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Electrical Resistivity Inversion Based on a Hybrid CCSFLA-MSVR Method

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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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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant Nos. 41604117, 41904127, 41874148, 61701179), Hunan Provincial Science and Technology Program, China (Grant No. 2018TP1018), Scientific Research Fund of Hunan Provincial Education Department, China (Grant No. 18A031, 16B147), Changsha Civic Science and Technology Program, China (Grant No. kq1706048). We would like to thank the anonymous reviewers for their valuable comments that allowed us to highly improve the paper quality.

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Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, People’s Republic of China

    Feibo Jiang

  2. Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, Hunan University of Technology and Business, Changsha, 410205, People’s Republic of China

    Li Dong

  3. School of Geosciences and Info-Physics, Central South University, Changsha, 410083, People’s Republic of China

    Qianwei Dai

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  1. Feibo Jiang
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  2. Li Dong
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  3. Qianwei Dai
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Correspondence to Li Dong.

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Jiang, F., Dong, L. & Dai, Q. Electrical Resistivity Inversion Based on a Hybrid CCSFLA-MSVR Method. Neural Process Lett 51, 2871–2890 (2020). https://doi.org/10.1007/s11063-020-10229-4

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