Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application
Computer simulation is an important task for reservoir modeling in screening process of seabed logging. Information acquired from the computer simulation could provide reliable information of electromagnetic (EM) profile and subsurface underneath the seabed. However, the computer simulation could be...
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Springer Science and Business Media B.V.
2021
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my.utp.eprints.292702022-03-25T01:26:39Z Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application Mohd Aris, M.N. Daud, H. Mohd Noh, K.A. Dass, S.C. Computer simulation is an important task for reservoir modeling in screening process of seabed logging. Information acquired from the computer simulation could provide reliable information of electromagnetic (EM) profile and subsurface underneath the seabed. However, the computer simulation could be a time-consuming task in the screening process due to its intricate mathematical equations. In this paper, a predictive model based on Gaussian process regression (GPR) is used to provide information of EM profile at various observations with low time consumption. Multivariate GPR model is developed based on computer simulation outputs. Normalized magnitude versus offset plots are analyzed to eliminate data from any undesired wave interaction. Root mean square error and coefficient of variation between the GPR model and the computer simulation outputs at untried observations are computed. On average, the resulting error was 0.0352 and the coefficient of variation was less than 0.5. This indicates the multivariate GPR model is well-fitted and capable of evaluating EM profile with low processing-time. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Springer Science and Business Media B.V. 2021 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123299195&doi=10.1007%2f978-981-16-4513-6_43&partnerID=40&md5=0d1719fe4a2071ea2f25b0bda0d981e8 Mohd Aris, M.N. and Daud, H. and Mohd Noh, K.A. and Dass, S.C. (2021) Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application. In: UNSPECIFIED. http://eprints.utp.edu.my/29270/ |
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Computer simulation is an important task for reservoir modeling in screening process of seabed logging. Information acquired from the computer simulation could provide reliable information of electromagnetic (EM) profile and subsurface underneath the seabed. However, the computer simulation could be a time-consuming task in the screening process due to its intricate mathematical equations. In this paper, a predictive model based on Gaussian process regression (GPR) is used to provide information of EM profile at various observations with low time consumption. Multivariate GPR model is developed based on computer simulation outputs. Normalized magnitude versus offset plots are analyzed to eliminate data from any undesired wave interaction. Root mean square error and coefficient of variation between the GPR model and the computer simulation outputs at untried observations are computed. On average, the resulting error was 0.0352 and the coefficient of variation was less than 0.5. This indicates the multivariate GPR model is well-fitted and capable of evaluating EM profile with low processing-time. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. |
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Conference or Workshop Item |
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Mohd Aris, M.N. Daud, H. Mohd Noh, K.A. Dass, S.C. |
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Mohd Aris, M.N. Daud, H. Mohd Noh, K.A. Dass, S.C. Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application |
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Mohd Aris, M.N. Daud, H. Mohd Noh, K.A. Dass, S.C. |
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Mohd Aris, M.N. |
title |
Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application |
title_short |
Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application |
title_full |
Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application |
title_fullStr |
Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application |
title_full_unstemmed |
Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application |
title_sort |
multivariate gaussian process regression for evaluating electromagnetic profile in screening process of seabed logging application |
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Springer Science and Business Media B.V. |
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2021 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123299195&doi=10.1007%2f978-981-16-4513-6_43&partnerID=40&md5=0d1719fe4a2071ea2f25b0bda0d981e8 http://eprints.utp.edu.my/29270/ |
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