Airwaves estimation in shallow water CSEM data: Multi-layer perceptron versus multiple regression
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...
| Main Authors: | Abdulkarim, M., Ahmad, W.F.W., Ansari, A., Nyamasvisva, E.T., Shafie, A. |
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| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.31236 / |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2014
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938772012&doi=10.1109%2fICCOINS.2014.6868367&partnerID=40&md5=3ac022e89cf2d912ca20f73818e47b26 http://eprints.utp.edu.my/31236/ |
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| Summary: |
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. |
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