地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (10): 2084-2092.doi: 10.12082/dqxxkx.2023.230274
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:
WEI Guanjun1,2,3(), GAO Maoning1,2,3,*(
)
Received:
2023-05-18
Revised:
2023-07-03
Online:
2023-10-25
Published:
2023-09-22
Contact:
* GAO Maoning, E-mail:Supported by:
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。
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
[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 |
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