis conclusively demonstrates the superiority of machine learning algorithms over traditional statistical methods for earthquake prediction. Additionally, including sampling time in the data sets significantly improves the model's predictive performance. However, it is important to note that the model's predictive performance varies across different hot spring and indicators type, highlighting the importance of identifying optimal indicators for specific scenarios. The model parameters, including the anomaly detection rate (P) and earthquake response time threshold (M), significantly impact the model's predictive capabilities. Therefore, adjustments are needed to optimize the model's performance for practical use. Despite limitations such as the inability to differentiate pre‐earthquake anomalies from post‐earthquake anomalies and pinpoint the precise location of earthquakes, this study successfully showcases the potential of machine learning algorithms in earthquake prediction, paving the way for further research and improved prediction methods. Plain Language Summary: This study explores the potential of utilizing machine learning algorithms for earthquake prediction based on hydrothermal fluid chemical anomaly data. An earthquake prediction model integrating six machine learning algorithms was developed using continuous hot spring hydrochemical monitoring data. The model's prediction performance was validated using actual earthquake event data, and various factors influencing its predictive capability were analyzed. The results demonstrate that the machine learning algorithm's prediction performance exceeds that of traditional methods, and the selection of hot springs, indicator types, and model parameters significantly impacts the model's prediction performance. This study successfully showcased the potential of machine learning algorithms in earthquake prediction, while also indicating new avenues for further research and improvement of prediction methods. Key Points: An earthquake prediction model based on machine learning using hydrochemical data from hot springs is proposed with sufficient accuracyThe earthquake prediction accuracy of the local outlier factor algorithm outperformed the traditional statistical methodThe predictive performance of the model varies across different hot springs, indicators type, and model parameters [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]

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]

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]

Short-term (with a lead time of about one week) earthquake (EQ) prediction is one of the most challenging subjects in geoscience and applied science; however, it is highly required by society because it is of essential importance in mitigating the human and economic losses associated with EQs. Electromagnetic precursors have recently been agreed to be the most powerful candidate for short-term prediction, because a lot of evidence has been accumulated on the presence of electromagnetic precursors (not only from the lithosphere, but also from the atmosphere and ionosphere) prior to EQs during the last three decades. On the other hand, unusual animal behavior associated with EQs, which is the main topic of this review, has been investigated as a macroscopic phenomenon for many years, with a much longer history than the study of seismo-electromagnetics. So, in this paper, we first summarize the previous research work on this general unusual animal behavior with reference to its relationship with EQs, and then we pay the greatest attention to our own previous work on dairy cows' milk yield changes. We recommend this unusual animal behavior as an additional potential tool for short-term EQ prediction, which may be a supplement to the above seismo-electromagnetic effects. Finally, we will present our latest case study (as an example) on unusual changes of cows' milk yields for a particular recent Tokyo EQ on 7 October 2021, and further propose that electromagnetic effects might be a possible sensory mechanism of unusual animal behavior, suggesting a close link between electromagnetic effects and unusual animal behavior. [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]

Geomagnetic field data have been found to contain earthquake (EQ) precursory signals; however, analyzing this high-resolution, imbalanced data presents challenges when implementing machine learning (ML). This study explored feasibility of principal component analyses (PCA) for reducing the dimensionality of global geomagnetic field data to improve the accuracy of EQ predictive models. Multi-class ML models capable of predicting EQ intensity in terms of the Mercalli Intensity Scale were developed. Ensemble and Support Vector Machine (SVM) models, known for their robustness and capabilities in handling complex relationships, were trained, while a Synthetic Minority Oversampling Technique (SMOTE) was employed to address the imbalanced EQ data. Both models were trained on PCA-extracted features from the balanced dataset, resulting in reasonable model performance. The ensemble model outperformed the SVM model in various aspects, including accuracy (77.50% vs. 75.88%), specificity (96.79% vs. 96.55%), F1-score (77.05% vs. 76.16%), and Matthew Correlation Coefficient (73.88% vs. 73.11%). These findings suggest the potential of a PCA-based ML model for more reliable EQ prediction. [ABSTRACT FROM AUTHOR]

The statistical analysis of the impact of the top 50 X-class solar flares (1997–2024) on global seismic activity as well as on the earthquake preparation zones located in the illuminated part of the globe and in an area of 5000 km around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases, an increase in seismicity is observed, especially in the region around the subsolar point (up to 33%) during the 10 days after the solar flare in comparison with the preceding 10 days. The case study of the aftershock sequence of a strong Mw = 9.1 earthquake (Sumatra–Andaman Islands, 26 December 2004) after the solar flare of X10.16 class (20 January 2005) demonstrated that the number of aftershocks with a magnitude of Mw ≥ 2.5 increases more than 17 times after the solar flare with a delay of 7–8 days. For the case of the Darfield earthquake (Mw = 7.1, 3 September 2010, New Zealand), it was shown that X-class solar flares and M probably triggered two strong aftershocks (Mw = 6.1 and Mw = 5.9) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from the point of view of the fault electrical conductivity and orientation. Based on the obtained results, the possible application of natural electromagnetic triggering of earthquakes is discussed for the earthquake forecast using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) has developed rapidly in recent years, achieving exciting advancements in applications such as data mining, computer vision, natural language processing, data feature extraction, and prediction. ML methods are increasingly being utilized in various aspects of seismic engineering, such as predicting the performance of various construction materials, monitoring the health of building structures or components, forecasting their seismic resistance, predicting potential earthquakes or aftershocks, and evaluating the residual performance of post-earthquake damaged buildings. This study conducts a scientometric-based review on the application of machine learning in seismic engineering. The Scopus database was selected for the data search and retrieval. During the data analysis, the sources of publications relevant to machine learning applications in seismic engineering, relevant keywords, influential authors based on publication count, and significant articles based on citation count were identified. The sources, keywords, and publications in the literature were analyzed and scientifically visualized using the VOSviewer software tool. The analysis results will help researchers understand the trending and latest research topics in the related field, facilitate collaboration among researchers, and promote the exchange of innovative ideas and methods. [ABSTRACT FROM AUTHOR]

