Processing synthetic seabed logging (SBL) data using Gaussian Process regression

This paper presents a study on processing one dimensional (1D) synthetic seabed logging (SBL) data which were generated through Computer Simulation Technology (CST) software using Gaussian Process Regression (GPR). Seabed Logging (SBL) is an application of electromagnetic (EM) wave emitted from a controlled source to discover hydrocarbon-saturated layers beneath the seabed. In this paper, GPR is proposed as the processing tool to provide any additional information for SBL application. GPR is able to provide predicted mean values and uncertainty measurement in terms of ± standard deviation. The procedures of regressing Gaussian Process (GP) are described thoroughly in this paper. Squared exponential (SE) is chosen as the covariance function used in the GPR. SE covariance function is capable of producing smooth and infinitely differentiable of predicted functional estimates. Log-marginal likelihood is then optimized in order to infer the hyper-parameters involved in the SE covariance function. For model validation, mean square error (MSE) is calculated and observed to determine the reliability of the GPR model in the synthetic SBL data. This shall give an indication that GPR is an appropriate tool for processing nonlinear SBL data with uncertainty quantification and low MSE.