【研究背景】
自然灾害是影响人们生命财产安全的重要因素之一。其中，山体滑坡是一种常见的地质灾害，给人们的生产和生活带来了极大的威胁。为了及时预警和应对山体滑坡，科学家们一直在探索有效的监测手段。然而，传统的监测系统往往需要高昂的设备成本，限制了地质灾害监测和管理的效力。因此，如何开发一种成本效益高、能够同时监测多个关键参数的监测系统，成为了当前研究领域的一个重要问题。
【研究内容】
近日，一项名为“Probing multi-physical process and deformation mechanism of a large-scale landslide using integrated dual-source monitoring”的研究成果在《Geoscience Frontiers》杂志上发表。南京大学朱鸿鹄教授团队开发了一种新型监测系统，将光纤布拉格光栅（FBG）和微机电系统（MEMS）技术相结合，以同时监测土壤湿度、温度、滑动阻力、应变、表面倾角和深部倾角等多个参数。该系统能够实现实时数据采集和交叉验证分析，成本效益高，是监测地质灾害关键参数的理想选择。南京大学研究人员成功将该系统应用于我国长江三峡库区新铺滑坡的原位监测。数据显示，该滑坡存在两个浅层滑动面和一个深层滑动面。其中，下部浅层滑动面主要由极端降水引起，而上部浅层滑动面则主要由水库水位的剧烈变化和集中降雨次生触发。此外，研究还揭示了滑坡内部存在渗流驱动和浮力驱动等多种物理过程。研究结果为类似地质环境下的滑坡工程措施评估和改进提供了重要参考。
【研究意义】
南京大学开发的滑坡监测系统能够同时监测多个关键参数，成本效益高，为地质灾害监测和风险管理提供了新的解决方案。此外，研究结果还揭示了滑坡内部存在多种物理过程，为该类地质灾害的预警和防治提供了新的思路和方法，对于保障人们的生命财产安全具有重要意义。
来源：Geoscience Frontiers, Volume 15, Issue 2, March 2024, 101773
作者：Zhu, Honghu, Ye, Xiao, Pei, Huafu, Wei, Zhang, Cheng, Gang, Zi-Li Li
机构：南京大学，大连理工大学，爱尔兰科克大学，美国麻省理工学院
DOI：10.1016/j.gsf.2023.101773
Probing multi-physical process and deformation mechanism of a large-scale landslide using integrated dual-source monitoring
Abstract: The implementation of isolated heterologous monitoring systems for spatially distant borehole deployments often comes with substantial equipment costs, which can limit the effectiveness of geohazard mitigation and georisk management efforts. To address this, we have developed a novel monitoring system that integrates fiber Bragg grating (FBG) and microelectromechanical system (MEMS) techniques to capture soil moisture, temperature, sliding resistance, strain, surface tilt, and deep-seated inclination. This system enables real-time, simultaneous data acquisition and cross-validation analyses, offering a cost-effective solution for monitoring critical parameters in geohazard-prone areas. We successfully applied this integrated monitoring system to the Xinpu landslide, an active super-large landslide located in the Three Gorges Reservoir Area (TGRA) of China. The resulting strain profile confirmed the presence of two shallow secondary sliding surfaces at depths of approximately 7 m and 12 m, respectively, in addition to the deep-seated sliding surface at a depth of 28 m. The lower secondary sliding surface was activated by extreme precipitation, while the upper one was primarily driven by significant changes in reservoir water levels and secondarily triggered by concentrated rainfalls. Anti-slide piles have remarkably reinforced the upper moving masses but failed to control the lower ones. The gap between the pile heads and the soil amplified the rainwater erosion effect, creating a preferential channel for rainwater infiltration. Multi-physical measurements revealed a mixture of seepage-driven and buoyancy-driven behaviors within the landslide. This study offers an integrated dual-source multi-physical monitoring paradigm that enables collaborative management of multiple crucial boreholes on a large-scale landslide, and contributes to the evaluation and improvement of engineering measures in similar geological settings.
【References】
Acharya, A., Kogure, T., 2023. Application of novel distributed fibre-optic sensing for slope deformation monitoring: a comprehensive review. Int. J. Environ. Sci. Technol. 20, 8217–8240. https://doi.org/10.1007/s13762-022-04697-5.
AghaKouchak, A., Huning, L.S., Chiang, F., Sadegh, M., Vahedifard, F., Mazdiyasni, O., Moftakhari, H., Mallakpour, I., 2018. How do natural hazards cascade to cause disasters? Nature 561, 458–460. https://doi.org/10.1038/d41586-018-06783-6.
