AI‐Based Digital Rocks Augmentation and Assessment Metrics
Abstract Reliable uncertainty model calculation in subsurface engineering from pore‐ and grain‐scale to field‐scale relies on sufficient data, but subsurface data set acquisition remains a challenge, particularly in domains where data collection is expensive or time‐consuming, such as Computed Topog...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR037939 |
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| author | Lei Liu Bernard Chang Maša Prodanović Michael J. Pyrcz |
| author_facet | Lei Liu Bernard Chang Maša Prodanović Michael J. Pyrcz |
| author_sort | Lei Liu |
| collection | DOAJ |
| description | Abstract Reliable uncertainty model calculation in subsurface engineering from pore‐ and grain‐scale to field‐scale relies on sufficient data, but subsurface data set acquisition remains a challenge, particularly in domains where data collection is expensive or time‐consuming, such as Computed Topography (CT) imaging for digital rock images. While AI‐based data augmentation may assist the model training, it still requires many training images as well as the quality assessment of generated data. Yet, most data quantitative metrics flatten spatial data into vectors; therefore, removing the essential spatial relationships within the data. We evaluate topology‐based metrics for quality assessment of AI‐based image augmentation, coupled with digital rocks augmentation practice using the Single image Generative Adversarial Network (SinGAN) for binarized (segmented) images. Compared to most traditional dimensionality reduction methods that process images into a flattened vector, we propose topological image analysis for dimensionality reduction while preserving the essential geometric and topological features of the high‐dimensional data. To demonstrate our proposed approach, we evaluate the generated images starting from four distinct digital rock samples, sorted sandstone, synthetic sphere pack, limestone, and poorly sorted sandstone, using Minkowski functionals, image graph network‐based measures, graph Laplacian‐based measures, local trend maps, and a homogeneity‐heterogeneity classifier. Our workflow suggests that AI‐based digital rock augmentation, combined with topological dimensionality reduction offers a powerful tool for enhanced quality assessment and diagnostic of digital rock augmentation and improved interpretation to support decision‐making. |
| format | Article |
| id | doaj-art-0d8e4196228b4d1a816b468f4f59315f |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-0d8e4196228b4d1a816b468f4f59315f2025-08-20T02:36:28ZengWileyWater Resources Research0043-13971944-79732025-05-01615n/an/a10.1029/2024WR037939AI‐Based Digital Rocks Augmentation and Assessment MetricsLei Liu0Bernard Chang1Maša Prodanović2Michael J. Pyrcz3Hildebrand Department of Petroleum and Geosystems Engineering The University of Texas at Austin Austin TX USAHildebrand Department of Petroleum and Geosystems Engineering The University of Texas at Austin Austin TX USAHildebrand Department of Petroleum and Geosystems Engineering The University of Texas at Austin Austin TX USAHildebrand Department of Petroleum and Geosystems Engineering The University of Texas at Austin Austin TX USAAbstract Reliable uncertainty model calculation in subsurface engineering from pore‐ and grain‐scale to field‐scale relies on sufficient data, but subsurface data set acquisition remains a challenge, particularly in domains where data collection is expensive or time‐consuming, such as Computed Topography (CT) imaging for digital rock images. While AI‐based data augmentation may assist the model training, it still requires many training images as well as the quality assessment of generated data. Yet, most data quantitative metrics flatten spatial data into vectors; therefore, removing the essential spatial relationships within the data. We evaluate topology‐based metrics for quality assessment of AI‐based image augmentation, coupled with digital rocks augmentation practice using the Single image Generative Adversarial Network (SinGAN) for binarized (segmented) images. Compared to most traditional dimensionality reduction methods that process images into a flattened vector, we propose topological image analysis for dimensionality reduction while preserving the essential geometric and topological features of the high‐dimensional data. To demonstrate our proposed approach, we evaluate the generated images starting from four distinct digital rock samples, sorted sandstone, synthetic sphere pack, limestone, and poorly sorted sandstone, using Minkowski functionals, image graph network‐based measures, graph Laplacian‐based measures, local trend maps, and a homogeneity‐heterogeneity classifier. Our workflow suggests that AI‐based digital rock augmentation, combined with topological dimensionality reduction offers a powerful tool for enhanced quality assessment and diagnostic of digital rock augmentation and improved interpretation to support decision‐making.https://doi.org/10.1029/2024WR037939AIdata augmentationdigital rockassessment metric |
| spellingShingle | Lei Liu Bernard Chang Maša Prodanović Michael J. Pyrcz AI‐Based Digital Rocks Augmentation and Assessment Metrics Water Resources Research AI data augmentation digital rock assessment metric |
| title | AI‐Based Digital Rocks Augmentation and Assessment Metrics |
| title_full | AI‐Based Digital Rocks Augmentation and Assessment Metrics |
| title_fullStr | AI‐Based Digital Rocks Augmentation and Assessment Metrics |
| title_full_unstemmed | AI‐Based Digital Rocks Augmentation and Assessment Metrics |
| title_short | AI‐Based Digital Rocks Augmentation and Assessment Metrics |
| title_sort | ai based digital rocks augmentation and assessment metrics |
| topic | AI data augmentation digital rock assessment metric |
| url | https://doi.org/10.1029/2024WR037939 |
| work_keys_str_mv | AT leiliu aibaseddigitalrocksaugmentationandassessmentmetrics AT bernardchang aibaseddigitalrocksaugmentationandassessmentmetrics AT masaprodanovic aibaseddigitalrocksaugmentationandassessmentmetrics AT michaeljpyrcz aibaseddigitalrocksaugmentationandassessmentmetrics |