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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]

Structural response prediction under earthquakes is crucial for evaluating the structural performance and subsequent functional restoration. Deep learning provides the potential to rapidly obtain the responses by skipping the time‐consuming nonlinear finite element analysis. However, a single deep learning network may only predict the time history responses of one specific structure, resulting in redundancy and resource waste when building multiple networks for modeling different structures. Thus, this study proposes a Structure Temporal Fusion Network (STFN) that can predict responses of various homogeneous structures using a single network. The key concept is that the seismic waves and the structural characteristics, such as story numbers, are fused together to predict diverse time history responses. Two numeric experiments are conducted, including predicting responses of ideal single‐degree‐of‐freedom (SDOF) structures and regular multistory reinforced concrete frames. Furthermore, a series of ablation analyses are carried out to validate the network architecture. The results indicate that STFN can predict nonlinear time history responses of different structures with mean square errors in the magnitude of 10−4$10^{-4}$ and 10−5$10^{-5}$ for two experiments, respectively. The solutions also highlight the importance of fusing static characteristics for the modeling of various structures with only one network. The STFN presents a promising solution for time history response prediction across multiple structures in regions. [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]

This editorial, published in the journal Frontiers in Earth Science, focuses on multidisciplinary investigations into earthquake processes. The research aims to improve risk evaluation and prediction in seismically active areas. The editorial provides an overview of the published contributions, which cover topics such as earthquake precursors, observation techniques, modeling fault movement, and earthquake forecasting experiments. The document discusses various approaches and techniques used in earthquake research, including seismometers, ambient noise, numerical modeling, and seismic hazard assessment. It also explores the impact of seismic wave effects from meteorite impacts and the potential use of machine learning in detecting earthquake precursors. [Extracted from the article]

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]

Intensity measures based on energy have proven to be robust indicators of damage for a variety of structural types. This article presents a modified ground-motion model (GMM) incorporating a deep neural network to predict elastic input energy spectra for both horizontal and vertical ground motions, considering the pulselike ground motions. The newly developed model employs six predictor variables, that is, moment magnitude MW, fault mechanism F, rupture distance Rrup, logarithmic rupture distance ln(Rrup), rupture directivity term Idir, and logarithmic shear-wave velocity ln(VS30)as inputs. A subset of records, sourced from the recently updated Next Generation Attenuation-West2 Project database constituted by 2745 ground motions from 97 earthquakes, have been employed in the development of the model. The performance of the developed model remains within the prescribed error range. In addition, the proposed model is compared against currently used GMMs. The predicted spectra obtained from the present study are in good agreement with those given by other literature, and the standard deviations of residuals have been reduced by ~20% and are more stable. Observations from these results indicate that the newly proposed model generates improved predictions. [ABSTRACT FROM AUTHOR]

We present the development and testing of multiple epidemic-type aftershock sequence (ETAS)-based earthquake forecasting models for Switzerland, aiming to identify suitable candidate models for operational earthquake forecasting (OEF) at the Swiss Seismological Service. We consider seven model variants: four variants use parameters obtained through fitting the ETAS model to the Swiss earthquake catalog, and three use generic parameters that were fit to Californian seismicity or global seismicity from regions tectonically similar to Switzerland. The model variants use different pieces of information from the current state-of-the-art time-independent earthquake rate forecast underlying the Swiss seismic hazard model (SUIhaz2015), and one is calibrated on a larger local data set that includes smaller earthquakes by allowing a time-dependent estimate of the completeness magnitude. We test all variants using pseudoprospective short-term (7-day) forecasting experiments and retrospective long-term (30-year) consistency tests. Our results suggest that all ETAS-based models outperform the time-independent SUIhaz2015 forecast in the short term, but two of the model variants overestimate event numbers in the long term. ETAS parameters are found not to be universally transferrable across tectonic regimes, and region-specific calibration is found to add value over generic parameters. Finally, we rank all model variants based on six criteria, including the results of the pseudoprospective and retrospective tests, as well as other criteria such as model run time or consistency with the existing long-term model, using a multicriteria decision analysis approach. Based on this ranking, we propose the ETAS model calibrated on the Swiss catalog, and with the spatial background seismicity distribution of SUIhaz2015 as the ideal candidate for the first Swiss OEF system. All procedures and choices involved in the development and testing of the Swiss ETAS model follow recently established expert recommendations and can act as a reference in the establishment of time-variant earthquake forecasting models for other regions. [ABSTRACT FROM AUTHOR]

