Application of spatial contiguity analysis to seismic data filtering
This paper describes spatial contiguity analysis (SCA) -- also called proximity analysis -- as a multivariate filtering method. The author uses this technique to split a multivariate signal into various spatial components on different scales. It permits the separation of local and regional structures; an the filtering of either spatial correlated or random noise. The author applies it, in particular, for the analysis of a 3D seismic data set. Its application requires a preliminary structural data study to determine an appropriate neighborhood on the data location plane. The results, compared with those obtained with the principal component analysis (PCA), particular case of the Karhunen-Loeve transform, demonstrate the advantage of accounting for the data spatial properties in seismic analysis and filtering.
- OSTI ID:
- 93113
- Report Number(s):
- CONF-941015--
- Country of Publication:
- United States
- Language:
- English
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