用微CT图像直接计算熵值，自动量化岩石不均一性。
Entropy-based measure of rock sample heterogeneity derived from micro-CT images
- 直接处理原始微CT图像，通过子体积熵值衡量纹理不均一性。
- 在4935张图像上验证，某属性与专家判断显著相关。
- 比传统方法更客观高效，适合大规模岩石样本分析。
本研究提出一种基于原始X射线微计算机断层扫描（micro-CT）图像的自动化方法，用于客观测量岩石不均一性，克服了传统方法耗时、昂贵且主观的局限。该方法不依赖图像分割，直接分析micro-CT图像，通过将图像划分为子体积并计算各子体积的属性，以熵作为不确定性度量。该方法可适应不同样品特性，实现跨样本的有意义比较。研究应用在来自巴西储层的4,935张圆柱形岩心样本图像上。结果显示，所选属性对生成良好结果（如与结构不均一性强相关）起关键作用。为评估方法有效性，四位专家对175个样本进行了分类（每名专家评估样本数不同），将样本标记为均质或非均质。其中一项属性在所有专家标注中均表现出显著差异，其余两项属性则在三名专家中未达显著水平。该方法在匹配专家判断方面优于已知提取不均一性的传统纹理属性。该纹理不均一性度量可作为岩石表征的补充参数，自动化设计确保可复现性和高成本效益。
原文摘要 · Abstract (English)
This study presents an automated method for objectively measuring rock heterogeneity via raw X-ray micro-computed tomography (micro-CT) images, thereby addressing the limitations of traditional methods, which are time-consuming, costly, and subjective. Unlike approaches that rely on image segmentation, the proposed method processes micro-CT images directly, identifying textural heterogeneity. The image is partitioned into subvolumes, where attributes are calculated for each one, with entropy serving as a measure of uncertainty. This method adapts to varying sample characteristics and enables meaningful comparisons across distinct sets of samples. It was applied to a dataset consisting of 4,935 images of cylindrical plug samples derived from Brazilian reservoirs. The results showed that the selected attributes play a key role in producing desirable outcomes, such as strong correlations with structural heterogeneity. To assess the effectiveness of our method, we used evaluations provided by four experts who classified 175 samples as either heterogeneous or homogeneous, where each expert assessed a different number of samples. One of the presented attributes demonstrated a statistically significant difference between the homogeneous and heterogeneous samples labelled by all the experts, whereas the other two attributes yielded nonsignificant differences for three out of the four experts. The method was shown to better align with the expert choices than traditional textural attributes known for extracting heterogeneous properties from images. This textural heterogeneity measure provides an additional parameter that can assist in rock characterization, and the automated approach ensures easy reproduction and high cost-effectiveness.
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