Анализ методов и средств изучения динамики перемещений оползневых склонов
Финансирование: -
Аннотация:
Реализация масштабных проектов строительства требует все большего внимания к исследованию экзогенных геологических процессов в сложных геоморфологических условиях. Склоновые процессы (развитие оползней, обвалов, лавин) относятся к одним из наиболее распространенных и опасных геологических процессов. Механизмы их формирования, выявление, а также регулярный контроль и мониторинг их состояния представляются наиболее важными элементами для качественной оценки оползневых явлений, включая своевременное выявление участков с наибольшей опасностью и ликвидацию их последствий. В статье представлены наиболее распространенные в настоящее время методы и средства выявления оползневых участков с целью эффективного управления рисками. Рассмотрены возможности традиционных подходов с использованием технологий дистанционного зондирования и возможностей наземных (TLS) и воздушных методов (ALS) лазерного сканирования для изучения оползневых явлений; методы фотограмметрии, геодезии, геофизики, геодинамики и математического моделирования; возможности ГИС в совокупности с алгоритмами машинного обучения, а также классические методы математической статистики с комплексной обработкой разнородных данных.
Ключевые слова (RU):
оползневый склон, мониторинг, геодезические наблюдения, цифровая модель рельефа, ГИС, математическое моделирование
Ключевые слова (EN):
landslide slope, monitoring, geodetic observations, digital elevation model, GIS, mathematical modeling
Библиографический список:
- Hungr O., Leroueil S., Picarelli L. The Varnes classification of landslide types, an update // Landslides. – 2014. – Vol. 11. – P. 167–194. doi: 10.1007/s10346-013-0436-y.
- Кризек Р. Оползни: исследование и укрепление / пер. с англ. А. А. Варги, Р. Р. Тиздель // под ред. Г. С. Золотарева. – М. : Мир, 1981. – 368 с.
- Van Den Eeckhaut M., Hervas J., Jaedicke C., Malet J.-P., Montanarella L., Nadim F. Statistical modeling of Europe-wide landslide susceptibility using limited landslide inventory data // Landslides. – 2012. – Vol. 9. – P. 357–369. doi: 10.1007/s10346-011-0299-z.
- Симонян В. В. Изучение оползневых процессов геодезическими методами. – М. : МГСУ, 2011. – 172 с.
- Баборыкин М. Ю., Жидиляева Е. В., Погосян А. Г. Выявление опасных геологических процессов при проведении инженерно-геологических изысканий на основе цифровых моделей рельефа // Инженерные изыскания. – 2015. – № 2. – С. 30–36.
- Federici P. R., Puccinelli A., Cantarelli E., Casarosa N., D’Amato Avanzi G., Falaschi F., Giannecchini R., Pochini A., Ribolini A., Bottai M., Salvati N., Testi C. Multidisciplinary investigations in evaluating landslide susceptibility – An example in the Serchio River valley (Italy) // Quaternary International. – 2007. – Vol. 171-172. – P. 52–63. doi: 10.1016/j.quaint.2006.10.018.
- Ali S., Biermanns P., Haider R., Reicherter K. Landslide susceptibility mapping by using a geographic information system (GIS) along the China–Pakistan Economic Corridor (Karakoram Highway), Pakistan // Natural Hazards Earth System Sciences. – 2019. – Vol. 19. – P. 999–1022. doi: 10.5194/nhess-19-999-2019.
- Wen T., Tang H., Wang Y., Lin Ch., Xiong Ch. Landslide displacement prediction using the GALSSVM model and time series analysis: a case study of Three Gorges Reservoir, China // Natural Hazards Earth System Sciences. – 2017. – Vol. 17. – P. 2181–2198. doi: 10.5194/nhess-17-2181-2017.