To estimate predicted ground motion from a teleseismic slip model, we use a low- and high-frequency hybrid method to simulate the regional, strong ground motions observed following the 18 April 2014 moment magnitude (MW) 7.3 Papanoa, Mexico, earthquake. To generate the regional ground motion at low frequencies (<1 Hz), a teleseismically derived, finite-fault, kinematic model is used to define the earthquake source, taking into account slip-model variations identified with a parameter sampling approach that considers possible errors in the fault geometry, the hypocenter depth, and the rupture velocity. A 3D crustal model is used to calculate the low-frequency ground motions using a finite-element calculation that includes topography and considers variations in the source model to estimate the uncertainty in the calculations. High frequencies (>1 Hz) are added using a 1D full-wave propagation code that estimates uncertainties by considering multiple random distributions of slip with different spatial correlation lengths. The synthetic, broadband (0.05-10.0 Hz) ground motions are obtained by combining the low- and high-frequency portions match filtered at 1 Hz. These synthetic ground motions are compared with the regional observations using velocity records, peak ground acceleration, and medians of the orientation-independent response spectra of the horizontal components (RotD50) calculated at periods of 0.2, 0.3, 0.5, 1.0, 2.0, 3.0, 5.0, 7.5, and 10.0 s. The results indicate that ground motions estimated at these periods using our hybrid approach based primarily on a teleseismically derived source model are comparable to the values observed for the 2014 Papanoa earthquake at regional distances. The approach could be used to estimate strong-motion spectral levels expected for regions with limited local and regional recordings and could also fill in magnitude or distance gaps in ground-motion prediction relations utilized in the assessment of seismic hazard. [ABSTRACT FROM AUTHOR]

Seismic performance investigation and earthquake damage prediction of buildings in old urban areas are of great significance for the resistance to earthquake risks. Building attributes are fundamental to earthquake damage prediction, but the information of buildings in old urban may be insufficient and outdated. In this paper, UAV (Unmanned Aerial Vehicle) based oblique photogrammetry is used to the building-scale seismic performance assessment of the old urban area in Jaxing, China. Based on obtained UAV data, the building footprint is primarily detected, and direct attributes of building that determine the seismic performance are extracted, including number of storeys, nearborhood attributes and color attributes etc. Then indirect attribute, the building age, is predicted by machine learning. Base on the attritubes obtained, the elasto-plastic time history analysis of each detected building is carried out in the simplified model of the floor shear model, and the building damage distribution in study aera is finally obtained. Highlights: • A method based on unmanned aerial vehicle (UAV) tilt photogrammetry was initially used for the building-scale seismic performance assessment in Chinese old urban area. • Building attributes that determine the seismic performance are directly recognized or indirectly acquired through UAV tilt photogrammetry. • The method of analyzing building age in Chinese old urban areas by machine learning using spatial features, neighborhood features and image features is proposed. [ABSTRACT FROM AUTHOR]

This study's main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index. The results reveal a significant negative impact on stock market value during the post-treatment period. The results indicate rapid divergence from counterfactual predictions, and the actual stock index is lower than would have been expected in the absence of an earthquake. The curve of the actual stock value and the counterfactual prediction after the earthquake suggest a reconvening pattern in the stock market when the stock market resumes its activities. The cumulative impact effect shows a negative effect in relative terms, as evidenced by the decrease in the BIST-100 index of − 30%. These results have significant implications for investors and policymakers, emphasizing the need to prepare for natural disasters to minimize their adverse effects on stock market valuations. [ABSTRACT FROM AUTHOR]

