Electrical Resistivity Inversion Based on a Hybrid CCSFLA-MSVR Method

摘要

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.