Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Fundamental and Applied Sciences
PublisherSpringer
Pages487-501
Number of pages15
ISBN (Electronic)9789811645136
ISBN (Print)9789811645129
DOIs
Publication statusPublished - 2021
Event6th International Conference on Fundamental and Applied Sciences 2020 - Virtual, Online
Duration: 13 Jul 202115 Jul 2021

Publication series

NameSpringer Proceedings in Complexity
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692

Conference

Conference6th International Conference on Fundamental and Applied Sciences 2020
Abbreviated titleICFAS 2020
CityVirtual, Online
Period13/07/2115/07/21

Keywords

  • Gaussian process
  • Multivariate regression
  • Seabed logging

ASJC Scopus subject areas

  • Applied Mathematics
  • Modelling and Simulation
  • Computer Science Applications

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