Consolidation settlement of Salt Lake County tailings impoundment revealed by time-series InSAR observations from multiple radar satellites

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Department of Geological and Mining Engineering and Sciences; Center for Data Sciences


Tailings impoundment failures may lead to catastrophically fatal, environmental and financial consequences. Monitoring the stability of tailings facility is therefore indispensable for sustainable mining development. Particularly, tailings experience gradual consolidation settlement as the pore pressure dissipates and the terrain subsides. However, field investigations and geotechnical analysis at tailings impoundment are limited by sparse field instrumentation due to high cost. Interferometric Synthetic Aperture Radar (InSAR) can provide a full spatial view of settlement rate at millimeter-scale precision with bi-weekly or monthly updates. Here we integrate big remotely sensed data including multi-temporal and multi-spaceborne SAR images of ENVISAT, ALOS PALSAR-1, and Sentinel-1A, and SRTM DEM and LIDAR DEM, as well as water level data, to investigate the dynamics of consolidation settlement over the tailings impoundment area in the vicinity of Great Salt Lake, Utah. We show that the reclaimed south pond is experiencing enormous quasi-linear settlements with the largest rate of 200 + mm/yr around the low-permeable decant pond clay at the northeast corner during 2004–2011, and the rate decreases to 100 + mm/yr during 2015–2016. The nearly decadal InSAR measurements can be well-explained by geotechnical consolidation model, which reveals the long-term exponentially decaying settlement and predicts the settlement process in the near future. InSAR-derived displacement maps also highlight active motions of surrounding infrastructures, such as some highway segments. There is no clear evidence that the fluctuating deformation at those locations and seasonal varied water level are correlated. Our results demonstrate that high-resolution surface displacement measurements from InSAR can significantly improve our understanding of tailings settlement process and facilitate the monitoring of dams/infrastructures stability.

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© 2017 Elsevier Inc. Publisher's version of record:

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Remote Sensing of Environment