结合TRIGRS模型的黄土滑坡危险性评价粒子滤波数据同化方法

doi:10.12082/dqxxkx.2023.230274

地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (10): 2084-2092.doi: 10.12082/dqxxkx.2023.230274

结合TRIGRS模型的黄土滑坡危险性评价粒子滤波数据同化方法

魏冠军¹^,²^,³(), 高茂宁¹^,²^,³^,^*()

1.兰州交通大学测绘与地理信息学院，兰州 730070
2.地理国情监测技术应用国家地方联合工程研究中心，兰州 730070
3.甘肃省地理国情监测工程实验室，兰州 730070

收稿日期:2023-05-18 修回日期:2023-07-03 出版日期:2023-10-25 发布日期:2023-09-22
通讯作者: * 高茂宁（1997—），男，四川中江人，硕士生，主要从事地质灾害监测与评价方法研究。 E-mail:932655441@qq.com
作者简介:魏冠军（1976—），男，甘肃庄浪人，博士，教授，主要从事误差理论与测量数据处理研究。E-mail: wchampion@mail.lzjtu.cn
基金资助:
国家自然科学基金项目(41964008)

Particle Filter Data Assimilation Method for Loess Landslide Risk Assessment Combined with TRIGRS Model

WEI Guanjun¹^,²^,³(), GAO Maoning¹^,²^,³^,^*()

1. Faculty of Geomatics Lanzhou Jiaotong University, Lanzhou 730070, China
2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China

Received:2023-05-18 Revised:2023-07-03 Online:2023-10-25 Published:2023-09-22
Contact: * GAO Maoning, E-mail:932655441@qq.com
Supported by:
National Natural Science Foundation of China(41964008)

摘要/Abstract

摘要：

滑坡灾害对国家基础设施工程产生了严重的威胁，开展工程区域滑坡灾害危险性评价对于铁路运行安全至关重要。滑坡危险性评价通常可以概括为频率分析法、概率分析法以及确定性分析法，其中基于降雨入渗-积水机制角度建立物理确定性模型可以获得更为客观的评价结果，具有良好的适用性，但其通常需要大量的岩土参数参与计算，易受岩土参数的时空变异性以及不确定性等因素影响，存在一定的局限性。为进一步提升滑坡危险性评价的预测精度，本文以高家湾滑坡为研究区，基于粒子滤波算法，利用SBAS-InSAR观测数据对TRIGRS模型中的安全系数（Fs）进行同化，同时更新模型的内摩擦角参数。结果表明：同化后高家湾滑坡的安全系数呈现出逐渐降低的趋势，且坡体前缘的安全系数明显低于坡体后缘，与当前观测更为接近；实现了内摩擦角参数的实时更新，使参数逐渐向观测值方向修正；模型的均方根偏差从0.17降低至0.04，使模型预测结果与实际观测更为接近。因此，基于粒子滤波同化方法的滑坡危险性评价可以更准确地体现当前滑坡的实际情况，具有更高的预测精度。

关键词: 滑坡, 危险性评价, TRIGRS模型, SBAS-InSAR, 数据同化, 粒子滤波, 高家湾滑坡

Abstract:

Landslide disasters pose a grave threat to national infrastructure projects, making landslide disaster risk assessment in engineering areas crucial for ensuring the safety of railway operations. Typically, such assessments involve employing frequency analysis, probability analysis, and deterministic analysis methods. Among these methods, the establishment of a physical deterministic model based on the mechanisms of rainfall infiltration and water accumulation yields more objective evaluation results and exhibits excellent applicability. However, physically deterministic models often necessitate the inclusion of numerous geotechnical parameters in their calculations, leading to certain limitations. Factors like temporal and spatial variability, as well as the uncertainty in geotechnical parameters, influence these models. To enhance the prediction accuracy of landslide risk assessments, this study focuses on the Gaojiawan landslide as the research area. By utilizing the particle filter algorithm, the study assimilates safety factor (Fs) data from the TRIGRS model, incorporating SBAS-InSAR observation data. Additionally, it updates the internal friction angle parameter of the model. The results reveal a gradual decrease in the safety factor of the Gaojiawan landslide following assimilation. Moreover, the safety factor at the front edge of the slope is significantly lower than that at the rear edge, aligning more closely with current observations. Real-time updates of the internal friction angle parameters are achieved, gradually aligning the parameters with the observed values. As a result, the root mean square deviation of the model decreases from 0.17 to 0.04, bringing the model's prediction closer to the observed values. Consequently, landslide risk assessment based on the particle filter assimilation method more accurately reflects the current landslide situation and exhibits higher prediction accuracy.

