Lossless seismic data compression using adaptive linear prediction

Mandyam, Giridhar; Magotra, N.; McCoy, W.

doi:10.1109/igarss.1996.516556

Cited by 7 publications

(1 citation statement)

References 3 publications

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“…Seismic data is usually correlative with each other in time and space, so it can be compressed by predicting or transforming algorithm to extract their characteristics. The predictors are designed to code data in prediction algorithm, for example the self-adaptation linear prediction code, (Mandyam, et al, 1996), prediction tree coding scheme (Memon, et al, 1994). Transform coding minimizes data correlation in transform such as DCT transform (WANG WU, 2000), Walsh transform (Wood, 1974) and wavelet transform (Villasenor, et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Seismic data compression based on integer wavelet transform

Wang

Teng

Gao

et al. 2004

Acta Seismologica Sinica

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Due to the particularity of the seismic data, they must be treated by lossless compression algorithm in some cases. In the paper, based on the integer wavelet transform, the lossless compression algorithm is studied. Comparing with the traditional algorithm, it can better improve the compression rate. CDF (2, n) biorthogonal wavelet family can lead to better compression ratio than other CDF family, SWE and CRF, which is owe to its capability in canceling data redundancies and focusing data characteristics. CDF (2, n) family is suitable as the wavelet function of the lossless compression seismic data.

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