Depth estimation of gravity anomalies using Hopfield Neural Networks

The method of Artificial Neural Network is used as a suitable tool for intelligentinterpretation of gravity data in this paper.We have designed a Hopfield Neural Network to estimate the gravity source depth.The designed network was tested by both synthetic and real data. As real data, thisArtificial Neural Network was used to estimate the depth of a Qanat (an undergroundchannel) located at north entrance of the Institute of Geophysics and the result was verynear to the real value of the depth