- Hosseinalizadeh M., Kariminejada N., Chen W., Pourghasemi H. R., Alinejad M., Behbahani A. M., Tiefenbacher J. P. Spatial modelling of gully headcuts using UAV data and four best-first decision classifier ensembles (BFTree, Bag-BFTree, RS-BFTree, and RF-BFTree) // Geomorphology. – 2019. – Vol. 349. – P. 184–193. doi: 10.1016/j.geomorph.2019.01.006.
- Boogar A. R., Salehi H., Pourghasemi H. R., Blaschke T. Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques // Water. – 2019. – Vol. 11, Issue 10. – P. 2049. doi: 10.3390/w11102049.
- Yousefi S., Pourghasemi H. R., Emami S. N., Pouyan S., Eskandari S., Tiefenbacher J. P. A machine learning framework for multi-hazards modeling and mapping in a mountainous area // Scientific Reports. – 2020. – Vol. 10. – P. 12144. doi: 10.1038/s41598-020-69233-2.
- Yousefi S., Khatami R., Mountrakis G., Mirzaee S., Pourghazemi H. R., Tazeh M. Accuracy assessment of land cover/land use classifiers in dry and humid areas of iran // Environmental monitoring and assessment. – 2015. – Vol. 187, Issue 10. – P. 641. doi: 10.1007/s10661-015-4847-1.
- Greco R., Sorrico-Valvo M., Catalano E. Logistic Regression analysis in the evaluation of mass movements susceptibility: The Aspromonte case study, Calabria, Italy // Engineering Geology. – 2007. – Vol. 89, Issue 1-2. – P. 47–66. doi: 10.1016/j.enggeo.2006.09.006.
- Pourghasemi H. R., Sadhasivam, Kariminejad N., Collins A. Gully erosion spatial modelling: Role of machine learning algorithms N. in selection of the best controlling factors and modelling process // Geoscience Frontiers. – 2020. – Vol. 11, Issue 6. – P. 2207–2219. doi: 10.1016/j.gsf.2020.03.005.
- Pourghasemi H. R., Rossi M. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS and M-AHP methods // Theoretical and Applied Climatology. – 2017. – Vol. 130. – P. 609–633. doi: 10.1007/s00704-016-1919-2.
- Huang F., Yao Ch., Liu W., Li Y., Liu X. Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector ma-chine // Geomatics, Natural Hazards and Risk. – 2018. – Vol. 9, Issue 1. – P. 919–938. doi: 10.1080/19475705.2018.1482963.
- Pradhan B. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS // Computers & Geosciences. – 2013. – Vol. 51. – P. 350–365. doi: 10.1016/j.cageo.2012.08.023.
- Akgun A., Sezer E. A., Nefeslioglu H. A., Pradhan C. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm // Computers & Geosciences. – 2012. – Vol. 38, Issue 1. – P. 23–34. doi: 10.1016/j.cageo.2011.04.012.
- Do H. M., Yin K. L., Guo Z. Zh. A comparative study on the integrative ability of the analytical hierarchy process, weights of evidence and logistic regression methods with the Flow-R model for landslide susceptibility assessment // Geomatics, Natural Hazards and Risk. – 2020. – Vol. 11, Issue 1. – P. 2449–2485. doi: 10.1080/19475705.2020.1846086.
- Hsu Y.-Ch., Chang Y.-L., Chang Ch.-H., Yang J.-Ch., Tung Y.-K. Physical-based rainfall-triggered shallow landslide forecasting // Smart Water. – 2018. – Vol. 3. – P. 3. doi: 10.1186/s40713-018-0011-8.
- Gayen A., Pourghasemi H. R., Saha S., Keesstra S., Bai S. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms // Science of the Total Environment. – 2019. – Vol. 668. – P. 124–138. doi: 10.1016/j.scitotenv.2019.02.436.
- Bui D. T., Tuan T. A., Klempe H., Pradhan B., Revhaug I. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree // Landslides. – 2016. –Vol. 13. – P. 361–378. doi: 10.1007/s10346-015-0557-6.
