Inversion of quasi-3D DC resistivity imaging data using artificial neural networks

Abstract

The objective of this paper is to investigate the applicability of artificial neural networks in inverting quasi-3D DC resistivity imaging data. An electrical resistivity imaging survey was carried out along seven parallel lines using a dipole-dipole array to confirm the validation of the results of an inversion using an artificial neural network technique. The model used to produce synthetic data to train the artificial neural network was a homogeneous medium of 100 Ωm resistivity with an embedded anomalous body of 1000 Ωm resistivity. The network was trained using 21 datasets (comprising 12159 data points) and tested on another 11 synthetic datasets (comprising 6369 data points) and on real field data. Another 24 test datasets (comprising 13896 data points) consisting of different resistivities for the background and the anomalous bodies were used in order to test the interpolation and extrapolation of network properties. Different learning paradigms were tried in the training process of the neural network, with the resilient propagation paradigm being the most efficient. The number of nodes, hidden layers, and efficient values for learning rate and momentum coefficient have been studied. Although a significant correlation between results of the neural network and the conventional robust inversion technique was found, the ANN results show more details of the subsurface structure, and the RMS misfits for the results of the neural network are less than seen with conventional methods. The interpreted results show that the trained network was able to invert quasi-3D electrical resistivity imaging data obtained by dipole-dipole configuration both rapidly and accurately.

Review on Resistivity Inversion of Underground Abnormal Bodies

“…Method Traditional machine learning converts real-world problems into mathematical models, trains historical data models, and finally converts the new data to real-world solutions. Among them, the artificial neural network is suitable for solving this kind of structure is complex changes of the underground geological inversion problem, not to need for data analysis and modeling, able to recognize and deal with the background and the law is not clear information [27][28][29], however, it is very hard to complex geological region has certain limitation, for example, hard training large networks and depth, In the inversion process, there are problems such as over-fitting, slow convergence rate and low accuracy [7,[30][31][32]. Based on this, domestic and foreign geophysicists have carried out in-depth research on this issue and put forward a series of improved methods, such as: Jiang et al proposed COSFLA optimization based on wavelet packet denoising and ANFIS network [33] and resistivity imaging inversion based on kernel principal component wavelet neural network based on ISFLA training [34], which achieved good results by using kernel principal component wavelet neural network.…”

A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

“…It is used to prevent the system from converging to a local minimum. The momentum and learning rate terms were analyzed by Ahmad Neyamadpour et al (2010) and suggested the learning rate of 0.01, and a momentum coefficient of 0.2.…”

A comparative study for the estimation of geodetic point velocity by artificial neural networks

“…The momentum term (α) dampens the amount of weight change by adding in a portion of the weight change from the previous iteration. The momentum term is credited with smoothing out large changes in the weights and with helping the network converge faster when the error is changing in the correct direction (Neyamadpour et al 2010). In this study, the estimation is started with a learning rate of 0.3 according to the guidelines suggested by Neuner (2010).…”