Tailings storage facilities (TSFs) are an essential infrastructure of mineral processing, but they represent a significant physical, chemical and biological hazard and must, therefore, be strictly and responsibly sited, managed and closed. Tailings can, for example, be dispersed by many processes (such as sinkholes, earthquakes, intense rainfall and flood events, and wind), substandard design and construction, and seepage. The stability and behaviour of TSFs needs to be continuously monitored and one highly effective way of doing this is through satellite Earth observation (EO). The EO industry is witnessing a technological revolution. Large and long-lifespan satellite sensors that have been the staple of national space agencies and commercial satellite manufacturers are now being complemented by constellations of low-cost, short-lifespan cube sats by companies with the ambition to image the whole earth daily. Satellites with synthetic aperture radar (SAR) sensors are also collecting high volumes of data, with the added benefit of being able to do so day or night and in different weather conditions. The range of data options and capabilities these provide open opportunities for novel data analysis techniques for TSFs. One of these is satellite InSAR (interferometric SAR; a technique used to map millimetric-precision changes in ground height over time), which is already used by mining companies to reduce risk in and of their operations. From monitoring the stability of TSFs, through to assessments of impacts of natural hazards, InSAR allows rapid and accurate targeting of high-risk areas and structures to identify those that require subsequent investigation through ground-based methods. To demonstrate the application of EO data and InSAR in identifying pre- and post-failure mine activities and TSF deformation, the authors will present a case study across Cadia mine, New South Wales, Australia, which had a localised TSF failure on 9 March 2018. The InSAR results presented show that low-magnitude subsidence signals were observed across the TSF dam during the year preceding the collapse. In January 2018 a notable change in behaviour was observed, with a concentrated area of subsidence focused on the region which initially failed on 9 March 2018. Furthermore, post-collapse InSAR measurements show an increased rate of subsidence for regions either side of the failure zone. Review of medium- and high-resolution satellite images show that the failure was phased, with an initial failure and then a subsequent failure at least two days after 9 March 2018. It also highlights what might be construction activity associated with a dam raise prior to failure.
Topics include the physics of remote sensing, data sources, image processing, and preparation of data for inclusion in a geographic information system (GIS) for further analyses. A summary of former and current Denver Office remote sensing studies is appended to show the variety of possible topics.
This paper presents the outcomes of a study that utilised predictive methods based on advanced synthetic aperture radar and multispectral data coupled with Google Earth Engine to develop a model for particular TSFs based on historical records, observations, and material types. Google Earth Engine brings the first opportunity to use a systematic and comprehensive combination of radar and visual-infrared satellite data. It was found that the synergy between the two data types could be used to offset the individual ambiguities of each, and the resulting method delivered a predictive dryness probability map and visual moisture/water depth/presence indicators that were able to be verified and made operational almost immediately. On-ground visual records and aerial imagery provided qualitative verification of the approach. The methodology allows TSF operators a free, open source platform with which to monitor and map surface moisture, enabling proactive deposition decision-making to mitigate the risk of tailings dusting. Additional benefits realised include increased data on beach formation, channel and pond location (extent and to some degree depth), improving the accuracy of the TSF water balance. For the particular TSFs studied, the water balance is a critical control from a safety perspective to mitigate potential failure mechanisms, and from an operational perspective to maximise tailings density and water return to the plant.