TY  - CONF
AV  - none
CY  - Serdang
KW  - Artificial Neural Network; Borehole logging; Geophysical inverse problems; Geophysical inversion; Multi layer perceptron; Synthetic data
KW  -  Electric loads; Geophysics; Inverse problems; Mean square error; Models; Multilayer neural networks; Multilayers; Research
KW  -  Well logging
ID  - scholars571
SP  - 93
TI  - Geophysical inversion using multilayer perceptron
N1  - cited By 0; Conference of 2009 IEEE Student Conference on Research and Development, SCOReD2009 ; Conference Date: 16 November 2009 Through 18 November 2009; Conference Code:80411
N2  - This paper is a continuation report of the previous research on seabed logging (SBL). In this paper, it was shown that a certain geophysical inverse problem (such as one posed by SBL) can be solved using an important class of artificial neural networks, which is a multilayer perceptron (MLP). To show this, several sets of synthetic data has been generated using some assumed models of a physical property (such as seabed resistivity) distribution. Then, these pairs of data and models were used to train a MLP with a certain architecture. Finally, the trained MLP was tested to do inversion with new data and produced a predicted model. The predicted model was reasonably close to the true model and the mean square error (MSE) between them was 0.016. ©2009 IEEE.
SN  - 9781424451876
Y1  - 2009///
EP  - 96
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952604172&doi=10.1109%2fSCORED.2009.5443293&partnerID=40&md5=64bc032669b5783d5ba541876e11ef45
A1  - Arif, A.
A1  - Al Asirvadam, V.S.
A1  - Bin Karsiti, M.N.
ER  -