Revolutionizing seismic data compression: unlocking the power of stable diffusion neural networks

Ayrat Abdullin*, Umair Bin Waheed, Naveed Iqbal

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


Seismic data compression is a significant concern with the growing interest in distributed acoustic sensing. The large amounts of data generated by fiber optic sensors must be processed and analyzed in real-time, requiring the use of sophisticated compression techniques. However, achieving high compression ratios without sacrificing accuracy or speed remains a challenge. We propose a novel implementation of a stable diffusion (SD) deep neural network for in-field seismic data compression. We show that the proposed SD-based method achieves a compression ratio of 307 on a synthetic data set.

Original languageEnglish
Pages (from-to)474-478
Number of pages5
JournalSEG Technical Program Expanded Abstracts
StatePublished - 14 Dec 2023
Event3rd International Meeting for Applied Geoscience and Energy, IMAGE 2023 - Houston, United States
Duration: 28 Aug 20231 Sep 2023

Bibliographical note

Publisher Copyright:
© 2023 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geophysics


Dive into the research topics of 'Revolutionizing seismic data compression: unlocking the power of stable diffusion neural networks'. Together they form a unique fingerprint.

Cite this