- Huang F., Huang J., Jiang Sh., Zhou Ch. Landslide displacement prediction based on multivariate chaotic model and extreme learning machine // Engineering Geology. – 2017. – Vol. 218. – P. 173–186. doi: 10.1016/j.enggeo.2017.01.016.
- Hong H. Y., Pradhan B., Xu C., Bui D. T. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines // Catena. – 2015. – Vol. 133. – P. 266–281. doi: 10.1016/j.catena.2015.05.019.
- Dou J., Yunus A. P., Bui D. T., Merghadi A., Sahana M., Zhu Z. F., Chen C. W., Han Z., Pham B. T. Improved Landslide assessment using support vector machine with bagging, boosting and stacking ensemble machine learning framework in a mountainous watershed, Japan // Landslides. – 2020. – Vol. 17. – P. 641–658. doi: 10.1007/s10346-019-01286-5.
- Bui D. T., Pradhan B., Lofman O., Revhaug I., Dick O. B. Landslide susceptibility assessment in the Noa Binh province of Vietnam: a comparison of the Levenberg-Marquardt and Bayesian regularized neural networks // Geomorphology. – 2012. – Vol. 171-172. – P. 12–29. doi: 10.1016/j.geomorph.2012.04.023.
- Кузин А. А., Санникова А. П. Методика оценки оползневой опасности при освоении территорий на основе геоинформационных систем по геодезическим данным // Геодезия и картография. – 2016. – №. 4. – С. 43–50.
- Mantovani M., Devoto S., Forte E., Mocnik A., Pasuto A., Piacentini D., Soldati M. A multidisciplinary approach for rock spreading and block sliding investigation in the north-western coast of Malta // Landslides. – 2012. – Vol. 10, Issue 5. – P. 611–622. doi: 10.1007/s10346-012-0347-3.
- Topographic lazer ranging and scanning: principles and processing / Edited by Shan J., Toth K. – 2nd edition. – CRC Press, 2018. – 654 p.
- Баборыкин М. Ю., Жидиляева Е. В. Мониторинг оползней с использованием лазерного сканирования и геодезических наблюдений // Инженерные изыскания. – 2014. – №. 3. – С. 16–24.
- Yamada M., Kumagai H., Matsushi Y., Matsuzawa T. Dynamic landslide processes revealed by broadband seismic records // Geophysical Research Letters. – 2013. – Vol. 40, Issue 12. – P. 2998–3002. doi: 10.1002/grl.50437.
- Godone D., Giordan D., Baldo M. Rapid mapping application of vegetated terraces based on high resolution airborne LIDAR // Geomatics, Natural Hazards Risk. – 2018. – Vol. 9, Issue 1. – P. 970–985. doi: 10.1080/19475705.2018.1478893.
- Jaboyedoff M., Oppikofer Th., Abellan A., Derron M.-H., Loye A., Metzger R., Pedrazzini A. Use of LIDAR in landslide investigations: a review // Natural Hazards. – 2012. – Vol. 61. – P. 5–28. doi: 10.1007/s11069-010-9634-2.
- Carter W., Shrestha R., Tuell G., Bloomquist D., Sartory M. Airborne laser swath mapping shines new light on Earth's topography // Edvancing Earth and Space science. – 2001. – Vol. 82, Issue 46. – P. 549–555. doi: 10.1029/01EO00321.
- Rowlands K. A., Jones L. D., Whitworth M. Landslide laser scanning: a new look at an old problem // Quarterly Journal of Engineering Geology and Hydrogeology. – 2003. – Vol. 36. – P. 155–157. doi: 10.1144/1470-9236/2003-08.
- Dunning S. A., Massey S. I., Rosser N. J. Structural and geomorphological features of landslides in the Bhutan Himalaya derived from Terrestrial laser Scanning // Geomorphology. – 2009. – Vol. 103, Issue 1. – P. 17–29. doi: 10.1016/j.geomorph.2008.04.013.
