%R 10.1007/978-3-642-25191-7₁₂
%N PART 1
%D 2011
%L scholars1811
%J Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
%O cited By 0; Conference of 2nd International Visual Informatics Conference, IVIC 2011 ; Conference Date: 9 November 2011 Through 11 November 2011; Conference Code:87315
%X Classification of Controlled Source Electro-Magnetic data into dichotomous groups based on the observed resistivity contrast measures is presented. These classifications may indicate the possible presence of hydrocarbon reservoir. Performance of Radial Basis Function of Neural network and Discriminant Function models were analyzed in this study. Both model's classification accuracy, Sensitivity and Specificity are compared and reported. Gaussian basis function was used for the hidden units in the RBF neural network, while quadratic form is used for the discriminant functions. The Controlled Source Electro-Magnetic data used for this study were obtained from simulating two known categories of data with and without hydrocarbon using COMSOL Multiphysics simulation software. The preliminary result indicates that the radial basis function neural network display superior accuracy, sensitivity and specificity in classifying CSEM data when compared to discriminant functions model. © 2011 Springer-Verlag.
%K Controlled source; Discriminant function analysis; hydrocarbon reservoir; Radial basis functions; Visual informatics, Computer software; Discriminant analysis; Hydrocarbons; Information science; Magnetic storage; Number theory; Radial basis function networks, Neural networks
%P 113-124
%C Selangor
%A M. Abdulkarim
%A A. Shafie
%A R. Razali
%A W.F. Wan Ahmad
%A A. Arif
%V 7066 L
%T Comparison on performance of radial basis function neural network and discriminant function in classification of CSEM data