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

Publish Year: 1384
Type: Conference paper
Language: English
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GCI12_008

Index date: 1 January 2006

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

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

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

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