FORWARD MODELING OF MARINE ELECTROMAGNETIC SURVEY USING ARTIFICIAL NEURAL NETWORKS

• Main Author: ARIF, AGUS
• Format: Thesis
• Language: English
• Published: 2012
• Subjects: Instrumentation and Control
• Online Access: http://utpedia.utp.edu.my/21668/1/2012%20-ELECTRICAL%20AND%20ELECTRONIC-FORWARD%20MODELING%20OF%20MARINE%20ELECTROMAGNET%20SURVEY%20USING%20ARTIFICIAL%20NEURAL%20NETWORKS-AGUS%20ARIF.pdf; http://utpedia.utp.edu.my/21668/

Marine electromagnetic (EM) survey is an engineering endeavor to determine the location and dimension of hydrocarbon reservoirs which are particularly situated under the sea floor. Forward modeling is one of the important step in processing the data of a marine EM survey. By this modeling, the distribution of resistivity values along the sea bed could be mapped and the location and dimension of the associated hydrocarbon layer could be predicted. As an alternative to the established methods in conducting forward modeling, in this research two types of artificial neural networks are employed to determine the possibil�ities of them as forward models for marine EM survey. The networks are a multi-layer perceptron (MLP) network and a radial basis function (RBF) network. The motivation of this work is to find out the possibilities of these networks as forward models for marine EM survey. To achieve the research goals, a set of synthetic data must be generated using a simulation software. These data are used to train and test the MLP and RBF networks until they attained a sufficient property of generalization in modeling marine EM survey data. To validate the correctness of the models, a reverse method of forward modeling has been employed, which is the inversion process. Occam's inversion has been specifically used to validate the neural networks' for�ward modeling. It is found that artificial neural networks, specifically MLP and RBF networks, have a possibility to become forward models for marine electromagnetic sur�vey. In addition, this research found that the forward modeling by RBF network is better than the corresponding one by MLP network.