## 1 Introduction
[18] investigated the representation of InSAR metadata as entity embeddings within a deep learning framework (EE-DL) to model the spatiotemporal deformation response. They successfully detected and predicted the fine spatial movement patterns at the Cardia Mining Proving Ground in Australia. Accurate early warning of the abnormal deformation caused by landslides requires rapid information acquisition and the analysis of the target, but conventional data acquisition and analysis methods have certain limitations and lags [19; 20; 21]. Consequently, it is of great scientific and practical value to study the integration of SBAS and InSAR in a landslide early warning model. The InSAR technique is an advanced space-based Earth observation method that is extensively employed for the monitoring of surface deformation [22; 23]. Compared to traditional methods, such as GPS measurements, the InSAR technique has the advantages of being all-day, all-weather, and wide-ranging and is unaffected by adverse weather conditions, such as clouds and rain. Moreover, it can acquire a wide area of ground deformity with precision up to the millimeter level [24; 25]. However, the traditional InSAR technique also has some problems in its application. For example, the images are susceptible to spatial incoherence, temporal incoherence, and atmospheric delay effects [26; 27; 28]. Thus, on this basis, different time-series InSAR techniques have been developed which not only overcome these problems but also improve the precision of the monitoring results. Currently, the mainstream time-series InSAR monitoring methods include SBAS-InSARand persistent scatterer interferometric synthetic aperture radar (PS-InSAR). SBAS-InSAR is mainly applied in the area of surface deformation monitoring [29; 30]. Deformation monitoring is the measurement of a monitored object to determine its spatial location and internal morphological characteristics over time, which can be analyzed by obtaining the landslide rate before the landslide deformation and combining it with the historical landslide sites to build a better early warning model [31; 32]. SBAS-InSAR is one of the most effective tools to obtain the deformation rate. In this study, based on ascending orbit data between July 2021 and September 2022 and descending orbit data between July 2021 and October 2022 from the Sentinel-1 satellite, we utilized the SBAS-InSAR technique to acquire the deformation rate of the Hongya Village landslide and analyze the relationship between the landslide and the fracture zone, stratigraphic lithology, and rainfall. In recent years, the landslide in Hongya Village, Mutual County, Qinghai Province, has moved several times. The frequent movement of this landslide has posed a great threat to the lives and safety of the residents. In order to address the limitations of traditional monitoring methods, we apply the SBAS-InSAR technique, utilizing Sentinel-1 satellite data from both ascending and descending orbits spanning from July 2021 to September/October 2022, to investigate the deformation rates of the landslide in Hongya Village. Our primary focus is on elucidating the intricate interactions between the landslide dynamics, fault zones, stratigraphic lithology, and precipitation patterns. Specifically, this study aims to explore the correlations between the deformation characteristics of the Hongya Village landslide and the underlying geological features, with a particular emphasis on the stratigraphic lithology, as well as their interactions with rainfall. By integrating InSAR-derived deformation measurements with geological and meteorological data, this study seeks to provide a comprehensive understanding of the factors influencing landslide activity. The insights gained from this study are anticipated to contribute to the broader field of landslide studies and enhance our capacity for effective landslide hazard prediction and management.