- Mazzanti P., Schilirò L., Martino S., Antonielli B., Brizi E., Brunetti A., Margottini C., Mugnozza G. S. The Contribution of Terrestrial Laser Scanning to the Analysis of Cliff Slope Stability in Sugano (Central Italy) // Remote Sensing. – 2018. – Vol. 10, Issue 9. – P. 1475. doi: 10.3390/rs10091475.
- Guo J., Yi Sh., Yin Y., Cui Y., Qin M., Li T., Wang Ch. The effect of topography on landslide kinematics: a case study of the Jichang town landslide in Guizhou, China // Landslides. – 2020. – Vol. 17, Issue 5. – P. 959–973. doi: 10.1007/s10346-019-0339-9.
- Hu Sh., Qiu H., Pei Y., Cui Y., Xie W., Wang X., Yang D., Tu X., Zou Q., Cao P., Cao M. Digital terrain analysis of a landslide on the loess tableland using high-resolution topography data // Landslides. – 2019. – Vol. 16. – P. 617–632. doi: 10.1007/s10346-018-1103-0.
- Овсюченко Н. И., Акопов Д. Н. Лазерное сканирование и мониторинг оползневых склонов // Инженерные изыскания. – 2012. – № 2. – С. 40–45.
- Westoby M. J., Brasington J., Glasser N. F., Hambrey M. J., Reynolds J. M. ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications // Geomorphology. – 2012. – Vol. 179. – P. 300–314. doi: 10.1016/j.geomorph.2012.08.021.
- Cignetti M., Godone D., Wrzesniak A., Giordan D. Structure from Motion Multisource Application for Landslide Characterization and Monitoring: The Champlas du Col Case Study, Sestriere, North-Western Italy // Sensors. – 2019. – Vol. 19, Issue 10. – P. 2364. doi: 10.3390/s19102364.
- Zeybek M., Sanlioglu I. Investigation of landslide detection using radial basis functions: a case study of the Taşkent landslide, Turkey // Environmental Monitoring and Assessment. – 2020. – Vol. 192, Issue 4. – P. 320. doi: 10.1007/s10661-020-8101-0.
- Thoma C. T., Makridou K. N., Kaloudis D. E. and Vlachos C. G. Evaluating the Potential of ThreeDimensional Laser Surface Scanning as an Alternative Method of Obtaining Morphometric Data / Annales Zoologici Fennici. – 2018. – 55 (1-3). – P. 55–66. doi: 10.1002/ajpa.24204.
- Chen L.-H., Chen C.-T., Pan Y.-G. Groundwater level prediction using SOM-RBFN multisite model // Journal of Hydrologic Engineering. – 2010. – Vol. 15, Issue 8. – P. 624–631. doi: 10.1061/(ASCE)HE.1943-5584.0000218.
- Mohanty S., Jha М. К., Kumar A., et al. Artificial neural network modeling for groundwater level forecasting in a river island of Eastern India // Water Resources Management. – 2010. – Vol. 24. – P. 1845–1865. doi: 10.1007/s11269-009-9527-x.
- Gundoglu K. S., Guney I. Spatial analyses of groundwater levels using universal kriging // Journal of Earth System Science. – 2007. – Vol. 116. – P. 49–55. doi: 10.1007/s12040-007-0006-6.
- Iverson R. M. Landslide triggering by rain infiltration // Water Resources Research. – 2000. – Vol. 36, Issue 7. – P. 1897–1910. doi: 10.1029/2000WR900090.
- Bermúdez J. D., Corberán-Vallet A., Vercher E. Multivariate exponential smoothing: a Bayesian forecast approach based on simulation // Mathematics and Computers Simulation. – 2009. – Vol. 79, Issue 5. – P. 1761–1769. doi: 10.1016/j.matcom.2008.09.004.
- Duan G., Chen D., Niu R. Forecasting Groundwater Level for Soil Landslide Based on a Dynamic Model and Landslide Evolution Pattern // Water. – 2019. – Vol. 11, Issue 10. – P. 2163. doi: 10.3390/w11102163.
