收藏切换
Generalizable digital rock image segmentation under limited data with the segment anything model
收藏切换
PDF
Ziqiang Wanga, Zhiyu Houa, b, Shuai Houa, Danping Caoa, *
Intelligent Geoengineering | 2026, 3(1) : 1 - 10
Less
收藏切换
Intelligent Geoengineering | 2026, 3(1): 1-10
Full length article
Generalizable digital rock image segmentation under limited data with the segment anything model
Full
Ziqiang Wanga, Zhiyu Houa, b, Shuai Houa, Danping Caoa, *
Affiliations
  • aState Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
  • bInstitute of Earth Sciences, University of Lausanne, Lausanne CH-1015, Switzerland
Published: 2026-03-10 doi: 10.1016/j.ige.2025.12.001
Outline
收藏切换

Accurate segmentation of digital rock images is essential for characterizing pore-matrix systems and predicting petrophysical properties. However, the diversity of rock textures across different lithologies poses a significant challenge for conventional segmentation networks, especially under limited training data. To address this, we introduce DRI-SAM (Digital Rock Image - Segment Anything Model), a hybrid segmentation framework that leverages the powerful visual prior of the Segment Anything Model (SAM) and adapts it to the digital rock domain. Specifically, we apply LoRA-based fine-tuning to SAM's image encoder to better capture rock-specific microstructures, while U-Net is employed to generate prompt points, guiding SAM toward accurate pore-matrix delineation. This approach retains the encoder's representational power while allowing domain-specific adaptation via LoRA, enabling effective cross-domain generalization under limited training data. The model is trained exclusively on 200 annotated images of Bentheimer sandstone, covering two distinct voxel resolutions, and is evaluated on digital rock images of varying lithologies, resolutions and imaging modalities. The results confirm that DRI-SAM achieves accurate segmentation on both sandstone and more challenging carbonate samples, including synthetic and SEM images, without additional retraining or parameter adjustments. Compared to DeepLabV3 + and the only LoRA-tuned SAM, DRI-SAM demonstrates superior performance under limited supervision, highlighting its strong generalization and practical value in digital rock image analysis. Moreover, the findings suggest that foundation models like SAM, when properly adapted, also hold great promise for broader geoscientific imaging tasks.

Digital rock physics  /  Image segmentation  /  Segment Anything Model
Ziqiang Wang, Zhiyu Hou, Shuai Hou, Danping Cao. Generalizable digital rock image segmentation under limited data with the segment anything model[J]. Intelligent Geoengineering, 2026 , 3 (1) : 1 -10 . DOI: 10.1016/j.ige.2025.12.001
  • National Natural Science Foundation of China(42325403)
  • National Science and Technology Major Project of China(2024ZD1004201)
  • China Scholarship Council(CSC202306450071)
Year 2026 volume 3 Issue 1
PDF
2
0
Cite this Article
BibTeX
Article Info
doi: 10.1016/j.ige.2025.12.001
  • Receive Date:2025-10-05
  • Online Date:2026-06-18
  • Published:2026-03-10
Article Data
Affiliations
History
  • Received:2025-10-05
  • Revised:2025-11-27
  • Accepted:2025-12-09
Funding
National Natural Science Foundation of China(42325403)
National Science and Technology Major Project of China(2024ZD1004201)
China Scholarship Council(CSC202306450071)
Affiliations
    aState Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
    bInstitute of Earth Sciences, University of Lausanne, Lausanne CH-1015, Switzerland

Corresponding:

* China University of Petroleum (East China), Qingdao 266580, China. E-mail address: (D. Cao).
References
Share
https://castjournals.cast.org.cn/joweb/igeo/EN/10.1016/j.ige.2025.12.001
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT