3-D Poststack Seismic Data Compression With a Deep Autoencoder

Schiavon, Ana Paula; Ribeiro, Kevyn Swhants; Navarro, João Paulo; Vieira, Marcelo Bernardes; Silva, Pedro Mário Cruz e

doi:10.1109/lgrs.2020.3028023

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…By contrast, other seismic compression algorithms based on learned transforms have recently emerged. Schiavon et al (2020) proposed a deep autoencoder to compress post-stack seismic data. Helal et al (2021) proposed two convolutional autoencoders, where the first model is adapted to low compression rates (CRs), whereas the second model is more efficient when the user needs to reach high CR.…”

Section: Introductionmentioning

confidence: 99%

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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%

“…Schiavon et al. (2020) proposed a deep autoencoder to compress post‐stack seismic data. Helal et al.…”

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

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“…However, this method is not a generalized method because the feature representation is not generated from different attributes of input traces. A 3D deep learning technique is proposed in Schiavon et al [18] to compress seismic data with low bit rate. This technique is less sensitive to noise and can reconstruct the seismic data with high quality.…”

Section: Introductionmentioning

confidence: 99%

Seismic Data Compression Using Deep Learning

et al. 2021

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The exponential growth of the size of seismic data recorded in seismic surveys and real time data monitoring makes seismic data compression strongly demanded. Furthermore, compression will lead to an efficient use of the bandwidth assigned for the communication link between the seismic stations and the main center. In this paper, two convolutional autoencoders (CAEs) are proposed for seismic data compression. The two algorithms are mainly based on the convolutional neural network (CNN), which has the capability to compress the seismic data into feature representations, thereby allowing the decoder to perfectly reconstruct the input seismic data. The results show that the first model is efficient at low compression ratios (CRs), while the second model improves the signal-to-noise ratio (SNR) from about 3 dB to 12 dB compared to the other benchmark algorithms at moderate and high CRs. INDEX TERMS Convolutional autoencoders (CAE), Deep learning, Seismic data compression.

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