DocumentCode :
297868
Title :
Lossless seismic data compression using adaptive linear prediction
Author :
Mandyam, Giridhar ; Magotra, Neeraj ; McCoy, Wes
Author_Institution :
Dept. of Electr. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA
Volume :
2
fYear :
1996
fDate :
27-31 May 1996
Firstpage :
1029
Abstract :
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
Keywords :
adaptive codes; adaptive signal processing; data compression; geophysical signal processing; geophysical techniques; seismology; adaptive linear prediction; adaptive linear predictor; adaptive signal processing; geophysical measurement technique; gradient adaptive lattice; lossless compression; lossless data compression; normalize least-mean square; recursive least squares lattice; seismic data compression; seismic waveform; seismology; Adaptive filters; Data compression; Decorrelation; Entropy; Equations; Finite impulse response filter; Lattices; Stochastic processes; Testing; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1996. IGARSS '96. 'Remote Sensing for a Sustainable Future.', International
Conference_Location :
Lincoln, NE
Print_ISBN :
0-7803-3068-4
Type :
conf
DOI :
10.1109/IGARSS.1996.516556
Filename :
516556
Link To Document :
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