Berkeley Seismological Laboratory, University of California, Berkeley, CA, USA.
Department of Earth and Planetary Science, University of California, Berkeley, CA, USA.
Nat Commun. 2020 Jun 3;11(1):2792. doi: 10.1038/s41467-020-16617-7.
Landslides modify the natural landscape and cause fatalities and property damage worldwide. Quantifying landslide dynamics is challenging due to the stochastic nature of the environment. With its large area of ~1 km and perennial motions at ~10-20 mm per day, the Slumgullion landslide in Colorado, USA, represents an ideal natural laboratory to better understand landslide behavior. Here, we use hybrid remote sensing data and methods to recover the four-dimensional surface motions during 2011-2018. We refine the boundaries of an area of ~0.35 km below the crest of the prehistoric landslide. We construct a mechanical framework to quantify the rheology, subsurface channel geometry, mass flow rate, and spatiotemporally dependent pore-water pressure feedback through a joint analysis of displacement and hydrometeorological measurements from ground, air and space. Our study demonstrates the importance of remotely characterizing often inaccessible, dangerous slopes to better understand landslides and other quasi-static mass fluxes in natural and industrial environments, which will ultimately help reduce associated hazards.
滑坡改变了自然景观，在全球范围内造成了人员伤亡和财产损失。由于环境的随机性，量化滑坡动力学具有挑战性。美国科罗拉多州的斯卢姆古利翁滑坡，面积约 1 平方公里，常年以每天 10-20 毫米的速度移动，是一个理想的自然实验室，可以更好地了解滑坡行为。在这里，我们使用混合遥感数据和方法来恢复 2011 年至 2018 年期间的四维表面运动。我们细化了史前滑坡山顶以下约 0.35 平方公里区域的边界。我们构建了一个力学框架，通过对地面、空中和空间的位移和水文气象测量进行联合分析，量化流变学、地下通道几何形状、质量流速和时空相关孔隙水压反馈。我们的研究表明，对通常难以进入和危险的边坡进行远程特征描述非常重要，这有助于更好地了解滑坡和自然及工业环境中的其他准静态质量流，从而最终有助于减少相关危害。