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Although ground motion duration significantly influences structural response, there is a lack of accurate prediction models for ground motion duration. Ground motion duration plays a vital role in structural response during an earthquake. Even a small magnitude event may cause severe structural damage if the duration of the earthquake is long. Thus, accurate estimation of ground motion duration is essential for structural seismic design and analysis. This article uses machine learning techniques to estimate ground motion duration for the New Zealand region. This paper investigates the use of emerging machine learning algorithms to address this critical issue using data from the New Zealand earthquakes database. The utility of the prediction models is also evaluated using numerous parameters related to filtering frequencies, fault dimensions, S-wave triggering flag, etc., apart from the traditional source, path, and site. Other parameters, e.g., the usable frequency range and the uncertainty of the available parameters, are also considered in evaluating the prediction of the considered machine learning models. Root mean squared error, along with the coefficient of determination (R2), is used to evaluate the performance of the machine learning models. The method with the least difference between actual and predicted values on the test set is presented for each duration metric available within the New Zealand database. Most importantly, the game theory-based SHapely Additive exPlanations (SHAP) are provided as to whether each independent variable would push the predictions toward higher or lower values. These explanations demonstrate the relative importance of the parameters within the strong motion database in the prediction of earthquake duration. [ABSTRACT FROM AUTHOR]

The emission of seismo-electromagnetic (EM) signatures prior to earthquake recorded in geomagnetic data has potential to reveal the pre-earthquake processes in focal zones. This study focused to analysis of vertical component of a geomagnetic field from Mar 2019 to Apr 2020 using fractal and multifractal approach to identify the EM signatures in Campbell Bay, a seismically active region of Andaman and Nicobar, subduction zone. The significant enhancements in monofractal dimension and spectrum width components of multifractal highlights the high frequency with less and more complex nature of EM signatures preceded by earthquakes respectively, which indicates that the pre-earthquake processes on West Andaman Fault (WAF) and Andaman Trench (AT) are due to micro fracturing. Moreover, the significant enhancements in holder exponents, components of multifractal highlight the less correlated, smooth, and low frequency characteristics of EM signatures preceded by earthquakes, which indicate that pre-earthquake processes on Seulimeum Strand (SS) fault are due to electrokinetic processes. Thus, the mono fractal, spectrum width, and holder exponent parameter respond differently to the earthquakes with different characteristics, causing EM signatures to be observed with an average of 10, 12, and 20 days prior to the earthquakes respectively, which are also lies in range of short -term earthquake prediction. [ABSTRACT FROM AUTHOR]

The Parkfield transitional segment of the San Andreas Fault (SAF) is characterized by the production of frequent quasi-periodical M6 events that break the very same asperity. The last Parkfield mainshock occurred on 28 September 2004, 38 years after the 1966 earthquake, and after the segment showed a ~22 years average recurrence time. The main reason for the much longer interevent period between the last two earthquakes is thought to be the reduction of the Coulomb stress from the M6.5 Coalinga earthquake of 2 May 1983, and the M6 Nunez events of June 11th and 22 July 1983. Plausibly, the transitional segment of the SAF at Parkfield is now in the late part of its seismic cycle and current observations may all be relative to a state of stress close to criticality. However, the behavior of the attenuation parameter in the last few years seems substantially different from the one that characterized the years prior to the 2004 mainshock. A few questions arise: (i) Does a detectable preparation phase for the Parkfield mainshocks exist, and is it the same for all events? (ii) How dynamically/kinematically similar are the quasi-periodic occurrences of the Parkfield mainshocks? (iii) Are some dynamic/kinematic characteristics of the next mainshock predictable from the analysis of current data? (e.g., do we expect the epicenter of the next failure to be co-located to that of 2004?) (iv) Should we expect the duration of the current interseismic period to be close to the 22-year "undisturbed" average value? We respond to the questions listed above by analyzing the non-geometric attenuation of direct S-waves along the transitional segment of the SAF at Parkfield, in the close vicinity of the fault plane, between January 2001 and November 2023. Of particular interest is the preparatory behavior of the attenuation parameter as the 2004 mainshock approached, on both sides of the SAF. We also show that the non-volcanic tremor activity modulates the seismic attenuation in the area, and possibly the seismicity along the Parkfield fault segment, including the occurrence of the mainshocks. [ABSTRACT FROM AUTHOR]