Nonlinear principal component analysis for seismic data compression

Seismic data processing to interpret subsurface features is both computationally and data intensive. It is necessary to keep the dimensionality of data as small as possible, for good generalization from limited data. Therefore it is worthwhile exploring methods to compress the size of seismic data. In this paper, we consider approaches for linear and nonlinear principal component analysis (PCA) methods for compression of data for seismic signal processing. Principal component analysis (PCA) can improve seismic interpretations. Linear compression is realized by Karhunen-Loeve transform (KLT) and also by three layer autoassociative neural network (AANN) models. The distribution capturing ability of five layer AANN model is explored for nonlinear principal component analysis for compression of seismic data.