Using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ML 6.6 Meinong, Taiwan Earthquake

Chen, Hong Jia; Chen, Chien Chih; Ouillon, Guy; Sornette, Didier

doi:10.3319/tao.2016.11.01.01

Cited by 12 publications

(9 citation statements)

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“…The SP signals occur in nature when electrolytes with different concentrations of ions come into galvanic contact with each other or where groundwater flows go through a capillary or a porous medium due to a hydraulic gradient (Revil & Jardani, 2013). In recent decades, the SP method has been becoming an important component in geophysical surveys on the areas of groundwater flow (Jardani et al, 2006;Mao et al, 2015;Sill, 1983), dam seepage monitoring (Al-Saigh et al, 1994;Ikard et al, 2014;Panthulu et al, 2001), volcanic monitoring (Aizawa et al, 2005;Corwin & Hoover, 1979;Kuwano et al, 2015), and earthquake precursor (Chen & Chen, 2016;Chen et al, 2017;Corwin & Morrison, 1977;Ramirez-Rojas et al, 2004). However, the SP signals are sensitive to anthropogenic factors, such as electric trains, factories, and power pipelines (Fraser-Smith & Coates, 1978;Harada et al, 2004;Ishikawa et al, 2007;Oettinger et al, 2001;Saito et al, 2011;Takahashi et al, 2007;Viljanen & Pirjola, 1994), and to natural factors, such as solar-terrestrial interactions, groundwater flows, and ocean tides (Fujii et al, 1995;Huang & Liu, 2006;Jardani et al, 2006;Lanzerotti et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…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). Moreover, associations between the time dynamics of the SP signals and crustal deformation processes were investigated by using the Fisher-Shannon method (Telesca et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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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%

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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“…We focused on 72 h time windows immediately before and after the main shock. The time windows used is for analysis of short-term precursors, which differs from those studies that focus on long-term precursors (e.g., Chen et al 2017;Wen and Chen 2017).…”

Section: Introductionmentioning

confidence: 99%

Understanding the 6 February 2018, Hualien earthquake sequence through catalog compilation

Chen¹,

Chen²,

et al. 2019

Terr. Atmos. Ocean. Sci.

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In this study, we compiled the Central Weather Bureau (CWB) data in order to study the Gutenberg-Richter magnitude-frequency slopes (i.e., b-values) and seismicity rates of significant earthquake sequences in the area of Hualien. A total of ten events between 1973 and 2018 were selected for analysis. Using time windows 72 h before and after the main shock, we first examined the existence of detectable foreshocks and then applied the Gutenberg-Richter law and Omori's law to determine the b-value and seismicity rate, respectively. The compiled results were used to assess the abnormalities and other characteristics of the 2018 Hualien earthquake for their forecast potential. We concluded that seismicity rates alone are not sufficient to forecast whether a greater main shock is forthcoming. The foreshock sequence of the 2018 Hualien earthquake was characterized by a low b-value and a high seismicity rate. Another earthquake with a prominent foreshock sequence occurred in 1990, but it showed a different relationship between the magnitude and the seismicity rate. For both the 1990 and the 2018 Hualien earthquakes, we found that the b-values of the foreshocks were lower than those of the respective aftershocks. The b-values for earthquake sequences are depressed relative to the background seismicity in the area. The mechanisms proposed for temporal variation in b-values are briefly reviewed to explain the observed b-value patterns. Finally, we established an empirical relationship with moment magnitude (M w ) in order to estimate the spatial range of aftershock distributions in the area of Hualien for shallow earthquakes (hypocenter depth ≤ 20 km) with M w ≥ 5.3.

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“…Zlotnicki [5] used the SP method to study volcano activities. Chen [6] used the SP skewness and kurtosis to forecast the earthquake. Roubinet [7] modeled SP signals in fractured rock to identify hydraulically active fractures.…”

Section: Introductionmentioning

confidence: 99%

Numerical modeling of biogeobattery system from microbial degradation of underground organic contaminant

et al. 2020

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Redox fields observed near contaminated sites are related to electrochemical and electrobiological reactions. The term biogeobattery is used to describe the redox processes in underground organic contaminated sites which are biogeochemical systems, where bacteria, conductive minerals, oxygen, and organic contaminants create numerous anode-cathode pairs by transferring electrons from reducing areas under the water table or other anaerobic conditions to oxidizing areas above the water table. A numerical biogeobattery model established based on the geobattery and inert electrode model is the theoretical basis for numerical modeling. A coupled method of tetrahedral finite elements and hexahedral infinite elements is proposed for the algorithm basis of numerical modeling. Then a small-scale biogeochemical system and a field-scale landfill system are modeled using the numerical biogeobattery model and the finite-infinite element coupling method. The results suggest that the accuracy of the coupled method is superior to the finite element method. The amplitude change in the embedded redox field has a great effect on self-potential anomalies, while the gradient change in the embedded redox field has no significant impact on self-potential anomalies. The resolution of the surface self-potential decreases with the depth of biogeochemical systems. The biogeobattery model based on the inert electrode model is an effective numerical model in biogeochemical systems.

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Using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ML 6.6 Meinong, Taiwan Earthquake

Cited by 12 publications

References 12 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

Understanding the 6 February 2018, Hualien earthquake sequence through catalog compilation

Numerical modeling of biogeobattery system from microbial degradation of underground organic contaminant

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