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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.

This research aims at improving the methods of prediction of shear wave velocity in underground layers. We propose and showcase our methodology using a case study on the Mashhad plain in north eastern part of Iran. Geotechnical investigations had previously reported nine measurements of the SASW (Spectral Analysis of Surface Waves) method over this field and above wells which have DHT (Down Hole Test) result. Since SASW utilizes an analytical formula (which suffers from some simplicities and noise) for evaluating shear wave velocity, we use the results of SASW in a trained artificial neural network (ANN) to estimate the unknown nonlinear relationships between SASW results and those obtained by the method of DHT (treated here as real values). Our results show that an appropriately trained neural network can reliably predict the shear wave velocity between wells accurately.