A dictionary learning method for seismic data compression

Mohammed Outhmane Faouzi Zizi

doi:10.1190/GEO2020-0948.1

A dictionary learning method for seismic data compression

2022-01-24

7

OA

AI

M

Mohammed Outhmane Faouzi Zizi *

P

Pierre Turquais

DOI：10.1190/GEO2020-0948.1

Original

Original request for help

Save

Abstract

En 中文

For a marine seismic survey, the recorded and processed data size can reach several terabytes. Storing seismic data sets is costly, and transferring them between storage devices can be challenging. Dictionary learning (DL) has been shown to provide representations with a high level of sparsity. This method stores the shape of the redundant events once and represents each occurrence of these events with a single sparse coefficient. Therefore, an efficient DL-based compression workflow, which is specifically designed for seismic data, is developed here. This compression method differs from conventional compression methods in three respects: (1) The transform domain is not predefined but data-driven, (2) the redundancy in seismic data is fully exploited by learning small-sized dictionaries from local windows of the seismic shot gathers, and (3) two modes (i.e., two different parameterizations) are proposed depending on the geophysical application. Based on a test seismic data set, we have determined superior performance of our workflow in terms of compression ratio (CR) for a wide range of signal-toresidual ratios, such as the zfp software or algorithms from the Seismic Unix package. Using a more realistic data set of marine seismic acquisition, we evaluate the capability of our workflow to preserve the seismic signal for different applications. For applications such as near-real-time transmission and long-term data storage, we observe insignificant signal leakage on a 2D line stack when the DL method reaches a CR higher than 20. For other applications, such as visual quality control of shot gathers, our method preserves the visual aspect of the data even when a CR of 95 is reached.

A dictionary learning method for seismic data compression

Abstract

Journal

Organization

Cited Papers