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Cole-cole model parameter estimation from multi-frequency complex resistivity spectrum based on the artificial neural network

Abstract

In near surface electrical exploration, it is often necessary to estimate the Cole-Cole model parameters according to the measured multi-frequency complex resistivity spectrum of ore and rock samples in advance. Parameter estimation is a nonlinear optimization problem, and the common method is least square fitting. The disadvantage of this method is that it relies on initial value and the result is unstable when data is confronted with noise interference. To further improve the accuracy of parameter estimation, this paper applied artificial neural network (ANN) method to the Cole-Cole model estimation. Firstly, a large number of forward models are generated as samples to train the neural network and when the data fitting error is lower than the error threshold, the training ends. The trained neural network is directly used to efficiently estimate the parameters of vast amounts of new data. The efficiency of the artificial neural network is analyzed by using simulated and measured spectral induced polarization data. The results show that artificial neural network method has a faster computing speed and higher accuracy in Cole-Cole model parameter estimation.


ISSN: 1083-1363
EISSN: 1943-2658
Serial Title: Journal of Environmental & Engineering Geophysics
Serial Volume: 26
Serial Issue: 1
Title: Cole-cole model parameter estimation from multi-frequency complex resistivity spectrum based on the artificial neural network
Affiliation: China University of Petroleum-Beijing (CUP), College of Geophysics, Beijing, China
Pages: 71-77
Published: 202103
Text Language: English
Publisher: Environmental and Engineering Geophysical Society, Englewood, CO, United States
References: 23
Accession Number: 2021-038932
Categories: Applied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 2 tables
Secondary Affiliation: Central South University, CHN, ChinaChinese Academy of Sciences (IGGCAS), CHN, China
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2021, American Geosciences Institute. Abstract, copyright, Environmental & Engineering Geophysical Society. Reference includes data from GeoScienceWorld, Alexandria, VA, United States
Update Code: 202112
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