Berti, M., Simoni, A., 2010. Field evidence of pore pressure diffusion in clayey soils prone to landsliding. J. Geophys. Res. Earth Surf. 115, F03031. https://doi.org/10.1029/2009JF001463.
Chen, M.L., Yang, X.G., Zhou, J.W., 2022. Spatial distribution and failure mechanism of water-induced landslides in the reservoir areas of Southwest China. J. Rock Mech. Geotech. Eng. 15 (2), 442–456. https://doi.org/10.1016/j.jrmge.2022.04.004.
Fang, K., Tang, H.M., Li, C.D., Su, X.X., An, P.J., Sun, S.X., 2023a. Centrifuge modelling of landslides and landslide hazard mitigation: A review. Geosci. Front. 14, (1). https://doi.org/10.1016/j.gsf.2022.101493 101493.
Fang, K., Zhang, J.F., Tang, H.M., Hu, X.L., Yuan, H.H., Wang, X.T., An, P.J., Ding, B.D., 2023b. A quick and low-cost smartphone photogrammetry method for obtaining 3D particle size and shape. Eng. Geol. 322,. https://doi.org/10.1016/j.enggeo.2023.107170 107170.
Ferrari, A., Ledesma, A., González, D.A., Corominas, J., 2011. Effects of the foot evolution on the behaviour of slow-moving landslides. Eng. Geol. 117, 217–218. https://doi.org/10.1016/j.enggeo.2010.11.001.
Finnegan, N.J., Perkins, J.P., Nereson, A.L., Handwerger, A.L., 2021. Unsaturated flow processes and the onset of seasonal deformation in slow-moving landslides. J. Geophys. Res. Earth Surf. 126, e2020JF005758. doi: 10.1029/2020JF005758.
Gariano, S.L., Guzzetti, F., 2016. Landslides in a changing climate. Earth-Sci. Rev. 162, 227–252. https://doi.org/10.1016/j.earscirev.2016.08.011.
Guo, J.Y., Shi, B., Sun, M.Y., Zhang, C.C., Tang, C.S., Wei, G.Q., Fang, J.H., Jiang, H.T., 2023. Soil total suction sensing using fiber-optic technology. Geoderma 439,. https://doi.org/10.1016/j.geoderma.2023.116687 116687.
Ho, Y.T., Huang, A.B., Lee, J.T., 2006. Development of a fibre Bragg grating sensored ground movement monitoring system. Meas. Sci. Technol. 17, 1733–1740.
https://doi.org/10.1088/0957-0233/17/7/011.
Hu, X., Bürgmann, R., Schulz, W.H., Fielding, E.J., 2020. Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing. Nat. Commun. 11, 2792. https://doi.org/10.1038/s41467-020-16617-7.
Hu, X.L., Liu, D.Z., Niu, L.F., Liu, C., Wang, X., Fu, R., 2021. Development of soil-pile interactions and failure mechanisms in a pile-reinforced landslide. Eng. Geol. 294. https://doi.org/10.1016/j.enggeo.2021.106389 106389.
Huang, X.H., Guo, F., Deng, M.L., Yi, W., Huang, H.F., 2020. Understanding the deformation mechanism and threshold reservoir level of the floating weightreducing
landslide in the Three Gorges Reservoir Area, China. Landslides 17, 2879–2894. https://doi.org/10.1007/s10346-020-01435-1.
Hurley, D.G., Pantelis, G., 1985. Unsaturated and saturated flow through a thin porous layer on a hillslope. Water Resour. Res. 21, 821–824. https://doi.org/10.1029/WR021i006p00821.
Jones, J.N., Boulton, S.J., Bennett, G.L., Stokes, M., Whitworth, M.R.Z., 2021. Temporal variations in landslide distributions following extreme events: Implications for landslide susceptibility modeling. J. Geophys. Res. Earth Surf. 126, e2021JF006067. doi: 10.1029/2021JF006067.
Kogure, T., Okuda, Y., 2018. Monitoring the vertical distribution of rainfall-induced strain changes in a landslide measured by distributed fiber optic sensing with Rayleigh backscattering. Geophys. Res. Lett. 45, 4033–4040. https://doi.org/10.1029/2018GL077607.
Lacroix, P., Handwerger, A.L., Bièvre, G., 2020. Life and death of slow-moving landslides. Nat. Rev. Earth Environ. 8, 1–419. https://doi.org/10.1038/s43017-020-0072-8.
Lehmann, P., Or, D., 2012. Hydromechanical triggering of landslides: From progressive local failures to mass release. Water Resour. Res. 48, W03535. https://doi.org/10.1029/2011WR010947.
