我国是世界上地质灾害频发的国家之一，滑坡占我国地质灾害发生总数的比重较大，由于滑坡发生的不确定性，每年都会造成很大的人力、物力、财力资源损失，时刻威胁着群众生命和财产安全。2017年10月21日凌晨重庆市巫溪县大河乡广安村发生大型山体滑坡，引起局部坡体产生剧烈变形，存在很大的安全隐患，因此进行广安村滑坡形变监测与分析研究具有重要的实际意义。

      随着科技不断发展，InSAR技术为滑坡形变监测提供了新方法，具有全天时、范围广、精度高和受气候条件限制小的优势，在滑坡识别和监测领域得到了广泛应用。但由于大型高位山体滑坡形变量级大、速度快，传统DInSAR技术易受时空失相干、大气延迟等因素干扰，难以获得可靠的形变结果，因此结合有效技术进行滑坡形变监测与分析研究具有重要的应用价值。针对以上问题，本文采用时序InSAR技术对广安村滑坡前后的形变进行监测分析与预计研究，主要研究内容和成果如下：

      （1）广安村滑坡前后时序形变监测与分析。分别采用SBAS-InSAR和IPTA技术对覆盖滑坡区域84景Sentinel-1A数据进行处理，其中滑坡前为2017年3月17日至2017年10月19日共18景数据，滑坡后为2017年10月31日至2019年12月26日共66景数据，优化数据处理流程，获取了滑坡前后的年平均形变速率和时序累积形变量。通过选择典型特征点，对两种方法获取的形变速率进行验证分析，滑坡前相关系数为0.86，滑坡后相关系数为0.85，说明了两种InSAR结果在位置和量级上都具有较好的一致性，滑坡发生前后在坡体上均存在有明显的形变区域。

     （2）广安村滑坡形变特征分析。采用Offset Tracking技术对滑坡前后两景ALOS2数据进行处理，获取了滑坡的二维偏移量，距离向和方位向形变与滑移区域位置分布一致。结合时序InSAR形变结果与已有的滑坡资料，从滑坡二维偏移量、滑坡前后形变空间分布以及影响因素三个角度分析滑坡形变特征。结果显示，广安村滑坡发生前后时序InSAR形变空间分布与滑坡失稳拉裂位置、形变分区的分布以及光学影像变化基本吻合，验证了时序InSAR结果的合理性，局部坡体在滑坡后仍持续形变，在异常降雨、灰岩溶蚀、冲沟切割、地形起伏以及人类活动等触发因素作用下仍有滑动的可能性，可以结合时序InSAR技术加强对滑坡稳定性的探测。

     （3）基于时序InSAR技术和PSO-SVR算法建立滑坡后形变预计模型。结合滑坡后形变的空间分布特征，在不同形变分区内选择具有显著形变信号的特征点，提取对应的时序累积形变量，将数据按照9:1的比例划分训练集和测试集，建立滑坡后形变预计模型，利用模型评价指标对预计模型精度进行评定。研究结果表明PSO-SVR算法可以对时序InSAR形变结果进行较好的预计，测试集数据的决定系数均大于0.5，模型预计拟合度较好，为滑坡稳定性分析提供了一种有效的预计方法。

       China is one of the countries with frequent geological disasters in the world, among which landslides account for a large proportion of the total number of geological disasters in China, and due to the uncertainty of landslide occurrence, it causes great loss of human, material, and financial resources every year, which always threatens the safety of people's lives and properties. In the early morning of October 21, 2017, a high-level landslide occurred in Guang'an Village, Dahe Township, Wuxi County, Chongqing City, which caused severe deformation of the local slope and great potential safety hazards. Therefore, it is of great practical significance to carry out the analysis and research of landslide deformation monitoring in Guang'an village.

        With the continuous development of science and technology, InSAR technology provides a new method for landslide deformation monitoring, which has the advantages of all-day, wide range, high accuracy and less restricted by climatic conditions, and has been widely used in the field of landslide identification and monitoring. However, due to the large magnitude and rapid deformation of high-level landslides, traditional DInSAR technology is susceptible to spatial and temporal decoherence and atmospheric delay, which makes it difficult to monitor single landslides comprehensively and obtain reliable deformation results, so it is important to combine effective technology for landslide deformation monitoring and prediction analysis research. To address the above problems, this paper adopts the time-series InSAR technique to analyze and predict the deformation characteristics before and after the landslide in Guang'an Village landslide. main research contents and results are as follows:

       (1) Monitoring and analysis of time series deformation before and after Guang'an village landslide. SBAS-InSAR and IPTA technologies were used to process 84 scenes of Sentinel-1A data covering the landslide area, of which 18 scenes were from March 17, 2017, to October 19, 2017, before the landslide, and October 2017 after the landslide, from the 31st to December 26th, 2019, a total of 66 scene data were collected. The data processing process was optimized, and the annual average deformation rate and time-series cumulative deformation variable before and after the landslide were obtained. By selecting typical feature points, the deformation rates obtained by the two methods are verified and analyzed, and the correlation coefficient was 0.86 before the landslide and 0.85 after the landslide, indicating that the two InSAR results are in good agreement in terms of location and magnitude. There are obvious deformation areas on the slope before and after the landslide occurs.

        (2) Analysis of the deformation characteristics of Guang'an Village landslide. Firstly, Offset Tracking technique was used to process the ALOS2 data before and after the landslide to obtain two-dimensional offset of the landslide, and the range and azimuthal deformations were consistent with the distribution position of the landslide area. Then, combining the time-series InSAR results with the landslide investigation data of Guang'an Village, the landslide deformation characteristics were comprehensively analyzed from three perspectives: the two-dimensional offset of the landslide, the spatial distribution of the deformation before and after the landslide, and the factors influencing of landslide. The results show that the spatial distribution of time-series InSAR deformation before and after the occurrence of the landslide in Guang'an Village is consistent with the distribution of the landslide instability rupture location, deformation zoning, and the changes in optical images, which verifies the rationality of the time-series InSAR results; and the local slope continues to deform after the landslide, under the action of triggering factors such as abnormal rainfall, chert dissolution, gully cutting, terrain fluctuation and human activities, there is still has the possibility of sliding again, which can be combined with time-series InSAR technology to enhance the detection and analysis of landslide stability.

      (3) The post-landslide deformation prediction model is established based on the time-series InSAR technique and PSO-SVR algorithm. Combining the spatial distribution characteristics of post-landslide deformation, feature points with significant deformation signals are selected in different deformation sub-areas, the corresponding time-series cumulative deformation variables are extracted, and the data are divided into training and test sets in the ratio of 9:1 to establish the post-landslide deformation prediction model, and the accuracy of the prediction model is evaluated by using model evaluation indexes. The results show that PSO-SVR algorithm can predict the time-series InSAR deformation results well, and the coefficient of determination of the test set data is greater than 0.5. The model fits well and can meet the requirements of engineering applications, which provides an estimation method for landslide stability analysis.

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