and operation. KEYWORDS TSF, MT-DInSAR, Vertical deformation, LightGBM forecasting, early warning monitoring 1. INTRODUCTION TSFs are essential infrastructure for modern mining and are expected to increase in number and size as demand for critical minerals grows. TSFs store mine tailings, commonly as slurry, behind embankment dams that are progressively raised during operations. The main construction methods, downstream, centreline, and upstream, differ in cost and constructability, but also in geotechnical risk, making instability and failure a continuing concern for design, monitoring, and long-term management. Tailings dams often operate under more variable conditions than conventional water-retaining dams, which can increase vulnerability. Key risk factors include the use of tailings or waste rock as construction material, progressive raising (especially upstream), historical gaps in design standards and regulation, and the need for long-term surveillance during operation and after closure when access and resources may be constrained (Canadian Dam Association, 2013). Although failures are relatively infrequent, they can be catastrophic, releasing large volumes of contaminated tailings with severe downstream impacts, including loss of life and environmental damage (Rico et al., 2008; Silva Rotta et al., 2020). These events highlight the need for lifecycle risk management and monitoring systems capable of detecting early signs of instability under changing operational and environmental conditions. Monitoring typically relies on visual inspections and in-situ instrumentation (e.g., piezometers, inclinometers, survey benchmarks). Instrumentation provides quantitative data that supports performance evaluation and stability assessment (Lumbroso et al., 2019), but measurements are usually sparse and require installation, maintenance, and safe site access. For large or remote TSFs, dense sensor deployment can be impractical due to cost and logistical risk (Zare et al., 2024), motivating complementary techniques that provide wide-area deformation measurements without direct human exposure. Over the last two decades, InSAR has become a standard remote-sensing technique for deformation monitoring. By exploiting phase differences between repeat SAR acquisitions, it measures displacement along the radar line of sight with centimetre- to millimetre-scale sensitivity over wide areas (Hanssen, 2001; Massonnet & Feigl, 1998; Rosen et al., 2000). Multi-temporal approaches, particularly Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), use long image stacks to reduce noise and improve detection of slow deformation. This capability is well suited to TSFs, where changes can develop gradually before visible distress. InSAR also provides dense spatial coverage across the dam and surrounding areas, enabling detection of localised deformation that sparse instrumentation may miss (Crosetto et al., 2016; Rana et al., 2024). Sentinel-1 time series have revealed accelerating deformation prior to several failures, including Brumadinho (2019) and Cadia Valley (2018), supporting its value for early detection when interpreted with site knowledge (Das et al., 2024; F. Gama et al., 2020). Consequently, InSAR is increasingly used as a complementary monitoring layer in TSF
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