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    杨志华, 吴瑞安, 郭长宝, 伍宇明, 邵慰慰, 郁鹏飞. 融合斜坡形变特征的复杂山区区域滑坡评价研究现状与展望[J]. 中国地质. DOI: 10.12029/gc20230630002
    引用本文: 杨志华, 吴瑞安, 郭长宝, 伍宇明, 邵慰慰, 郁鹏飞. 融合斜坡形变特征的复杂山区区域滑坡评价研究现状与展望[J]. 中国地质. DOI: 10.12029/gc20230630002
    YANG Zhihua, WU Ruian, GUO Changbao, Wu Yuming, SHAO Weiwei, YU Pengfei. Research status and prospect of regional landslide assessment integrating slope deformation characteristics in the complex mountainous area[J]. GEOLOGY IN CHINA. DOI: 10.12029/gc20230630002
    Citation: YANG Zhihua, WU Ruian, GUO Changbao, Wu Yuming, SHAO Weiwei, YU Pengfei. Research status and prospect of regional landslide assessment integrating slope deformation characteristics in the complex mountainous area[J]. GEOLOGY IN CHINA. DOI: 10.12029/gc20230630002

    融合斜坡形变特征的复杂山区区域滑坡评价研究现状与展望

    Research status and prospect of regional landslide assessment integrating slope deformation characteristics in the complex mountainous area

    • 摘要:研究目的】复杂山区斜坡形变普遍存在,促进了滑坡发育发生,增加了滑坡危险性,是区域滑坡评价研究需要考虑的重要因素。【研究方法】梳理文献资料,总结了融合斜坡形变特征的区域滑坡评价研究现状。【研究结果】该领域的相关理论模型和技术方法尚不成熟,还处于探索研究阶段。区域斜坡形变获取的时间分辨率有待于进一步提高,加强捕捉长时序斜坡形变过程的关键特征,深入分析区域斜坡形变的时空分布规律。初步构建了融合斜坡形变特征的区域滑坡评价体系,包含流程步骤、技术方法和因子指标。技术方法主要有:基于专家经验的定性判断、基于专家经验和校正矩阵的加权图层叠加、斜坡形变作为区域滑坡评价的因子指标、斜坡形变作为区域滑坡评价的滑坡样本。斜坡形变因子可以进一步划分为斜坡形变类型、强度、分布位置和时间变化等次一级因子。【结论】需要结合机器学习、人工智能等新技术,提出或优化融合斜坡形变特征的区域滑坡定量化评价新模型,提高区域滑坡评价精度。研究成果期望推动融合斜坡形变特征的区域滑坡评价研究,支撑服务复杂山区滑坡灾害早期防控。

       

      Abstract: Objective The slope deformation is common in the mountainous areas, which significantly promotes landslide development and increases landslide risk. So, it is the important factor for the regional landslide assessment. Methods By reviewing literatures, the research status of regional landslide assessment integrating slope deformation characteristics were summarized. Results The relevant theoretical models and technical methods are not mature and are still in the stage of preliminary research. The temporal resolution of regional slope deformation should be further improved, and key characteristics of the long time series slope deformation should be captured, and the spatio-temporal distribution of regional slope deformation should be deeply analyzed. A preliminary regional landslide assessment system integrating slope deformation was constructed, including process steps, technical methods and factor indicators. The main technical methods include the qualitative judgment based on expert experience, weighted layer overlay based on expert experience and correction matrix, slope deformation as a factor index of regional landslide assessment, slope deformation as a landslide sample of regional landslide assessment. The slope deformation factor can be further divided into slope deformation type, intensity, distribution position and time change. Conclusions It is necessary to combine new technologies such as machine learning and artificial intelligence to propose or optimize the new quantitative regional landslide assessment models that integrate the slope deformation characteristics to improve regional landslide assessment accuracy. It is expected to promote the study on regional landslide assessment integrating slope deformation, and support the early landslide prevention in the complex mountainous areas.

       

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