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There is increasing evidence that anomalies in the ionosphere could appear a few days before large earthquakes. Many significant successes with using anomalies for predictions have been reported, although they are usually limited, both in space, to a specific geographic area, and in time, to one or a few events. To date, no solution has been presented that consistently yields the location and magnitude of future earthquakes and thus can be used to develop a warning service. The purpose of this research is to improve on the possible use of Global Ionospheric Maps for earthquake prediction. The use of three-dimensional data matrices, having spatiotemporal information to feed a convolutional neural network, is proposed in this contribution. This network was trained on all large earthquakes occurring from the beginning of the year 2011 to the beginning of October 2024 but it is proposed that it be periodically retrained with new data. This network has reached an accuracy of around 60% in the validation data for a division into eight categories of different earthquake magnitudes. Nevertheless, this percentage increases considerably if the classification into neighboring categories is also accepted, something that could be clearly admissible for the purposes of a warning system. The author believes that success in this endeavor has to come from a collaborative effort. For this reason, the training and validation data with three-dimensional matrices (latitude/longitude/time) of total electron content values along with the subsequent earthquake magnitudes are provided in this paper along with the trained network. Researchers are strongly encouraged to improve on the current neural network with or without the inclusion of additional information.

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.

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.

We designed a convolutional neural network application to detect seismic precursors in geomagnetic field records. Earthquakes are among the most destructive natural hazards on Earth, yet their short-term forecasting has not been achieved. Stress loading in dry rocks can generate electric currents that cause short-term changes to the geomagnetic field, yielding theoretically detectable pre-earthquake electromagnetic emissions. We propose a CNN model that scans windows of geomagnetic data streams and self-updates using nearby earthquakes as labels, under strict detectability criteria. We show how this model can be applied in three key seismotectonic settings, where geomagnetic observatories are optimally located in high-seismicity-rate epicentral areas. CNNs require large datasets to be able to accurately label seismic precursors, so we expect the model to improve as more data become available with time. At present, there is no synthetic data generator for this kind of application, so artificial data augmentation is not yet possible. However, this deep learning model serves to illustrate its potential usage in earthquake forecasting in a systematic and unbiased way. Our method can be prospectively applied to any kind of three-component dataset that may be physically connected to seismogenic processes at a given depth.

Earthquake forecasting is the process of forecasting the time, location, and magnitude of an earthquake, hoping to gain some time to prepare to reduce the disasters caused by earthquakes. In this paper, the possible relationship between the maximum electron density, the corresponding critical frequency, and the occurrence of earthquakes is explored by means of radio occultation data based on mechanism analysis and actual earthquake-nearby data. A new disturbance frequency index is proposed in this paper as a novel method to help forecast earthquakes. Forecasting of the location and timing of earthquakes is based on the connection between proven new frequency distributions and earthquakes. The effectiveness of this index is verified by backtracking observation around the 2022 Ya’an earthquake. Using this index, occultation data can forecast the occurrence of earthquakes five days ahead of detection, which can help break the bottleneck in earthquake forecasting.

A complex for measuring the electrical parameters of the surface layer of the atmosphere, which has been successfully operating in Kamchatka since 1997, is described. The basic principles of organization of such measurements and methods for their implementation under specific conditions are given. The main reactions of the electric field to meteorological phenomena, as well as to seismic and space events, are shown. A feature of the observatory is that it is located at mid-latitudes with a temperate maritime climate with a very small number of thunderstorms and in a region with high seismic activity. The paper presents the main results of studies of geophysical processes obtained at an observatory with such features. The measurement results of this complex are broadcast on the Internet and are available through a shared use center.

Due to their potential application in earthquake forecasting, seismo-electromagnetic phenomena were intensively studied for several decades all over the world. At present, measurements from ground to space have accumulated a large amount of observation data, proving an excellent opportunity for seismo-electromagnetic study. Using a variety of analytical methods to examine past earthquake events, many electromagnetic changes associated with earthquakes have been independently reported, supporting the existence of pre-earthquake anomalies. This study aimed to give a brief review of the seismo-electromagnetic studies preceding earthquakes and to discuss possible ways for the application of seismo-electromagnetic signals at the current stage. In general, seismo-electromagnetic signals can be classified into electric and magnetic changes in the lithosphere and perturbations in the atmosphere. We start with seismo-electromagnetic research in the lithosphere, and then we review the studies in the lower atmosphere and upper atmosphere, including some latest topics that aroused intense scholarly interest. The potential mechanisms of seismo-electromagnetic phenomena are also discussed. It was found that although a number of statistical tests show that electromagnetic anomalies may contain predictive information for major earthquakes, with probability gains of approximately 2–6, it is still difficult to make use of seismo-electromagnetic signals efficiently in practice. To address this, finally, we put forward some preliminary ideas about how to apply the seismo-electromagnetic information in earthquake forecasting.

With the continuous development of human society, earthquakes are becoming more and more dangerous to the production and life of human society. Researchers continue to try to predict earthquakes, but the results are still not significant. With the development of data science, sensing and communication technologies, there are increasing efforts to use machine learning methods to predict earthquakes. Our work raises a method that applies big data analysis and machine learning algorithms to earthquakes prediction. All data are accumulated by the Acoustic and Electromagnetic Testing All in One System (AETA). We propose the multi-station Principal Component Analysis (PCA) algorithm and extract features based on this method. At last, we propose a weekly-scale earthquake prediction model, which has a 60% accuracy using LightGBM (LGB).

Many studies have shown that b values tend to decrease prior to large earthquakes. To evaluate the forecast information in b value variations, we conduct a systematic assessment in Yunnan Province, China, where the seismicity is intense and moderate–large earthquakes occur frequently. The catalog in the past two decades is divided into four time periods (January 2000–December 2004, January 2005–December 2009, January 2010–December 2014, and January 2015–December 2019). The spatial b values are calculated for each 5-year span and then are used to forecast moderate-large earthquakes (M ≥ 5.0) in the subsequent period. As the fault systems in Yunnan Province are complex, to avoid possible biases in b value computation caused by different faulting regimes when using the grid search, the hierarchical space–time point-process models (HIST-PPM) proposed by Ogata are utilized to estimate spatial b values in this study. The forecast performance is tested by Molchan error diagram (MED) and the efficiency is quantified by probability gain (PG) and probability difference (PD). It is found that moderate–large earthquakes are more likely to occur in low b regions. The MED analysis shows that there is considerable precursory information in spatial b values and the forecast efficiency increases with magnitude in the Yunnan Province. These results suggest that the b value might be useful in middle- and long-term earthquake forecasts in the study area.