3D modeling of gravity anomalies using the forced neural networks method

Potential field data (gravity and magnetic data) are usually analyzed by employing linear transformations, the spectral method, inversion techniques and analytic signal methods. Nowadays, there are different methods of modeling the gravity data; but each has limitations. One of the limitations of these methods is the assumption of a simple shape for buried structures whereas the actual shape could be entirely different. This study uses cubic units (3D model) to solve this limitation because affords the ability to make any shape for unknown underground structures by arranging these cubics. In this paper, a new method called Forced Neural Networks (FNN) to find the density variation of buried deposits or underground structures in different depth sections by assuming the cubic model is described. The aim of the geological modeling is to determine the shape and location of underground structures in 3-D sections. Here, one neuron network and back propagation algorithm are applied to discover the density difference. In this method, weights of the neurons are assigned as density for each cubic and the activation function has a linear property such that the outputs are the same as the inputs. After using the back propagation, densities for each cubic are updated and the output of the neurons gives the gravity anomaly. Hence, the density differences are found. However, the results of this system are insufficient because non-uniqueness and horizontal locations are constrained; therefore, the value of density difference is set to zero if its value is very close to zero according to the density difference which is obtained from geological features of the region. Otherwise these values are set to the density difference of the geological region after back propagation. Using a forced neural network, after sufficient epoch is applied, fixed values are assigned to the output of the neuron according to the density difference, and this process is continued until the mean square error of the output becomes sufficiently small. The method is used for both noise-free and noise-corrupted synthetic data and, after obtaining satisfactory results for three synthetic data models, this method was used for modeling of the real data. The Dehloran Bitumen map in Iran was chosen as a real data application. The area under consideration is located in the Zagros tectonic zone, west of Iran where we are looking for Bitumen. Layers of Medium-bedded limestone with intermediate marl-limestone are the dominant formations in the area and the hydrocarbon zone is one of the most important characteristics of the area. A program was written using the Anomaly modeling method. The final result of this method shows that the deposit begins from the low depth to approximately less than 40 meters. This modeling yeilded satisfactory results for the drilling in the region. The results of the drillings show that the lowest depth of the deposit varies from 7 to 10 meters. This method can easily be applied for gravity, microgravity and magnetic data especially for porphyry deposits.