@inproceedings{scholars882,
            note = {cited By 3; Conference of 2010 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2010 ; Conference Date: 9 November 2010 Through 11 November 2010; Conference Code:84329},
            year = {2010},
             doi = {10.1109/APACE.2010.5720090},
         journal = {2010 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2010 - Proceedings},
         address = {Port Dickson},
           title = {Forward modeling of seabed logging with controlled source electromagnetic method using multilayer perceptron},
        keywords = {Artificial Neural Network; Controlled source; Data sets; Electromagnetic methods; Forward modeling; Hidden layers; Hydrocarbon layers; Input layers; Multi-physics; Multilayer perceptron; Output layer; Performance comparison; Sea floor; Seabed logging; Simulation software, Computer software; Data handling; Electromagnetism; Hydrocarbon refining; Hydrocarbons; Multilayers; Neural networks; Wireless sensor networks, Electromagnetic logging},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79953048315&doi=10.1109\%2fAPACE.2010.5720090&partnerID=40&md5=353fe62768963a29c26da0b61a34a2a7},
        abstract = {Forward modeling is an important step in processing data of seabed logging (SBL) with controlled source electromagnetic (CSEM) method to determine the location of a hydrocarbon layer under the seafloor. In this research, forward modeling was conducted using a multi layer perceptron (MLP), which is an important type of artificial neural networks. To train this MLP, a data set was generated using simulation software: COMSOL Multiphysics. The MLP designed has 3 layers with 3 neurons in input layer and 1 neuron in output layer. The single hidden layer contained neurons whose number had been varied between 3 until 15 neurons. The performance comparison showed that the MLP with 10 neurons in its hidden layer was the best to model SBL with CSEM method. {\^A}{\copyright} 2010 IEEE.},
          author = {Arif, A. and Asirvadam, V. S. and Karsiti, M. N.},
            isbn = {9781424485666}
}