| 基于多钻进参数和概率分类方法的地层识别研究 |
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| 引用本文： | 梁栋才,汤华,吴振君,张勇慧,房昱纬.基于多钻进参数和概率分类方法的地层识别研究[J].岩土力学,2022,43(4):1123-1134. |
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| 作者姓名： | 梁栋才 汤华 吴振君 张勇慧 房昱纬 |
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| 作者单位： | 1. 中国科学院武汉岩土力学研究所 岩土力学与工程国家重点实验室，湖北 武汉 430071；2. 中国科学院大学，北京 100049 |
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| 基金项目： | 云南省交通运输厅科技计划（云交科教（2020）74号）;云南省交通运输厅科技计划（云交科教（2018）18号）~~； |
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| 摘 要： | 传统的超前钻探地质预报常以某个钻进参数的变化率作为地层识别的主要依据。钻头破岩是一个复杂的力学过程，应考虑多个参数的协同作用，仅采用单钻进参数识别地层的不确定性较大。首先，对超前钻探数据进行预处理，包括标准化、频数分布分析和敏感性分析，筛选出对地层变化敏感的关键钻进参数；其次，基于能量守恒、二元无序逻辑回归分析和多参数变异性分析原理分别建立了破岩能量、逻辑回归概率和地层硬度3种地层识别综合指标；最后，采用基于贝叶斯原理的概率分类方法建立地层识别模型，利用ROC分析方法得到模型参数，实现基于多钻进参数和概率分类方法的地层识别。以地质条件复杂的隧道工程为例，介绍了该地层识别方法的应用，结果表明：3种地层识别综合指标均具有较好的跨孔地层识别能力，识别准确率超过80%；破岩能量和逻辑回归概率指标适用于较近距离的跨孔地层识别，平均识别准确率分别为86.3%和84.1%；逻辑回归概率指标对软弱夹层识别能力较强，准确率达到94.2%；地层硬度指标适用于较远距离的跨孔地层识别；灰岩识别准确率最大达到93.2%。
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| 关 键 词： | 超前钻探 破岩能量 逻辑回归概率 地层硬度指标 概率分类 地层识别 |
| 收稿时间： | 2021-04-10 |
| 修稿时间： | 2021-12-07 |
Stratum identification based on multiple drilling parameters and
probability classification |
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| LIANG Dong-cai,TANG Hua,WU Zhen-jun,ZHANG Yong-hui,FANG Yu-wei.Stratum identification based on multiple drilling parameters and
probability classification[J].Rock and Soil Mechanics,2022,43(4):1123-1134. |
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| Authors: | LIANG Dong-cai TANG Hua WU Zhen-jun ZHANG Yong-hui FANG Yu-wei |
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| Institution: | 1. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei 430071, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | The traditional geological prediction method of advanced drilling usually takes the change rate of one specific drilling parameter as the main basis for stratum identification. The rock breaking of drill bit is a complicated mechanical process. Stratum identification with single drilling parameter results in great uncertainty. The cooperative effect of multiple parameters in drilling process should be considered. Firstly, the advanced drilling data was preprocessed, including standardization, frequency distribution analysis and sensitivity analysis, to select the key drilling parameters sensitive to stratum changes. Secondly, based on the principles of energy conservation, binary disordered logistic regression analysis and multi-parameter variability analysis, three comprehensive identification indexes of rock breaking energy, logistic regression probability and stratum hardness index were established respectively. Finally, a stratum identification model was established by probability classification method based on Bayesian principle, and the model parameters were obtained by ROC analysis method, and the stratum identification based on multiple drilling parameters and probability classification method was realized. Taking the tunnel project with complex geological conditions as an example, the application of the proposed stratum identification method is introduced. The results show that: Three comprehensive indexes have great performance in cross-hole stratum identification, and the identification accuracy exceeds 80%. The rock breaking energy and the logic regression probability are suitable for the cross-hole stratum identification with short distance, and the average identification accuracies are 86.3% and 84.1%, respectively. The logical regression probability index has strong identification capability for the weak interlayer, and the identification accuracy reaches 94.2%. The stratum hardness index is suitable for the cross-hole stratum identification with long distance, and the maximum identification accuracy of limestone is 93.2%. |
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| Keywords: | advanced drilling rock breaking energy logistic regression probability stratum hardness index probability classification stratum identification |
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