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Article(id=1149738956503298837, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, articleNumber=1003-3033(2024)04-0145-08, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.04.1693, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1702569600000, receivedDateStr=2023-12-15, revisedDate=1705507200000, revisedDateStr=2024-01-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1752048728346, onlineDateStr=2025-07-09, pubDate=1714233600000, pubDateStr=2024-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752048728346, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752048728346, creator=13701087609, updateTime=1752048728346, updator=13701087609, issue=Issue{id=1149738954913661267, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='4', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='1714233600000', pubDateStr='2024-04-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752048727968, creator='13701087609', updateTime=1756468927830, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1168278616925286857, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1168278616925286858, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=145, endPage=152, ext={EN=ArticleExt(id=1149738956796900120, articleId=1149738956503298837, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Intelligent monitoring and prediction of deformation of uranium tailings dam based on SBAS-InSAR and BPNN, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

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.

, authors=null, authorsList=Yi ZHOU, Guowen PENG, Zhao HUANG, Pengfei YANG, Dandan LIU, Xiaoli CHEN, authorCompany=null, correspAuthors=Guowen PENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1149738973611864476, articleId=1149738956503298837, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于SBAS-InSAR和BPNN的铀尾矿坝形变智能监测与预测, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

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。

, authors=

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：。

, authorsList=周怡, 彭国文, 黄召, 阳鹏飞, 刘丹丹, 陈小丽, authorCompany=null, correspAuthors=彭国文, authorNote=null, correspAuthorsNote=
**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：。
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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：。

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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：。

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language=CN, label=图8, caption=BPNN模型预测的结果, figureFileSmall=4XjMAPq+yQWPHVOgA6F/4g==, figureFileBig=bOmY6wyRyF4o9Jx5isjmqw==, tableContent=null), ArticleFig(id=1168150968098108114, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738956503298837, language=EN, label=Tab.1, caption=

GNSS measurement values and InSAR monitoring values and their error statistics mm/a

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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
), ArticleFig(id=1168150968181994195, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738956503298837, language=CN, label=表1, caption=

GNSS测量值与InSAR监测值及其误差统计

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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
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基于SBAS-InSAR和BPNN的铀尾矿坝形变智能监测与预测
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周怡 1 , 彭国文 1, ** , 黄召 2 , 阳鹏飞 1 , 刘丹丹 1 , 陈小丽 1
中国安全科学学报 | 安全工程技术 2024,34(4): 145-152
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中国安全科学学报 |安全工程技术 2024 , 34 (4) : 145 -152
基于SBAS-InSAR和BPNN的铀尾矿坝形变智能监测与预测
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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：。

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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：。

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周怡1 , 彭国文1, ** , 黄召2, 阳鹏飞1, 刘丹丹1, 陈小丽1
作者信息
  • 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
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：。
Intelligent monitoring and prediction of deformation of uranium tailings dam based on SBAS-InSAR and BPNN
Yi ZHOU1 , Guowen PENG1, ** , Zhao HUANG2, Pengfei YANG1, Dandan LIU1, Xiaoli CHEN1
Affiliations
  • 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
出版时间: 2024-04-28 doi: 10.16265/j.cnki.issn1003-3033.2024.04.1693
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摘要
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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。

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

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.

