Inversion of magnetic anomalies due to isolated thin dike-like sources using artificial neural networks

A new approach is proposed to interpret magnetic anomalies caused by isolated thin dike-like causative targets. The approach is essentially based on utilizing artificial neural network (ANN) inversion for estimating the problem parameters. Particularly, the modular neural network (MNN) is used for the inversion process in order to quantitatively interpret the magnetic anomalies. The MNN inversion has been first tested on a synthetic data with and without random white Gaussian noise. The effect of random noise has been clearly investigated where it showed that the approach provided satisfactory results. Furthermore, three field examples have been inverted in order to investigate the applicability of the proposed approach. The results showed good agreement with the techniques that have been stated in the literatures.