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Seismic data compression: an overview
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DOI：10.1007/s00530-023-01233-4.png)
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Abstract
En 中文
Seismic Data (SD) have been for several decades used as one of the main inspection and exploration tools in various fields, particularly petroleum industry and geoscience. However, seismic datasets are huge and involve many terabytes, whose handling and storage are expensive for industry activities and computers capabilities. Driven by the large volume of SD, several compression methods have been proposed the last five decades to significantly reduce the SD size, while aiming the highest possible preservation of rocks structural and lithological characteristics. Considering the importance of SD compression, this paper is expected to make the first overview of a large number of relevant state-of-the-art papers related to SD compression, where the papers are divided into two main classes, with respect to the approach they use to extract the SD relevant features. Our aim is to review recent achievements in SD compression, and to go over covering scope, key techniques and performances of the main representative methods on this topic. This, along with issues of these latter, can help raise some open challenges and future directions for upcoming SD compression efforts.
Keywords:
Seismic data
Compression
Designed transforms
Learned transforms
Overview
Journal
IF:
3.1
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2.8K
Citations:
2.7K
Organization
Cited Papers
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