small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR)  /  back propagation neural network(BPNN)  /  uranium tailings pond  /  intelligent deformation monitoring  /  sentinel-1A
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. 中国安全科学学报, 2024 , 34 (4) : 145 -152 . DOI: 10.16265/j.cnki.issn1003-3033.2024.04.1693
Yi ZHOU, Guowen PENG, Zhao HUANG, Pengfei YANG, Dandan LIU, Xiaoli CHEN. 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 . DOI: 10.16265/j.cnki.issn1003-3033.2024.04.1693
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0 引言
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随着铀矿冶工业的不断发展，我国对核能资源需求量日益增加，其中，在运核电机组为55台，装机容量达到5703万kW，建设了200多个铀矿床(田)以及十几个厂矿，铀矿资源总量超过280万t。铀尾矿库储存铀矿石冶炼和浓缩过程中产生的放射性废物，含有铀、镭、钍等多种天然放射性核素以及衰变产生的氡和氡子体，铀尾矿库如果堤坝破裂或溢流，可能对周围水体和土壤造成污染，对人类和生态系统构成严重威胁。2010年广东信宜紫金矿业溃坝共造成22人死亡，直接损失1900万元[1];2019年CÓrrego do Feijao铁矿山尾矿库的倒塌释放约1 200万m3 的尾矿，造成240多人死亡[2]。提升铀尾矿库的监测效率，及时识别危险因素和提取灾害信息，精准监测和预测坝体形变，不仅是确保铀矿业可持续发展的必要条件，也是关乎自然环境保护、人体健康和社会稳定的重要保障[3]。
目前，铀尾矿库形变的监测方法主要为测量机器人、全球导航卫星系统(Global Navigation Satellite System，GNSS)和传统水准测量等[4]。测量机器人和GNSS虽然能够提供实时、精确的监测结果，但是只能监测铀尾矿库的局部形变，工作效率低。传统水准测量虽然监测精度高，但易受复杂地形的限制，不能全面监测铀尾矿库。相比之下，合成孔径雷达干涉测量(Interferometric Synthetic Aperture Radar，InSAR)技术具有低成本、全范围监测、高精度等优势[5]，它能够不受地面条件的限制，提供高分辨率的坝体形变信息[6]。ZHANG Peng等[7]将永久散射体(Persistent Scatterer，PS)-InSAR与小基线(Small Baseline Subset，SBAS)-InSAR技术相结合，利用不同雷达反射区域融合不同干涉测量的数据来监测宿州地面沉降，发现SBAS-InSAR技术在监测乡村植被区时精度更高；陈娅男等[4]利用SBAS-InSAR技术监测覆砂石尾矿坝的整体形变，发现SBAS-InSAR能事后追溯尾矿坝的时序形变演化过程；王雪丽等[8]运用SBAS-InSAR升降轨融合技术监测尾矿库地表形变，融合升轨和降轨的合成孔径雷达(Synthetic Aperture Radar，SAR)数据能有效提高监测精度；李洋洋等[9]利用SBAS-InSAR技术监测了大理市地面的沉降，并利用反向传播神经网络(Back Propagation Neural Network，BPNN)模型预测大理市的地表沉降。综上，SBAS-InSAR技术能够智能监测地表沉降和尾矿库形变，铀尾矿库是一种大型的人工构筑物，由于其稳定性高、形变较缓慢，具备长期有效监测的高相干性，但目前研究较少将InSAR技术应用于铀尾矿库的形变智能监测。
鉴于此，笔者拟基于SARscape软件，利用SBAS-InSAR技术对铀尾矿库进行形变智能监测，获取铀尾矿库2020年12月—2022年12月的累积形变量和年平均形变速率图，并利用BPNN模型选取100个沉降点的累积沉降数据，预测铀尾矿坝的形变，以期为铀尾矿库的退役治理提供一定的参考依据。
1 铀尾矿库概况及SAR数据
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1.1 铀尾矿库概况
