Characterization of mechanical properties of shale constituent minerals (viz., the mechanical genes of shale) has been challenging but of great significance for engineering applications in shale formations. In this study, a phase‐identified nanoindentation is proposed to decode the mechanical genes of shale using a large nanomechanical dataset. With the consideration of uniform prior probability density functions (PDFs) and Gaussian posterior PDFs, the evidence of the measured dataset generated by the candidate model classes was assessed by applying the expectation–maximization algorithm and solving the Hessian matrix of the likelihood function. In contrast with Bayesian information criterion analysis, which has been widely used in prior studies, the proposed phase‐identified nanoindentation approach is insensitive to the size of the dataset. Here, the identified clusters are well matched with the constituent phases measured by coupling grid nanoindentation and surface physicochemical identification.

中文翻译：

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使用相识别纳米压痕表征页岩成分矿物的机械性能


页岩成分矿物（即页岩的力学基因）的机械特性表征一直具有挑战性，但对于页岩地层的工程应用具有重要意义。在这项研究中，提出了一种相识别的纳米压痕，使用大型纳米力学数据集来解码页岩的力学基因。考虑到统一先验概率密度函数（PDF）和高斯后验 PDF，通过应用期望最大化算法并求解似然函数的 Hessian 矩阵来评估候选模型类生成的测量数据集的证据。与先前研究中广泛使用的贝叶斯信息准则分析相比，所提出的相识别纳米压痕方法对数据集的大小不敏感。在这里，识别出的簇与通过耦合网格纳米压痕和表面物理化学识别测量的组成相很好地匹配。