Petrographic observations are vital for carbonate pore-typing, linking geological frameworks to petrophysical behavior. However, current petrographic pore typing is manual, with the qualitative to semi-quantitative results not easily fitted into quantitative subsurface characterization. Some recent studies have automated this process using supervised machine learning (ML) and deep learning (DL), focusing on simple pore morphological features, and have reported high classification accuracies for several complex pore types. However, there are concerns about the validity of these studies due to conceptual and technical flaws in their collective approach. This study was aimed at a more fundamental problem, classifying between open microfractures and open pores in petrographic thin sections using an object-based approach and explainable supervised ML. We analyzed 18 carbonate thin sections from the USA, numerically representing them using five shape features: compactness, aspect ratio, extent, solidity, and formfactor. Using a labeled data set of 400 microfractures and 400 pores, we evaluated nine of the most widely used supervised models. All models showed high testing accuracies (89.58%–90.42%). Interestingly, complex non-linear models did not significantly outperform simpler linear ones. Compactness and aspect ratio were the most informative features. However, the labeled data sets did not reflect the overall data set's complexity, which suggested that high accuracies in similar studies might be due to curated data sets rather than accounting for the true complexity of carbonate pore systems. The study concludes that simple shape features are ineffective for classifying carbonate pore types. It is hoped that this study will provide a foundation for more robust artificial intelligence-assisted pore typing.

中文翻译：

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使用可解释的监督机器学习区分岩相分析中的孔隙和微裂缝的基于对象的方法

岩相观测对于碳酸盐岩孔隙分类至关重要，将地质框架与岩石物理行为联系起来。然而，目前的岩相孔隙定型是手动的，定性到半定量的结果不容易适应定量的地下表征。最近的一些研究使用监督机器学习 (ML) 和深度学习 (DL) 自动化了这一过程，重点关注简单的孔隙形态特征，并报告了几种复杂孔隙类型的高分类精度。然而，由于这些研究的集体方法存在概念和技术缺陷，人们对这些研究的有效性感到担忧。这项研究针对一个更基本的问题，即使用基于对象的方法和可解释的监督机器学习对岩相薄片中的开放微裂缝和开放孔隙进行分类。我们分析了来自美国的 18 个碳酸盐薄片，并使用五个形状特征对它们进行了数字表示：致密性、纵横比、范围、坚固性和形状因子。使用包含 400 个微裂缝和 400 个孔隙的标记数据集，我们评估了 9 个最广泛使用的监督模型。所有模型均表现出较高的测试精度（89.58%–90.42%）。有趣的是，复杂的非线性模型并没有明显优于更简单的线性模型。紧凑性和纵横比是信息最丰富的特征。然而，标记的数据集并没有反映整个数据集的复杂性，这表明类似研究中的高精度可能是由于精心策划的数据集而不是考虑到碳酸盐孔隙系统的真正复杂性。研究得出的结论是，简单的形状特征对于碳酸盐岩孔隙类型的分类是无效的。希望这项研究能为更强大的人工智能辅助孔隙分型提供基础。