Desai Vrinda D, Fazelpour Farnaz, Handwerger Alexander L, Daniels Karen E
Physics Department, North Carolina State University, Raleigh, North Carolina 27695, USA.
Joint Institute for Regional Earth System Science and Engineering, University of California Los Angeles, Los Angeles, California 90095, USA and Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA.
Phys Rev E. 2023 Jul;108(1-1):014901. doi: 10.1103/PhysRevE.108.014901.
As a result of extreme weather conditions, such as heavy precipitation, natural hillslopes can fail dramatically; these slope failures can occur on a dry day, due to time lags between rainfall and pore-water pressure change at depth, or even after days to years of slow motion. While the prefailure deformation is sometimes apparent in retrospect, it remains challenging to predict the sudden transition from gradual deformation (creep) to runaway failure. We use a network science method-multilayer modularity optimization-to investigate the spatiotemporal patterns of deformation in a region near the 2017 Mud Creek, California landslide. We transform satellite radar data from the study site into a spatially embedded network in which the nodes are patches of ground and the edges connect the nearest neighbors, with a series of layers representing consecutive transits of the satellite. Each edge is weighted by the product of the local slope (susceptibility to failure) measured from a digital elevation model and ground surface deformation (current rheological state) from interferometric synthetic aperture radar (InSAR). We use multilayer modularity optimization to identify strongly connected clusters of nodes (communities) and are able to identify both the location of Mud Creek and nearby creeping landslides which have not yet failed. We develop a metric, i.e., community persistence, to quantify patterns of ground deformation leading up to failure, and find that this metric increased from a baseline value in the weeks leading up to Mud Creek's failure. These methods hold promise as a technique for highlighting regions at risk of catastrophic failure.
由于极端天气条件，如强降水，天然山坡可能会发生剧烈坍塌；这些边坡失稳可能在干燥天气发生，这是由于降雨与深层孔隙水压力变化之间存在时间滞后，甚至可能在经历数天至数年的缓慢移动之后发生。虽然事后有时能明显看出失稳前的变形，但预测从逐渐变形（蠕变）到失控失稳的突然转变仍然具有挑战性。我们使用一种网络科学方法——多层模块化优化——来研究2017年加利福尼亚州泥溪滑坡附近区域变形的时空模式。我们将研究地点的卫星雷达数据转换为一个空间嵌入网络，其中节点是地面斑块，边连接最近邻，一系列层代表卫星的连续过境。每条边由从数字高程模型测量的局部坡度（失稳敏感性）与干涉合成孔径雷达（InSAR）测量的地表变形（当前流变状态）的乘积加权。我们使用多层模块化优化来识别节点的强连接簇（群落），并能够识别泥溪的位置以及附近尚未失稳的蠕动滑坡。我们开发了一种度量标准，即群落持续性，以量化失稳前的地面变形模式，并发现该度量标准在泥溪失稳前的几周内从基线值开始增加。这些方法有望成为突出有灾难性失稳风险区域的一种技术。
