Fault detection using seismic attributes and visual saliency

Abdulmajid Lawal*, Suhai Al-Dharrab, Mohamed Deriche, M. Amir Shafiq, Ghassan AlRegib

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

In order to detect accurately faults in seismic inline sections, we propose a new bottom-up saliency based approach using different seismic attributes such as coherence, curvature, dip, and gradient in parallel. Each attribute is calculated independently from the original seismic section. The saliency maps of aforementioned attributes are computed using covariance matrix, which are later combined to form a consolidated saliency map that highlights the seismic fault regions. The covariance matrix is used to characterize the seismic patches and captures local structures. By thresholding the variance maps and optimizing the binary points for curve fitting, the proposed workflow yields good results for faults labeling.

Original languageEnglish
Pages (from-to)1939-1943
Number of pages5
JournalSEG Technical Program Expanded Abstracts
Volume35
DOIs
StatePublished - 2016

Bibliographical note

Publisher Copyright:
© 2016 SEG.

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geophysics

Fingerprint

Dive into the research topics of 'Fault detection using seismic attributes and visual saliency'. Together they form a unique fingerprint.

Cite this