The method of Artificial Neural Network is used as a suitable tool for intelligent interpretation 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, this Artificial Neural Network was used to estimate the depth of a Qanat (an underground channel) located at north entrance of the Institute of Geophysics and the result was very near to the real value of the depth.
Hajian,A R , Ebrahim Zadeh Ardestani,V and Lucas,C . (2011). Depth estimation of gravity anomalies using Hopfield Neural Networks. (e23099). Journal of the Earth and Space Physics, 37(2), e23099
MLA
Hajian,A R , , Ebrahim Zadeh Ardestani,V , and Lucas,C . "Depth estimation of gravity anomalies using Hopfield Neural Networks" .e23099 , Journal of the Earth and Space Physics, 37, 2, 2011, e23099.
HARVARD
Hajian A R, Ebrahim Zadeh Ardestani V, Lucas C. (2011). 'Depth estimation of gravity anomalies using Hopfield Neural Networks', Journal of the Earth and Space Physics, 37(2), e23099.
CHICAGO
A R Hajian, V Ebrahim Zadeh Ardestani and C Lucas, "Depth estimation of gravity anomalies using Hopfield Neural Networks," Journal of the Earth and Space Physics, 37 2 (2011): e23099,
VANCOUVER
Hajian A R, Ebrahim Zadeh Ardestani V, Lucas C. Depth estimation of gravity anomalies using Hopfield Neural Networks. JESP. 2011;37(2):e23099 (In Persian).
