基于SBAS-InSAR和BPNN的铀尾矿坝形变智能监测与预测

doi:10.16265/j.cnki.issn1003-3033.2024.04.1693

中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (4): 145-152.doi: 10.16265/j.cnki.issn1003-3033.2024.04.1693

基于SBAS-InSAR和BPNN的铀尾矿坝形变智能监测与预测

周怡¹(), 彭国文¹^,^**(), 黄召², 阳鹏飞¹, 刘丹丹¹, 陈小丽¹

¹ 南华大学资源环境与安全工程学院,湖南衡阳 421001
² 中核二七二铀业有限责任公司,湖南衡阳 421004

收稿日期:2023-12-15 修回日期:2024-01-18 出版日期:2024-04-28
通讯作者:
**彭国文(1978—),男,江西抚州人,博士,教授,主要从事核设施退役处理与环境修复等方面的研究。E-mail: pgwnh78@163.com。
作者简介:
周怡 (2000—),女,江苏盐城人,硕士研究生,研究方向为InSAR技术、安全监测与预警、放射性废物处置。E-mail: 1273003687@qq.com。
基金资助:
国家自然科学基金资助(42377076); 湖南省自然科学基金资助(2023JJ50129)

Intelligent monitoring and prediction of deformation of uranium tailings dam based on SBAS-InSAR and BPNN

ZHOU Yi¹(), PENG Guowen¹^,^**(), HUANG Zhao², YANG Pengfei¹, LIU Dandan¹, CHEN Xiaoli¹

¹ School of Resource & Environment and Safety Engineering, University of South China, Hengyang Hunan 421001, China
² China National Nuclear Corporation 272 Uranium Industry Co., Ltd., Hengyang Hunan 421001, China

Received:2023-12-15 Revised:2024-01-18 Published:2024-04-28

摘要/Abstract

摘要：

为提高铀尾矿库退役治理的监测工作效率,提出一个基于小基线合成孔径雷达干涉测量(SBAS-InSAR)技术和反向传播神经网络(BPNN)的铀尾矿库形变智能监测与预测模型。首先,利用SBAS-InSAR技术得到铀尾矿库2020年12月—2022年12月的累计形变量与年均形变速率,并用第一拦水坝的7个全球导航卫星系统(GNSS)监测站验证InSAR监测值的精度;然后,选取铀尾矿库中的雷公塘坝、南坡横坝、战斗坝和松林坝4个坝段的累计沉降量并结合降雨量进行沉降分析;最后,随机提取铀尾矿坝100个沉降点的累积沉降数据,通过BPNN预测铀尾矿坝的形变。结果表明:2年间铀尾矿库的形变速率在-60.06~34.94 mm/a,铀尾矿坝整体处于下沉状态,累计沉降量最大为-46.67 mm。BPNN预测值与实际监测值的平均绝对误差为0.586 mm,均方误差为0.624 mm。

关键词: 小基线合成孔径雷达干涉测量(SBAS-InSAR), 反向传播神经网络(BPNN), 铀尾矿库, 形变智能监测, Sentinel-1A

Abstract:

To improve the efficiency of monitoring work in the retirement treatment of uranium tailings ponds, an intelligent monitoring and prediction model of deformation of uranium tailings ponds was proposed based on SBAS-InSAR technology and BPNN. Firstly, SBAS-InSAR technology was used to obtain the cumulative deformation and annual deformation rate of the uranium tailings pond over the past two years. The accuracy of InSAR monitoring values was verified using seven Global Navigation Satellite System (GNSS) monitoring stations on the first dam. Then, the cumulative settlement of four dam sections, including Leigongtang dam, nanpo cross dam, Battle dam and Songlin dam, was selected and analyzed in conjunction with rainfall. Finally, the cumulative settlement data of 100 settlement points of the uranium tailings dam were randomly extracted to predict the deformation of the uranium tailings dam. The results show that from December 2020 to December 2022, the deformation rate of uranium tailings dam is between -60.06-34.94 mm/a. The overall settlement of the uranium tailings dam is in a sinking state, with a maximum cumulative settlement of -46.67 mm. The average absolute error between the predicted values of BPNN and the actual monitoring values is 0.586 mm, and the mean square error is 0.624 mm.

