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Geoelectric time series (TS) have long been studied for their potential for probabilistic earthquake forecasting, and a recent model (GEMSTIP) directly used the skewness and kurtosis of geoelectric TS to provide times of increased probability (TIPs) for earthquakes for several months in the future. We followed up on this work by applying the hidden Markov model (HMM) to the correlation, variance, skewness, and kurtosis TSs to identify two hidden states (HSs) with different distributions of these statistical indexes. More importantly, we tested whether these HSs could separate time periods into times of higher/lower earthquake probabilities. Using 0.5 Hz geoelectric TS data from 20 stations across Taiwan over 7 years, we first computed the statistical index TSs and then applied the Baum–Welch algorithm with multiple random initializations to obtain a well-converged HMM and its HS TS for each station. We then divided the map of Taiwan into a 16-by-16 grid map and quantified the forecasting skill, i.e., how well the HS TS could separate times of higher/lower earthquake probabilities in each cell in terms of a discrimination power measure that we defined. Next, we compare the discrimination power of empirical HS TSs against those of 400 simulated HS TSs and then organized the statistical significance values from this cellular-level hypothesis testing of the forecasting skill obtained into grid maps of discrimination reliability. Having found such significance values to be high for many grid cells for all stations, we proceeded with a statistical hypothesis test of the forecasting skill at the global level to find high statistical significance across large parts of the hyperparameter spaces of most stations. We therefore concluded that geoelectric TSs indeed contain earthquake-related information and the HMM approach is capable of extracting this information for earthquake forecasting. [ABSTRACT FROM AUTHOR]

The seismo-electrical coupling is critical to understand the mechanism of geoelectrical precursors to earthquakes. A novel seismo-electrical model, called Chen–Ouillon–Sornette (COS) model, has been developed by combining the Burridge–Knopoff spring-block system with the mechanisms of stress-activated charge carriers (i.e., electrons and holes) and pressure-stimulated currents. Such a model, thus, can simulate fracture-induced electrical signals at a laboratory scale or earthquake-related geoelectrical signals at a geological scale. In this study, by using information measures of time series analysis, we attempt to understand the influence of diverse electrical conditions on the characteristics of the simulated electrical signals with the COS model. We employ the Fisher–Shannon method to investigate the temporal dynamics of the COS model. The result showed that the electrical parameters of the COS model, particularly for the capacitance and inductance, affect the levels of the order/disorder in the electrical time series. Compared to the field observations, we infer that the underground electrical condition has become larger capacitance or smaller inductance in seismogenic processes. Accordingly, this study may provide a better understanding of the mechanical–electrical coupling of the earth's crust. [ABSTRACT FROM AUTHOR]

Geoelectric self‐potential (SP) signals are sensitive to natural and anthropogenic factors. The SP spectral characteristics under the different factors in Taiwan were investigated, and the SP spectral scalings were correlated with urbanization level, seismicity, and crustal deformation. The ambient SP noise models were first established by estimating the probability density functions of the spectrograms at each frequency. The effects of the natural and anthropogenic factors on the SP signals are understood by comparing the SP noise models under various conditions, such as precipitation, urbanization, and electric trains. Results show that the SP signals in areas of high industrialization and human activity and areas close to train stations behave as white noises and exhibit a distinct spectral ripple at frequencies around 1 Hz. On the other hand, the SP spectral power law parameters, Gutenberg‐Richter b values, and dilation strain rates were estimated by using the SP, earthquake catalog, and GPS data, respectively, during 2012–2017. By investigating the correlations of the SP spectral parameters with the Gutenberg‐Richter b value, dilation strain rates, and urbanization level, the SP optimal frequency band is found between 0.006 and 1 Hz due to the high correlation between the SP and seismicity data and between the SP and dilation data and the low correlation between the SP and urbanization data. Hence, this study may help the filtering and screening of the SP data and facilitate the understanding of the mechano‐electric behavior in the crust. Plain Language Summary: Self‐potential is a naturally electric potential difference beneath the Earth's surface, measured between any two points on the ground or in boreholes. The self‐potential signals are sensitive to natural factors, such as ionospheric disturbance, solar wind, and tidal forces, and to artificial factors, such as electric trains, factories, and power pipelines. Hence, the investigation of the background noises is critical. The self‐potential noise models were established by estimating the probability density functions of the daily power spectral densities. We compared the noise models under several conditions, such as urbanization, electric trains, and rainfalls. We found that the noise models behave as white noises in areas of high industrialization and human activity. Furthermore, we also estimated the self‐potential power law parameters, seismic b values, and dilation strain rates. We then studied correlations of the self‐potential power law parameters with the urbanization levels, b values, and dilation strain rates. We found out the frequency‐dependent correlations of the spectral scalings with the other parameters and determined the self‐potential signals with a high signal‐to‐noise ratio in the frequency band of 0.006–1 Hz. Understanding such correlations may understand the feature of the mechano‐electric behavior in the crust and facilitate the development of a seismic‐electric theory. Key Points: Self‐potential (SP) signals show white noises or spectral ripples in areas of high industrialization and areas close to train stationsThe SP spectral exponent seems to positively relate to the seismic b value and dilation rate and negatively to the urbanization levelThe SP spectral exponent shows frequency‐dependent correlations with the seismic b value, dilation rate, and urbanization level [ABSTRACT FROM AUTHOR]