基于GB-InSAR技术的水电工程高边坡变形监测

Surface deformation monitoring of high slope in hydropower project based on GB-InSAR technology

为解决大型水电工程复杂边坡表面变形监测存在的监测范围受地形限制、设备安装运维成本高、点位布设易遗漏等问题,采用地基合成孔径雷达(SAR)干涉测量技术(GB-InSAR),开展水电工程地质条件复杂的百米级高边坡表面变形三维非接触实时监测。首先将合成孔径雷达架设于所需监测边坡对岸,设定监测数据采集频率和监测预警值;然后构建一种基于三维激光扫描技术的改进雷达数据降噪模型,智能化判断和筛查现场异常数据;最后采用数据解缠和变形分析获取变形监测结果,通过云端服务器和人工智能算法,实时查询监测区域的历史形变及位移数据。结果表明:较传统监测技术,采用非接触式监测技术具有分辨率高、自动化程度高、受地形等条件限制少、有效识别和穿透掩盖物等优点;可实现短时间内识别亚毫米级的变形;建立的雷达数据降噪模型可提升监测数据的可视性、有效性和可靠性。

In monitoring surface deformation of complex slopes,large hydropower projects faced problems such as limited monitoring range due to terrain,expensive equipment installation and operation,and incomplete monitoring point layout.In order to solve these problems,the GB-InSAR technology was used to carry out three-dimensional(3D),non-contact,and real-time monitoring on surface deformation of 100-meter high slopes.These slopes had complex geological conditions in hydropower projects.First,the synthetic aperture radar(SAR)was erected on the opposite side of the slope to be monitored.The collection frequency of monitoring data and warning values of monitoring were set.Then,an improved radar data noise reduction model based on 3D laser scanning technology was constructed to intelligently judge and screen abnormal data on site.Finally,the deformation monitoring results were obtained by data unwrapping and deformation analysis.The historical deformation and displacement data of the monitored area were queried in real time through cloud servers and artificial intelligence algorithms.The results show that compared with traditional monitoring technology,non-contact monitoring technology has a series of advantages,such as high resolution and high degree of automation.In addition,it is less restricted by terrain and other conditions and can effectively identify and penetrate the cover.Furthermore,submillimeter-level deformation can be identified in a short time.The established radar data noise reduction model can improve the visibility,effectiveness,and reliability of monitoring data.