基于声发射监测的滑坡深部变形量化方法

邓李政; 袁宏永; 陈建国; 苏国锋; 张鸣之; 陈杨; 潘睿

doi:10.16511/j.cnki.qhdxxb.2024.26.015

清华大学学报（自然科学版） >

2024 , Vol. 64 >Issue 11: 1849 - 1859

DOI: https://doi.org/10.16511/j.cnki.qhdxxb.2024.26.015

专题：公共安全科学与技术

基于声发射监测的滑坡深部变形量化方法

邓李政 ,
袁宏永 ,
陈建国 ,
苏国锋 ,
张鸣之 ,
陈杨 ,
潘睿

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1. 清华大学安全科学学院, 北京 100084;
2. 清华大学, 公共安全研究院, 北京 100084;
3. 清华大学合肥公共安全研究院, 合肥 230601;
4. 中国地质环境监测院(自然资源部地质灾害技术指导中心), 北京 100081;
5. 自然资源部地质灾害智能监测与风险预警工程技术创新中心, 北京 100081

收稿日期: 2023-10-23

网络出版日期: 2024-11-05

基金资助

自然资源部地质灾害智能监测与风险预警工程技术创新中心开放基金资助项目(TICGM-2023-03);北京市自然科学基金青年项目(8244051);博士后创新人才支持计划项目(BX20230175);中国博士后科学基金资助项目(2022M721845);地质调查项目(DD20211364)

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Quantitative methods for landslide subsurface deformation based on acoustic emission monitoring

DENG Lizheng ,
YUAN Hongyong ,
CHEN Jianguo ,
SU Guofeng ,
ZHANG Mingzhi ,
CHEN Yang ,
PAN Rui

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1. School of Safety Science, Tsinghua University, Beijing 100084, China;
2. Institute of Public Safety Research, Tsinghua University, Beijing 100084, China;
3. Hefei Institute of Public Safety Research, Tsinghua University, Hefei 230601, China;
4. China Institute of Geo-Environment Monitoring (Geological Disaster Technical Guidance Center of Ministry of Natural Resources), Beijing 100081, China;
5. Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources, Beijing 100081, China

Received date: 2023-10-23

Online published: 2024-11-05

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摘要

滑坡变形破坏过程会持续产生声发射信号, 而声发射技术可用于监测斜坡稳定性。有源波导声发射技术是监测土质滑坡深部变形的有效方法, 能灵敏感知微小变形并持续监测较大变形, 可实现滑坡灾害早期预警。基于一系列滑坡物理模型实验和原位监测研究, 声发射监测数据分析方法由定性化向定量化发展。首先, 该文综述了利用声发射数据量化滑坡深部变形行为的传统方法, 通过经验公式计算反演滑坡位移、速度和加速度, 提取滑坡运动信息并分析演化规律, 指出传统量化方法面临的挑战; 其次, 阐述了运用机器学习方法自动量化声发射监测数据, 建立滑坡运动状态自动分类模型和滑坡位移预测模型的方法, 用以准确量化滑坡速度、加速度和位移等关键变形特征; 再次, 在声发射监测和机器学习方法的基础上, 提出了滑坡早期风险分级预警方法, 考虑了数据缺失等不利状况的应对方案; 最后, 讨论了不同应用场景下声发射数据量化方法的选择倾向, 指出了各方法的应用局限和发展趋势。

关键词： 滑坡变形; 声发射监测; 量化方法; 机器学习; 预警方法

本文引用格式

邓李政 , 袁宏永 , 陈建国 , 苏国锋 , 张鸣之 , 陈杨 , 潘睿 . 基于声发射监测的滑坡深部变形量化方法[J]. 清华大学学报（自然科学版）, 2024 , 64(11) : 1849 -1859 . DOI: 10.16511/j.cnki.qhdxxb.2024.26.015

Abstract

[Significance] Slope instability early warning systems are used to monitor landslide deformation to ensure proper stakeholders make timely safety decisions and take emergency actions. Acoustic emission signals are constantly produced during landslide deformation and can be employed to monitor slope stability. Acoustic emission technology using an active waveguide has gradually become an effective monitoring method for subsurface deformation of soil landslides. It has the characteristics of low cost and high sensitivity for early detection of minor deformation within slopes. Therefore, acoustic emission technology is anticipated to increase the success rate of landslide risk early warning. [Progress] Based on large-scale landslide model experiments and field monitoring studies, the interpretation methods for acoustic emission monitoring data evolved from qualitative to quantitative. Many landslide on-site tests using acoustic emission monitoring revealed a proportional correlation between acoustic emission rate and landslide velocity. This paper described an empirical formula method for quantifying landslide subsurface deformation behavior using acoustic emission data, extracting landslide movement information, and examining the change pattern by inversely calculating landslide displacement, velocity, and acceleration. The threshold of acoustic emission parameters that trigger landslide warnings could be obtained based on the landslide velocity classification standard. However, challenges remained in developing widely applicable methods for quantifying acoustic emission data, such as the diversity of conditions in the monitoring equipment and the complexity of the interaction within the active waveguide. These challenges limited the accurate quantification of the deformation-acoustic emission response relationship, and thus, the reliability of landslide warning results could not be ensured. To overcome the above limitations, a machine learning approach was proposed, which could automatically interpret acoustic emission monitoring data and quantify the response relationship between deformation and acoustic emission. An automatic classification model for the landslide motion state and a prediction model for landslide displacement were developed to accurately measure the representative deformation characteristics, including landslide velocity, acceleration, and displacement. The classification model was trained using two acoustic emission parameters (ring down count, change rate of ring down count) and the actual labels of the landslide kinematic state. Only the two acoustic emission parameters were input to the trained classifier and kinematic labels were produced through model prediction. A machine learning-based landslide displacement prediction method was developed, where landslide displacement can be automatically measured using acoustic emission data and related parameters (e.g., rainfall). Based on the output results from machine learning classification and prediction, a method for landslide early warning with graded risk was then developed, considering the response to negative circumstances such as missing data. [Conclusions and Prospects] Finally, this article discusses the tendency to choose acoustic emission data interpretation methods for different application scenarios and alludes to the limitations and development trends of these interpretation methods. Machine learning is the current trend in acoustic emission data analysis methods, which can increase the reliability of landslide risk warning systems. In the future, a full waveform data-based acoustic emission analysis method will be introduced for landslide deformation monitoring. It is hoped that acoustic emission technology will be developed as a universal monitoring technique for soil landslide subsurface deformation.

Key words： landslide deformation; acoustic emission monitoring; quantification methods; machine learning; early warning methods

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