A new approach for residual gravity anomaly profile interpretations: Forced Neural Network (FNN)

摘要

This paper presents a new approach for interpretation of residual gravity anomaly profiles, assuming horizontalcylinders as source. The new method, called Forced Neural Network (FNN), is introduced to determine the undergroundstructure parameters which cause the anomalies. New technologies are improved to detect the bordersof geological bodies in a reliable way. In a first phase one neuron is used to model the system and a back propagationalgorithm is applied to find the density difference. In a second phase, density differences are quantifiedand a mean square error is computed. This process is iterated until the mean square error is small enough. Afterobtaining reliable results in the case of synthetic data, to simulate real data, the real case of the Gulf of Mexicogravity anomaly map, which has the form of anticline structure, is examined. Gravity anomaly values from across section of this real case, result to be very close to those obtained with the proposed method.