Search

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 bvalues, 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 bvalue, 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. 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 bvalues, and dilation strain rates. We then studied correlations of the self‐potential power law parameters with the urbanization levels, bvalues, 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. 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 bvalue and dilation rate and negatively to the urbanization levelThe SP spectral exponent shows frequency‐dependent correlations with the seismic bvalue, dilation rate, and urbanization level