Using artificial neural networks to invert 2D DC resistivity imaging data for high resistivity contrast regions: A MATLAB application

MATLAB is a high-level matrix/array language with control flow statements and functions. MATLAB has several useful toolboxes to solve complex problems in various fields of science, such as geophysics. In geophysics, the inversion of 2D DC resistivity imaging data is complex due to its non-linearity, especially for high resistivity contrast regions. In this paper, we investigate the applicability of MATLAB to design, train and test a newly developed artificial neural network in inverting 2D DC resistivity imaging data. We used resilient propagation to train the network. The model used to produce synthetic data is a homogeneous medium of 100 Ω m resistivity with an embedded anomalous body of 1000 Ω m. The location of the anomalous body was moved to different positions within the homogeneous model mesh elements. The synthetic data were generated using a finite element forward modeling code by means of the RES2DMOD. The network was trained using 21 datasets and tested on another 16 synthetic datasets, as well as on real field data. In field data acquisition, the cable covers 120 m between the first and the last take-out, with a 3 m x-spacing. Three different electrode spacings were measured, which gave a dataset of 330 data points. The interpreted result shows that the trained network was able to invert 2D electrical resistivity imaging data obtained by a Wenner–Schlumberger configuration rapidly and accurately.