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Abstract

Summary

This study presents an innovative deep learning-based workflow for the semantic segmentation of sedimentary components in core images. The approach focuses on identifying and quantifying major components of reef systems (corals, coralline algae, microbialites, bioclastic sands...), from cores collected during IODP Expedition 389 – Hawaiian Drowned Reefs.

High-resolution images of the cores were obtained with a linescan camera, resulting in continuous images. A U-Net architecture, widely recognized for its efficiency in image segmentation, was employed using a weighted Dice-Sorensen coefficient to address class imbalances. The model achieved a segmentation accuracy of 81.1% after post-processing with Conditional Random Fields (CRF), improving the quality of segmentation results. Secondly, the model also quantifies the relative proportions of sedimentary components along core depths, aiming at facilitating the interpretation of paleoenvironmental variations recorded by reef systems.

This workflow provides a robust, automated solution for core analysis, reducing the time and expertise required. It holds significant potential for industrial applications like reservoir characterization and societal studies, such as paleoenvironmental reconstructions. Perspectives includes technical challenges (color normalization, benchmark the loss weight...) and methodological improvements (model refinement for unusual or complex sedimentary patterns, integration of complementary datasets to develop a multiple data source approach.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202539025
2025-03-24
2026-02-15

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References

  1. Anjom, F.K., Vaccarino, F. and Socco, L.V. [2024] Machine learning for seismic exploration: Where are we and how far are we from the holy grail?GEOPHYSICS, 89, WA157–WA178.
     [Google Scholar]
  2. Azzam, F., Blaise, T. and Brigaud, B. [2024] Automated petrographic image analysis by supervised and unsupervised machine learning methods. Sedimentologika, 2(2). Number: 2.
     [Google Scholar]
  3. Bouziat, A., Lechevallier, A., Koroko, A., Lecomte, J., Feraille, M., Rohais, S. and Desroziers, S. [2022] Assisted interpretation of thin sections and core samples with Deep Learning workflows. 2022(1), 1–5.
     [Google Scholar]
  4. Deo, R., Webster, J.M., Salles, T. and Chandra, R. [2024] ReefCoreSeg: A Clustering-Based Framework for Multi-Source Data Fusion for Segmentation of Reef Drill Cores. IEEE Access, 12, 12164–12180.
     [Google Scholar]
  5. Dice, L.R. [1945] Measures of the Amount of Ecologic Association Between Species. Ecology, 26(3), 297–302. https://onlinelibrary.wiley.com/doi/pdf/10.2307/1932409.
     [Google Scholar]
  6. Lafferty, J.D., McCallum, A. and Pereira, F.C.N. [2001] Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML '01. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 282–289.
     [Google Scholar]
  7. Ronneberger, O., Fischer, P. and Brox, T. [2015] U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv: 1505.04597 [cs].
     [Google Scholar]
  8. Sorensen, T. [1948] A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species Content and Its Application to Analyses of the Vegetation on Danish Commons. Munksgaard in Komm. Google-Books-ID: rpS8GAAACAAJ.
     [Google Scholar]
  9. Webster, J.M., Ravelo, A.C., Grant, H.L.J. and Expedition 389 Scientists [2024] Expedition 389 -Hawaiian Drowned Reefs Preliminary Report. https://doi.org/10.14379/iodp.pr.389.2024.
     [Google Scholar]
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Automatic Characterization and Quantification of Sedimentary Components in Reef Cores using Deep Learning for Image Segmentation
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.202539025
/content/papers/10.3997/2214-4609.202539025
/content/papers/10.3997/2214-4609.202539025
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