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A Weighting Inversion Method of Spectrum Induced Polarization
Earth Sciences
Volume 8, Issue 3, June 2019, Pages: 178-189
Received: Dec. 28, 2018; Accepted: May 23, 2019; Published: Jul. 4, 2019
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Wu Yu Hao, Tianjin Institute of Geotechnical Investigation and Surveying, Tianjin, China; College of Geo-exploration Sciences and Technology, Jilin University, Changchun, China
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The inversion results of complex resistivity method are four Cole-Cole model parameters. Among the four parameters, the frequency dependence and the time constant are more difficult to invert. It is necessary to study an algorithm that can invert the four Cole-Cole model parameters at the same time. In this paper, the least squares and OCCAM inversion algorithms are used to invert four Cole-Cole model parameters. In other words, two model constraints are added to the objective function. When inverting different Cole-Cole model parameters, the real and imaginary parts of the data are weighted to adjust the proportion of real part and imaginary part of data in inversion. Firstly, the formula of the algorithm is deduced. Then the theoretical models are designed for inversion trial calculation. In the inversion process, the inversion converges steadily by adjusting the Lagrange factor, and the inversion effect is improved by adjusting the weighting coefficients of real part and imaginary part. This method can get better inversion results by adjusting the proportion of the real and imaginary parts of the data in the inversion. The model trial results show that the weighting inversion algorithm significantly improves the results of the inversion of the four Cole-Cole parameters.
Spectral Induced Polarization, Cole–Cole Model Parameters, Occam Inversion, The Least Squares Inversion, Weighting Inversion
To cite this article
Wu Yu Hao, A Weighting Inversion Method of Spectrum Induced Polarization, Earth Sciences. Vol. 8, No. 3, 2019, pp. 178-189. doi: 10.11648/
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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