Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models

Seabed logging (SBL) is an application of controlled-source electromagnetic (CSEM) waves to discover marine hydrocarbon-filled reservoirs based on the resistivity contrast of subsurface underneath the seabed. Current practice for processing marine CSEM responses utilizes meshes-based algorithms. The...

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Main Authors: Mohd Aris, M.N., Daud, H., Mohd Noh, K.A., Dass, S.C.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.28902 /
Published: Springer Science and Business Media Deutschland GmbH 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115422736&doi=10.1007%2f978-3-030-79606-8_17&partnerID=40&md5=41cd5c48eadc2e6caa194faca25641be
http://eprints.utp.edu.my/28902/
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spelling utp-eprints.289022022-03-17T02:01:54Z Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models Mohd Aris, M.N. Daud, H. Mohd Noh, K.A. Dass, S.C. Seabed logging (SBL) is an application of controlled-source electromagnetic (CSEM) waves to discover marine hydrocarbon-filled reservoirs based on the resistivity contrast of subsurface underneath the seabed. Current practice for processing marine CSEM responses utilizes meshes-based algorithms. The ad hoc algorithms require high computational time to solve the integrals and linear equations. Therefore, this synthetic-based study proposes Gaussian process regression (GPR) to estimate marine CSEM responses at various resistivities of target layer. Synthetic multifrequency SBL responses with target depth of 500 m from the seabed are modelled by finite element (FE) method using computer simulation technology (CST) software. As the prior information to the GPR, the target layer is parameterized with resistivity of 30�510 Ωm with an increment of 60 Ωm. By using MATLAB software, a two-dimensional (2D) GPR model is developed to estimate the marine CSEM responses at unobserved resistivities. For the validation, the mean absolute deviation (MAD), mean squared error (MSE) and root mean squared error (RMSE) between the 2D GP model and the CST outputs (i.e., true values) at the unobserved resistivities are calculated. The computational time for evaluating the marine CSEM using GPR and FE are computed and compared. The resulting error measurements and the computational time revealed that GPR can estimate the marine CSEM responses efficiently and at par to the current methods. © 2022, Institute of Technology PETRONAS Sdn Bhd. Springer Science and Business Media Deutschland GmbH 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115422736&doi=10.1007%2f978-3-030-79606-8_17&partnerID=40&md5=41cd5c48eadc2e6caa194faca25641be Mohd Aris, M.N. and Daud, H. and Mohd Noh, K.A. and Dass, S.C. (2022) Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models. Studies in Systems, Decision and Control, 383 . pp. 235-247. http://eprints.utp.edu.my/28902/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Seabed logging (SBL) is an application of controlled-source electromagnetic (CSEM) waves to discover marine hydrocarbon-filled reservoirs based on the resistivity contrast of subsurface underneath the seabed. Current practice for processing marine CSEM responses utilizes meshes-based algorithms. The ad hoc algorithms require high computational time to solve the integrals and linear equations. Therefore, this synthetic-based study proposes Gaussian process regression (GPR) to estimate marine CSEM responses at various resistivities of target layer. Synthetic multifrequency SBL responses with target depth of 500 m from the seabed are modelled by finite element (FE) method using computer simulation technology (CST) software. As the prior information to the GPR, the target layer is parameterized with resistivity of 30�510 Ωm with an increment of 60 Ωm. By using MATLAB software, a two-dimensional (2D) GPR model is developed to estimate the marine CSEM responses at unobserved resistivities. For the validation, the mean absolute deviation (MAD), mean squared error (MSE) and root mean squared error (RMSE) between the 2D GP model and the CST outputs (i.e., true values) at the unobserved resistivities are calculated. The computational time for evaluating the marine CSEM using GPR and FE are computed and compared. The resulting error measurements and the computational time revealed that GPR can estimate the marine CSEM responses efficiently and at par to the current methods. © 2022, Institute of Technology PETRONAS Sdn Bhd.
format Article
author Mohd Aris, M.N.
Daud, H.
Mohd Noh, K.A.
Dass, S.C.
spellingShingle Mohd Aris, M.N.
Daud, H.
Mohd Noh, K.A.
Dass, S.C.
Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models
author_sort Mohd Aris, M.N.
title Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models
title_short Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models
title_full Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models
title_fullStr Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models
title_full_unstemmed Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models
title_sort estimating marine csem responses using gaussian process regression based on synthetic models
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115422736&doi=10.1007%2f978-3-030-79606-8_17&partnerID=40&md5=41cd5c48eadc2e6caa194faca25641be
http://eprints.utp.edu.my/28902/
_version_ 1741197168092381184
score 11.62408

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