Abstract
A new approach is proposed for the interpretation of magnetic anomalies caused by dipping dikes. This approach is mainly based on modular neural network inversion for estimating the parameters of dipping dike model. Suitable network training examples and test data have been generated using forward models based on known true parameters. The training procedures adopt supervised learning routine using modular neural networks. The effect of random noise has been examined where the proposed technique showed stability and satisfactory results. The applicability of this technique has been tested on synthetic and field examples data. This technique is particularly applied to two field examples, namely magnetic anomaly over an outcropping quartz dike-like body in Karimnagar area, Andhra Pradesh, India, and Marcona magnetic anomaly, Marcona district, Peru. The results of using this technique showed good agreement with the measured field data compared with most conventional ones. Furthermore, neural networks proved to be efficient and flexible in the interpretation of magnetic anomaly of dipping dike. Copyright 2015 Saudi Society for Geosciences