宋家苇, 杨莹辉, 许强, 等. 2024. 滑坡灾害InSAR早期识别关键技术方法研究[J]. 工程地质学报, 32(3): 963-977. doi: 10.13544/j.cnki.jeg.2023-0411.
    引用本文: 宋家苇, 杨莹辉, 许强, 等. 2024. 滑坡灾害InSAR早期识别关键技术方法研究[J]. 工程地质学报, 32(3): 963-977. doi: 10.13544/j.cnki.jeg.2023-0411.
    Song Jiawei, Yang Yinghui, Xu Qiang, et al. 2024. Research on key theoretical methods for early landslide detection using InSAR technology[J]. Journal of Engineering Geology, 32(3): 963-977. doi: 10.13544/j.cnki.jeg.2023-0411.
    Citation: Song Jiawei, Yang Yinghui, Xu Qiang, et al. 2024. Research on key theoretical methods for early landslide detection using InSAR technology[J]. Journal of Engineering Geology, 32(3): 963-977. doi: 10.13544/j.cnki.jeg.2023-0411.

    滑坡灾害InSAR早期识别关键技术方法研究

    RESEARCH ON KEY THEORETICAL METHODS FOR EARLY LANDSLIDE DETECTION USING INSAR TECHNOLOGY

    • 摘要: 本研究重点关注了合成孔径雷达干涉测量(InSAR)技术在滑坡识别方面的应用,并着重分析了InSAR技术进行滑坡识别时所遇到的典型技术难题。首先,论文分析了InSAR多视处理作为预处理关键步骤的重要性,探讨了多视因子选择在噪声抑制和空间分辨率之间的平衡问题,并获得了最佳的多视因子参数。其次,本文讨论了干涉图滤波窗口对形变提取精度的影响,发现最佳的滤波窗口可有效保留形变信息,并抑制InSAR干涉噪声,有利于滑坡隐患的准确识别。此外,本研究还发现采用InSAR干涉图层面的大气校正处理,可避免InSAR相位解缠误差的传播,并有效削弱大气噪声,提高InSAR形变提取精度。最后,本研究讨论了InSAR干涉对筛选中的长短时间基线问题,发现仅利用短时间基线干涉对较难捕捉滑坡小量级形变,而长时间基线又不可避免面临干涉失相关的挑战,因此,以短时间基线干涉对为基础,辅以一定数量的高质量长时间基线干涉对,是更为可靠的小量级运动滑坡隐患InSAR识别干涉对筛选策略。最后,研究以金沙江上游某滑坡密集区域为例,基于最优参数和对照参数组开展了实验,验证了最优参数组的有效性和适用性。上述研究成果显著深化了InSAR技术在地质灾害识别应用中的适用性和局限性,为利用InSAR技术开展滑坡灾害早期识别提供了科学的支撑,具有重要的理论和实用价值。

       

      Abstract: This study focuses on the application of Synthetic Aperture Radar Interferometry (InSAR) technology in landslide identification and intensively explores the typical technical challenges encountered during landslide detection via InSAR technology. Firstly, the paper examines the significance of InSAR multi-view processing as a crucial pre-processing step, discussing the balance between noise suppression and spatial resolution in the selection of multi-view factors, eventually obtaining the optimal multi-view parameter set. Furthermore, this paper discusses the influence of interferogram filtering windows on deformation extraction accuracy, finding that the optimal filtering window effectively preserves deformation information while suppressing InSAR interferometric noise, thus facilitating accurate detection of landslide risks. In addition, this research proposes atmospheric correction at the interferogram level to avoid the propagation of InSAR phase unwrapping errors and effectively mitigate atmospheric noise, thereby enhancing the accuracy of InSAR deformation extraction. Finally, the study explores the long and short temporal baselines in InSAR interferometry, discovering that solely relying on short temporal baseline interferograms is insufficient for capturing small-magnitude landslide deformations. Long temporal baselines, although unavoidable, face challenges of interferometric decorrelation. Therefore, a strategy combining a base of short temporal baseline interferograms with a certain number of high-quality long temporal baseline interferograms proves to be more reliable for identifying small-magnitude landslide hazards. Lastly, the study exemplifies its findings through a landslide-prone area in the upper reaches of the Jinsha River, conducting experiments based on the optimal and control parameter sets to validate the effectiveness and applicability of the optimal set. The aforementioned research outcomes significantly deepen the practicality and limitations of InSAR technology in geological hazard identification applications, providing scientific support for the early identification of landslide hazards using InSAR technology, with important theoretical and practical implications.

       

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