357 window for response and limits the ability of geotechnical teams to act proactively. The purpose of a Geotechnical Monitoring System is to ensure that we understand, quantify, and manage the uncertainty in ground conditions—so that slopes remain predictable, people remain safe, and production continues without unplanned disruption. Ground-based radar (GBInSAR/SSR) delivers sub-millimetric displacement monitoring with minute-level update rates, fundamentally improving real-time detection of rapidly evolving instabilities in open pits. However, because standard processing pipelines depend on short aggregation windows, they remain largely insensitive to very low deformation rates; slow, spatially diffuse precursors are often filtered out or misinterpreted as atmospheric noise. Slow Movement Analysis (SMA)—which recalculates displacement and velocity over longer, multi-interval windows—lowers the effective detection threshold and uncovers long-horizon deformation trends that real-time views cannot reveal. By making these early precursors visible, SMA significantly increases lead-time and shifts geotechnical monitoring from a predominantly mitigating control to a genuinely preventive one, reducing the consequences of slope failure and improving operational stability. Figure 1. Extending the Automatic Detection of Velocities for Slope Monitoring The work presented here emerged from the recognition that addressing this problem requires more than improved signal processing. It requires an operating model in which geotechnical engineering judgement and data science capability are tightly integrated. This paper therefore focuses on the collaborative structure that enabled two prior technical studies on slow movement analysis and automated multi-interval SMA, rather than representing those technical results themselves. The intent is to articulate how collaboration functioned as a critical system component in translating analytics into trusted engineering insights. 2. OBJECTIVES AND SCOPE The primary objective of this work is to document the transdisciplinary collaboration that enabled the development of an interpretable, data driven approach to identifying low velocity slope movement. The study demonstrates how sustained integration between geotechnical engineers and technology specialists supported the translation of analytical techniques into practical engineering decision support.
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