Lossless seismic data compression using adaptive linear prediction

This paper presents a comparison of adaptive linear predictors as applied to the area of lossless compression of seismic waveform data. Three methods are explored: the normalize least-mean square (NLMS) algorithm, the gradient adaptive lattice (GAL) algorithm, and the recursive least squares lattice (RLSL) algorithm. When compared to standard linear prediction techniques, all three of these methods require little overhead, are more computationally efficient, and can be implemented using floating point techniques. With respect to a standard seismic database, the RLSL filter outperforms the other two methods in nearly all cases tested.