%K Artificial Neural Network; Controlled source; Data sets; Electromagnetic methods; Forward modeling; Hidden layers; Hydrocarbon layers; Input layers; Multi-physics; Multilayer perceptron; Output layer; Performance comparison; Sea floor; Seabed logging; Simulation software, Computer software; Data handling; Electromagnetism; Hydrocarbon refining; Hydrocarbons; Multilayers; Neural networks; Wireless sensor networks, Electromagnetic logging
%X Forward modeling is an important step in processing data of seabed logging (SBL) with controlled source electromagnetic (CSEM) method to determine the location of a hydrocarbon layer under the seafloor. In this research, forward modeling was conducted using a multi layer perceptron (MLP), which is an important type of artificial neural networks. To train this MLP, a data set was generated using simulation software: COMSOL Multiphysics. The MLP designed has 3 layers with 3 neurons in input layer and 1 neuron in output layer. The single hidden layer contained neurons whose number had been varied between 3 until 15 neurons. The performance comparison showed that the MLP with 10 neurons in its hidden layer was the best to model SBL with CSEM method. © 2010 IEEE.
%L scholars882
%J 2010 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2010 - Proceedings
%O cited By 3; Conference of 2010 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2010 ; Conference Date: 9 November 2010 Through 11 November 2010; Conference Code:84329
%R 10.1109/APACE.2010.5720090
%D 2010
%A A. Arif
%A V.S. Asirvadam
%A M.N. Karsiti
%T Forward modeling of seabed logging with controlled source electromagnetic method using multilayer perceptron
%C Port Dickson