Correlations and variability in electrical signals related to earthquake activity

Guzmán‐Vargas, L.; Ramírez-Rojas, Alejandro; Hernández-Pérez, R.; Angulo‐Brown, F.

doi:10.1016/j.physa.2009.06.019

Cited by 15 publications

(22 citation statements)

References 50 publications

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“…We observed that for the different time‐spanned data and both components, the

\overset{β}{true}

values for most of the SP stations range from 1 to 1.5 with

σ_{\overset{true¯}{β}}

from 0.2 to 0.4. This indicates that the ambient SP noises are often characterized by the flicker‐noise and Brownian‐noise processes (Guzmán‐Vargas et al, ; Ramirez‐Rojas et al, ). On the other hand, the

\overset{α}{true}

values for most of the SP stations range from −2 to 0 with

σ_{\overset{true¯}{α}}

from 0.5 to 1.5.…”

Section: Methodsmentioning

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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“…We observed that for the different time‐spanned data and both components, the

\overset{β}{true}

values for most of the SP stations range from 1 to 1.5 with

σ_{\overset{true¯}{β}}

\overset{α}{true}

values for most of the SP stations range from −2 to 0 with

σ_{\overset{true¯}{α}}

from 0.5 to 1.5.…”

Section: Methodsmentioning

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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“…(c), (d) As in (a) and (b) but for randomized data. Note that in these shuffled cases the data display mostly white noise profile (Guzmán-Vargas et al, 2009). m: r = 0.15 and m = 2.…”

Section: Mse Resultsmentioning

confidence: 98%

“…In order to apply the MSE procedure to the geoelectric time series we considered non overlapped time windows of 5,400 data points each, corresponding approximately to 3 hours of records Guzmán-Vargas et al (2009). First, the data points of the original signal are divided by its standard deviation and S E is calculated for each time scale according to the MSE method.…”

Section: Mse Resultsmentioning

confidence: 99%

Some Complexity Studies of Electroseismic Signals from Mexican Subduction Zone

Guzmán‐Vargas¹,

Hernández-Pérez²,

Angulo‐Brown³

et al. 2012

Earthquake Research and Analysis - Seismology, Seismotectonic and Earthquake Geology

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“…Two decades ago, the so-called Higuchi`s method [12,13] for calculating the fractal dimension of complex time series has been applied to investigate correlations and non-linear dynamic properties embedded in non-stationary time series. For example, this method has been used for analysing electroseismic time series [33]. Recently, Higuchi's method has been used to detect periodic components mixed with fractal signals [34][35][36].…”

Section: Higuchi's Methodsmentioning

confidence: 99%

Detrended Fluctuation Analysis and Higuchi's Windowing Method Applied to an Analysis of Southern California Seismicity

Muñoz-Diosdado¹

2015

Earthquake Engineering - From Engineering Seismology to Optimal Seismic Design of Engineering Structures

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Recent studies have shown that many complex natural systems are characterized by correlations [7]; however, the identification and quantification of the presence of these long range correlations using spectral analysis is inadequate, because the data are non-stationary. However, the detrended fluctuation method DFA enables the detection of correlations in nonstationary time series, thereby avoiding spurious detections [8, 9]. Telesca et al. [10] described the scale behaviour of seismic sequences in Southern California from 1981 to 1998 (Figure 1), represented as a two-dimensional sequence of jumps and interevent intervals. They used the catalogue of Richards-Dinger and Shearer (RDS) [11], which in the years mentioned above lists 284925 events. The catalogue is complete from a magnitude of M ≥ 1.5, this means that no earthquake greater than 1.5 is missing (Figures 2 and 3). They discussed long-range entire catalogue properties using the DFA method and applying it to the time series of jumps and

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Correlations and variability in electrical signals related to earthquake activity

Cited by 15 publications

References 50 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

Some Complexity Studies of Electroseismic Signals from Mexican Subduction Zone

Detrended Fluctuation Analysis and Higuchi's Windowing Method Applied to an Analysis of Southern California Seismicity

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