Leshchinsky, B., Olsen, M.J., Mohney, C., O’Banion, M., Bunn, M., Allan, J., McClung, R., 2019. Quantifying the sensitivity of progressive landslide movements to failure geometry, undercutting processes and hydrological changes. J. Geophys. Res. Earth Surf. 124, 616–638. https://doi.org/10.1029/2018JF004833.
Li, Y., Utili, S., Milledge, D., Chen, L.X., Yin, K.L., 2021. Chasing a complete understanding of the failure mechanisms and potential hazards of the slow moving Liangshuijing landslide. Eng. Geol. 281,. https://doi.org/10.1016/j.enggeo.2020.105977 105977.
Liu, Y., Xu, C., Huang, B., Ren, X.W., Liu, C.Q., Hu, B.D., Chen, Z., 2020. Landslide displacement prediction based on multi-source data fusion and sensitivity states. Eng. Geol. 271,. https://doi.org/10.1016/j.enggeo.2020.105608 105608.
Ma, J.W., Tang, H.M., Liu, X., Hu, X.L., Sun, M.J., Song, Y.J., 2017. Establishment of a deformation forecasting model for a step-like landslide based on decision tree C5.0 and two-step cluster algorithms: a case study in the Three Gorges Reservoir area, China. Landslides 14, 1275–1281. https://doi.org/10.1007/s10346-017-0804-0.
Matsuura, S., Asano, S., Okamoto, T., 2008. Relationship between rain and/or meltwater, pore-water pressure and displacement of a reactivated landslide. Eng. Geol. 101, 49–59. https://doi.org/10.1016/j.enggeo.2008.03.007.
Miao, F.S., Wu, Y.P., Török, A., Li, L.W., Xue, Y., 2022. Centrifugal model test on a riverine landslide in the Three Gorges Reservoir induced by rainfall and water level fluctuation. Geosci. Front. 13, (3). https://doi.org/10.1016/j.gsf.2022.101378 101378.
Nava, L., Carraro, E., Reyes-Carmona, C., Puliero, S., Bhuyan, K., Rosi, A., Monserrat, O., Floris, M., Meena, S.R., Galve, J.P., Catani, F., 2023. Landslide displacement forecasting using deep learning and monitoring data across selected sites. Landslides 20, 2111–2129. https://doi.org/10.1007/s10346-023-02104-9.
Nereson, A.L., Davila Olivera, S., Finnegan, N.J., 2018. Field and remote-sensing evidence for hydro-mechanical isolation of a long-lived earthflow in central California. Geophys. Res. Lett. 45, 9672–9680. https://doi.org/10.1029/2018GL079430.
Prokešová, R., Medved’ová, A., Táborˇík, P., Snopková, Z., 2013. Towards hydrological triggering mechanisms of large deep-seated landslides. Landslides 10, 239–254. https://doi.org/10.1007/s10346-012-0330-z.
Rana, K., Bhuyan, K., Ferrer, J.V., Cotton, F., Ozturk, U., Catani, F., Malik, N., 2023. Landslide topology uncovers failure movements. arXiv preprint arXiv:2310.09631, doi: 10.48550/arXiv.2310.09631.
Scaringi, G., Loche, M., 2022. A thermo-hydro-mechanical approach to soil slope stability under climate change. Geomorphology 401. https://doi.org/10.1016/j.geomorph.2022.108108 108108.
Schenato, L., 2014. Fiber-optic sensors for geo-hydrological applications: basic concepts and applications. Rendiconti Online Della Soc. Geol. Italiana 30, 51–54.
Sheikh, M.R., Nakata, Y., Shitano, M., Kaneko, M., 2021. Rainfall-induced unstable slope monitoring and early warning through tilt sensors. Soils Found. 61 (4), 1033–1053. https://doi.org/10.1016/j.sandf.2021.05.010.
Shi, B., 2013. On fields and their coupling in engineering geology. J. Eng. Geol. 21 (5), 673–680. https://doi.org/10.3969/j.issn.1004-9665.2013.05.001.
Stormont, J.C., Morris, C.E., 1998. Method to estimate water storage capacity of capillary barriers. J. Geotech. Geoenviron. Eng. 124 (4), 297–302.
Sun, M.Y., Shi, B., Zhang, C.C., Zheng, X., Guo, J.Y., Wang, Y.Q., He, M.N., Liu, J., 2021. Quasi-distributed fiber-optic in-situ monitoring technology for large-scale measurement of soil water content and its application. Eng. Geol. 294. https://doi.org/10.1016/j.enggeo.2021.106373 106373.
