Handwerger Alexander L, Lacroix Pascal, Bell Andrew F, Booth Adam M, Huang Mong-Han, Mudd Simon M, Bürgmann Roland, Fielding Eric J
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, 91109, USA.
Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, 90095, USA.
Sci Rep. 2025 Aug 14;15(1):29831. doi: 10.1038/s41598-025-11399-8.
Landslides pose a significant hazard worldwide. Despite advances in landslide monitoring, predicting their size, timing, and location remains a major challenge. We revisit the 2017 Mud Creek landslide in California using radar interferometry, pixel tracking, and elevation change measurements from satellite and airborne radar, lidar, and optical data. Our analysis shows that pixel tracking of optical imagery captured the transition from slow motion to runaway acceleration starting ~ 1 month before catastrophic failure-an acceleration undetected by satellite InSAR alone. Strain rate maps revealed a new slip surface formed within the landslide body during acceleration, likely a key weakening mechanism. Failure forecast analysis indicates the acceleration followed a hyperbolic trend, suggesting failure time could have been predicted at least 6 days in advance. We also inverted for the landslide thickness during the slow-moving phase and found variations from < 1 to 36 m. While thickness inversions provide important first-order information on landslide size, more work is needed to better understand how landslide subsurface properties and deforming volumes may evolve during the transition from slow-to-fast motion. Our findings underscore the need for integrated remote sensing techniques to improve landslide monitoring and forecasting. Future advancements in operational monitoring systems and big data analysis will be critical for tracking slope instability and improving regional-scale failure predictions.
滑坡在全球范围内构成重大危害。尽管滑坡监测取得了进展，但预测其规模、发生时间和位置仍然是一项重大挑战。我们利用雷达干涉测量、像素跟踪以及来自卫星和机载雷达、激光雷达和光学数据的高程变化测量，重新审视了2017年加利福尼亚州的泥溪滑坡。我们的分析表明，光学图像的像素跟踪捕捉到了从缓慢移动到失控加速的转变，这一加速始于灾难性破坏前约1个月，而仅靠卫星合成孔径雷达（InSAR）无法检测到这一加速。应变率图显示，在加速过程中滑坡体内形成了一个新的滑动面，这可能是一个关键的弱化机制。破坏预测分析表明，加速遵循双曲线趋势，这表明至少可以提前6天预测破坏时间。我们还反演了滑坡缓慢移动阶段的厚度，发现厚度在小于1米到36米之间变化。虽然厚度反演提供了有关滑坡规模的重要一阶信息，但仍需要更多工作来更好地理解滑坡地下特性和变形体积在从缓慢移动到快速移动的转变过程中可能如何演变。我们的研究结果强调了需要综合遥感技术来改善滑坡监测和预测。运营监测系统和大数据分析的未来进展对于跟踪边坡失稳和改善区域尺度的破坏预测至关重要。
