Principal component analysis of seismic facies

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Thilo Wrona , Indranil Pan, Rebecca E. Bell, Haakon Fossen, Robert Leslie Gawthorpe


Seismic facies analyses are fundamental to the study of sedimentary, tectonic and magmatic systems using seismic reflection data. These analyses generally assume that seismic facies are: (1) well defined, (2) distinct and (3) prevalent patterns in the data. Here, we examine these assumptions critically. First, we demonstrate how to extract the main seismic facies from conventional industry seismic reflection data using principal component analysis. Applying principle component analysis on a large number (up to 1 000 000) of windows (150×150 samples) reveals typical seismic facies showing: (1) horizontal, (2) dipping, (3) displaced and (4) crisscrossing reflections. These seismic facies are distinct in the sense that the principal components are orthogonal to one another, i.e. we cannot express any one component as a linear combination of the others. Next, we show that a small number of seismic facies (100) can explain most of the variance in the data (>0.6); an assumption that is critical to seismic facies analyses. Lastly, we show a simple way to map these facies across a seismic section.



Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics


Geophysics, Principal component analysis, Seismic reflection data, Unsupervised learning


Published: 2020-08-06 01:52

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GNU Lesser General Public License (LGPL) 2.1

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