Key words: landslide, hazard assessment, TRIGRS model, SBAS-InSAR, data assimilation, particle filter, Gaojiawan landslide

魏冠军, 高茂宁. 结合TRIGRS模型的黄土滑坡危险性评价粒子滤波数据同化方法[J]. 地球信息科学学报, 2023, 25(10): 2084-2092.DOI:10.12082/dqxxkx.2023.230274

WEI Guanjun, GAO Maoning. Particle Filter Data Assimilation Method for Loess Landslide Risk Assessment Combined with TRIGRS Model[J]. Journal of Geo-information Science, 2023, 25(10): 2084-2092.DOI:10.12082/dqxxkx.2023.230274

图/表 10

图1

图2

图3

表1

图4

表2

图5

图6

图7

图8

参考文献 32

[1]	许领, 戴福初, 邝国麟, 等. 黄土滑坡典型工程地质问题分析[J]. 岩土工程学报, 2009, 31(2):287-293.
	[Xu L, Dai F C, KWONG A K L, et al. Analysis of some special engineering-geological problems of loess landslide[J]. Chinese Journal of Geotechnical Engineering, 2009, 31(2):287-293.]
[2]	冉林, 马鹏辉, 彭建兵, 等. 甘肃黑方台“10·5”黄土滑坡启动及运动特征分析[J]. 中国地质灾害与防治学报, 2022, 33(6):1-9.
	[Ran L, Ma P H, Peng J B, et al. The initiation and motion characteristics of the “10·5” loess landslide in the Heifangtai platform, Gansu Province[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(6):1-9.] DOI:10.16031/j.cnki.issn.1003-8035.202111006
[3]	周侯伯, 肖桂荣, 林炫歆, 等. 基于特征筛选与差分进化算法优化的滑坡危险性评估方法[J]. 地球信息科学学报, 2022, 24(12):2373-2388. doi: 10.12082/dqxxkx.2022.220158
	[Zhou H B, Xiao G R, Lin X X, et al. Landslide hazard assessment method based on feature screening and differential evolution algorithm optimization[J]. Journal of Geo-information Science, 2022, 24(12):2373-2388.] DOI:10.12082/dqxxkx.2022.220158
[4]	Montgomery D R, Dietrich W E. A physically based model for the topographic control on shallow landsliding[J]. Water Resources Research, 1994, 30(4):1153-1171. DOI:10.1029/93wr02979
[5]	Pack R T, Tarboton D G, Goodwin C N. The SINMAP approach to terrain stability mapping[C]. 1998. 8th congress of the international association of engineering geology, Vancouver, British Columbia, Canada. 1998:21-25.
[6]	Baum R L, Savage W Z, Godt J W. TRIGRS: A Fortran program for transient rainfall infiltration and grid- based regional slope-stability analysis, version 2.0[R]. USGS, Colorado, Open-File Report, 2008.
[7]	Reid M E, Christian S B, Brien D L, et al. Scoops3D-software to analyze three-dimensional slope stability throughout a digital landscape[M]. US Geological Survey Techniques and Methods, book, 2015.
[8]	同霄, 彭建兵, 朱兴华, 等. 降雨作用下黄土浅层滑坡的危险性分析[J]. 水土保持通报, 2016, 36(3):109-113.
	[Tong X, Peng J B, Zhu X H, et al. Risk analysis of loess shallow landslides under different rainfall conditions[J]. Bulletin of Soil and Water Conservation, 2016, 36(3):109-113.] DOI:10.13961/j.cnki.stbctb.2016.03.020
[9]	高波, 王晓勇. 基于SINMAP模型的延安市滑坡危险性区划[J]. 水土保持通报, 2019, 39(3):211-216.
	[Gao B, Wang X Y. Risk zoning of landslide based on SINMAP model in Yan'an city[J]. Bulletin of Soil and Water Conservation, 2019, 39(03):211-216.] DOI:10.13961/j.cnki.stbctb.2019.03.035
[10]	徐增辉, 金继明, 蔡耀辉, 等. 气候变化对黄土高原浅层滑坡影响的模拟研究——以延安宝塔区为例[J]. 水土保持研究, 2021, 28(1):387-393.
