In this context, lossless compression can enhance the performance of lossy one. Since lossless compression is based on exploiting some kind of data redundancy or predictability, not all data can be compressed. A successful compression algorithm must be tuned for compressing a particular data type. Most lossless compression technology is designed primarily for text compression applications where text data is loosely defined as data consisting of variable length words. Unfortunately, such text based lossless compression schemes do not work well on seismic data.
Seismic data is a subclass of data known as signal data.
Signal data consists of a sequence of fixed length and arithmetically formatted words, usually one to four bytes in length. It is usually band-limited or relatively smooth and thus highly serially correlated, i.e., sequential pairs of signal data have small arithmetic differences. These characteristics are generally not well exploited by most text compression algorithms. Except for the run encoding, these preprocessing schemes do not in themselves directly result in any compression, but rather enhance the effectiveness of any subsequent compression processes by reducing the range of the data values. In this paper we exploit the above mentioned signal properties to achieve lossless compression on seismic data.