Phase-induced polarization method based on processing noise signals of the natural electromagnetic field of the Earth

Shaidurov, Georgy Ya.; Potylitsyn, V. S.; Kudinov, D. S.

doi:10.1016/j.rgg.2016.08.022

Cited by 9 publications

(6 citation statements)

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“…The seismo-electric effect is the physical basis of a new method of geophysical prospecting, significantly improving traditional seismic methods. Cross-correlation of seismic and electric signals provides additional important information about oil and gas deposit (Shaidurov et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Possible mechanisms of co-seismic electromagnetic effect

2018

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We analyze theoretically two possible sources of co-seismic electromagnetic response to the propagation of various types of seismic waves caused either by the electrokinetic phenomena or geomagnetic inductive effect. The differences between these two generation mechanisms have been examined for different types of seismic waves (P, S, and Rayleigh-Love). Theoretical relationships describing the dependence of the co-seismic signal amplitude, polarization and apparent impedance on the earthquake seismic moment and magnitude have been derived as a function of distance. We indicate an observational possibility to discriminate seismo-electrokinetic and seismo-magnetic effects and to estimate their contribution into a recorded co-seismic electromagnetic signal. Magnitudes and polarization of these signals are shown to depend strongly on the type of seismic wave and local crust parameters, such as streaming potential coupling coefficient, conductivity, inhomogeneity, etc. Co-seismic electromagnetic signals, though not directly applied for earthquake prediction, contain a useful information on local crustal phenomena, and can be used to identify ''sensitive'' zones perspective for the monitoring of precursory electromagnetic disturbances.

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Section: Introductionmentioning

confidence: 99%

Possible mechanisms of co-seismic electromagnetic effect

2018

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“…Due to the influence of terrain and environment, the polarization direction of the measured AMT sites is highly affected by active sources in the ore concentration area and concentrated on a certain direction. 29,30 Therefore, we used the polarization direction to test the effectiveness of the above methods. Figure 9 shows the comparison of polarization direction before and after de-noising for AMT measured site D012530 at frequencies of 0.2 Hz and 6 Hz, respectively.…”

Section: D012530mentioning

confidence: 99%

Audio Magnetotelluric Signal-Noise Identification and Separation Based on Multifractal Spectrum and Matching Pursuit

Zhang

Tang

et al. 2019

Fractals

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To avoid the blindness of the overall de-noising method and retain useful low frequency signals that are not over processed, we proposed a novel audio magnetotelluric (AMT) signal-noise identification and separation method based on multifractal spectrum and matching pursuit. We extracted two sets of multifractal spectrum characteristic from AMT time-series data to analyze the singularity. We used a support vector machine approach to learn the multifractal spectrum characteristics in a sample’s library and generate a model of support vector machine to distinguish between sections with and without interference in the measured AMT data. The matching pursuit algorithm was used to separate only those sections identified as having interference. Experimental results showed that the proposed method can effectively identify interference in the EMTF mathematical model and measured AMT data. Sections without interference were accurately preserved and reconstructed AMT signals were close to the natural electromagnetic field. The resulting apparent resistivity-phase curve is more continuous and smooth, and effectively improves the quality of AMT data. Moreover, the proposed method provides more reliable AMT data for subsequent electromagnetic inversion.

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“…According to the authors of this article, the passive method of induced polarization is devoid of this limitation and, according to the experience of various field experimental observations [8][9] on real deposits, allows solving the problem of detecting ore anomalies at depths of up to 1 km.…”

Section: Introductionmentioning

confidence: 99%

Physical modeling of the passive induced polarization method for the possibility of estimating the depth of the ore body simulator

Belolipetskii

Shajdurov

Potylitsyn

et al. 2022

J. Phys.: Conf. Ser.

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The article presents physical modeling data for the passive method of induced polarization for the possibility of estimating the depth of immersion of the ore body simulator. Experimental observations were carried out on a laboratory tank filled with sand, the observation time for each measurement point was 180 s, the sampling frequency was 1 kHz. In the course of physical modeling, measurements of the induced polarization parameter were carried out in the absence of an ore body simulator, with its immersion by 10 cm and 20 cm, as well as with full immersion and immersion to a depth of 30 cm. As a result of physical modeling with an ore body simulator, the following results: the dependence of the induced polarization parameter was obtained with increasing depth; with increasing depth, the change in the induced polarization parameter decreases; the background value of the induced polarization parameter in the absence of an ore body simulator leads to a decrease in the sensitivity of the measurement equipment.

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Phase-induced polarization method based on processing noise signals of the natural electromagnetic field of the Earth

Cited by 9 publications

References 0 publications

Possible mechanisms of co-seismic electromagnetic effect

Possible mechanisms of co-seismic electromagnetic effect

Audio Magnetotelluric Signal-Noise Identification and Separation Based on Multifractal Spectrum and Matching Pursuit

Physical modeling of the passive induced polarization method for the possibility of estimating the depth of the ore body simulator

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