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Paper Information

Title: 

DEPTH ESTIMATION OF SUBSURFACE CAVITIES VIA MULTI-LAYER PERCEPTRON NEURAL NETWORK FROM MICROGRAVITY DATA

Type: PAPER
Author(s): HAJIAN ALIREZA*,ARDESTANI V.E.,LUCAS CARO,HAJIAN MOHADDESEH
 
 *INSTITUTE OF GEOPHYSICS, TEHRAN UNIVERSITY
 
Name of Seminar: CONFRANCE ZHEOFIZIKE IRAN
Type of Seminar:  CONFERENCE
Sponsor:  ANJOMANE MELI ZHEOFIZIK IRAN
Date:  2006Volume 12
 
 
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 30% NOISE (S/N=.3), AND ALSO TESTED FOR REAL DATA. IT PRESENTED GOOD RESULTS FOR DEPTH ESTIMATION OF SUBSURFACE CAVITIES.

 
Keyword(s): MICROGRAVITY, DEPTH ESTIMATION, SUBSURFACE CAVITIES, ARTIFICIAL NEURAL NETWORKS, MULTI-LAYER PERCEPTRON
 
 
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