	[Xu Z H, Jin J M, Cai Y H, et al. Impact of climate change on shallow landslides in the loess plateau - a case study in Baota Region, Yan'an city[J]. Research of Soil and Water Conservation, 2021, 28(1):387-393.] DOI:10.13869/j.cnki.rswc.2021.01.048
[11]	do Pinho T M, Filho O A. Landslide susceptibility mapping using the infinite slope, SHALSTAB, SINMAP, and TRIGRS models in Serra do Mar, Brazil[J]. Journal of Mountain Science, 2022, 19(4):1018-1036. DOI:10.1007/s11629-021-7057-z
[12]	Wei X, Zhang L L, Gardoni P, et al. Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales[J]. Acta Geotechnica, 2023:1-24. DOI:10.1007/s11440-023-01841-4
[13]	Zhang S, Jiang Q G, Wu D Z, et al. Improved method of defining rainfall intensity and duration thresholds for shallow landslides based on TRIGRS[J]. Water, 2022, 14(4):524. DOI:10.3390/w14040524
[14]	Nie Y P, Li X Z, Xu R C. Dynamic hazard assessment of debris flow based on TRIGRS and flow-R coupled models[J]. Stochastic Environmental Research and Risk Assessment, 2022, 36(1):97-114. DOI:10.1007/s00477-021-02093-y
[15]	张波, 唐萌萌, 刘润泽. 动态数据驱动多模型耦合的滑坡稳定性分析方法[J]. 测绘与空间地理信息, 2017, 40(6):142-144,147.
	[Zhang B, Tang M M, Liu R Z. Dynamic data driven multi-model coupled for analyzing the stability of landslide[J]. Geomatics & Spatial Information Technology, 2017, 40(6):142-144,147.] DOI:10.3969/j.issn.1672-5867.2017.06.047
[16]	马建文, 秦思娴. 数据同化算法研究现状综述[J]. 地球科学进展, 2012, 27(7):747-757. doi: 10.11867/j.issn.1001-8166.2012.07.0747
	[Ma J W, Qin S X. Recent advances and development of data assimilation algorithms[J]. Advances in Earth Sciences, 2012, 27(7):747-757.]
[17]	李新, 刘丰, 方苗. 模型与观测的和弦:地球系统科学中的数据同化[J]. 中国科学:地球科学, 2020, 50(9):1185-1194.
	[Li X, Liu F, Fang M. Harmonizing models and observations: Data assimilation in Earth system science[J]. Sciential Sinica(Terrae), 2020, 50(9):1185-1194.]
[18]	Geer A J. Learning earth system models from observations: Machine learning or data assimilation?[J]. Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 2021, 379(2194):20200089. DOI:10.1098/rsta.2020.0089
[19]	张智韬, 陈策, 贾江栋, 等. 基于EnKF和PF的沙壕渠灌域土壤含盐量监测模型研究[J]. 农业机械学报:, 2023, 54(6):361-372.
	[Zhang Z T, Chen C, Jia J D, et al. Soil salinity monitoring model of shahaoqu irrigation area based on EnKF and PF algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(6):361-372.]
[20]	金哲, 汪涛, 张洪芹, 等. 基于中国大气反演系统的卫星CO₂数据同化对全球碳收支的评估[J]. 中国科学:地球科学, 2023, 53(3):587-597.
	[Jin Z, Wang T, Zhang H Q, et al. Constraint of satellite CO₂ retrieval on the global carbon cycle from a Chinese atmospheric inversion system[J]. Scientia Sinica (Terrae), 2023, 53(3):587-597.]
[21]	Jiang C H, Zhu D J, Li H B, et al. Improving the particle filter for data assimilation in hydraulic modeling by using a Cauchy likelihood function[J]. Journal of Hydrology, 2023, 617:129050. DOI:10.1016/j.jhydrol.2022.129050