Hyperparameter tuning is crucial for enhancing the accuracy and reliability of artificial neural networks (ANNs). This study presents an optimization of the Levenberg-Marquardt backpropagation neural network (LM-BPNN) by integrating an improved seagull optimization algorithm (ISOA). The proposed ISOA-LM-BPNN model is designed to forecast earthquakes in the Caribbean region. The study further explores the impact of data and model parallelism, revealing that hybrid parallelism effectively mitigates the limitations of both. This leads to substantial gains in throughput and overall performance. To address computational demands, this model leverages the compute unified device architecture (CUDA) framework, enabling hybrid parallelism on graphics processing units (GPUs). This approach significantly enhances the model's computational speed. The experimental results demonstrate that the ISOALM-BPNN model achieves a 20% improvement in accuracy compared to four baseline algorithms across three diverse datasets. The integration of ISOA with LM-BPNN refines the neural network's hyperparameters, leading to more precise earthquake predictions. Additionally, the model's computational efficiency is evidenced by a 56% speed increase when utilizing a single GPU, and an even greater acceleration with dual GPUs connected via NVLink compared to traditional CPU-based computations. The findings underscore the potential of ISOA-LM-BPNN as a robust tool for earthquake forecasting, combining high accuracy with enhanced computational speed, making it suitable for real-time applications in seismic monitoring and early warning systems. [ABSTRACT FROM AUTHOR]

The Load/Unload Response Ratio (LURR) is a seismic prediction method based on the dynamic evolution of the stress-strain relationship of rocks. By adopting Benioff strain as the response indicator, an in-depth analysis was conducted on the LURR anomalies within 400 km of the epicenter and its surrounding areas before the Ms7.4 earthquake in Madoi, Qinghai Province, on 22 May 2021. The analysis revealed that the LURR value peaked 1 month before the earthquake and then declined within half a month, indicating that the rock medium in the seismic gestation area was approaching the end of its yielding phase. Further investigation using the Outgoing Longwave Radiation (OLR) data from the National Oceanic and Atmospheric Administration (NOAA) satellite was conducted to study the short-term and imminent anomalous evolution of ground longwave radiation values after the high values declined (from May 14 to 25). The results showed that, spatially, significant and continuous OLR anomalies were observed only in the northern area of the epicenter before the earthquake, and these anomalies exhibited a trend of expanding towards the epicenter. Temporally, the entire anomalous evolution process can be divided into six phases: initial warming, anomaly expansion, peak intensity, anomaly weakening, earthquake occurrence, and anomaly disappearance. To validate this finding, the spatiotemporal evolution characteristics of LURR and OLR within 300 km of the epicenter and its surrounding areas before the Ms6.4 earthquake in Yangbi, Yunnan Province, on 21 May 2021, were analyzed, and similar patterns were found. These results suggest that the high LURR value before its decline may mark the end of the rock medium's yielding phase, and OLR data can reflect, to some extent, the state of tectonic stress accumulation along active faults in a critical condition. A comprehensive study of the anomalous evolution characteristics of these two physical parameters before the earthquake is not only conducive to the continuity from long-term to short-term forecasts but also of great significance for more accurately assessing the trend of seismic activity. [ABSTRACT FROM AUTHOR]

