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.

In order to find out whether the geomagnetic storms and large-mega earthquakes are correlated or not, statistical studies based on Superposed Epoch Analysis (SEA), significance analysis, and Z test have been applied to the Dst index data and M ≥ 7.0 global earthquakes during 1957–2020. The results indicate that before M ≥ 7.0 global earthquakes, there are clearly higher probabilities of geomagnetic storms than after them. Geomagnetic storms are more likely to be related with shallow earthquakes rather than deep ones. Further statistical investigations of the results based on cumulative storm hours show consistency with those based on storm days, suggesting that the high probability of geomagnetic storms prior to large-mega earthquakes is significant and robust. Some possible mechanisms such as a reverse piezoelectric effect and/or electroosmotic flow are discussed to explain the statistical correlation. The result might open new perspectives in the complex process of earthquakes and the Lithosphere-Atmosphere-Ionosphere (LAI) coupling.

Search space design is a fundamental procedure for the inverse problem in Rayleigh wave exploration. In practice, such a crucial task mainly depends on researchers’ experience-based judgment. However, intricate near-surface materials may lead to erroneous search space design, and consequently, accurate inversion results cannot always be ensured. By the forward calculation, it is found that there is a strong relationship between the fundamental dispersion curves of a given earth model and its miniature model; namely, if the parameters (layer thicknesses and $S$ -wave velocities) of the given earth model are shrunk at a certain scale, the phase velocities on the fundamental dispersion curve will decrease at the same scale. Taking advantage of this relation, we propose a broad learning framework for search space design in a data-driven manner. First, a training set of minified models is generated using the forward calculation of dispersion curves. Then, a mapping relationship between dispersion curves and minified earth models is built via a broad learning network. Finally, a search space for the actual earth model is designed using the minified model based on the relation. As the ranges of parameters in the minified model are much smaller, the network can find the model parameters for the corresponding dispersion curve quickly and easily. Numerical simulations and field data applications demonstrate the reliability and effectiveness of the proposed method for search space design. It is concluded that the proposed method can promote accurate estimation of $S$ -wave velocities by Rayleigh wave inversion without experience-based judgment.

Earthquake forecast and risk assessment are of key importance in reducing casualties and property losses. However, they have not been fully achieved due to the complexity of earthquakes. Numerous studies have explored the correspondence of the b-value with changes in effective stress, leveraging temporal and spatial variations to identify precursor characteristics of destructive events in both natural and induced seismic activities. However, robust interpretation of predictive b-values hinges on rigorous estimation, as biased results can mislead conclusions. This paper provides a comprehensive review of spatiotemporal b-value estimation methods alongside statistical significance tests. A pilot b-value analysis of natural earthquakes and induced seismicity manifested the valid impression. The expansion of monitoring datasets with the development of acquisition technology or dense array and advanced estimation methodology will augment the utility of b-value analysis in seismic research and hazard assessment.

The salinity of groundwater in Qinzhou Bay has been increasing since 2013. This phenomenon affects livelihoods and the local agricultural economy, specifically in the Longmengang area. In this work, we investigate the causes of water salinization to provide evidence for effectively managing the local water resources and improving water quality. We collected 110 samples of water from spatially distributed local wells, rivers, reservoirs, and the sea, and conducted a hydrochemical study complemented by numerical modeling. We found that water pumping and mariculture practices are the principal contributors to seawater intrusion into the groundwater table, which leads to increased water salinization.

One important question in earthquake prediction is whether a moderate or large earthquake will be followed by an even bigger one. Through temporal b-value evolution analysis, the traffic light system can be used to estimate if an earthquake is a foreshock. However, the traffic light system does not take into account the uncertainty of b-values when they constitute a criterion. In this study, we propose an optimization of the traffic light system with the Akaike Information Criterion (AIC) and bootstrap. The traffic light signals are controlled by the significance level of the difference in b-value between the sample and the background rather than an arbitrary constant. We applied the optimized traffic light system to the 2021 Yangbi earthquake sequence, which could be explicitly recognized as foreshock–mainshock–aftershock using the temporal and spatial variations in b-values. In addition, we used a new statistical parameter related to the distance between earthquakes to track earthquake nucleation features. We also confirmed that the optimized traffic light system works on a high-resolution catalog that includes small-magnitude earthquakes. The comprehensive consideration of b-value, significance probability, and seismic clustering might improve the reliability of earthquake risk judgment.