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<p>Ground-based radar (GBR) delivers rich multi-component observations, including spatially continuous surface-deformation fields and coherence maps. Yet conventional early-warning models rely heavily on single-point displacement time series, underutilizing GBR’s inherent spatial information and proving inadequate for complex landslides deviating from classic creep behavior. To address this, we propose an improved landslide early-warning and forecasting method centered on a novel Extent of Channel Deformation (EOCD) index. First, we formalize “channel-deformation data” — conceptualizing the four-dimensional spatiotemporal evolution of a slope’s deformation zone as an integrated data channel capturing macroscopic intensity and scale. Second, EOCD fuses three complementary parameters: cumulative channel deformation, deformation-zone area, and mean coherence — with coherence inverted quadratically to enhance physical sensitivity to pre-failure acceleration, per the theoretical coherence–deformation-velocity relationship. Third, the EOCD tangent angle enables fine-grained identification of acceleration stages; terminal-stage channel-deformation velocities, combined with the inverse-velocity method, yield precise failure-time forecasts. Validated using data from two western China open-pit mines with contrasting geology and deformation modes, results show: for a classic creep landslide, EOCD achieves a 6-minute prediction error (ahead), outperforming the improved-tangent-angle method (21-minute lag); for a composite landslide where single-point analysis fails entirely, EOCD delivers effective warning 36&#xa0;min prior, with only 4-minute error (behind). By fully leveraging GBR’s area-monitoring capability, EOCD overcomes single-point limitations under atypical deformation, offering a more robust framework for integrated multi-component radar-data interpretation.</p>

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An improved landslide early warning and forecasting method based on multi-component ground-based radar data

  • 期刊论文

  • 发表日期：

    2026年7月18日

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  • Pingping Huang,
  • Xingyan Guo,
  • Weixian Tan,
  • Hui Wu,
  • Yaolong Qi,
  • Wei Xu,
  • Yongguang Zhai

摘要

Ground-based radar (GBR) delivers rich multi-component observations, including spatially continuous surface-deformation fields and coherence maps. Yet conventional early-warning models rely heavily on single-point displacement time series, underutilizing GBR’s inherent spatial information and proving inadequate for complex landslides deviating from classic creep behavior. To address this, we propose an improved landslide early-warning and forecasting method centered on a novel Extent of Channel Deformation (EOCD) index. First, we formalize “channel-deformation data” — conceptualizing the four-dimensional spatiotemporal evolution of a slope’s deformation zone as an integrated data channel capturing macroscopic intensity and scale. Second, EOCD fuses three complementary parameters: cumulative channel deformation, deformation-zone area, and mean coherence — with coherence inverted quadratically to enhance physical sensitivity to pre-failure acceleration, per the theoretical coherence–deformation-velocity relationship. Third, the EOCD tangent angle enables fine-grained identification of acceleration stages; terminal-stage channel-deformation velocities, combined with the inverse-velocity method, yield precise failure-time forecasts. Validated using data from two western China open-pit mines with contrasting geology and deformation modes, results show: for a classic creep landslide, EOCD achieves a 6-minute prediction error (ahead), outperforming the improved-tangent-angle method (21-minute lag); for a composite landslide where single-point analysis fails entirely, EOCD delivers effective warning 36 min prior, with only 4-minute error (behind). By fully leveraging GBR’s area-monitoring capability, EOCD overcomes single-point limitations under atypical deformation, offering a more robust framework for integrated multi-component radar-data interpretation.