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Magnetic inverse modelling of a dike using the artificial neural network approach
Author(s) -
Alimoradi Andisheh,
Angorani Saeed,
Ebrahimzadeh Mehrnoosh,
Shariat Panahi Masoud
Publication year - 2011
Publication title -
near surface geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.639
H-Index - 39
eISSN - 1873-0604
pISSN - 1569-4445
DOI - 10.3997/1873-0604.2011008
Subject(s) - artificial neural network , dike , geology , backpropagation , field (mathematics) , inverse , inverse problem , artificial intelligence , geophysics , computer science , mathematics , geometry , mathematical analysis , pure mathematics , geochemistry
ABSTRACT Artificial neural systems have been used in a variety of problems in the fields of science and engineering. Here we describe a study of the application of neural networks in solving some geophysical inverse problems. In particular, we try to estimate the depth of dikes using magnetic data and a three‐layer feed forward neural network. The network is trained by synthetic data as input and output. For forward neural network training we use the back‐propagation algorithm. Results indicate that forward neural networks, if adequately trained, can predict a reasonably accurate depth for dikes. The proposed method was applied to magnetic data over the Darmian Iron field in Iran. Results were compared to real values from well data and proved the good performance of the trained neural network in predicting the dike’s depth.