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<p>Rock slope failures in mining operations pose significant geotechnical risks, necessitating advanced monitoring to decipher deformation mechanisms and enable timely risk mitigation. The study addresses the need for advanced analytical frameworks that can identify early warning signals of progressive instability in mining rock slopes. This study employs multi-scale time-series analysis of GNSS displacement to evaluate slope stability dynamics in an active mining environment. Time-series displacement data (November 2024–January 2025) from five monitoring points were analyzed using frequency-domain spectral analysis, wavelet transforms, change point detection (Pelt algorithm), Prophet forecasting model, and spatial correlation techniques. Point 5 exhibited critical progressive displacement, accelerating from ~ 3&#xa0;mm to &gt; 30&#xa0;mm within 15 days and stabilizing at 43–45&#xa0;mm. In contrast, Points 1–4 showed displacements of less than 12&#xa0;mm with multi-regime fluctuations. Spectral analysis revealed long-term periodicities at stable points, whereas Point 5 displayed spectral collapse, indicating transition to irreversible shear. Spatial correlation identified a coherent kinematic cluster (Points 1, 2, 3, 5) with correlations up to <i>r</i> = 0.994, while Point 4 showed anti-correlation (<i>r</i> = − 0.58 to − 0.75) with a phase lag of 25 days. The Prophet forecasts continued acceleration at Points 1–2 beyond 20&#xa0;mm, with Point 5 exhibiting high uncertainty (± 70&#xa0;mm) after stabilization. The findings highlight the utility of multi-scale, multi-method monitoring for early warning and risk mitigation in slope stability management.</p>

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Multi-scale Analysis of Rock Slope Failure Dynamics in Mining Operations Using Time-Series Monitoring and Advanced Signal Processing for Improved Geotechnical Risk Assessment

  • 期刊论文

  • 发表日期：

    2026年2月24日

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  • Ibrahim Haruna Umar,
  • Hang Lin,
  • Müge Elif Fırat,
  • Chaoyi Yang

摘要

Rock slope failures in mining operations pose significant geotechnical risks, necessitating advanced monitoring to decipher deformation mechanisms and enable timely risk mitigation. The study addresses the need for advanced analytical frameworks that can identify early warning signals of progressive instability in mining rock slopes. This study employs multi-scale time-series analysis of GNSS displacement to evaluate slope stability dynamics in an active mining environment. Time-series displacement data (November 2024–January 2025) from five monitoring points were analyzed using frequency-domain spectral analysis, wavelet transforms, change point detection (Pelt algorithm), Prophet forecasting model, and spatial correlation techniques. Point 5 exhibited critical progressive displacement, accelerating from ~ 3 mm to > 30 mm within 15 days and stabilizing at 43–45 mm. In contrast, Points 1–4 showed displacements of less than 12 mm with multi-regime fluctuations. Spectral analysis revealed long-term periodicities at stable points, whereas Point 5 displayed spectral collapse, indicating transition to irreversible shear. Spatial correlation identified a coherent kinematic cluster (Points 1, 2, 3, 5) with correlations up to r = 0.994, while Point 4 showed anti-correlation (r = − 0.58 to − 0.75) with a phase lag of 25 days. The Prophet forecasts continued acceleration at Points 1–2 beyond 20 mm, with Point 5 exhibiting high uncertainty (± 70 mm) after stabilization. The findings highlight the utility of multi-scale, multi-method monitoring for early warning and risk mitigation in slope stability management.