An enhanced correlation identification algorithm and its application on spread spectrum induced polarization data

Abstract. In spread spectrum induced polarization (SSIP) data processing, attenuation of background noise from the observed data is the essential step that improves the signal-to-noise ratio (SNR) of SSIP data. The traditional correlation identification (TCI) algorithm has been proposed to improve the SNR of these data. However, signal processing in background noise is still a challenging problem. We propose an enhanced correlation identification (ECI) algorithm to attenuate the background noise. In this algorithm, the cross-correlation matching method is helpful for the extraction of useful components of the raw SSIP data and suppression of background noise. Then the formula of the TCI algorithm is used for identifying the frequency response of the observation system. Even when the signal to noise ratio (SNR) is −37.5 dB, this ECI algorithm can still be able to keep 3.0 % relative error. Experiments on both synthetic and real SSIP data show that the ECI algorithm can not only suppress the background noise but also better preserves the valid information of the raw SSIP data to display the actual location and shape of adjacent high resistivity anomalies, which can improve subsequent steps in SSIP data processing and imaging.

[1]  Ahmad Kalhor,et al.  Random noise attenuation by Wiener-ANFIS filtering , 2018 .

[2]  Heping Pan,et al.  A study on the discrete image method for calculation of transient electromagnetic fields in geological media , 2015, Applied Geophysics.

[3]  Hongzhu Cai,et al.  Robust statistical methods for impulse noise suppressing of spread spectrum induced polarization data, with application to a mine site, Gansu province, China , 2016 .

[4]  Rujun Chen,et al.  CHROMITE MAPPING USING INDUCED POLARIZATION METHOD BASED ON SPREAD SPECTRUM TECHNOLOGY , 2013 .

[5]  Weiqiang Liu,et al.  A modified empirical mode decomposition method for multiperiod time-series detrending and the application in full-waveform induced polarization data , 2019, Geophysical Journal International.

[6]  Mei Li,et al.  Improved Correlation Identification of Subsurface Using All Phase FFT Algorithm , 2020 .

[7]  Chen Rujun,et al.  High Precision Multi-frequency Multi-function Receiver for Electrical Exploration , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[8]  R. Neelamani,et al.  Coherent and random noise attenuation using the curvelet transform , 2008 .

[9]  M. Karaoulis,et al.  Review: Some low-frequency electrical methods for subsurface characterization and monitoring in hydrogeology , 2012, Hydrogeology Journal.

[10]  Jishan He,et al.  Wide field electromagnetic sounding methods , 2015 .

[11]  H. Seigel.  Mathematical formulation and type curves for induced polarization , 1959 .

[12]  Weibin Luo,et al.  Time-Domain Spectral Induced Polarization Based on Pseudo-random Sequence , 2012, Pure and Applied Geophysics.

[13]  J. R. Wait.  THE VARIABLE-FREQUENCY METHOD , 1959 .

[14]  Guang Li,et al.  De-noising low-frequency magnetotelluric data using mathematical morphology filtering and sparse representation , 2020, Journal of Applied Geophysics.

[15]  Jia-Wen Liu,et al.  Controlled-source electromagnetic data processing based on gray system theory and robust estimation , 2017, Applied Geophysics.

[16]  Hongzhu Cai,et al.  Correlation analysis for spread-spectrum induced-polarization signal processing in electromagnetically noisy environments , 2017 .

[17]  André Revil,et al.  Induced polarization response of porous media with metallic particles — Part 9: Influence of permafrost , 2019, Geophysics.

[18]  Lin Pin-rong,et al.  Signal processing approaches to obtain complex resistivity and phase at multiple frequencies for the electrical exploration method , 2017 .

[19]  Jianxin Liu,et al.  Integrated interpretation of dual frequency induced polarization measurement based on wavelet analysis and metal factor methods , 2013 .

[20]  K. A. Ruddock,et al.  LABORATORY INVESTIGATION OF OVERVOLTAGE , 1959 .

[21]  Hong Wu,et al.  LARGE-SCALE DISTRIBUTED 2D/3D FDIP SYSTEM BASED ON ZIGBEE NETWORK AND GPS , 2014 .