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Neural Processing Letters

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28-03-2020

Electrical Resistivity Inversion Based on a Hybrid CCSFLA-MSVR Method

Authors: Feibo Jiang, Li Dong, Qianwei Dai

Published in: Neural Processing Letters | Issue 3/2020

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Abstract

2D electrical resistivity inversion is a complicated nonlinear optimization problem, which is high-dimensional and non-convex. Using traditional neural networks to solve resistivity inversion problem is cost effective but suffers from trapping in local minima. In order to solve the above problem, a multi-output support vector regression (MSVR) nonlinear inversion method with limited ERI learning samples is researched in this paper, which considers the combined fitting errors of all outputs. Moreover, a Cauchy random and chaotic oscillation shuffled frog leaping algorithm is applied to optimize the RBF kernel widths and penalty coefficients of MSVR for improving the inversion accuracy and the computational efficiency. The key issues of data sets generation, data preprocessing and inversion flowchart are analyzed. The experimental results based on the synthetic and field examples demonstrated that the proposed algorithm is accurate, efficient and can be applied in practical engineering applications.

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Title: Electrical Resistivity Inversion Based on a Hybrid CCSFLA-MSVR Method
Authors: Feibo Jiang
Li Dong
Qianwei Dai
Publication date: 28-03-2020
Publisher: Springer US
Published in: Neural Processing Letters / Issue 3/2020
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-020-10229-4

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