## 1 Introduction  
Geological hazards are catastrophic events caused by natural or man-made factors [1, 2] and include collapses, landslides, debris flows, karst surface collapses, ground fissures, and subsidence [3, 4]. They typically occur in mountainous areas and are characterized by a sudden occurrence, accompanied by casualties, property losses, and environmental destruction [5, 6, 7]. For example, the Xinmo landslide on 24 June 2017 caused 83 casualties [8], and the Baige landslide on 10 October 2018 caused economic losses of approximately 6.8 billion yuan [9]. Therefore, the early identification of geological disasters is of great significance for disaster prevention and mitigation. The identification of geological hazards is complex [10]. It is generally considered that surface deformation is an important indicator of potential geological disasters. In the past, experts analyzed macro-physiological features using field surveys to identify potential disasters such as landslides [11, 12]. Traditional measurement methods, such as leveling measurements, global positioning systems (GPS), and global navigation satellite systems(GNSS), with a monitoring accuracy of centimeter-to-millimeter, have also been used to monitor surface deformation [13; 14; 15]. These methods are performed on a point-to-point basis. Therefore, for a large research area, the acquisition of monitoring data is relatively time-consuming and laborious [16; 17], and in underdeveloped areas, the high cost limits their applications [18]. Moreover, the low spatial coverage of data is also a major challenge for researching geological hazards of the entire study area [19]. Due the generation of various high-precision, large-scale satellite Synthetic Aperture Radar (SAR) images [20], Interferometric Synthetic Aperture Radar (InSAR) technology has been increasingly used in recent years for surface deformation monitoring. InSAR technology has the advantages of a large coverage area, high precision, and all-weather applications. Differential Interferometric Synthetic Aperture Radar (D-InSAR) technology was first used for ground deformation monitoring in 1989 and exhibited high monitoring accuracy [21; 22]. Subsequently, the method was increasingly used for the surface deformation monitoring of geological disaster areas [23; 24; 25]. However, with the continuous improvement of InSAR technology, experts found that D-InSAR is susceptible to external factors and has decorrelation problems, limiting its monitoring accuracy. Therefore, time-series InSAR technologies, such as Persistent Scatterer Interferometric SAR (PS-InSAR) and Small Baseline Subset (SBAS)-InSAR, were proposed by [PERSON] et al. [26] and [PERSON] et.al. [27] in 2000 and 2002, respectively. PS-InSAR was used for urban surface deformation monitoring [28; 29; 30], whereas SBAS-InSAR is applicable to urban and non-urban areas [31; 32; 33; 34]. Therefore, SBAS-InSAR technology has been widely used in research on geological disasters. The SBAS-InSAR technology uses a singular value decomposition (SVD) algorithm to perform time-series analysis on SAR images [27] and obtain the average annual deformation rate of the study area. Monitoring data obtained by SBAS-InSAR technology have the characteristics of many data points, wide distribution and high spatial correlation. In order to quickly and accurately identify abnormal deformation regions, some studies used hot spot analysis to process the results of monitoring. Hot spot analysis is a viable method for semi-automatic ground movements extraction [35]. By means of spatial statistics, the aggregation degree of deformation points is calculated. Then, high-low clustering is divided according to the standard normal distribution to highlight the regions with fast motion [36; 37; 38; 39].