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。
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 SBASInSAR 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.
作者
魏冠军
高茂宁
WEI Guanjun;GAO Maoning(Faculty of Geomatics Lanzhou Jiaotong University,Lanzhou 730070,China;National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China;Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China)
出处
《地球信息科学学报》
EI
CSCD
北大核心
2023年第10期2084-2092,共9页
Journal of Geo-information Science
基金
国家自然科学基金项目(41964008)。
