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

Muhammad Naeim Mohd Aris, Hanita Daud, Khairul Arifin Mohd Noh, Sarat Chandra Dass

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish
Title of host publicationTowards Intelligent Systems Modeling and Simulation
PublisherSpringer
Pages235-247
Number of pages13
ISBN (Electronic)9783030796068
ISBN (Print)9783030796051
DOIs
Publication statusE-pub ahead of print - 18 Sep 2021

Publication series

NameStudies in Systems, Decision and Control
Volume383
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

Keywords

  • Controlled-source electromagnetic
  • Finite element
  • Gaussian process regression
  • Seabed logging
  • Simulation

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Systems Engineering
  • Automotive Engineering
  • Social Sciences (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)
  • Control and Optimization
  • Decision Sciences (miscellaneous)

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