Airwaves estimation in shallow water CSEM data: Multi-layer perceptron versus multiple regression

Abdulkarim, M. and Ahmad, W.F.W. and Ansari, A. and Nyamasvisva, E.T. and Shafie, A. (2014) Airwaves estimation in shallow water CSEM data: Multi-layer perceptron versus multiple regression. In: UNSPECIFIED.

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Abstract

In this study, a Multi-Layer Perceptron Neural Network and Multiple Regression techniques are used to estimate airwaves associated with shallow water Controlled-Source Electro-Magnetic (CSEM) data. Both techniques are appropriate for the development of estimation models. However, multiple regression models make some assumptions about the underlying data. These assumptions include independence, normality and homogeneity of variance. Conversely, neural network based models are not constrained by such assumptions. The performance of the two techniques is calculated based on coefficient of determination (R2) and mean square error (MSE). The results indicate that MLP produced better estimate for the airwaves with MSE of 0.0113 and R2 of 0.9935. © 2014 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 0
Uncontrolled Keywords: Mean square error; Network layers; Regression analysis, Airwaves; Coefficient of determination; Controlled Source Electro-Magnetic; Multi-layer perceptron neural networks; Multiple regrssion; Shallow waters, Multilayer neural networks
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 09:03
Last Modified: 25 Mar 2022 09:03
URI: http://scholars.utp.edu.my/id/eprint/31236

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