Independent Component Analysis for Filtering Airwaves in Seabed Logging Application

TL;DR

This paper applies the FASTICA independent component analysis algorithm to filter out interfering airwaves in seabed logging electromagnetic signals, improving hydrocarbon detection accuracy.

Contribution

It introduces the use of FASTICA for separating airwave interference from seabed electromagnetic signals in marine CSEM applications.

Findings

FASTICA effectively isolates airwave components

Improved signal clarity for hydrocarbon detection

Demonstrated applicability in seabed logging scenarios

Abstract

Marine controlled source electromagnetic (CSEM) sensing method used for the detection of hydrocarbons based reservoirs in seabed logging application does not perform well due to the presence of the airwaves (or sea-surface). These airwaves interfere with the signal that comes from the subsurface seafloor and also tend to dominate in the receiver response at larger offsets. The task is to identify these air waves and the way they interact, and to filter them out. In this paper, a popular method for counteracting with the above stated problem scenario is Independent Component Analysis (ICA). Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional or multivariate dataset into its constituent components (sources) that are statistically as independent from each other as possible. ICA-type de-convolution algorithm that is FASTICA is considered for mixed signals de-convolution and considered convenient depending upon the nature of the source and noise model. The results from the FASTICA algorithm are shown and evaluated. In this paper, we present the FASTICA algorithm for the seabed logging application.