In this paper, we introduce a new method called Forced Neural Network
(FNN) to find the parameters of the object in geophysical section respect to gravity
anomaly assuming the prismatic model. The aim of the geological modeling is to find
the shape and location of underground structure, which cause the anomalies, in 2D
cross section. At the first stage, we use one neuron to model the system and apply
back propagation algorithm to find out the density difference. At the second level,
quantization is applied to the density differences and mean square error of the system
is computed. This process goes on until the mean square error of the system is small
enough. First, we use FNN to two synthetic data, and then the Sivas–Gürün basin map
in Turkey is chosen as a real data application. Anomaly values of the cross section,
which is taken from the gravity anomaly map of Sivas–Gürün basin, are very close to
those obtained from the proposed method.
In this paper, we introduce a new method called Forced Neural Network (FNN) to find the parameters of the object in geophysical section respect to gravity anomaly assuming the prismatic model. The aim of the geological modeling is to find the shape and location of underground structure, which cause the anomalies, in 2D cross section. At the first stage, we use one neuron to model the system and apply back propagation algorithm to find out the density difference. At the second level, quantization is applied to the density differences and mean square error of the system is computed. This process goes on until the mean square error of the system is small enough. First, we use FNN to two synthetic data, and then the Sivas-Gurun basin map in Turkey is chosen as a real data application. Anomaly values of the cross section, which is taken from the gravity anomaly map of Sivas-Gurun basin, are very close to those obtained from the proposed method.