ABSTRACT This paper aims to demonstrate that the elastic stiffnesses and the anisotropic parameters of rocks can be accurately predicted from geophysical features such as the porosity, the density, the compression stress, the pore pressure and the burial depth using relevant machine learning methods. It also suggests that the extreme gradient boosting method is the best method for this purpose. It is more accurate, extremely faster to train and more robust than the artificial neural networks and the support vector machine methods. Very high R‐squared scores was obtained for the predicted elastic stiffnesses of a relevant dataset that is available in the literature. This dataset contains different types of rocks, and the values of the features are in large ranges. An optimal set of parameters was obtained by considering an appropriate sensitivity analysis. The optimized model is very easy to implement in Python for practical applications.

中文翻译：

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使用不同的机器学习方法研究岩石的弹性特性和各向异性

摘要 本文旨在证明岩石的弹性刚度和各向异性参数可以使用相关的机器学习方法从孔隙度、密度、压缩应力、孔隙压力和埋藏深度等地球物理特征进行准确预测。它还表明，极端梯度提升方法是实现此目的的最佳方法。与人工神经网络和支持向量机方法相比，它更准确、训练速度极快且更健壮。对于文献中可用的相关数据集的预测弹性刚度，获得了非常高的 R 平方分数。该数据集包含不同类型的岩石，特征值范围较大。通过考虑适当的敏感性分析获得一组最佳参数。优化后的模型很容易在 Python 中实现以用于实际应用。