Abstract Predicting the proximity of large-scale dynamic failure is a critical concern in the engineering and geophysical sciences. Here we use evolving contractive, dilatational, and shear strain deformation preceding failure in dynamic X-ray tomography experiments to examine which strain components best predict the proximity to failure. We develop machine learning models to predict the proximity to failure using time series of three-dimensional local incremental strain tensor fields acquired in rock deformation experiments under stress conditions of the upper crust. Three-dimensional scans acquired in situ throughout triaxial compression experiments provide a distribution of density contrasts from which we estimate the three-dimensional incremental strain that accumulates between each scan acquisition. Training machine learning models on multiple experiments of six rock types provides suites of feature importance that indicate the predictive power of each feature. Comparing the average importance of groups of features that include information about each strain component quantifies the ability of the contractive, dilatational and shear strain to predict the proximity of macroscopic failure. A total of 24 models of four machine learning algorithms with six rock types indicate that 1) the dilatational strain provides the best predictive power of the strain components, and 2) the intermediate values (25th-75th percentile) of the strain population provide the best predictive power of the statistics of the strain populations. In addition, the success of the predictions of models trained on one rock type and tested on other rock types quantifies the similarities and differences of the precursory strain accumulation process in the six rock types. These similarities suggest the potential existence of a unified theory of brittle rock deformation for a range of rock types.

中文翻译：

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利用岩石上动态原位 X 射线断层扫描三轴压缩实验的局部应变群预测宏观破坏的接近程度

摘要 预测大规模动态破坏的邻近性是工程和地球物理科学中的一个关键问题。在这里，我们使用动态 X 射线断层扫描实验中失效前不断变化的收缩、膨胀和剪切应变变形来检查哪些应变分量最能预测失效的接近程度。我们开发了机器学习模型，使用在上地壳应力条件下的岩石变形实验中获得的三维局部增量应变张量场的时间序列来预测失效的接近程度。在整个三轴压缩实验中原位获取的三维扫描提供了密度对比的分布，我们从中可以估计每次扫描获取之间累积的三维增量应变。在六种岩石类型的多次实验上训练机器学习模型提供了一组特征重要性，表明每个特征的预测能力。比较包括关于每个应变分量的信息的特征组的平均重要性，量化了收缩应变、膨胀应变和剪切应变预测宏观失效接近度的能力。4 种机器学习算法的 24 个模型与 6 种岩石类型共 24 个模型表明：1）膨胀应变提供了最好的应变分量预测能力，2）应变群的中间值（第 25-75 个百分位数）提供了最好的预测能力。菌株种群统计数据的预测能力。此外，在一种岩石类型上训练并在其他岩石类型上测试的模型预测的成功量化了六种岩石类型中前体应变积累过程的异同。这些相似之处表明，对于一系列岩石类型，脆性岩石变形的统一理论可能存在。