Depth estimation of subsurface cavities via Multi Layer Perceptron Neural Network from microgravity data  Fulltext 

[ Alireza Hajian ] - Institute of geophysics, Tehran University
[ V.E.Ardestani ] - Head of gravity, Institute of geophysics, Tehran University
[ Caro Lucas ] - Head of control, Electrical Engineering Department, Tehran university
[ Mohaddeseh Hajian ] - Geology Department,Isfahan University

خلاصه مقاله:

We aim to estimate the depth of subsurface cavities from microgravity data through a Multi Layer Perceptron(MLP) neural network.Infact, this method is an intelligent way to interpret microgravity data and gain an estimation of depth. The MLP neural network was trained for two main models of cavities: sphere and cylinder in a domain of radius and depth. We tested different MLP’s with different number of neurons in the hidden layer and obtained the optimum value for number of neurons in the hidden layer. Then it was tested in present of ٣٠٪ noise(S/N=.٣), and also tested for real data. It presented good results for depth estimation of subsurface cavitie

Microgravity, depth estimation, subsurface cavities, artificial neural networks, Multi Layer Perceptron