Key words: small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR), back propagation neural network(BPNN), uranium tailings pond, intelligent deformation monitoring, sentinel-1A

中图分类号:

周怡, 彭国文, 黄召, 阳鹏飞, 刘丹丹, 陈小丽. 基于SBAS-InSAR和BPNN的铀尾矿坝形变智能监测与预测[J]. 中国安全科学学报, 2024, 34(4): 145-152.

ZHOU Yi, PENG Guowen, HUANG Zhao, YANG Pengfei, LIU Dandan, CHEN Xiaoli. Intelligent monitoring and prediction of deformation of uranium tailings dam based on SBAS-InSAR and BPNN[J]. China Safety Science Journal, 2024, 34(4): 145-152.

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参考文献 17

[1]	刘晓杰. 星载雷达遥感广域滑坡早期识别与监测预测关键技术研究[D]. 西安: 长安大学, 2022.
	LIU Xiaojie. Research on key technologies for early identification, monitoring and forecasting of wide-area landslides with spaceborne radar remote sensing[D]. Xi'an: Chang'an University, 2022.
[2]	DU Zheyuan, GE Linlin, ALEX H, et al. Risk assessment for tailings dams in Brumadinho of Brazil using InSAR time series approach[J]. Science of the Total Environment, 2020, 717: DOI: 10.1016/j.scitotenv.2020.137125.
[3]	潘英杰. 铀尾矿库的辐射安全及事故预防对策[J]. 铀矿冶, 2002 (4): 200-204.
	PAN Yingjie. Radiation safety and accident prevention countermeasures for uranium tailing impoundment[J]. Uranium Mining and Metallurgy, 2002(4): 200-204.
[4]	陈娅男, 李素敏, 郭瑞, 等. 基于时序InSAR的覆砂石尾矿坝形变演化研究[J]. 中国安全生产科学技术, 2020, 16(4): 31-37.
	CHEN Ya'nan, LI Sumin, GUO Rui, et al. Study on deformation evolution of sandstone-covered tailings dam based on time series InSAR[J]. Journal of Safety Science and Technology, 2020, 16(4): 31-37.
[5]	李勇发, 左小清, 麻源源, 等. 基于PS-InSAR技术和遗传神经网络算法的矿区地表沉降监测与预计[J]. 地球物理学进展, 2020, 35(3): 845-851.
	LI Yongfa, ZUO Xiaoqing, MA Yuanyuan, et al. Surface subsidence monitoring and prediction based on PS-InSAR technology and genetic neural network algorithm[J]. Progress in Geophysics, 2020, 35(3): 845-851.
[6]	程刚, 王振雪, 李刚强, 等. 滑坡一体化全维度监测预警技术进展与展望[J]. 中国安全科学学报, 2023, 33(1): 105-114. doi: 10.16265/j.cnki.issn1003-3033.2023.01.1144
	CHENG Gang, WANG Zhenxue, LI Gangqiang, et al. Progress and prospect of integrated full-dimensional monitoring and early warning technology for landslides[J]. China Safety Science Journal, 2023, 33(1): 105-114. doi: 10.16265/j.cnki.issn1003-3033.2023.01.1144
[7]	ZHANG Peng, GUO Zihao, GUO Shuangfeng, et al. Land subsidence monitoring method in regions of variable radar reflection characteristics by integrating PS-InSAR and SBAS-InSAR techniques[J]. Remote Sensing, 2022, 14(14): DOI: 10.3390/rs14143265.
[8]	王雪丽, 王卫红, 王立娟, 等. 基于SBAS-InSAR升降轨融合的尾矿库地表形变监测[J]. 化工矿物与加工, 2023, 52(4): 43-48,78.
	WANG Xueli, WANG Weihong, WANG Lijuan, et al. Monitoring of ground deformation around tailings pond by integrating of ascending and descending path based on SBAS-InSAR[J]. Industrial Minerals & Processing, 2023, 52(4): 43-48, 78.
