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