intrinsically optimised for short time scale behaviour and are less sensitive to very low displacement rates that evolve over weeks to months. Research across multiple industries reveals that analytics initiatives often fail not due to a lack of technical capability, but rather due to a misalignment between analytical outputs and decision-making contexts. Studies in team science and organisational analytics consistently identify linear handover models, opaque algorithms, and weak integration with domain expertise as primary causes of failure (National Research Council, 2015; Bergmann et al., 2021). In engineering domains, this disconnect is amplified when analytical outputs cannot be reconciled with physical understanding or operational constraints. Low velocity slope movement exemplifies this challenge. Such behaviour is often ambiguous, spatially diffuse, and easily rationalised as benign deformation. Without structured analytical support and expert interpretation, early precursors are frequently recognised only in hindsight. This creates a narrow 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.
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