[22]	Jiang Y N, Liao M S, Zhou Z W, et al. Landslide deformation analysis by coupling deformation time series from SAR data with hydrological factors through data assimilation[J]. Remote Sensing, 2016, 8(3):179. DOI:10.3390/rs8030179
[23]	Wang J, Nie G G, Xue C H. Landslide displacement prediction based on time series analysis and data assimilation with hydrological factors[J]. Arabian Journal of Geosciences, 2020, 13(12):460. DOI:10.1007/s12517-020-05452-1
[24]	Xue C H, Nie G G, Li H Y, et al. Data assimilation with an improved particle filter and its application in the TRIGRS landslide model[J]. Natural Hazards and Earth System Sciences, 2018, 18(10):2801-2807. DOI:10.5194/nhess-18-2801-2018
[25]	朱建军, 胡俊, 李志伟, 等. InSAR滑坡监测研究进展[J]. 测绘学报, 2022, 51(10):2001-2019. doi: 10.11947/j.AGCS.2022.20220294
	[Zhu J J, Hu J, Li Z W, et al. Recent progress in landslide monitoring with InSAR[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10):2001-2019.] doi: 10.11947/j.AGCS.2022.20220294
[26]	何涯舟, 张珂, 晁丽君, 等. 基于多源遥感土壤湿度与模型数据同化的流域径流模拟[J]. 水资源保护, 2023, 39(2):145-151,189.
	[He Y Z, Zhang K, Chao L J, et al. Watershed runoff simulation based on multi-source remotely sensed soil moisture and data assimilation[J]. Water Resources Protection, 2023, 39(2):145-151,189.] DOI:10.3880/j.issn.1004-6933.2023.02.018.
[27]	包姗宁, 曹春香, 黄健熙, 等. 同化叶面积指数和蒸散发双变量的冬小麦产量估测方法[J]. 地球信息科学学报, 2015, 17(7):871-882. doi: 10.3724/SP.J.1047.2015.00871
	Cao C X, Huang J X, et al. Research on winter wheat yield estimation based on assimilation of leaf area index and evapotranspiration data[J]. Journal of Geo-information Science, 2015, 17(7):871-882.] DOI:10.3724/SP.J.1047.2015.00871
[28]	刘良云, 陈良富, 刘毅, 等. 全球碳盘点卫星遥感监测方法、进展与挑战[J]. 遥感学报, 2022, 26(2):243-267.
	[Liu L Y, Chen L F, Liu Y, et al. Satellite remote sensing for global stocktaking: methods, progress and perspectives. National Remote Sensing Bulletin, 2022, 26(2):243-267.] DOI:10.11834/jrs.20221806
[29]	Zhou S H, Tian Z Y, Di H G, et al. Investigation of a loess-mudstone landslide and the induced structural damage in a high-speed railway tunnel[J]. Bulletin of Engineering Geology and the Environment, 2020, 79(5):2201-2212. DOI:10.1007/s10064-019-01711-y
[30]	王占巍, 赵发睿, 谢文苹, 等. 青海省高家湾滑坡的形成条件分析及稳定性评价[J]. 水土保持通报, 2020, 40(3):81-87.
	[Wang Z W, Zhao F R, Xie W P, et al. Formation condition analysis and stability evaluation of Gaojiawan landslide in Qinghai province[J]. Bulletin of Soil and Water Conservation, 2020, 40(3):81-87.] DOI:10.13961/j.cnki.stbctb.2020.03.012
[31]	Meng X M, Qi T J, Zhao Y, et al. Deformation of the Zhangjiazhuang high-speed railway tunnel: An analysis of causal mechanisms using geomorphological surveys and D-InSAR monitoring[J]. Journal of Mountain Science, 2021, 18(7):1920-1936. DOI:10.1007/s11629-020-6493-5
[32]	Zhu Y, Qiu H, Liu Z, et al. Detecting long-term deformation of a loess landslide from the phase and amplitude of satellite SAR images: A retrospective analysis for the closure of a tunnel event[J]. Remote Sensing, 2021, 13(23):4841. DOI:10.3390/rs13234841

