Borehole breakout is a widely utilised phenomenon in horizontal stress orientation determination, and breakout geometrical parameters, such as width and depth, have been used to estimate both horizontal stress magnitudes. However, the accuracy of minimum horizontal stress estimation from borehole breakout remains relatively low in comparison to maximum horizontal stress estimation. This paper aims to compare and improve the minimum horizontal stress estimation via a number of machine learning (ML) regression techniques, including parametric and non-parametric models, which have rarely been explored. ML models were trained based on 79 laboratory data from published literature and validated against 23 field data. A systematic bias was observed in the prediction for the validation dataset whenever the horizontal stress value exceeded the maximum value in the training data. Nevertheless, the pattern was captured, and the removal of systematic bias showed that the artificial neural network is capable of predicting the minimum horizontal stress with an average error rate of 10.16% and a root mean square error of 3.87 MPa when compared to actual values obtained through conventional in-situ measurement techniques. This is a meaningful improvement considering the importance of in-situ stress knowledge for underground operations and the availability of borehole breakout data.

钻孔突破是在水平应力方向确定中广泛使用的现象，并且突破几何参数，例如宽度和深度，已被用于估计两种水平应力大小。然而，与最大水平应力估计相比，钻孔突破的最小水平应力估计精度仍然相对较低。本文旨在通过一些机器学习 (ML) 回归技术来比较和改进最小水平应力估计，包括很少探索的参数和非参数模型。ML 模型基于来自已发表文献的 79 个实验室数据进行训练，并针对 23 个现场数据进行了验证。每当水平应力值超过训练数据中的最大值时，就会在验证数据集的预测中观察到系统偏差。然而，模式被捕获，系统偏差的消除表明人工神经网络能够预测最小水平应力，与获得的实际值相比，平均误差率为 10.16%，均方根误差为 3.87 MPa通过传统的原位测量技术。考虑到现场应力知识对地下作业的重要性以及钻孔突破数据的可用性，这是一项有意义的改进。消除系统偏差表明，与传统原位测量技术获得的实际值相比，人工神经网络能够预测最小水平应力，平均误差率为 10.16%，均方根误差为 3.87 MPa . 考虑到现场应力知识对地下作业的重要性以及钻孔突破数据的可用性，这是一项有意义的改进。消除系统偏差表明，与传统原位测量技术获得的实际值相比，人工神经网络能够预测最小水平应力，平均误差率为 10.16%，均方根误差为 3.87 MPa . 考虑到现场应力知识对地下作业的重要性以及钻孔突破数据的可用性，这是一项有意义的改进。