Forward Modeling with Forced Neural Networks for Gravity Anomaly Prof?le |
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Authors: | Onur Osman A Muhittin Albora and Osman Nuri Ucan |
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Institution: | (1) Istanbul Commerce University, Ragip Gumuspala Cad. No. 80, Eminonu, Istanbul, Turkey;(2) Engineering Faculty, Geophysical Department, Istanbul University, 34850 Avcilar, Istanbul, Turkey;(3) Engineering Faculty, Electrical and Electronics Department, Istanbul University, 34850 Avcilar, Istanbul, Turkey |
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Abstract: | 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. |
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Keywords: | Neural network Gravity anomaly Modeling Sivas– Gürün basin |
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