基于树结构Parzen 估计器优化后两层Stacking模型的岩石脆性指数预测

doi:10.3969/j.issn.1672-7703.2025.02.009

中国石油勘探 ›› 2025, Vol. 30 ›› Issue (2): 115-132.DOI: 10.3969/j.issn.1672-7703.2025.02.009

基于树结构Parzen 估计器优化后两层Stacking模型的岩石脆性指数预测

王芷含¹,温韬^1,2

1 长江大学地球科学学院；2 湖北长大科技开发有限公司加查县分公司

出版日期:2025-03-14 发布日期:2025-03-14
通讯作者: 温韬(1990-)，男，江西宜春人，博士，2018 年毕业于中国地质大学（武汉），教授，主要从事地质灾害方面的科研与教学工作。地址：湖北省武汉市蔡甸区大学路111 号，邮政编码：430100。
作者简介:王芷含(2004-)，男，湖北荆州人，在读学士，主要从事机器学习算法预测与滑坡地质灾害工作。地址：湖北省武汉市蔡甸区大学路111 号，邮政编码：430100。
基金资助:
国家自然科学基金“冻融循环作用下藏东南高海拔山区岩质滑坡解锁力学机制与启滑判据”（42477174）；西藏自治区重大科技专项“西藏重大自然灾害风险预判与防治关键技术及示范应用”（XZ202402ZD0001）；西藏自治区科技项目“复杂地质环境下边境地区滑坡风险识别及应急预警方案研究——以西藏山南市为例”（XZ202301YD0034C）；青海省基础研究计划项目“河湟谷地多灾种山地灾害形成机制及预判预警研究”（2024-ZJ-904）。

Prediction of rock brittleness index using two-layer Stacking model optimized by tree-structured Parzen estimator

Wang Zhihan¹, Wen Tao^1,2

1 School of Geosciences, Yangtze University; 2 Jiacha County branch, Hubei Yangtze University Technology Development Co., Ltd.

Online:2025-03-14 Published:2025-03-14

摘要/Abstract

摘要： 目前岩石脆性指数的评价方法众多，主要基于矿物组分或岩石力学性质开展评价，但多数评价指标获取费用高昂、耗时长。采用机器学习的手段，提出一种基于Stacking 集成学习思想的岩石脆性指数预测方法，并行训练梯度提升决策树模型（GBDT）、随机森林模型（RF）、朴素决策树模型（DT）、支持向量回归模型（SVR）以及LightGBM 模型等，并加以树结构Parzen 估计器对各模型进行超参数调优后，串行使用XGBoost 模型对基模型训练结果进行融合，从而实现各参数的快速寻优和岩石脆性指数的预测。结果表明，基于树结构Parzen 估计器优化后的两层Stacking 模型预测结果与使用的基模型预测结果相比具有明显优势，其可释方差得分（EVS）最高达到0.97，决定系数（R²）最高达到0.967，在同样的数据集表现中，该模型平均绝对误差（MAE）和均方根误差（RMSE）均最小，表明该模型能够在有监督学习的技术背景下较好地拟合岩石脆性指数的变化规律，验证了其在预测岩石脆性指数方面具有一定的实用价值。

关键词: 岩石脆性指数, Stacking 模型, 集成学习, 树结构Parzen 估计器

Abstract: Currently, there are numerous methods for evaluating rock brittleness index, which are mainly based on mineral composition or
rock mechanical properties, but most evaluation indicators are costly and time-consuming to obtain. By utilizing machine learning technique, a rock brittleness index prediction method based on Stacking ensemble learning concept has been proposed, which involves parallel training of Gradient Boosting Decision Tree model (GBDT), Random Forest model (RF), Naive Decision Tree model (DT), Support Vector Regression model (SVR), and LightGBM model. After hyperparameter optimization using a tree-structured Parzen estimator for each model, the XGBoost model has sequentially been used to fuse training results of the base models to achieve rapid parameter optimization and prediction of rock brittleness index. The study results indicate that the prediction results using two-layer Stacking model optimized by tree-structured Parzen estimator show significant advantages compared to those by the base models, with the explained variance score (EVS) reaching up to 0.97, and the coefficient of determination (R²) reaching a maximum of 0.967. Given the same dataset, this model obtains the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), indicating that it can effectively fit the variation pattern of rock brittleness index in the technical context of supervision and learning, which verifies its practical value in predicting rock brittleness index.

Key words: rock brittleness index, Stacking model, ensemble learning, tree-structured Parzen estimator

中图分类号:

P631.9

王芷含, 温韬. 基于树结构Parzen 估计器优化后两层Stacking模型的岩石脆性指数预测[J]. 中国石油勘探, 2025, 30(2): 115-132.

Wang Zhihan, Wen Tao. Prediction of rock brittleness index using two-layer Stacking model optimized by tree-structured Parzen estimator[J]. China Petroleum Exploration, 2025, 30(2): 115-132.

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基于树结构Parzen 估计器优化后两层Stacking模型的岩石脆性指数预测

Prediction of rock brittleness index using two-layer Stacking model optimized by tree-structured Parzen estimator

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