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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]