Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 27, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Fault detection in seismic data using a true 3D global attention convolutional network with self-supervised denoising

Matin Mahzad1, Majid Bagheri2

  • 1Institute of Geophysics, University of Tehran, Tehran, Iran. matinmahzad@yahoo.com.

Scientific Reports
|May 25, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dual-attention residual U-Net with Huber loss for robust and efficient porosity prediction from well logs.

Scientific reports·2026
Same author

Enhancing crystallization and structural modification in SiO<sub>2</sub>-CaO-Al<sub>2</sub>O<sub>3</sub> glass-ceramics using mineral-based Mn source.

Scientific reports·2026
Same author

Music therapy and Benson relaxation in postoperative care: A randomized controlled trial on pain and kinesiophobia after lumbar discectomy.

Explore (New York, N.Y.)·2026
Same author

Integrating multiple seismic attributes for fault detection using a new hybrid machine learning.

Scientific reports·2025
Same author

The effect of educational intervention using surgery preference cards on anxiety and clinical competence among operating room nursing students in general surgeries: a randomized controlled trial study.

BMC medical education·2025
Same author

Psychometric Assessment of the Persian Version of the Surgical Anxiety Questionnaire in Patients Undergoing Cosmetic Surgery.

Journal of nursing measurement·2025

This study introduces unfactorized 3D global attention for seismic fault detection, significantly improving accuracy and generalization by capturing continuous fault planes and reducing noise misclassification.

Area of Science:

  • Geophysics
  • Artificial Intelligence
  • Deep Learning

Background:

  • Accurate fault detection in 3D seismic data is crucial for understanding subsurface geology.
  • Current methods using hybrid CNN-transformer models fragment fault geometries, limiting the capture of continuous fault planes.
  • Seismic noise often leads to misclassification of faults due to limited data and noise exposure.

Purpose of the Study:

  • To present the first application of unfactorized 3D global attention for seismic fault segmentation.
  • To improve the accuracy and generalization of fault detection models.
  • To address the limitations of fragmented attention mechanisms in capturing continuous geological structures.

Main Methods:

  • Developed a hybrid U-Net model integrating 3D convolution with unfactorized 3D global self-attention.
Keywords:
3D seismic fault segmentationHybrid convolutional-attention networkSelf-supervised pretext trainingUnfactorized attentionVolumetric global attention

Related Experiment Videos

Last Updated: May 27, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

  • Employed a two-stage training approach: self-supervised denoising pretext training followed by discriminative transfer learning.
  • Utilized layer-wise learning rate decay and Unified Focal Loss for training on fault-labeled data.

    • Main Results:

    • Achieved Dice coefficient of 0.853 and IoU of 0.744 on Thebe survey data, showing statistically significant improvements over state-of-the-art 3D CNN+Swin architecture.
    • Demonstrated wider improvements of 9.9-16.7% on unseen test data, indicating superior generalization.
    • Validated the effectiveness of unfactorized global attention and noise-aware pretext training in enhancing fault detection.

    Conclusions:

    • Unfactorized 3D global attention, combined with volumetric convolution, effectively models continuous fault planes in seismic data.
    • Noise-aware pretext training significantly reduces misclassification of seismic noise as faults.
    • The proposed approach establishes a new state-of-the-art in seismic fault detection accuracy and generalization.