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

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.