Slope failures occur in open-pit mining areas worldwide, producing considerable damage in addition to economic loss. Identifying the triggering factors and detecting unstable slopes and precursory displacements —which can be achieved by exploiting remote sensing data— are critical for reducing their impact. Here we present a methodology that combines digital photogrammetry, satellite radar interferometry, and geo-mechanical modeling, to perform remote analyses of slope instabilities in open-pit mining areas. We illustrate this approach through the back analysis of a massive landslide that occurred in an active open-pit mine in southwest Spain in January 2019. Based on pre- and post-event high-resolution digital elevation models derived from digital photogrammetry, we estimate an entire sliding mass volume of around 14 million m 3 . Radar interferometry reveals that during the year preceding the landslide, the line of sight accumulated displacement in the slope reached − 5.7 and 4.6 cm in ascending and descending geometry, respectively, showing two acceleration events clearly correlated with rainfall in descending geometry. By means of 3D and 2D stability analyses we located the slope instability, and remote sensing monitoring led us to identify the likely triggers of failure. Las Cruces event can be attributed to delayed and progressive failure mechanisms triggered by two factors: (i) the loss of historical suction due to a pore-water pressure increase driven by rainfall and (ii) the strain-softening behavior of the sliding material. Finally, we discuss the potential of this methodological approach either to remotely perform post-event analyses of mining-related landslides and evaluate potential triggering factors or to remotely identify critical slopes in mining areas and provide pre-alert warning.

中文翻译：

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露天矿边坡失稳的远程分析：西班牙拉斯克鲁塞斯案例研究

世界各地的露天矿区都发生边坡失稳，除了经济损失外，还会造成相当大的破坏。确定触发因素并检测不稳定的斜坡和前兆位移——这可以通过利用遥感数据来实现——对于减少它们的影响至关重要。在这里，我们提出了一种结合数字摄影测量、卫星雷达干涉测量和地球力学建模的方法，以对露天矿区的边坡不稳定性进行远程分析。我们通过对 2019 年 1 月西班牙西南部一个活跃露天矿场发生的大规模滑坡的反向分析来说明这种方法。基于从数字摄影测量中获得的事前和事后高分辨率数字高程模型，我们估计了整个滑动质量体积约为 1400 万立方米。雷达干涉测量显示，在滑坡前一年，斜坡的视线累积位移在上升和下降几何中分别达到 - 5.7 和 4.6 厘米，显示两个加速事件与下降几何中的降雨明显相关。通过 3D 和 2D 稳定性分析，我们定位了边坡失稳，遥感监测使我们确定了可能的故障触发因素。Las Cruces 事件可归因于由两个因素触发的延迟和渐进式失效机制：（i）由于降雨驱动的孔隙水压力增加而导致的历史吸力损失，以及（ii）滑动材料的应变软化行为。最后，