[9]	李洋洋, 左小清, 肖波, 等. 基于SBAS-InSAR和BP算法的大理市地面沉降监测及预测[J]. 贵州大学学报:自然科学版, 2023, 40(4):39-47.
	LI Yangyang, ZUO Xiaoqing, XIAO Bo, et al. Monitoring and prediction of ground subsidence in Dali city based on SBAS-InSAR and BP algorithm[J]. Journal of Guizhou University: Natural Science Edition, 2023, 40(4): 39-47.
[10]	蒋复量, 吴浩楠, 刘永, 等. 基于灰色关联度和变权理论的地震条件下铀尾矿库群坝安全评价研究[J]. 南华大学学报:自然科学版, 2020, 34(1): 22-28.
	JIANG Fuliang, WU Haonan, LIU Yong, et al. Safety evaluation of uranium tailings dams under earthquake based on gray relational analysis and variable weight theory[J]. Journal of University of South China: Science and Technology, 2020, 34(1): 22-28.
[11]	赵发, 刘玉生, 沈立华, 等. 基于北斗的铀尾矿库综合监控平台设计[J]. 铀矿冶, 2023, 42(2): 76-82.
	ZHAO Fa, LIU Yusheng, SHEN Lihua, et al. Comprehensive monitoring platform design of uranium tailings pond based on BDS[J]. Uranium Mining and Metallurgy, 2023, 42(2): 76-82.
[12]	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
[13]	朱建军, 李志伟, 胡俊. InSAR变形监测方法与研究进展[J]. 测绘学报, 2017, 46(10): 1717-1733. doi: 10.11947/j.AGCS.2017.20170350
	ZHU Jianjun, LI Zhiwei, HU Jun. Research progress and methods of InSAR for deformation monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1717-1733. doi: 10.11947/j.AGCS.2017.20170350
[14]	ZHANG Qingyun, LI Yongsheng, ZHANG Jingfa, et al. InSAR technique applied to the monitoring of the Qinghai-Tibet railway[J]. Natural Hazards and Earth System Sciences, 2019, 19(10): 2229-2240. doi: 10.5194/nhess-19-2229-2019
[15]	ZHANG Rui, ZHANG Weiguang, HUANG Wei, et al. High-precision monitoring method for airport deformation based on time-series InSAR technology[J]. Construction and Building Materials, 2023, 366: DOI: 10.1016/j.conbuildmat.2022.130144.
[16]	眭嘉里, 严波, 林翔, 等. 基于BP神经网络的悬垂绝缘子串风偏角预测模型[J]. 重庆大学学报, 2021, 44(8): 114-124.
	SUI Jiali, YAN Bo, LIN Xiang, et al. BP neural network model for dynamic swing angle of suspended insulator string[J]. Journal of Chongqing University, 2021, 44(8): 114-124.
[17]	董道国, 朱建龙, 方瑾. 基于神经网络的建筑能耗及设备故障预测方法研究[J]. 智能建筑与智慧城市, 2022(4): 148-150.
	DONG Daoguo, ZHU Jianlong, FANG Jin. Research on building energy consumption and equipment failuret prediction method based on neural network[J]. Intelligent Building and Smart City, 2022(4): 148-150.

GNSS站点	H	P	R
a	-14.09	-15.12	1.03
b	-16.12	-16.59	0.47
c	-8.33	-7.41	-0.92
d	-8.56	-7.15	-1.41
e	-12.84	-14.44	1.6
f	-7.46	-10.01	2.55
g	-10.76	-12.63	1.87

基于SBAS-InSAR和BPNN的铀尾矿坝形变智能监测与预测

Intelligent monitoring and prediction of deformation of uranium tailings dam based on SBAS-InSAR and BPNN

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