Anticlinal Structure Modeling with Feed Forward Neural Networks for Residual Gravity Anomaly Profile

Abstract

Geologically, Anticlines are the most important geological structures amongst regional studies and hydrocarbon exploration methods. In general, inversion of gravity anomalies is non-unique in the sense that the observed gravity anomalies in a survey can be explained by a variety of density distributions. To resolve such an ambiguity, the anomalous mass should be estimated by a suitable geometry with a defined density contrast. Since anticlinal structures have mostly two non-isocline skirt, therefore utilization of the isosceles triangular model will be accompanied by a large error in the forward modeling. We have proposed using two adjoining right triangle for resolving mentioned problem. The density has been assumed constant. In this paper, a new method for anticline structure modeling based on feed forward neural network is presented. The network is trained by synthetic data as input and output. For feed forward neural network training we have used the back-propagation algorithm. The results indi¬cate that feed forward neural networks, if adequately trained, can predict the 2D form of anticline structure. The proposed method was applied to gravity data from Korand in Iran. The modeling results show high similarity with the attained results from seismic operation.