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。
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.
关键词
滑坡变形 /
声发射监测 /
量化方法 /
机器学习 /
预警方法
Key words
landslide deformation /
acoustic emission monitoring /
quantification methods /
machine learning /
early warning methods
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参考文献
[1] CRUDEN D M, VARNES D J. Landslides: Investigation and mitigation. Chapter 3-Landslide types and processes [J]. Transportation Research Board Special Report, 1996(247): 36-75.
[2] HUNGR O, LEROUEIL S, PICARELLI L. The Varnes classification of landslide types, an update [J]. Landslides, 2014, 11(2): 167-194.
[3] 许强, 曾裕平. 具有蠕变特点滑坡的加速度变化特征及临滑预警指标研究 [J]. 岩石力学与工程学报, 2009, 28(6): 1099-1106. XU Q, ZENG Y P. Research on acceleration variation characteristics of creep landslide and early-warning prediction indicator of critical sliding [J]. Chinese Journal of Rock Mechanics and Engineering, 2009, 28(6): 1099-1106. (in Chinese)
[4] UHLEMANN S, SMITH A, CHAMBERS J, et al. Assessment of ground-based monitoring techniques applied to landslide investigations [J]. Geomorphology, 2016, 253: 438-451.
[5] 易武, 孟召平. 岩质边坡声发射特征及失稳预报判据研究 [J]. 岩土力学, 2007, 28(12): 2529-2533, 2538. YI W, MENG Z P. Study on acoustic emission feature of rock slope and its instability forecast criterion [J]. Rock and Soil Mechanics, 2007, 28(12): 2529-2533, 2538. (in Chinese)
[6] SMITH A, DIXON N. Acoustic emission behaviour of dense sands [J]. Géotechnique, 2019, 69(12): 1107-1122.
[7] MATSUURA S, ASANO S, OKAMOTO T. Relationship between rain and/or meltwater, pore-water pressure and displacement of a reactivated landslide [J]. Engineering Geology, 2008, 101(1-2): 49-59.
[8] SMITH A, DIXON N, FOWMES G J. Early detection of first-time slope failures using acoustic emission measurements: Large-scale physical modelling [J]. Géotechnique, 2017, 67(2): 138-152.
[9] DIXON N, SMITH A, FLINT J A, et al. An acoustic emission landslide early warning system for communities in low-income and middle-income countries [J]. Landslides, 2018, 15(8): 1631-1644.
[10] DIXON N, SPRIGGS M, SMITH A, et al. Quantification of reactivated landslide behaviour using acoustic emission monitoring [J]. Landslides, 2015, 12(3): 549-560.
[11] SMITH A. Quantification of slope deformation behaviour using acoustic emission monitoring [D]. Loughborough: Loughborough University, 2015.
[12] SMITH A, DIXON N, MELDRUM P, et al. Acoustic emission monitoring of a soil slope: Comparisons with continuous deformation measurements [J]. Géotechnique Letters, 2014, 4(4): 255-261.
[13] CODEGLIA D, DIXON N, FOWMES G J, et al. Analysis of acoustic emission patterns for monitoring of rock slope deformation mechanisms [J]. Engineering Geology, 2017, 219: 21-31.
[14] BERG N, SMITH A, RUSSELL S, et al. Correlation of acoustic emissions with patterns of movement in an extremely slow-moving landslide at Peace River, Alberta, Canada [J]. Canadian Geotechnical Journal, 2018, 55(10): 1475-1488.
[15] MICHLMAYR G, COHEN D, OR D. Sources and characteristics of acoustic emissions from mechanically stressed geologic granular media: A review [J]. Earth-Science Reviews, 2012, 112(3-4): 97-114.
[16] DIXON N, HILL R, KAVANAGH J. Acoustic emission monitoring of slope instability: Development of an active waveguide system [J]. Proceedings of the Institution of Civil Engineers-Geotechnical Engineering, 2003, 156(2): 83-95.
[17] KOERNER R M, MCCABE W M, LORD JR A E. Acoustic emission monitoring of soil stability [J]. Journal of the Geotechnical Engineering Division, 1978, 104(5): 571-582.
[18] LORD JR A E, FISK C L, KOERNER R M. Utilization of steel roads as AE waveguides [J]. Journal of the Geotechnical Engineering Division, 1982, 108(2): 300-305.
[19] VOIGHT B. A relation to describe rate-dependent material failure [J]. Science, 1989, 243(4888): 200-203.
[20] SPRIGGS M. Quantification of acoustic emission from soils for predicting landslide failure [D]. Loughborough: Loughborough University, 2005.
[21] CODEGLIA D, DIXON N, FOWMES G J, et al. Strategies for rock slope failure early warning using acoustic emission monitoring [J]. IOP Conference Series: Earth and Environmental Science, 2015, 26: 012028.
