基于GB-InSAR技术的水电工程高边坡变形监测

doi:10.16265/j.cnki.issn1003-3033.2022.S2.0046

中国安全科学学报 ›› 2022, Vol. 32 ›› Issue (S2): 64-69.doi: 10.16265/j.cnki.issn1003-3033.2022.S2.0046

基于GB-InSAR技术的水电工程高边坡变形监测

段斌¹(), 何加平², 覃事河¹^,^**(), 严思源¹, 陈志超¹

¹ 国家能源投资集团有限责任公司国能大渡河金川水电建设有限公司, 四川阿坝 624100
² 国家能源投资集团有限责任公司国能大渡河流域水电开发有限公司, 四川成都 610065

收稿日期:2022-08-20 修回日期:2022-10-18 出版日期:2022-12-30 发布日期:2023-06-30
通讯作者: ** 覃事河(1984—),男,湖北五峰人,硕士,高级工程师,主要从事水电工程建设和安全管理方面的工作。E-mail:shihemaster@126.com。
作者简介:
段斌 (1980—),男,四川北川人,工学博士,正高级工程师,从事水电工程建设管理与技术工作。E-mail:iamduanbin@163.com。

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

DUAN Bin¹(), HE Jiaping², QIN Shihe¹^,^**(), YAN Siyuan¹, CHEN Zhichao¹

¹ Dadu River Jinchuan Hydropower Project Construction Co., Ltd., China Energy, Aba Sichuan 624100, China
² Dadu River Hydropower Development Co., Ltd., China Energy, Chengdu Sichuan 610065, China

Received:2022-08-20 Revised:2022-10-18 Online:2022-12-30 Published:2023-06-30

摘要/Abstract

摘要：

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。

关键词: 水电工程, 高边坡, 变形监测, 地基合成孔径雷达干涉测量技术(GB-InSAR), 实时监测

Abstract:

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.

Key words: hydropower project, high slope, deformation monitoring, ground-based interferometric synthetic aperture radar (GB-InSAR) technology, real-time monitoring

段斌, 何加平, 覃事河, 严思源, 陈志超. 基于GB-InSAR技术的水电工程高边坡变形监测[J]. 中国安全科学学报, 2022, 32(S2): 64-69.

DUAN Bin, HE Jiaping, QIN Shihe, YAN Siyuan, CHEN Zhichao. Surface deformation monitoring of high slope in hydropower project based on GB-InSAR technology[J]. China Safety Science Journal, 2022, 32(S2): 64-69.

