Two dimensional direct current resistivity mapping for subsurface investigation using computational intlligence techniques

Abstract

The purpose of this study is to investigate the application of artificial neural network (ANN) in solving two dimensional Direct Current (DC) resistivity mapping for subsurface investigation. Neural network algorithms were proposed based on radial basis function (RBF) model and multi-layer perceptron (MLP) model. Conventional approach of least square (LS) method was used as the benchmark and comparison for the proposed algorithm. In order to train the proposed algorithm, several synthetic data were generated using RES2DMOD software based on hybrid Wenner-Schlumberger configurations. Results were compared between the proposed algorithm and least square method in term of its effectiveness and error variations to actual values. It was discovered that the proposed algorithms have better performance in term of effectiveness and have minimum error difference to actual model as compared to least square method. Simulations result demonstrated that proposed algorithm can solve the inverse problem and can be illustrated by graphical means.