[22] SMITH A, HEATHER-SMITH H J, DIXON N, et al. Acoustic emission generated by granular soil-steel structure interaction [J]. Géotechnique Letters, 2020, 10(2): 119-127.
[23] SMITH A, DIXON N, FOWMES G. Monitoring buried pipe deformation using acoustic emission: Quantification of attenuation [J]. International Journal of Geotechnical Engineering, 2017, 11(4): 418-430.
[24] INGRAHAM M D, ISSEN K A, HOLCOMB D J. Use of acoustic emissions to investigate localization in high-porosity sandstone subjected to true triaxial stresses [J]. Acta Geotechnica, 2013, 8(6): 645-663.
[25] DENG L Z, YUAN H Y, CHEN J G, et al. Experimental investigation and field application of acoustic emission array for landslide monitoring [J]. Landslides, 2023, 21(1): 71-81.
[26] DENG L Z, YUAN H Y, CHEN J G, et al. Prefabricated acoustic emission array system for landslide monitoring [J]. Engineering Geology, 2023, 323: 107185.
[27] MICHLMAYR G, COHEN D, OR D. Shear-induced force fluctuations and acoustic emissions in granular material [J]. Journal of Geophysical Research: Solid Earth, 2013, 118(12): 6086-6098.
[28] CADMAN J D, GOODMAN R E. Landslide noise [J]. Science, 1967, 158(3805): 1182-1184.
[29] MA K, TANG C A, LIANG Z Z, et al. Stability analysis and reinforcement evaluation of high-steep rock slope by microseismic monitoring [J]. Engineering Geology, 2017, 218: 22-38.
[30] KOERNER R M, MCCABE W M, LORD JR A E. Acoustic emission behavior and monitoring of soils [M]. West Conshohocken: ASTM International, 1981.
[31] DENG L Z, YUAN H Y, CHEN J G, et al. Experimental investigation on progressive deformation of soil slope using acoustic emission monitoring [J]. Engineering Geology, 2019, 261: 105295.
[32] HU W, SCARINGI G, XU Q, et al. Acoustic emissions and microseismicity in granular slopes prior to failure and flow-like motion: The potential for early warning [J]. Geophysical Research Letters, 2018, 45(19): 10406-10415.
[33] DIXON N, SPRIGGS M. Quantification of slope displacement rates using acoustic emission monitoring [J]. Canadian Geotechnical Journal, 2007, 44(8): 966-976.
[34] SMITH A, DIXON N. Quantification of landslide velocity from active waveguide-generated acoustic emission [J]. Canadian Geotechnical Journal, 2015, 52(4): 413-425.
[35] CAMPBELL C S. Granular material flows: An overview [J]. Powder Technology, 2006, 162(3): 208-229.
[36] MIRGHASEMI A A, ROTHENBURG L, MATYAS E L. Influence of particle shape on engineering properties of assemblies of two-dimensional polygon-shaped particles [J]. Géotechnique, 2002, 52(3): 209-217.
[37] CUI L, O'SULLIVAN C, O'NEILL S. An analysis of the triaxial apparatus using a mixed boundary three-dimensional discrete element model [J]. Géotechnique, 2007, 57(10): 831-844.
[38] LI H J, XU Q, HE Y S, et al. Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models [J]. Landslides, 2018, 15(10): 2047-2059.
[39] DENG L Z, SMITH A, DIXON N, et al. Machine learning prediction of landslide deformation behaviour using acoustic emission and rainfall measurements [J]. Engineering Geology, 2021, 293: 106315.
[40] DENG L Z, SMITH A, DIXON N, et al. Automatic classification of landslide kinematics using acoustic emission measurements and machine learning [J]. Landslides, 2021, 18(8): 2959-2974.
[41] THIRUGNANAM H, RAMESH M V, RANGAN V P. Enhancing the reliability of landslide early warning systems by machine learning [J]. Landslides, 2020, 17(9): 2231-2246.
[42] SMITH A, DIXON N. Listening for deterioration and failure: Towards smart geotechnical infrastructure [J]. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 2018, 171(4): 131-143.
[43] ZAKI A, CHAI H K, RAZAK H A, et al. Monitoring and evaluating the stability of soil slopes: A review on various available methods and feasibility of acoustic emission technique [J]. Comptes Rendus Geoscience, 2014, 346(9-10): 223-232.
[44] MICHLMAYR G, OR D. Mechanisms for acoustic emissions generation during granular shearing [J]. Granular Matter, 2014, 16(5): 627-640.
[45] MAO W W, AOYAMA S, TOWHATA I. A study on particle breakage behavior during pile penetration process using acoustic emission source location [J]. Geoscience Frontiers, 2020, 11(2): 413-427.
基金
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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