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参考文献 14

[1]	樊启祥, 王义锋, 裴建良, 等. 大型水电工程建设岩石力学工程实践[J]. 人民长江, 2018, 49(16):76-86.
	FAN Qixiang, WANG Yifeng, PEI Jianliang, et al. Engineering practice of rock mechanics in large-scale hydropower projects[J]. Yangtze River, 2018, 49(16):76-86.
[2]	段斌, 吴晓铭, 陈刚, 等. 建在深厚覆盖层和强卸荷岩体上的混凝土面板堆石坝筑坝关键技术研究[C]. 中国大坝协会2013学术年会暨第三届堆石坝国际研讨会论文集, 2013:679-687.
[3]	DL/T 5353—2006, 水电水利工程边坡设计规范[S].
	DL/T 5353-2006, Design specification for slope of hydropower and water conservancy project[S].
[4]	王军飞, 肖慎轲, 刘东烈, 等. 地基合成孔径雷达的边坡形变监测应用研究[J]. 测绘科学, 2021, 46(5):58-65.
	WANG Junfei, XIAO Shenke, LIU Donglie, et al. Research on application of GB-SAR in slope deformation monitoring[J]. Science of Surveying and Mapping, 2021, 46(5):58-65.
[5]	张劲松, 吴军, 王星杰. 地基合成孔径雷达在矿区边坡监测预警应用研究[J]. 工程勘察, 2021, 49(12):59-62.
	ZHANG Jinsong, WU Jun, WANG Xingjie. Application of ground based synthetic aperture radar in monitoring and early warning of mining area slope[J]. Geotechnical Investigation & Surveying, 2021, 49(12):59-62.
[6]	董建军, 梅媛, 李昕, 等. 高海拔排土场边坡安全稳定性SBAS-INSAR监测[J]. 中国安全科学学报, 2022, 32(1):92-101. doi: 10.16265/j.cnki.issn1003-3033.2022.01.013
	DONG Jianjun, MEI Yuan, LI Xin, et al. SBAS-InSAR monitoring of slope safety and stability of high altitude dumps[J]. China Safety Science Journal, 2022, 32(1):92-101. doi: 10.16265/j.cnki.issn1003-3033.2022.01.013
[7]	郭延辉, 杨溢, 杨志全, 等. 国产GB-InSAR在特大型水库滑坡变形监测中的应用[J]. 中国地质灾害与防治学报, 2021, 32(2):66-72.
	GUO Yanhui, YANG Yi, YANG Zhiquan, et al. Application of GB-InSAR in deformation monitoring of huge landslide in reservoir area[J]. The Chinese Journal of Geological Hazard and Control, 2021, 32(2):66-72.
[8]	谢博, 施富强, 廖学燕, 等. 边坡位移的EEMD-PSO-ELM模型预测方法[J]. 中国安全科学学报, 2020, 30(3):157-162. doi: 10.16265/j.cnki.issn1003-3033.2020.03.024
	XIE Bo, SHI Fuqiang, LIAO Xueyan, et al. Slope displacement prediction method based on EEMD-PSO-ELM model[J]. China Safety Science Journal, 2020, 30(3): 157-162. doi: 10.16265/j.cnki.issn1003-3033.2020.03.024
[9]	董佳慧, 牛瑞卿, 亓梦茹, 等. InSAR技术和孕灾背景指标相结合的地灾隐患识别[J]. 地质科技通报, 2022, 41(2):187-196.
	DONG Jiahui, NIU Ruiqing, QI Mengru, et al. identification of geological hazards based on the combination of InSAR technology and disaster background indicators[J]. Bulletin of Geological Science and Technology, 2022, 41(2):187-196.
[10]	周振凯, 姚鑫. 基于InSAR技术的2016年11月25日西昆仑Mw6.6地震构造变形研究[J]. 地质学报, 2018, 92(2):232-243.
	ZHOU Zhenkai, YAO Xin. Tectonic deformation study of Nov.25,2016 Mw 6 6 earthquake in West Kunlun mountain based on InSAR technology[J]. Acta Geologica Sinica, 2018, 92(2):232-243.
[11]	曾琪明, 朱猛, 焦健. 地震地壳形变InSAR测量中的关键技术分析[J]. 遥感学报, 2018, 22(增1):17-31.
	ZENG Qiming, ZHU Meng, JIAO Jian. Key technical issues in seismic crustal deformation measurement by InSAR[J]. Journal of Remote Sensing, 2018, 22(S1):17-31.
[12]	肖儒雅, 何秀凤. 时序InSAR水库大坝形变监测应用研究[J]. 武汉大学学报:信息科学版, 2019, 44(9):1 334-1 341.
	XIAO Ruya, HE Xiufeng. Deformation monitoring of reservoirs and dams using time? series InSAR[J]. Geomatics and Information Science of Wuhan University, 2019, 44(9):1 334-1 341.
[13]	何子鑫, 于海洋, 谢赛飞, 等. 小基线集InSAR技术的小浪底大坝形变监测[J]. 测绘科学, 2022, 47(5):66-72,82.
	HE Zixin, YU Haiyang, XIE Saifei, et al. Deformation monitoring of Xiaolangdi dam based on SBAS-InSAR technology[J]. Science of Surveying and Mapping, 2022, 47(5):66-72,82.
[14]	中国电建集团西北勘测设计研究院. 四川大渡河金川水电站可行性研究报告[R], 2013.

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

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