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Intelligent signal and noise separation (SNR) technology for near‐surface 3D distributed spectral induced polarization exploration
Author(s) -
Weiqiang Liu,
zhihui Wang,
Qingtian Lv,
Pinrong Lin,
Rujun Chen
Publication year - 2019
Publication title -
acta geologica sinica ‐ english edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 61
eISSN - 1755-6724
pISSN - 1000-9515
DOI - 10.1111/1755-6724.14057
Subject(s) - polarization (electrochemistry) , separation (statistics) , materials science , computer science , chemistry , machine learning
Now, spectral induced polarization (SIP) exploration has developed rapidly, based on a 3D distributed instrument system such as Newmont distributed data acquisition system (NEWDAS) [Eaton et al., 2010], Quantec’s 3D system [Gharibi et al., 2012], IRIS instrument [Fichtner et al., 2010] and Spread Spectrum IP system [Xi et al., 2013, 2014]. 3D distributed SIP is effective to characterize the complex resistivity structure of the shallow crust. However, observing the SIP response at low frequency band (10-3~10-1 Hz) is challenging and difficult in field surveys, because electromagnetic (EM) interferences caused by natural and artificial sources are strong at these frequencies. A complete intelligent signal and noise separation (SNR) technology, including correlation analysis, empirical mode decomposition, robust statistics, and relative phase calculation, was proposed to process the full waveform IP data. Through the analysis and testing, we found that the method can effectively suppress electromagnetic interference in the IP data, especially when the current electrode space is large, and the frequency is low. The method was applied to the practical data collected from a mining area in southern China. The data quality was considerably improved by the proposed method.