- Jia G. W., Zhan Т. L. T., Chen Y. M., Fredlund D. G. Performance of a large-scale slope model subjected to rising and lowering water levels // Engineering Geology. – 2009. – Vol. 106, Issues 1-2. – P. 92–103. doi: 10.1016/j.enggeo.2009.03.003.
- Huang F., Yin K., He T., Zhou Ch., Zhang J. Influencing factor analysis and displacement prediction in reservoir landslides – A case study of Three Gorges Reservoir (China) // Tehnicki Vjesnik. – 2016. – Vol. 23. – P. 617–626. doi: 10.17559/TV-20150314105216.
- Huang F. M., Huang J. S., Jiang S. H., Zhou C. B. Landslide displacement prediction based on multivariate chaotic model and extreme learning machine // Engineering Geology. – 2017. – Vol. 218. – P. 173–186. doi: 10.1016/j.enggeo.2017.01.016.
- Sun G., Zheng H., Tang H., Dai F. Huangtupo landslide stability under water level fluctuations of the Three Gorges reservoir // Landslides. – 2016. – Vol. 13, Issue 5. – P. 1167–1179. doi: 10.1007/s10346-015-0637-7.
- Liu S. Y., Shao L. T., Li H. J. Slope stability analysis using the limit equilibrium method and two finite element methods // Computers and Geotechnics. – 2015. – Vol. 63. – Р. 291–298. doi: 10.1016/j.compgeo.2014.10.008.
- Кожогулов К. Ч., Нифадьев В. И., Усманов С. Ф. Прогнозирование устойчивости откосов и склонов на основе численного моделирования напряженно-деформированного состояния горных пород // Фундаментальные и прикладные вопросы горных наук. – 2017. – Т. 4, № 3. – С. 54–59.
- Немирович-Данченко M. M. Численное моделирование трехмерных динамических задач сейсмологии // Физическая мезомеханика. – 2002. – Т. 5, № 5. – С. 99–106.
- Guo J., Yi Sh., Yin Y., Cui Y., Oin M., Li T., Wang Ch. The effect of topography on landslide kinematics: a case study of the Jichang town landslide in Guizhou, China // Landslides. – 2020. – Vol. 17. – P. 959–973. doi: 10.1007/s10346-019-01339-9.
- Moretti L., Allstadt K., Mangeney A., Capdeville Y., Stutzmann E., Bouchut F. Numerical modeling of the Mount Meager landslide constrained by its force history derived from seismic data // Journal of Geophysical Research: Solid Earth. – 2015. – Vol. 120. – Р. 2579–2599. doi: 10.1002/2014JB011426.
- Bai X., Jian J., He S., Liu W. Dynamic progress of massive Xinmo landslide, Sichuan (China), from joint seismic signal and morphodynamic analysis // Bulletin of Engineering Geology and the Environment. – 2019. – Vol. 78. – P. 3269–3279. doi: 10.1007/s10064-018-1360-0.
- Allstadt K. Extracting source characteristics and dynamics of the August 2010 Mount Meager landslide from broadband seismograms // Journal of Geophysical Research: Solid Earth. – 2013. – Vol. 118, Issue 3. – P. 1472–1490. doi: 10.1002/jgrf.20110.
- Fuchs F., Lenhardt W., Bokelmann G., the AlpArray Working Group. Seismic detection of rockslides at regional scale: examples from the Eastern Alps and feasibility of kurtosis-based event location // Earth Surface Dynamics. – 2018. – Vol. 6. – P. 955–970. doi: 10.5194/esurf-6-955-2018.
- Lin Ch.-H. Insight into landslide kinematics from a broadband seismic network // Earth, Planets and Space. – 2015. – Vol. 67. – P. 8. doi: 10.1186/s40623-014-0177-8.
