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 break-out 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 when-ever the horizontal stress value exceeded the maximum value in the training data.Nevertheless,the pat-tern 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 mea-surement techniques.This is a meaningful improvement considering the importance of in-situ stress knowledge for underground operations and the availability of borehole breakout data.

Huasheng Lin;Sarvesh Kumar Singh;Zizhuo Xiang;Won Hee Kang;Simit Raval;Joung Oh;Ismet Canbulat

Glencore Coal Assets Australia,Mount Thorley 2330,Australia;School of Minerals and Energy Resources Engineering,University of New South Wales,Sydney 2052,Australia;Centre for Infrastructure Engineering,School of Engineering,Western Sydney University,Penrith 2751,Australia

矿业科学技术学报（英文版）

[1]Huasheng Lin;Sarvesh Kumar Singh;Zizhuo Xiang;Won Hee Kang;Simit Raval;Joung Oh;Ismet Canbulat-.An investigation of machine learning techniques to estimate minimum horizontal stress magnitude from borehole breakout)[J].矿业科学技术学报（英文版）,2022(05):1021-1029

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