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Inversion of DC resistivity data using neural networks
Author(s) -
ElQady Gad,
Ushijima Keisuke
Publication year - 2001
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1046/j.1365-2478.2001.00267.x
Subject(s) - inversion (geology) , electrical resistivity and conductivity , artificial neural network , inverse problem , synthetic data , backpropagation , regional geology , computer science , depth sounding , algorithm , geophysics , geology , artificial intelligence , hydrogeology , mathematics , seismology , geotechnical engineering , engineering , electrical engineering , tectonics , mathematical analysis , oceanography , metamorphic petrology
The inversion of geoelectrical resistivity data is a difficult task due to its non‐linear nature. In this work, the neural network (NN) approach is studied to solve both 1D and 2D resistivity inverse problems. The efficiency of a widespread, supervised training network, the back‐propagation technique and its applicability to the resistivity problem, is investigated. Several NN paradigms have been tried on a basis of trial‐and‐error for two types of data set. In the 1D problem, the batch back‐propagation paradigm was efficient while another paradigm, called resilient propagation, was used in the 2D problem. The network was trained with synthetic examples and tested on another set of synthetic data as well as on the field data. The neural network gave a result highly correlated with that of conventional serial algorithms. It proved to be a fast, accurate and objective method for depth and resistivity estimation of both 1D and 2D DC resistivity data. The main advantage of using NN for resistivity inversion is that once the network has been trained it can perform the inversion of any vertical electrical sounding data set very rapidly.