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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. [ABSTRACT FROM AUTHOR]

An integrated geophysical investigation was performed at S dam located at Dadu basin in China to assess the condition of the dam curtain. The key methodology of the integrated technique used was flow-field fitting method, which allowed identification of the hydraulic connections between the dam foundation and surface water sources (upstream and downstream), and location of the anomalous leakage outlets in the dam foundation. Limitations of the flow-field fitting method were complemented with resistivity logging to identify the internal erosion which had not yet developed into seepage pathways. The results of the flow-field fitting method and resistivity logging were consistent when compared with data provided by seismic tomography, borehole television, water injection test, and rock quality designation. [ABSTRACT FROM AUTHOR]

The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve above problems, a kernel principal component wavelet neural network (KPCWNN) trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study. An additional kernel principal component (KPC) layer is applied to reduce the dimensionality of apparent resistivity data and increase the computational efficiency of wavelet neural network (WNN). Meanwhile, a novel ISFLA algorithm is adopted for improving the learning ability and inversion quality of WNN. In the proposed ISFLA, a hybrid LC mutation attractor is used to enhance the exploitation ability and a differential updating rule is used to enhance the exploration ability. Four groups of experiments are considered to demonstrate the feasibility of the proposed inversion method. The inversion results of the synthetic and field examples show that the introduced method is superior to other algorithms in terms of prediction accuracy and computational efficiency, which contribute to better inversion results. [ABSTRACT FROM AUTHOR]

Electrical resistivity imaging (ERI) inversion is a complicated non-linear inversion problem, which is high dimensional and non-convex. Using traditional neural networks to solve the ERI inversion problem has an over-fitting phenomenon and easily falls into local minima. Moreover, the artificial neural network (ANN) model is a black box; its relationships between the inputs and outputs are not easy to interpret. In order to solve these problems, a wavelet packet denoising (WPD) procedure and an improved adaptive neuro-fuzzy inference system (ANFIS) based on Cauchy oscillation shuffled frog leaping algorithm (COSFLA) are proposed in this paper. The wavelet packet denoising methodology is based on soft thresholding and Shannon entropy using a Db10 wavelet which is applied to remove the noise component from the measured apparent resistivity data. Meanwhile, COSFLA is introduced for updating the premise parameters of the ANFIS to improve the learning ability and prediction accuracy of the algorithm. The development of the proposed method and the total inversion process are also presented. The inversion of the synthetic and field examples are provided to demonstrate the feasibility and validity of the proposed method. Additionally, the introduced method is transparent and its if–then rules are easy to understand and interpret. [ABSTRACT FROM AUTHOR]