Hydrological changes in groundwater coupled with earthquakes had been documented in previous studies by global researchers. Although few reports investigate multiple geochemical parameters that respond to earthquakes, trace elements received less attention, whereas they were suggested to be more sensitive to small earthquakes than the commonly used geochemical parameters. Beijing is located in the Zhangjiakou-Bohai(Zhang-Bo) seismic belt of North China, and although the occurrence of small earthquakes is frequent, the great historic earthquake in the Sanhe-Pinggu area M8 in 1679 in the adjoining southeast of Beijing gained widespread public attention. To find effective precursors that are significant for operational earthquake forecasting of the Beijing area, we carried out a one year test research project through weekly collection of groundwater samples during June 2021 to June 2022 from the seismic monitoring well of Wuliying in northwest Beijing. The 41 trace elements chemical compositions were analyzed for each sample. During the project ongoing period, the biggest earthquake with a magnitude of ML3.3 occurred in the Chaoyang District of Beijing on 3 February 2022. The content changes in these trace elements were systematically monitored before and after the earthquake. Through retrospective research, it was found that a few sensitive trace elements were anomalous to be coupled to the earthquake, including Li,Sc, Rb, Mo, Cs, Ba, W, U, Sr, Mn, Ni,andZn. In addition to trace elements, we examined stable isotopes of hydrogen and oxygen and the existing hydrological data on groundwater level, temperature, major ions, and gases to assess the validity of geochemistry as a monitoring and predictive tool. We only found that F(fluorine) ions and He (helium) gas had apparent shifts related to the earthquakes, while no shifts in the groundwater level were observed. Such characteristics of multiple geochemical parameters indicate that trace elements are likely to be more sensitive to crustalstrain than the groundwater level and major ions. We assumed a most likely mechanism of the combination of mixing and water-rock interactions to explain the phenomenon. The probable scenario was that minor stresses caused by the earthquakes might create microcracks in bedrocks, thereby leading to a small volume of chemically distinct water mixing with the original water of the aquifer, and finally, the earthquakeinduced rock fractures enhance the water-rock interactions, resulting in the post-seismic recovery of trace elements and δ180 value migration to the GWML. More testing works to find other sensitive sites to investigate multiple geochemical characteristics aiming at long-term to short-term earthquake prediction in the Beijing area and Zhang-Bo seismic belt are in progress. [ABSTRACT FROM AUTHOR]

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]

The evaluation of available material in the search for earthquakes indicates a contradiction of opinion on the physical models of earthquake mechanisms. The cycle of seismoenergy requires an innovative approach to understanding the seismotectonic environment in focus of future earthquakes. The focus-rapture model retaining fault as the source of seismic burst is inconsistent with reality and temporizing elaboration of prediction of earthquakes. Whereas the three-dimensional volume outlines the shape of the focus of future seismic bursts in seismogenic layers of the Earth's crust and upper mantle. The spatially threedimensional volumetric focus can be lateral, vertical or angular in the space of the seismogenic layers. The stagnant, burstable and emission zones are defined based on seismic activity characteristics. The stagnant enhances the perpetuation capacity of volumetric focus. Therefore, stagnant is an aseismic zone, and the burstable zone is a potential seismic active zone. While the emission zone is the channel for the gentle seepage of geostress energy to the atmosphere, the continuous geodynamic processes can change the initial configuration of the complexity of stagnant and burstable zones. The volumetric focus is easily contoured making it easy to delineate spatial angle, and depth of future focus of seismic bursts in the seismogenic layers. The volume and depth articulate the possible magnitude and intensity of future earthquakes. [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.

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]

Yogyakarta, an Indonesian province prone to earthquakes, frequently suffers extensive damage to buildings, necessitating insurance coverage to mitigate potential losses. This study aims to forecast earthquake insurance premiums by predicting building damage levels resulting from earthquakes. Utilizing data from buildings affected by the June 30, 2023, earthquake in Yogyakarta, we employ K-Medoids Clustering and Support Vector Machine (SVM) to predict two categories of building damage: minor (labelled as 1) and heavy (labelled as 2). The total premiums for minor damage range from approximately USD 86.55 to USD 288.50, while for heavy damage, they range from USD 120.05 to USD 400.18 using the K-Medoids algorithm. Meanwhile, premiums for minor damage range from USD 83.14 to USD 277.13, and for heavy damage, they range from USD 223.67 to USD 745.55 using the SVM algorithm. [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]