【摘要】 兰州作为西部大开发及丝绸之路的重要节点，在现代经济、文化和交通发展中具有关键战略地位。然而由于特殊的地理位置和黄土地质，地面沉降一直是威胁其生态环境和城市基础设施安全的重要问题。因此，准确识别沉降及评估驱动因子的影响，对防治黄土地区地面沉降至关重要。本文提出将SBAS-InSAR技术与时间主成分分析法（TPCA）相结合，深入分析兰州市地面沉降及其驱动因子，从时空角度挖掘沉降信息，定量分析各驱动因子的影响。首先利用SBAS-InSAR技术获取兰州市地表时序形变数据，在此基础上利用TPCA方法从时序形变数据中提取占比较大的主成分，最后结合各主成分信号对兰州市地面沉降驱动因子进行定量分析。结果表明：兰州市地面沉降主要集中在红古区西北部、七里河区南部、城关区北部和新区西岔镇，最大沉降速率达到45.87 mm/a。在沉降严重的七里河区和新区，前4个主成分可以解释90%以上的数据特征，其中第一主成分（PC1）包含原始数据75%以上的信息。特征区域PC1特征向量变化与不同监测井水位变化的相关系数均超过0.6，表明地下水变化是兰州市地面沉降的主要影响因素。本文方法在兰州市地面沉降分析中具有一定的应用价值，为黄土地区地面沉降的治理提供参考依据。更多还原

【Abstract】 Lanzhou is an important node in the development of the western region and the Silk Road, holding a key strategic position in the development of modern economy, culture, and transportation. However, due to its unique geographical location and distinctive loess topography, land subsidence has been a persistent issue that threatens the city’s ecological environment and the safety of its urban infrastructure. Therefore, it is very important to accurately identify subsidence and evaluate the impact of driving factors to prevent and control land subsidence in loess areas. This paper proposes combining SBAS-InSAR technology with Time Series Principal Component Analysis(TPCA) to deeply analyze land subsidence and its driving factors in Lanzhou. By examining subsidence information from both temporal and spatial perspectives, the study quantitatively analyze the influence of various driving factors. Firstly, SBAS-InSAR technology is used to obtain time series deformation data of land surface in Lanzhou City. Then, TPCA is applied to extract the principal components that represent a significant proportion of the deformation data. Finally, the driving factors of land subsidence in Lanzhou city are quantitatively analyzed by combining the signals of each principal component. The results show that the land subsidence of Lanzhou city is concentrated in the northwestern part of Honggu District, the southern part of Qilihe District, the Northern part of Chengguan District,and the Xicha Town of the new district,with the maximum subsidence rate reaching 45.87 mm/year. In the Qilihe District and the new district, where the subsidence is severe, the first four principal components explain more than 90% of the data characteristics, with the first Principal Component(PC1)alone containing over 75% of the original data information. The correlation coefficient between the change in the PC1 characteristic vector in the characteristic area and the change in water levels in different monitoring wells is above 0.6, indicating that groundwater change is the main influencing factor of land subsidence in Lanzhou City. The method proposed in this paper has practical value in analyzing land subsidence in Lanzhou, providing a reference for managing land subsidence in loess areas.更多还原