Sun, Y.J., Zhang, D., Shi, B., Tong, H.J., Wei, G.Q., Wang, X., 2014. Distributed acquisition, characterization and process analysis of multi-field information in slopes. Eng. Geol. 182, 49–62. https://doi.org/10.1016/j.enggeo.2014.08.025.
Tang, H.M., Wasowski, J., Juang, C.H., 2019. Geohazards in the Three Gorges Reservoir area, China-Lessons learned from decades of research. Eng. Geol. 261,. https://doi.org/10.1016/j.enggeo.2019.105267 105267.
Towhata, I., Uchimura, T., Seko, I., Wang, L., 2015. Monitoring of unstable slopes by MEMS tilting sensors and its application to early warning. IOP Conf.: Earth Environ. Sci. 26,. https://doi.org/10.1088/1755-1315/26/1/012049 012049.
Uchimura, T., Towhata, I., Wang, L., 2010. Simple monitoring method for precaution of landslides watching tilting and water contents on slopes surface. Landslides 7(3), 351–357. https://doi.org/10.1007/s10346-009-0178-z.
Uchimura, T., Towhata, I., Wang, L., Nishie, S., Yamaguchi, H., Seko, I., Qiao, J.P., 2015. Precaution and early warning of surface failure of slopes by using tilt sensors. Soils Found. 55 (5), 1086–1099. https://doi.org/10.1016/j.sandf.2015.09.010.
Uhlemann, S., Chambers, J., Wilkinson, P., Maurer, H., Merritt, A., Meldrum, P., Kuras, O., Gunn, D., Smith, A., Dijkstra, T., 2017. Four-dimensional imaging of moisture dynamics during landslide reactivation. J. Geophys. Res. Earth Surf. 122, 398–418. https://doi.org/10.1002/2016JF003983.
Veness, W.A., Butler, A.P., Ochoa-Tocachi, B.F., Moulds, S., Buytaert, W., 2022. Localizing hydrological drought early warning using in situ groundwater sensors. Water Resour. Res. 58, e2022WR032165. doi: 10.1029/2022WR032165.
Wang, D.Y., Zhu, H.H., Wang, J., Sun, Y.J., Schenato, L., Pasuto, A., Shi, B., 2023. Characterization of sliding surface deformation and stability evaluation of landslides with fiber-optic strain sensing nerves. Eng. Geol. 314,. https://doi.org/10.1016/j.enggeo.2023.107011 107011.
Wasko, C., Sharma, A., Johnson, F., 2015. Does storm duration modulate the extreme precipitation-temperature scaling relationship? Geophys. Res. Lett. 42, 8783–8790. https://doi.org/10.1002/2015GL066274.
Wen, B.P., Aydin, A., Duzgoren-Aydin, N.S., Li, Y.R., Chen, H.Y., Xiao, S.D., 2007. Residual strength of slip zones of large landslides in the Three Gorges area, China. Eng. Geol. 93 (3–4), 82–98. https://doi.org/10.1016/j.enggeo.2007.05.006.
Wu, B., Zhu, H.H., Cao, D.F., Xu, L., Shi, B., 2021. Feasibility study on ice content measurement of frozen soil using actively heated FBG sensors. Cold Reg. Sci. Technol. 39,. https://doi.org/10.1016/j.coldregions.2021.103332 103332.
Xie, J.R., Uchimura, T., Wang, G.H., Selvarajah, H., Maqsood, Z., Shen, Q., Mei, G.X., Qiao, S.F., 2020. Predicting the sliding behavior of rotational landslides based on the tilting measurement of the slope surface. Eng. Geol. 269,. https://doi.org/10.1016/j.enggeo.2020.105554 105554.
Yang, B.B., Liu, Z.Q., Lacasse, S., Wang, L.Q., Xiao, T., 2023. Deformation triggers and stability evolution of landslide from multiple observations. Front. Ecol. Evol. 11, 1242093. https://doi.org/10.3389/fevo.2023.1242093.
Yang, B.B., Yin, K.L., Lacasse, S., Liu, Z.Q., 2019. Time series analysis and long shortterm memory neural network to predict landslide displacement. Landslides 16, 677–694. https://doi.org/10.1007/s10346-018-01127-x.
Ye, X., Zhu, H. H., Wang, J., Zhang, Q., Shi, B., Schenato, L., Pasuto, A, 2022. Subsurface multi-physical monitoring of a reservoir landslide with the fiber-optic nerve system. Geophys. Res. Lett. 49, e2022GL098211. doi: 10.1029/2022GL098211.
