Geoelectric precursors are considered to be predictors of earthquakes, but this issue is still under debate. The objective of this research is to statistically test the relationship between the geoelectric system and earthquakes. We observed that anomalies of skewness and kurtosis of geoelectric fields may precede large earthquakes. Next, we developed an alarm model of time of increased probability to quantitatively determine their relationship. Performing binary classification and C1–F1 analysis on both statistical anomalies and earthquake occurrences, the alarm model implies that the statistical correlation between the geoelectric system and earthquakes exists with high confidence. We explained the results by critical transition, which refers to the state of a system becoming slower as it recovers from small perturbations when the system approaches critical points. Hence, generic symptoms, such as autocorrelation, variance, skewness, and kurtosis, can vary appreciably. Early warning signals for critical transitions of the geoelectric system might correspond to impending large earthquakes, in agreement with independent suggestions by other authors that appeared very recently. Consequently, we suggest that the critical transition will take place in the crustal system. Furthermore, we establish a standard procedure to examine the relationship between potential precursor indexes and earthquakes.

The effect of tidal triggering on earthquake occurrence has been controversial for many years. This study considered earthquakes that occurred near Taiwan between 1973 and 2008. Because earthquake data are nonlinear and non-stationary, we applied the empirical mode decomposition (EMD) method to analyze the temporal variations in the number of daily earthquakes to investigate the effect of tidal triggering. We compared the results obtained from the non-declustered catalog with those from two kinds of declustered catalogs and discuss the aftershock effect on the EMD-based analysis. We also investigated stacking the data based on in-phase phenomena of theoretical Earth tides with statistical significance tests. Our results show that the effects of tidal triggering, particularly the lunar tidal effect, can be extracted from the raw seismicity data using the approach proposed here. Our results suggest that the lunar tidal force is likely a factor in the triggering of earthquakes.

Treating geoelectric field distributions as earthquake precursors Anomalous skewness and kurtosis of geoelectric fields before the Meinong earthquake...

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.