Location of subsurface targets in geophysical data using neural networks

Abstract

Abstract Neural networks were used to estimate the offset, depth, and conductivity-area product of a conductive target given an electromagnetic ellipticity image of the target. Five different neural network paradigms and five different representations of the ellipticity image were compared. The networks were trained with synthetic images of the target and tested on field data and more synthetic data. The extrapolation capabilities of the networks were also tested with synthetic data lying outside the spatial limits of the training set. The data representations consisted of the whole image, the subsampled image, the peak and adjacent troughs, the peak, and components from a two-dimensional (2-D) fast Fourier transform. The paradigms tested were standard back propagation, directed random search, functional link, extended delta bar delta, and the hybrid combination of self-organizing map and back propagation. For input patterns with less than 100 elements, the directed random search and functional link networks gave the best results. For patterns with more than 100 elements, self-organizing map to back propagation was most accurate. Using the whole ellipticity image gave the most accurate results for all the network paradigms. The fast Fourier transform data representation also yielded good results with a much faster computation time. Average accuracies of offset, depth, and conductivity-area product as high as 97 percent could be achieved for test and field data and 88 percent for extrapolation data.