Seismic data compression based on integer wavelet transform

Wang, Xizhen; Teng, Yuntian; Gao, M.; Jiang, Hui

doi:10.1007/s11589-004-0075-4

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…Indeed, the compressed sensing fields have helped to tackle many difficulties related to seismic data starting from acquisition to full waveform inversion by exploiting the sparse structure of seismic data (Herrmann et al., 2013; Lin & Herrmann, 2013; Mansour et al., 2012). Conventionally, seismic compression algorithms are based on fixed sparse transforms (Averbuch et al., 2001; Duval & Rosten, 2000; Fajardo et al., 2015; Wang et al., 2004; Zheng & Liu, 2012), where the basis functions are analytically predefined and already known by the encoder and decoder, such as discrete cosines, wavelets and others (Elad, 2010; Mallat, 2008). By contrast, other seismic compression algorithms based on learned transforms have recently emerged.…”

Section: Introductionmentioning

confidence: 99%

Dual‐sensor wavefield separation in a compressed domain using parabolic dictionary learning

Zizi

Turquais

Day

et al. 2023

Geophysical Prospecting

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In the marine seismic industry, the size of the recorded and processed seismic data is continuously increasing and tends to become very large. Hence, applying compression algorithms specifically designed for seismic data at an early stage of the seismic processing sequence helps to save cost on storage and data transfer. Dictionary learning methods have been shown to provide state‐of‐the‐art results for seismic data compression. These methods capture similar events from the seismic data and store them in a dictionary of atoms that can be used to represent the data in a sparse manner. However, as with conventional compression algorithms, these methods still require the data to be decompressed before a processing or imaging step is carried out. Parabolic dictionary learning is a dictionary learning method where the learned atoms follow a parabolic travel time move out and are characterized by kinematic parameters such as the slope and the curvature. In this paper, we present a novel method where such kinematic parameters are used to allow the dual‐sensor (or two‐components) wavefield separation processing step directly in the dictionary learning compressed domain for 2D seismic data. Based on a synthetic seismic data set, we demonstrate that our method achieves similar results as an industry‐standard FK‐based method for wavefield separation, with the advantage of being robust to spatial aliasing without the need for data preconditioning such as interpolation and reaching a compression rate around 13. Using a field data set of marine seismic acquisition, we observe insignificant differences on a 2D stacked seismic section between the two methods, whereas reaching a compression ratio higher than 15 when our method is used. Such a method could allow full bandwidth data transfer from vessels to onshore processing centres, where the compressed data could be used to reconstruct not only the recorded data sets, but also the up‐ and down‐going parts of the wavefield.

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Section: Introductionmentioning

confidence: 99%

Dual‐sensor wavefield separation in a compressed domain using parabolic dictionary learning

Zizi

Turquais

Day

et al. 2023

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…Indeed, the compressed sensing fields have helped to tackle many difficulties related to seismic data starting from acquisition to full waveform inversion by exploiting the sparse structure of seismic data (Herrmann et al, 2013;Lin & Herrmann, 2013;Mansour et al, 2012). Conventionally, seismic compression algorithms are based on fixed sparse transforms (Averbuch et al, 2001;Duval & Rosten, 2000;Fajardo et al, 2015;Wang et al, 2004;Zheng & Liu, 2012), where the basis functions are analytically predefined and already known by the encoder and decoder, such as discrete cosines, wavelets and others (Elad, 2010;Mallat, 2008). By contrast, other seismic compression algorithms based on learned transforms have recently emerged.…”

Section: Introductionmentioning

confidence: 99%

A dictionary learning method for seismic data compression

Zizi

Turquais

2022

GEOPHYSICS

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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 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 dictionary learning 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; 3) two modes are proposed depending on the geophysical application. Based on a test seismic data set, we demonstrate superior performance of the proposed workflow in terms of compression ratio for a wide range of signal-to-residual ratios, compared to standard seismic data methods, 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 the proposed 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 dictionary learning method reaches a compression ratio of 24.85. For other applications such as visual QC of shot gathers, our method preserves the visual aspect of the data even when a compression ratio of 95 is reached.

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“…Wavelet transforms are commonly used at the decorrelation stage of compression schemes, as presented in Stigant et al (1995) and Khkne et al (2000). For lossless compression of integer samples, the integer wavelet transform (IWT) is a suitable approach as described in Giurcaneanu et al (1999) and Wang et al (2004). In this work, a study was performed on seismic data compression evaluating the improvement of the compression ratio when the same encoding strategy is applied on integer wavelet coefficients instead of the original samples.…”

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confidence: 99%