- Moretti L., Allstadt K., Mangeney A., Capdeville Y., Stutzmann E., Bouchut F. Numerical modeling of the Mount Meager landslide constrained by its force history derived from seismic data // Journal of Geophysical Research: Solid Earth. – 2015. – Vol. 120, Issue 4. – P. 2579–2599. doi: 10.1002/2014JB011426.
- Manconi A., Picozzi M., Coviello V., de Santis F., Elia L. Real-time detection, location, and characterization of rockslides using broadband regional seismic networks // Geophysical Research Letters. – 2016. – Vol. 43. – P. 6960–6967. doi: 10.1002/2016GL069572.
- Walter M., Schwaderer U., Joswig M. Seismic monitoring of precursory fracture signals from a destructive rockfall in the Vorarlberg Alps, Austria // Natural Hazards and Earth System Sciences. – 2012. – Vol. 12, Issue 11. – P. 3545–3555. doi: 10.5194/nhess-12-3545-2012.
- Yan Y., Cui Y., Tian X., Hu S., Guo J., Wang Z., Yin S., Liao L. Seismic signal recognition and interpretation of the “7.23” Shuicheng landslide by seismogram station // Landslides. – 2020. – Vol. 17, Issue 5. – P. 1191–1206. doi: 10.1007/s10346-020-01358-x.
- Zh.-Y. Fend, Ch.-M. Lo, Q.-F. Lin. The characteristics of the seismic signals induced by landslides using a coupling of discrete element and finite difference methods // Landslides. – 2017. – Vol. 14. – P. 661–674. doi: 10.1007/s10346-016-0714-6.
- Guinau M., Tapia M., Peres-Guillen C., Surinach E., Roig P., Khazaradze G., Torne M., Royan M. J., Echeverria A. Remote sensing and seismic data integration for the characterization of a rock slide and an artificially triggered rock fall // Engineering Geology. – 2019. – Vol. 257, P. 105113. doi: 10.1016/j.enggeo.2019.04.010.
- Hilbert C., Ekstrom G., Stark C. P. Dynamics of the Bingham Canyon Mine landslides from seismic signal analysis // Geophysical Research Letters. – 2014. – Vol. 41, Issue 13. – P. 4535–4541. doi: 10.1002/2014GL060592.
- Dammeier F., Moore J. R., Haslinger F., Loew S. Characterization of alpine rockslides using statistical analysis of seismic signals // Journal of Geophysical Research: Solid Earth. – 2011. – Vol. 116, Issue F4. doi: 10.1029/2011JF002037.
- M. Dong, H. Hu, J. Song. Combined methodology for three-dimensional slope stability analysis coupled with time effect: a case study in Germany // Environmental Earth Sciences. – 2018. – Vol. 77, Issue 8. – P. 311. doi: 10.1007/s12665-018-7497-0.
- Saria P. T. K., Putria Y. E., Savitria Y. R., Amaliaa A. R., Marginia N. F., Nusantara D. A. D. The Comparison Between 2-D and 3-D Slope Stability Analysis Based on Reinforcement Requirements // International Journal on Advanced Science Engineering and Information Technology. – 2020. – Vol. 10, Issue 5. – P. 2082. doi: 10.18517/ijaseit.10.5.12815.
- Zhang R., Zhao J., Wang G. Stability Analysis of Anchored Soil Slope Based on Finite Element Limit Equilibrium Method // Mathematical Problems in Engineering. – 2016. – Article ID 7857490. doi: 10.1155/2016/7857490.
- Dong M., Wu H., Hu H., Azzam R., Zhang L., Zheng Z., Gong X. Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model // Sensors. – 2021. – Vol. 21, Issue 1. – P. 14. doi: 10.3390/s21010014.
- Dammeier F., Moore J. R., Hammer C., Haslinger F., Loew S. Automatic detection of alpine rockslides in continuous seismic data using hidden Markov models // Journal of Geophysical Research: Earth Surface. – 2016. – Vol. 121, Issue 2. – P. 351–371. doi: 10.1002/2015JF003647/.