An ICA Approach to Purify Components of Spatial Components of Seismic Recordings

Abstract

Abstract Decomposing linear mixtures or superpositions into their components is a problem occurring in many different branches of science, such as telecommunications, Seismology, and biomedical signal analysis. Blind source separation (BSS) in particular, deals with the case where neither the sources, nor the mixing matrix or process of mixing are known, the only available data are the mixed signals. The standard approach to BSS is Independent Component Analysis (ICA). In fact ICA is a statistical technique that represents a multidimensional random vector as a linear combination of nongaussian random variables - ‘independent components’-that are as independent as possible. In 3D seismic surveys involved in exploration operations, recorded time series in each dimension are taken to be independent in nature and behavior, which is a direct result of physical response of materials into which seismic waves penetrate. But as an observation one dimension is sometimes contaminated up to one fifth by information from another dimension, resulting an increase in SNR. Here we have applied FAST-ica algorithm to a 3D seismic record sample to extract least dependent recordings for all three spatial dimensions. In order to test the reliability of the decomposition we used mutual information (MI) transfer between signals to confirm the result of outputs from FAST-ica algorithm as, least dependent components, leading to more accurate interpretations of petrophysical parameters.