@inproceedings{scholars6611,
           title = {Squared exponential covariance function for prediction of hydrocarbon in seabed logging application},
       publisher = {American Institute of Physics Inc.},
         journal = {AIP Conference Proceedings},
          volume = {1787},
            note = {cited By 2; Conference of 4th International Conference on Fundamental and Applied Sciences, ICFAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125141},
             doi = {10.1063/1.4968061},
            year = {2016},
          author = {Mukhtar, S. M. and Daud, H. and Dass, S. C.},
            issn = {0094243X},
            isbn = {9780735414518},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006013732&doi=10.1063\%2f1.4968061&partnerID=40&md5=2a3efda7e71c464c053a27aebc0eebf9},
        abstract = {Seabed Logging technology (SBL) has progressively emerged as one of the demanding technologies in Exploration and Production (E\&P) industry. Hydrocarbon prediction in deep water areas is crucial task for a driller in any oil and gas company as drilling cost is very expensive. Simulation data generated by Computer Software Technology (CST) is used to predict the presence of hydrocarbon where the models replicate real SBL environment. These models indicate that the hydrocarbon filled reservoirs are more resistive than surrounding water filled sediments. Then, as hydrocarbon depth is increased, it is more challenging to differentiate data with and without hydrocarbon. MATLAB is used for data extractions for curve fitting process using Gaussian process (GP). GP can be classified into regression and classification problems, where this work only focuses on Gaussian process regression (GPR) problem. Most popular choice to supervise GPR is squared exponential (SE), as it provides stability and probabilistic prediction in huge amounts of data. Hence, SE is used to predict the presence or absence of hydrocarbon in the reservoir from the data generated. {\^A}{\copyright} 2016 Author(s).}
}