Ye, X., Zhu, H.H., Cheng, G., Pei, H.F., Shi, B., Schenato, L., Pasuto, A., 2023. Thermohydro-poro-mechanical responses of a reservoir-induced landslide tracked by
high-resolution fiber optic sensing nerves. J. Rock Mech. Geotech. Eng. https://doi.org/10.1016/j.jrmge.2023.04.004.
Yin, Y.P., Huang, B.L., Wang, W.P., Wei, Y.J., Ma, X.H., Ma, F., Zhao, C., 2016. Reservoir-induced landslides and risk control in three Gorges Project on Yangtze River, China. J. Rock Mech. Geotech. Eng. 8, 577–595. https://doi.org/10.1016/j.jrmge.2016.08.001.
Yin, K.L., Zhang, G.R., Zhu, L.F., 2004. Overview landslide hazard assessment of China. J. Earth Sci. 15 (3), 306–311.
Yu, H.B., Li, C.D., Zhou, J.Q., Gu, X.P., Duan, Y., Liao, L.F., Chen, W.Q., Long, J.J., 2022. A large-scale obliquely inclined bedding rockslide triggered by heavy rainstorm on the 8th of July 2020 in Shiban Village, Guizhou, China. Landslides 19, 1119–1130. https://doi.org/10.1007/s10346-022-01850-6.
Zeng, T.R., Glade, T., Xie, Y.Y., Yin, K.L., Peduto, D., 2023a. Deep learning powered long-term warning systems for reservoir landslides. Int. J. Disast. Risk Re. 94. https://doi.org/10.1016/j.ijdrr.2023.103820 103820.
Zeng, T.R., Wu, L.Y., Peduto, D., Glade, T., Hayakawa, Y.S., Yin, K.L., 2023b. Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy. Geosci. Front. 14, (6). https://doi.org/10.1016/j.gsf.2023.101645 101645.
Zhang, W.G., Ching, J.Y., Goh, A.T.C., Leung, A.Y.F., 2021. Big data and machine learning in geoscience and geoengineering: Introduction. Geosci. Front. 12 (1), 327–329. https://doi.org/10.1016/j.gsf.2020.05.006.
Zhang, L., Cui, Y.F., Zhu, H.H., Wu, H., Han, H.M., Yan, Y., Shi, B., 2023a. Shear deformation calculation of landslide using distributed strain sensing technology considering the coupling effect. Landslides 20, 1583–1597. https://doi.org/10.1007/s10346-023-02051-5.
Zhang, C.C., Zhu, H.H., Liu, S.P., Shi, B., Zhang, D., 2018. A kinematic method for calculating shear displacements of landslides using distributed fiber optic strain measurements. Eng. Geol. 234, 83–96. https://doi.org/10.1016/j.enggeo.2018.01.002.
Zhang, L., Zhu, H.H., Han, H.M., Shi, B., 2023b. Fiber optic monitoring of an anti-slide pile in a retrogressive landslide. J. Rock Mech. Geotech. Eng. https://doi.org/10.1016/j.jrmge.2023.02.011.
Zheng, W.J, Hu, J., Lu, Z., Hu, X., Sun, Q., Liu, J.H., Zhu, J.J., Li, Z.W., 2023. Enhanced kinematic inversion of 3-D displacements, geometry, and hydraulic properties of a north-south slow-moving landslide in Three Gorges Reservoir. J. Geophys. Res. Solid Earth 128, e2022JB026232. doi: 10.1029/2022JB026232.
Zhou, C., Cao, Y., Hu, X., Yin, K.L., Wang, Y., Catani, F., 2022a. Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area. Landslides 19, 1585–1597. https://doi.org/10.1007/s10346-021-01796-1.
Zhou, C., Cao, Y., Yin, K.L., Intrieri, E., Catani, F., Wu, L.X., 2022b. Characteristic comparison of seepage-driven and buoyancy-driven landslides in Three Gorges Reservoir area, China. Eng. Geol. 301,. https://doi.org/10.1016/j.enggeo.2022.106590 106590.
Zhu, H.H., 2023. Engineering geological interface: From multivariate characterization to evolution mechanism. Bull. Geol. Sci. Technol. 42 (1), 1–20. https://doi.org/10.19509/j.cnki.dzkq.tb20220661.
Zhu, H.H., Wang, J.C., Reddy, N.G., Garg, A., Cao, D.F., Liu, X.F., Shi, B., 2022. Monitoring infiltration of capillary barrier with actively heated fiber Bragg gratings. Environ. Geotech. 40, 1–16. https://doi.org/10.1680/jenge.21.00130.