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Filter bank decomposition of seismic data with application to compression and denoising

The use of discrete wavelet based analysis, feature extraction, denoising, and compression methods have led to extremely interesting developments in the field of seismic data processing. Notwithstanding, discrete wavelets belong to a wider class of filter banks. The use of more general filter banks allows the design of filter coefficients matching the signal’s properties. Consequently, general filter banks bring forth the performance of discrete wavelet based seismic data processing techniques. In this paper, we discuss basics of general filter bank theory, and its applications to seismic data compression and denoising. We show that properly designed filter banks are able to outperform discrete wavelets in both instances.