Characterizing heterogeneous rocks in 3D with a multimodal deep learning approach – Implications for transport simulations

Tomography of Materials and Structures Pub Date : 2025-02-13 DOI:10.1016/j.tmater.2025.100055

Jukka Kuva , Mohammad Jooshaki , Ester M. Jolis , Juuso Sammaljärvi , Marja Siitari-Kauppi , Filip Jankovský , Milan Zuna , Alan Bischoff , Paul Sardini

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Abstract

Investigating the heterogeneous transport properties of rock is vital for accurate assessment of radionuclide migration, which is essential for the safety assessment of a nuclear waste disposal facility. Previous studies have combined x-ray computed tomography (XCT) with other methods to obtain three-dimensional (3D) mineral and porosity maps, but such approaches are time consuming and somewhat dependent on the operator. To address these limitations, we have developed a deep learning-based method that combines XCT with fast and modern characterization techniques such as scanning micro x-ray fluorescence (μXRF) and carbon 14 polymethylmethacrylate (PMMA) autoradiography. This innovative approach produces 3D mineral and porosity maps with minimal operator dependency and manual work. The results obtained from our analysis of various rock samples demonstrate the method’s suitability for transport simulation studies in various geological settings.

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用多模态深度学习方法表征非均质岩石的三维特征-对运输模拟的影响

研究岩石的非均质输运特性对于准确评估放射性核素的迁移是至关重要的，这对核废料处理设施的安全性评估至关重要。之前的研究将x射线计算机断层扫描（XCT）与其他方法相结合，以获得三维（3D）矿物和孔隙度图，但这种方法既耗时又依赖于操作人员。为了解决这些限制，我们开发了一种基于深度学习的方法，将XCT与快速和现代的表征技术相结合，如扫描微x射线荧光（μXRF）和碳14聚甲基丙烯酸甲酯（PMMA）自放射成像。这种创新的方法可以生成3D矿物和孔隙度图，减少了对操作员的依赖和手工操作。我们对各种岩石样品的分析结果表明，该方法适用于各种地质条件下的输运模拟研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Tomography of Materials and Structures

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