1887

Abstract

Summary

Fault segmentation in seismic data is still a challenge, even with the advent of Deep Learning and neural architectures like U-Net ( ) and Transformers ( ). While end-to-end solutions abstract the need to extract useful information from the data, real-world applications might benefit from hybrid, pipeline approaches. In this work, we introduce a two-step method for the fault segmentation task: an initial model produces a segmentation mask with sufficient precision so that a posterior robust estimator like RANSAC ( ) fits plane geometries to cover faults. Combining the initial mask with the fitted planes seems to increase the Recall, Fl -Score, and IoU (Intersection over Union) by sacrificing some precision.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202639093
2026-03-09
2026-02-13
Loading full text...

Full text loading...

References

  1. Fischler, M.A. and Bolles, R.C. [1981] Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6), 381–395.
    [Google Scholar]
  2. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J. and Chintala, S. [2019] PyTorch: an imperative style, high-performance deep learning library. Curran Associates Inc., Red Hook, NY, USA.
    [Google Scholar]
  3. Ronneberger, O., Fischer, R and Brox, T. [2015] U- Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, WM. and Frangi, A.F (Eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer International Publishing, Cham, 234–241.
    [Google Scholar]
  4. Salehi, S.S.M., Erdogmus, D. and Gholipour, A. [2017] Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. In: Wang, Q., Shi, Y., Suk, H.I. and Suzuki, K. (Eds.) Machine Learning in Medical Imaging. Springer International Publishing, Cham, 379–387.
    [Google Scholar]
  5. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.u. and Polosukhin, I. [2017] Attention is All you Need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. and Garnett, R. (Eds.) Advances in Neural Information Processing Systems, 30. Curran Associates, Inc.
    [Google Scholar]
  6. Wu, X. and Fomel, S. [2018] Automatic fault interpretation with optimal surface voting. Geophysics, 83(5), 067–082.
    [Google Scholar]
  7. Yan, Z., Zhang, Z. and Liu, S. [2021] Improving Performance of Seismic Fault Detection by Fine-Tuning the Convolutional Neural Network Pre-Trained with Synthetic Samples. Energies, 14(12).
    [Google Scholar]
/content/papers/10.3997/2214-4609.202639093
Loading
/content/papers/10.3997/2214-4609.202639093
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error