摘要
本文对典型的黄土滑坡进行研究,研究区域位于我国甘肃省永靖县盐锅峡的黑方台地区.具体方法为:首先对不同时序的黑方台滑坡监控视频截屏图片进行了亮度和对比度调整,以消除光照的影响,其次进行消除运动模糊处理;采用数字图像相关(DIC)方法对矫正后的图像进行整像素搜索和亚像素拟合,使滑动前后的滑坡体互相匹配,求出了滑坡上各点的位移方向和大小,从而得到了整体下滑时段滑坡上所有点的位移分布图及其发展变化图像.计算结果表明,在开始滑动的8 s之内,坡体做为一个整体滑动由慢到快,当总体滑动位移量达到0.3 m的量级以后,滑坡迅速解体分散下滑.用该方法计算滑坡地表位移场及其变化过程不仅成本低、操作简便,而且可以清晰地描绘滑坡的位移过程以及尺度.
This paper studies the typical loess landslide,which is located in the Heifangtai area of Yanguo Gorge,Yongjing County,Gansu Province.The specific methods are as follows:firstly,adjust the brightness and contrast of Heifangtai landslide monitoring video screenshots with different time sequences to eliminate the influence of light,and then remove the motion blur;Digital Image Correlation(DIC)method is used to conduct integer pixel search and sub-pixel fitting on the corrected image so that the landslide mass before and after sliding can match with each other.And the displacement direction and size of each point on the landslide can be calculated.Therefore,the displacement distribution map of all points on the landslide during the overall sliding period and its development and change image can be obtained.The calculation results show that the slope as a whole slid from slow to fast within 8 seconds of the beginning of sliding.When the total sliding displacement reaches the order of 0.3 m,the landslide rapidly disintegrates and slides in a decentralized manner.Using this method to calculate the landslide surface displacement field and its change process is not only cheap and easy to operate but also can clearly describe the landslide displacement process and scale.
作者
赵永红
周云帆
李小凡
肖彦君
ZHAO YongHong;ZHOU YunFan;LI XiaoFan;XIAO YanJun(School of Earth and Space Science,Peking University,Beijing 100871,China;SinoPEC Petroleum Exploration and Production Research Institute,Beijing 100083,China)
出处
《地球物理学进展》
CSCD
北大核心
2023年第4期1543-1550,共8页
Progress in Geophysics
基金
国家重点研发专项(SQ2017YFSF040025,2018YFC1504203)资助。
关键词
滑坡
数字图像相关方法
黑方台
位移场
Landslide
Digital Image Correlation(DIC)method
Black square platform
Displacement field