Abstract
Magnetic surveys have been used for mineral exploration where different data processing techniques were used to derive the parameters of causative targets. In this respect, the neural network (NN) technique was used to estimate the magnetic causative target parameters. Examples of NN inversion have been tested on synthetic examples where the NN was trained well using forward models of the vertical magnetic effect of a vertical sheet and a horizontal circular cylinder. Specifically, modular neural network (MNN) inversion has been used for the parameter estimation of the causative targets, where the sigmoid function was used as the activation function. The effect of random noise and the error estimation of the horizontal location have been analyzed. When NN is applied to real data, it estimates successfully the parameters of the causative targets such as burial depths, magnetic constants, and angle of polarization. Hilbert transform has been used to locate the source origin, which is important for the NN inversion. This approach has more advantages than the conventional data inversions in terms of its efficiency and flexibility. It also gives fast solutions. The MNN approach has been applied to the Kursk and Manjampalli anomalies, where the results were shown to be in good agreement with the other techniques published in the literature.