TY - GEN
T1 - Multivariate Gaussian Process Regression for Evaluating Electromagnetic Profile in Screening Process of Seabed Logging Application
AU - Aris, Muhammad Naeim Mohd
AU - Daud, Hanita
AU - Mohd Noh, Khairul Arifin
AU - Dass, Sarat Chandra
N1 - Funding Information:
Acknowledgements The authors would like to thank to those who have contributed to this research work. This research work is sponsored by Yayasan Universiti Teknologi PETRONAS-Fundamental Research Grant (YUTP-FRG) (cost center: 015LC0-055).
Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Gaussian process
KW - Multivariate regression
KW - Seabed logging
UR - http://www.scopus.com/inward/record.url?scp=85123299195&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-4513-6_43
DO - 10.1007/978-981-16-4513-6_43
M3 - Conference contribution
AN - SCOPUS:85123299195
SN - 9789811645129
T3 - Springer Proceedings in Complexity
SP - 487
EP - 501
BT - Proceedings of the 6th International Conference on Fundamental and Applied Sciences
PB - Springer
T2 - 6th International Conference on Fundamental and Applied Sciences 2020
Y2 - 13 July 2021 through 15 July 2021
ER -