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Filtration of Airwave in Seabed Logging Using Principal Component Analysis

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Part of the Communications in Computer and Information Science book series (CCIS,volume 414)

Abstract

In this research, Independent component analysis using Principal Component Analysis (ICA-PCA) technique has been applied in the field of seabed logging application for the filtration of airwaves. Independent component analysis (ICA) is a statistical approach for transforming data of multivariate nature into its constituent components (sources) which are considered to be statistically independent of each other. ICA-PCA is applied in the domain of marine controlled source electromagnetic (CSEM), called seabed logging (SBL) sensing method used for the detection of hydrocarbons based reservoirs in SBL application. ICA-PCA has not been applied before in SBL application, and therefore may reduce exploration costs in deep sea areas. The task is to identify the air waves and to filter them out, hence, the ICA-PCA algorithm is carried out for airwave filtration, at varying seawater depth from 100 m to 3000 m. It is observed that the results are favorable upto 2500 m depth. Upon increasing seawater depth, the component representing the presence of hydrocarbon becomes more dispersed, vague and indistinguishable.

Keywords

  • Principal Component Analysis
  • Seabed logging
  • Independent component analysis
  • ICA
  • PCA
  • Data mining

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Correspondence to Adeel Ansari .

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Ansari, A., Shafie, A.B., Ansari, S., Said, A.B.M., Nyamasvisva, E.T. (2014). Filtration of Airwave in Seabed Logging Using Principal Component Analysis. In: Shaikh, F., Chowdhry, B., Zeadally, S., Hussain, D., Memon, A., Uqaili, M. (eds) Communication Technologies, Information Security and Sustainable Development. IMTIC 2013. Communications in Computer and Information Science, vol 414. Springer, Cham. https://doi.org/10.1007/978-3-319-10987-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-10987-9_6

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