Interpreting Electrical-Resistivity Tomography measurements using Neural Network

TL;DR

This paper introduces a neural network-based method for interpreting Electrical Resistivity Tomography data, enabling efficient 2D underground resistivity imaging up to 50 meters depth with superior accuracy compared to traditional inversion techniques.

Contribution

A novel supervised learning approach using neural networks for ERT data interpretation, achieving improved imaging accuracy and efficiency.

Findings

Neural network outperforms traditional inversion methods.

Effective interpretation of ERT data up to 50 meters depth.

Uses simple Wenner-Schlumberger survey configuration.

Abstract

Electrical Resistivity Tomography (ERT) has been extensively used for imaging the subsurface resistivity distribution and structure. Over the years, many algorithms have been developed in order to solve the subsurface resistivity distribution from the ERT measurements. In this paper a new method for interpreting the ERT measurements is presented. Using supervised learning to train a neural network, we are able to interpret the ERT measurement into a 2D image of the underground resistivity up to depths of 50 meters while using a simple Wenner-Schlumberger survey of 96 electrodes with 1 meter spacing. The neural network is trained and tested using simulative data and it is shown to have superior results over a well established inversion method.