彭双麒, 许强, 李骅锦, 郑光. 2019: 基于高精度图像识别的堆积体粒径分析. 工程地质学报, 27(6): 1290-1301. DOI: 10.13544/j.cnki.jeg.2018-305
    引用本文: 彭双麒, 许强, 李骅锦, 郑光. 2019: 基于高精度图像识别的堆积体粒径分析. 工程地质学报, 27(6): 1290-1301. DOI: 10.13544/j.cnki.jeg.2018-305
    PENG Shuangqi, XU Qiang, LI Huajin, ZHENG Guang. 2019: GRAIN SIZE DISTRIBUTION ANALYSIS OF LANDSLIDE DEPOSITS WITH RELIABLE IMAGE IDENTIFICATION. JOURNAL OF ENGINEERING GEOLOGY, 27(6): 1290-1301. DOI: 10.13544/j.cnki.jeg.2018-305
    Citation: PENG Shuangqi, XU Qiang, LI Huajin, ZHENG Guang. 2019: GRAIN SIZE DISTRIBUTION ANALYSIS OF LANDSLIDE DEPOSITS WITH RELIABLE IMAGE IDENTIFICATION. JOURNAL OF ENGINEERING GEOLOGY, 27(6): 1290-1301. DOI: 10.13544/j.cnki.jeg.2018-305

    基于高精度图像识别的堆积体粒径分析

    GRAIN SIZE DISTRIBUTION ANALYSIS OF LANDSLIDE DEPOSITS WITH RELIABLE IMAGE IDENTIFICATION

    • 摘要: 高位滑坡具有高隐蔽特性,失稳破坏后往往转化为流动性强大的碎屑流,成灾时间短暂且破坏性极强。由于碎屑流本质为碎裂岩体在重力作用下的高速远程运动,研究其堆积体的粒径分布情况用以分析灾害过程,对碎屑流危害预测具有重要意义。基于该认识,本文选用PCAS系统分析碎屑流堆积体影像数据,并基于此结果开展后续分析。本文以2017年贵州普洒村崩塌碎屑流为研究案例,步骤如下:首先,结合现场调查,拍摄无人机航拍图像;随后,统计灾害前后受灾房屋情况;最后,使用PCAS系统开展堆积体图像识别,并分析堆积体粒径分布情况以及分析粒径分布与房屋破坏之间的关系;研究可知:(1)PCAS系统识别堆积体颗粒效果好,精度高。(2)随着碎屑流的运移,堆积物小粒径占比增加。(3)大粒径出现了双峰分布,但总体上呈减少趋势。(4)在横向分布上,其一致性越来越优。(5)堆积体房屋具有"拦粗排细"的作用。综上可知,运用PCAS图像识别分析碎屑流粒径分布具有高效可靠的特点,能够在堆积体粒径识别领域发挥一定积极作用。

       

      Abstract: High-locality landslide is one of the most catastrophic hazards because of its insidious feature. Analyzing the grain size distribution form the basis of landslide failure process inversion, which is significate for prediction of rock avalanche hazards. In this paper, we propose a novel framework to handle this issue. It contains two main parts:field investigation and PCAS software. First, the high precision images of landslide deposit is obtained using UAV. Then, damage situation of local houses is analyzed in detail. Last, we quantify the grain size of the rock avalanche deposit using image data with PCAS software, and analyze the relationship between the grain size distribution and the damage situation of local houses. A case study is conducted on Pusacun rock avalanches of August 28, 2017. It turns out the following results. (1) The PCAS software can results in reliable image identifications. (2) The proportion of small particle size increases in pace with the runout distance. (3) The proportion of large particle shows bimodal distribution, but the peak value decreases along the sliding deration. (4) The similarity of lateral distribution enhances as the runout distance increases. (5) The houses have the ability for "trapping the coarse particles and discharging the fine particles". The results demonstrate the framework proposed has superior performance in related researches.

       

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