Relationship Between Seismic Electric Signals and Tectonics Derived from Dense Geoelectric Observations in Taiwan

Jiang, Feng; Chen, Xiaobin; Chen, Chien Chih; Chen, Hong Jia

doi:10.1007/s00024-018-2077-5

Cited by 12 publications

(8 citation statements)

References 54 publications

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“…It includes 20 SP stations about 50 km apart (Figure 1). One of the main purposes of the GEMS network is studying correlations between SP signals and crustal activities (Chen et al, 2017;Chen & Chen, 2016;Jiang et al, 2019;Telesca et al, 2014). For instance, the SP anomalies were found to be associated with M L ≥ 5 earthquakes by using skewness and kurtosis and were attempted to create an earthquake forecasting algorithm (Chen et al, 2017;Chen & Chen, 2016).…”

Section: Introductionmentioning

confidence: 99%

Self‐Potential Ambient Noise and Spectral Relationship With Urbanization, Seismicity, and Strain Rate Revealed via the Taiwan Geoelectric Monitoring Network

Chen

Chiu

et al. 2020

JGR Solid Earth

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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 selfpotential 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 spe...

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Section: Introductionmentioning

confidence: 99%

Self‐Potential Ambient Noise and Spectral Relationship With Urbanization, Seismicity, and Strain Rate Revealed via the Taiwan Geoelectric Monitoring Network

Chen

Chiu

et al. 2020

JGR Solid Earth

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“…Rebetsky and Guo (2019) discussed the relation of tectonic stress and the nucleation of megaearthquakes. Jiang et al (2019a) used the shifting correlation method to analyze 2-month recordings of 20 geoelectric stations in Taiwan and found that at least two stations had recorded geoelectric signals significantly correlated with known seismic events. These two stations are installed on large reverse faults, which are probably characterized by high stress concentrations before earthquakes.…”

Section: Theoretical Discussion and Innovative Methods Proposed/discumentioning

confidence: 99%

Continental Earthquakes: Physics, Simulation, and Data Science—Introduction

Zhang

Goebel

et al. 2019

Pure Appl. Geophys.

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During May 12-14, 2018, the International Conference for the Decade Memory of the Wenchuan Earthquake, in connection to the 4th International Conference on Continental Earthquakes (4th ICCE) and the 12th General Assembly of the Asian Seismological Commission (ASC), was held in Chengdu, China. There have been several outcomes of the Conference (for example, the book published jointly by Higher Education Press and Springer) as well as the present special issue. Among the 58 submissions, 34 have been accepted after peer review. Some of the papers came from the presentations during the Conference, but with additional submissions, the scope of this special issue is far beyond that of the proceedings of the meeting.

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“…EQ forecasting (Chen and Chen, 2016;Chen et al, 2020;Jiang et al, 2020;Telesca et al, 2014;. In 2016 and 2017, Chen and his colleagues (Chen and Chen, 2016;Chen et al, 2017) analyzed the data of 20 geoelectric stations in Taiwan (Fig.…”

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confidence: 99%

“…TIPs were identified through differences in the distributions of skewness and kurtosis with those found during normal periods. Moreover, Jiang et al (2020) investigated the geoelectric signals before, during, and after EQs by the shifting correlation method, and found that the lateral and vertical electrical resistivity variation and subsurface conductors might amplify SESs, which agreed with the findings by Sarlis (Sarlis et al, 1999) and Huang (Huang and Lin, 2010). Inspired by these findings, in this paper we wanted to take a closer look at the relationship between the EQ times and statistical indexes of geoelectric TSs, namely correlation (𝐶), variance (𝑉), skewness (𝑆), and kurtosis (𝐾).…”

mentioning

confidence: 99%

Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan

Wen

Chen

et al. 2022

Preprint

Self Cite

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Abstract. Geoelectric time series (TS) has long been studied for its potential for probabilistic earthquake forecasting, and a recent model (GEMSTIP) directly used the skewness and kurtosis of geoelectric TS to provide Time of Increased Probabilities (TIPs) for earthquakes in several months in future. We followed up on this work by applying the Hidden Markov Model (HMM) on 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, then organized the statistical significance values from these 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 to be capable at extracting this information for earthquake forecasting.

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Relationship Between Seismic Electric Signals and Tectonics Derived from Dense Geoelectric Observations in Taiwan

Cited by 12 publications

References 54 publications

Self‐Potential Ambient Noise and Spectral Relationship With Urbanization, Seismicity, and Strain Rate Revealed via the Taiwan Geoelectric Monitoring Network

Self‐Potential Ambient Noise and Spectral Relationship With Urbanization, Seismicity, and Strain Rate Revealed via the Taiwan Geoelectric Monitoring Network

Continental Earthquakes: Physics, Simulation, and Data Science—Introduction

Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan

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