No abstract

Artificial neural systems have been used in a variety of problems in the fields of science and engineering. Here we describe a study of the application of neural networks in solving some geophysical inverse problems. In particular, we try to estimate the depth of dikes using magnetic data and a three-layer feed forward neural network. The network is trained by synthetic data as input and output. For forward neural network training we use the back-propagation algorithm. Results indicate that forward neural networks, if adequately trained, can predict a reasonably accurate depth for dikes. The proposed method was applied to magnetic data over the Darmian Iron field in Iran. Results were compared to real values from well data and proved the good performance of the trained neural network in predicting the dike's depth. same depth and also the same width. In Fig. 1(c) the effect of dike width is illustrated by comparing the anomaly profile over two dikes, which have different widths but are situated at the same depth and angle.