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

• 遥感科学与应用技术 • 上一篇    下一篇

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. 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. 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:

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