{"title":"In-Situ Stress Prediction Model for Tight Sandstone Based on XGBoost Algorithm","authors":"Du Tong, Li Yuwei","doi":"10.1134/s1062739124020157","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This article uses XGBoost algorithm to calculate rock in-situ stress. By using Pearson correlation coefficient method, it is determined that the logging parameters with the best correlation with minimum horizontal principal stress are Depth, GR, LLD, ILD, AC, VCA, with maximum horizontal principal stress are: Depth, GR, SP, CAL, DEN. In order to verify the performance of the model, linear regression, support vector machine, and random forest models are used for comparison. In order to improve the generalization performance, the <span>\\(k\\)</span>-fold cross-validation method is used. The results show that using XGBoost algorithm to predict rock in-situ stress with a small amount of data has a high average accuracy of 94% and good generalization performance. The linear regression model has a faster fitting speed, but the fitting accuracy is the lowest. The random forest and support vector machine models are in-between. The result confirms that the research method in this article has certain universality and can be extended to solve other rock in-situ stress prediction problems.</p>","PeriodicalId":16358,"journal":{"name":"Journal of Mining Science","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mining Science","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1134/s1062739124020157","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MINING & MINERAL PROCESSING","Score":null,"Total":0}
引用次数: 0
Abstract
This article uses XGBoost algorithm to calculate rock in-situ stress. By using Pearson correlation coefficient method, it is determined that the logging parameters with the best correlation with minimum horizontal principal stress are Depth, GR, LLD, ILD, AC, VCA, with maximum horizontal principal stress are: Depth, GR, SP, CAL, DEN. In order to verify the performance of the model, linear regression, support vector machine, and random forest models are used for comparison. In order to improve the generalization performance, the \(k\)-fold cross-validation method is used. The results show that using XGBoost algorithm to predict rock in-situ stress with a small amount of data has a high average accuracy of 94% and good generalization performance. The linear regression model has a faster fitting speed, but the fitting accuracy is the lowest. The random forest and support vector machine models are in-between. The result confirms that the research method in this article has certain universality and can be extended to solve other rock in-situ stress prediction problems.
期刊介绍：
The Journal reflects the current trends of development in fundamental and applied mining sciences. It publishes original articles on geomechanics and geoinformation science, investigation of relationships between global geodynamic processes and man-induced disasters, physical and mathematical modeling of rheological and wave processes in multiphase structural geological media, rock failure, analysis and synthesis of mechanisms, automatic machines, and robots, science of mining machines, creation of resource-saving and ecologically safe technologies of mineral mining, mine aerology and mine thermal physics, coal seam degassing, mechanisms for origination of spontaneous fires and methods for their extinction, mineral dressing, and bowel exploitation.
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