## 3 Methods  
Initially, we conducted a reconnaissance survey utilizing high-resolution satellite imagery from Google Earth. Subsequently, we mapped the entire JRNF and systematically classified the alluvial-proluvial fans into three distinct categories (F1-F3). To identify the most suitable trenching locations, field mapping was executed at a scale of 1:50,000. By identifying the fault's geomorphic modification of the alluvial-proluvial fans, two optimal trenching sites were chosen and subsequently excavated. Advanced 3D surface reconstruction methods, including Structure-from-Motion (SfM) [32,33], were utilized to create high-resolution digital elevation models (DEMs) for each trenching site, leveraging an unmanned aerial vehicle (UAV). A DJI Phantom 4 RTK UAV (Shenzhen, China) was deployed to navigate a predetermined path within a rectangular zone at an elevation of 200 m. More than 300 high-resolution photographs, each boasting 20 million pixels and featuring a 75-80% overlap, were taken to facilitate the reconstruction of the terrain with a precision of 0.5 m. The integration of the Real-Time Kinematic (RTK) module into the UAV significantly enhances the absolute accuracy of image metadata, allowing for precise data collection without ground control points (GCPs). RTK technology  
Figure 2: Digital elevation model (DEM) shaded relief map (**A**) and satellite image interpretation (**B**) of JRNF (elevation data with a 30 m resolution were obtained from the Geospatial Data Cloud ASTER GDEM for the DEM, and the image data from Google Earth (accessed on 2 August 2023)). minimizes the time delay between positional information and camera data, resulting in improved positioning accuracy for the captured images. This advancement ensures high precision in aerial imaging applications ([[https://enterprise.dji.com/cn/phantom-4-rtk](https://enterprise.dji.com/cn/phantom-4-rtk)]([https://enterprise.dji.com/cn/phantom-4-rtk](https://enterprise.dji.com/cn/phantom-4-rtk)), accessed on 7 April 2025). In this study, the Agisoft Metashape Professional software (Version 1.5.2 build 7838 (64 bit)) was used to produce the DEM data for the study area. The location of the fault scarp is determined by DEM. Due to restrictions from the no-fly zone around Jining Airport, the UAV-based mapping was limited in coverage at the study site. To more accurately define the height of the escarpment, a Trimble R8 Global Navigation Satellite System (GNSS) real-time kinematic (RTK) system was used for vertical measurements of the fault scarp. As vegetation is scarce in the Jining region, the flat, treeless terrain offered an ideal environment for a global positioning system (GPS), allowing for a baseline survey across a 2 km area to be internally accurate within 2 cm. The precise accuracy would be contingent upon the satellite geometry's details at the time of the survey [34]. The Surfer 20 software (Version 20.1.195 (64 bit)) was used to produce the topography profile across the fault. Paleoseismic trenching stands as the preeminent method for paleoseismic investigation [35]. Utilizing photo-based 3D reconstruction techniques [36; 37], which incorporate horizontal and vertical scales in situ, mosaic orthoimages birming with the richest paleoseismic data were obtained. The trench walls were meticulously categorized into distinct layers, guided by attributes such as color, grain size, content, and texture. OSL (Optically Stimulated Luminescence) dating samples were collected to constrain the active timing of the fault. The OSL samples were collected with consideration for potential null results, unforeseen issues, and restricted sampling conditions, which were attributed to the pluvial-slope facies deposits within the trenches. For samples with insufficient quartz (Q) content, K-feldspar (Kf) testing was used, employing a combination of stepwise heating infrared post-IR luminescence and Standard growth curve (SGC) methods, effectively overcoming the anomalous fading of Kf.