输入参数	选取参数值
内聚力/kPa	22.3
内摩擦角/°	25.8
土壤容重/(kN/m³)	18.9×10³
饱和导水率/(m/s)	15×10^-7
扩散率/(m/s)	15×10^-5
饱和土壤含水率/%	0.12
残余土壤含水率/%	0.109

日期	模型输出	同化结果
2018-05-02	0.808	0.706
2018-08-30	0.796	0.675
2018-12-28	0.791	0.653

结合TRIGRS模型的黄土滑坡危险性评价粒子滤波数据同化方法

Particle Filter Data Assimilation Method for Loess Landslide Risk Assessment Combined with TRIGRS Model

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 32

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	周超, 甘露露, 王悦, 吴宏阳, 喻进, 曹颖, 殷坤龙. 综合非滑坡样本选取指数与异质集成机器学习的区域滑坡易发性建模[J]. 地球信息科学学报, 2023, 25(8): 1570-1585.
[2]	林炫歆, 肖桂荣, 周侯伯. 顾及土地利用动态变化的滑坡易发性评估方法[J]. 地球信息科学学报, 2023, 25(5): 953-966.
[3]	蒋伟杰, 张春菊, 徐兵, 罗晨晨, 周晗, 周康. AED-Net：滑坡灾害遥感影像语义分割模型[J]. 地球信息科学学报, 2023, 25(10): 2012-2025.
[4]	刘佳, 伍宇明, 高星, 司文涛. 基于GEE和U-net模型的同震滑坡识别方法[J]. 地球信息科学学报, 2022, 24(7): 1275-1285.
[5]	周侯伯, 肖桂荣, 林炫歆, 尹玉环. 基于特征筛选与差分进化算法优化的滑坡危险性评估方法[J]. 地球信息科学学报, 2022, 24(12): 2373-2388.
[6]	王毅, 方志策, 牛瑞卿, 彭令. 基于深度学习的滑坡灾害易发性分析[J]. 地球信息科学学报, 2021, 23(12): 2244-2260.
[7]	麦鉴锋, 冼宇阳, 刘桂林. 气候变化情景下广东省降雨诱发型滑坡灾害潜在分布及预测[J]. 地球信息科学学报, 2021, 23(11): 2042-2054.
[8]	田乃满, 兰恒星, 伍宇明, 李郎平. 人工神经网络和决策树模型在滑坡易发性分析中的性能对比[J]. 地球信息科学学报, 2020, 22(12): 2304-2316.
[9]	郑诗晨, 盛业华, 吕海洋. 基于粒子滤波的行车轨迹路网匹配方法[J]. 地球信息科学学报, 2020, 22(11): 2109-2117.
[10]	郭瑞, 李素敏, 陈娅男, 袁利伟. 基于SBAS-InSAR的矿区采空区潜在滑坡综合识别方法[J]. 地球信息科学学报, 2019, 21(7): 1109-1120.
[11]	张婷, 程昌秀. 顾及空间集聚程度的中国高温灾害危险性评价[J]. 地球信息科学学报, 2019, 21(6): 865-874.
[12]	马小艳, 摆玉龙, 唐丽红, 王月, 李珊珊. 一种新的基于模糊分析观测信息的局地化方法[J]. 地球信息科学学报, 2019, 21(12): 1855-1866.
[13]	杨根云, 周伟, 方教勇. 基于信息量模型和数据标准化的滑坡易发性评价[J]. 地球信息科学学报, 2018, 20(5): 674-683.
[14]	李云君, 刘志红, 吕远洋, 柳锦宝, 王平. 四川省滑坡灾害气象预警模型建立与验证[J]. 地球信息科学学报, 2017, 19(7): 941-949.
[15]	陈天博, 胡卓玮, 魏铼, 胡顺强. 无人机遥感数据处理与滑坡信息提取[J]. 地球信息科学学报, 2017, 19(5): 692-701.