A Fusion Approach for Suppression of Environmental Noise in Spread Spectrum Induced Polarization Data

2021 ◽  
Author(s):  
Siming He ◽  
Jian Guan ◽  
Dachuan Chen ◽  
Hang Xu ◽  
Yi Wang
Keyword(s):  
Spread Spectrum ◽  
Environmental Noise ◽  
Induced Polarization ◽  
Polarization Data ◽  
Fusion Approach

Reliability of Time Domain Induced Polarization Data

2011 ◽  
Author(s):  
Aurélie Gazoty ◽  
Esben Auken ◽  
Jesper Pedersen ◽  
Gianluca Fiandaca ◽  
Anders Vest Christiansen
Keyword(s):  
Time Domain ◽  
Induced Polarization ◽  
Polarization Data

Inversion of induced polarization data

Geophysics ◽  
1994 ◽  
Vol 59 (9) ◽  
pp. 1327-1341 ◽  
Author(s):  
Douglas W. Oldenburg ◽  
Yaoguo Li
Keyword(s):  
Inverse Problem ◽  
Objective Function ◽  
Effective Conductivity ◽  
Optimization Theory ◽  
Induced Polarization ◽  
Dc Resistivity ◽  
Earth Structure ◽  
Nonlinear Inverse Problem ◽  
Polarization Data ◽  
The Earth

We develop three methods to invert induced polarization (IP) data. The foundation for our algorithms is an assumption that the ultimate effect of chargeability is to alter the effective conductivity when current is applied. This assumption, which was first put forth by Siegel and has been routinely adopted in the literature, permits the IP responses to be numerically modeled by carrying out two forward modelings using a DC resistivity algorithm. The intimate connection between DC and IP data means that inversion of IP data is a two‐step process. First, the DC potentials are inverted to recover a background conductivity. The distribution of chargeability can then be found by using any one of the three following techniques: (1) linearizing the IP data equation and solving a linear inverse problem, (2) manipulating the conductivities obtained after performing two DC resistivity inversions, and (3) solving a nonlinear inverse problem. Our procedure for performing the inversion is to divide the earth into rectangular prisms and to assume that the conductivity σ and chargeability η are constant in each cell. To emulate complicated earth structure we allow many cells, usually far more than there are data. The inverse problem, which has many solutions, is then solved as a problem in optimization theory. A model objective function is designed, and a “model” (either the distribution of σ or η)is sought that minimizes the objective function subject to adequately fitting the data. Generalized subspace methodologies are used to solve both inverse problems, and positivity constraints are included. The IP inversion procedures we design are generic and can be applied to 1-D, 2-D, or 3-D earth models and with any configuration of current and potential electrodes. We illustrate our methods by inverting synthetic DC/IP data taken over a 2-D earth structure and by inverting dipole‐dipole data taken in Quebec.

Three-dimensional inversion of induced polarization data from simulated waste

2000 ◽  
Vol 44 (2-3) ◽  
pp. 67-83 ◽  
Author(s):  
A. Weller ◽  
W. Frangos ◽  
M. Seichter
Keyword(s):  
Three Dimensional ◽  
Induced Polarization ◽  
Polarization Data ◽  
Simulated Waste

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

2021 ◽  
Vol 28 (2) ◽  
pp. 247-256
Author(s):  
Siming He ◽  
Jian Guan ◽  
Xiu Ji ◽  
Hang Xu ◽  
Yi Wang
Keyword(s):  
Data Processing ◽  
Spread Spectrum ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Induced Polarization ◽  
High Resistivity ◽  
Observation System ◽  
Pseudorandom Sequence ◽  
Identification Algorithm ◽  
The Time Domain

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 time-domain spectral induced polarization based on pseudorandom sequence (TSIP) 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 frequency-domain IP (FDIP) method is used for extracting the frequency response of the observation system. Experiments on both synthetic and real SSIP data show that the ECI algorithm will not only suppress the background noise but also better preserve 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.

Intelligent denoising framework and algorithms for distributed full-waveform induced polarization data

2021 ◽  
Author(s):  
Wei-Qiang Liu ◽  
Qing-Tian Lü ◽  
Pin-Rong Lin ◽  
Ru-Jun Chen
Keyword(s):  
Induced Polarization ◽  
Polarization Data ◽  
Full Waveform

A hybrid correlation denoising method based on complex resistivity and application on spread spectrum induced polarization data

Geophysics ◽  
2021 ◽  
pp. 1-35
Author(s):  
Siming He ◽  
Jian Guan ◽  
Yi Wang ◽  
Xiu Ji ◽  
Hui Wang
Keyword(s):  
Hybrid Method ◽  
Spread Spectrum ◽  
Field Experiments ◽  
Noise Suppression ◽  
Random Noise ◽  
Induced Polarization ◽  
Data Simulation ◽  
Denoising Method ◽  
Complex Resistivity ◽  
Geophysical Surveys

In electrical exploration techniques, an effective suppression method for Gaussian and impulsive random noise in spread spectrum induced polarization (SSIP) continues to be challenging for conventional denoising methods. Remnant noise influences the complex resistivity spectrum and damages the subsequent interpretation of geophysical surveys. We present a hybrid method based on a correlation function and complex resistivity, which introduces the correlation analyses between the transmitting source, the measured potential, and the injected current signal. According to the analyses, reliable results for complex resistivity spectra can be calculated, which can be further used for noise suppression. We apply the hybrid method to both numerical and field experiments to process measured SSIP data. Simulation tests show that the hybrid method not only suppresses the two types of noise but also improves the relative error of the complex resistivity spectrum. Field data processing shows that the hybrid method can minimize the standard deviation of the data and possess a greater ability to distinguish adjacent objects, which can improve the reliability of the data in subsequent processing and interpretation.

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