某铀尾矿库位于湖南衡阳市珠晖区东阳渡(东经112°38'，北纬26°47')，占地面积约170万m3，库内的滩面面积约136万m3[10]。该铀尾矿库属于二等库，是铀矿冶系统中最大的尾矿库，已被国家列入环保安全应急管理重点尾矿库[11]。研究区域地理位置如图1所示。
1963年，该铀尾矿库建成并投入使用，1994年，国家决定将该企业铀水冶线停产，铀尾矿库也随之进行退役处理，设计采用的退役治理方法是将尾矿坝坡全部覆盖1m厚的黏土，并用砂卵石垫层及干砌块石护砌；尾矿滩面覆盖1m厚的黏土，并在其表面植草护面。该铀尾矿库与居民生活区距离较近，除此之外该尾矿库已退役近30年，地形也随之发生一些改变，存在一些安全隐患，如坝面排水渠及坝端截水沟护砌的形变和磨蚀在短期的强降雨时会导致沟内淤堵；堆积坝在长时间内沉降不均会造成坝体结构破坏，形成坝坡渗流。铀尾矿库一旦发生溃坝事故，将对周围的居民及生态系统造成极大的威胁，因此，急需采取可靠且有效的方法长期监测铀尾矿库的形变。
1.2 SAR数据
SAR数据来自欧空局网站(https://scihub.copernicus.eu/dh-us/#/home)中的Sentinel-1A卫星。Sentinel-1A卫星配备了C波段合成孔径雷达，具有高分辨率、高抗干扰性，可进行全天候、大范围的地表监测[12]。
2020年12月—2022年12月，每月从研究区随机选取一天某时刻的SAR图像，总共24景Sentinel-1A数据进行试验。采用基于美国航天航空局空间分辨率为30m的数字高程模型(Digital Elevation Model，DEM)校正地表特征，使用对应图像日期的卫星精密轨道星历数据修正轨道信息。
2 铀尾矿库监测与预测技术
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2.1 SBAS-InSAR技术
SBAS-InSAR技术是一种基于多主影像的InSAR时间序列方法，其主要特点在于利用时空基线较短的干涉对来提取地表形变信息[13]。相对于传统的PS-InSAR技术，SBAS-InSAR技术克服了PS-InSAR因选取一幅影像作为公共主影像而引起的部分干涉图相干性较差的不足；还同时降低了对SAR数据的需求量，运算效率较高[14]。因此，SBAS-InSAR技术被广泛应用于低相干地区的地表形变智能监测[15]。
2.2 SBAS-InSAR技术与BP神经网络相结合
1986年，RUMELHART等[16]提出BP神经网络，它由输入层、隐藏层、输出层3部分组成，其核心为前向传播和误差反向传播[17]。
将SBAS-InSAR技术与BPNN相结合，构建出一个基于SBAS-InSAR技术和BPNN的铀尾矿库形变智能监测与预测模型。采用SBAS-InSAR技术，提取形变时间序列作为BPNN的输入，构建BPNN的训练数据集。利用已知的一部分铀尾库形变数据训练BPNN，另一部分形变数据作为期望输出值，不断调整网络权值和参数，提高预测精度。SBAS-InSAR技术与BPNN主要处理流程如图2所示。
3 SBAS-InSAR数据处理及结果分析
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3.1 SBAS-InSAR数据处理
基于ENVI-SARscape 软件处理2020年12月9日—2022 年12月23日的24景SAR数据。为避免时间和空间失相干，设置时间基线为150天，空间基线阈值为3%，系统自动主选定2021年12月16日为超级主影像，生成的时间和空间基线连接图如图3所示。图中图像两两配对组成干涉对，不存在单独影像，总共生成86个干涉对。
在SAR影像配对组合后再进行差分干涉处理，包括影像配准、生成干涉图、去平、滤波和相位解缠。为减小大气干扰、提高影像的相干性，将多视比设置为4∶1;采用30 m精度的外部DEM消除地形及平地相位；滤波采用Goldstein自适应滤波方法；相位解缠采用最小费用流进行2D解缠。在较稳定的区域选取连续的30个地面控制点，通过地面控制点对干涉图进行轨道精炼和重去平，估算和消除残余的恒定相位。然后进行地表形变反演，设定输出相干系数阈值为0.25，第1次反演在估算形变速率和残余地形的同时再进行2次解缠；第2次反演在时间域和空间域上分别采用高通滤波和低通滤波来估算与去除大气相位。在得到最精确的时序形变结果后，通过地理编码将其转换到1984年世界大地测量系统下。
3.2 精度验证
选择位于第一拦水坝的7个GNSS监测站来验证SBAS-InSAR技术处理数据的精确度，GNSS监测站点分布位置如图4所示，其中，坝顶的3个监测站代号为a、b、c，环坝公路与排水渠间坡面的2个监测站代号为d、c，排水沟平面的2个监测站代号为f、g。选取2022年2—11月期间以各个GNSS监测站为圆心，半径为20 m 的圆形缓冲区内的5个SBAS-InSAR年形变速率，并求平均值H，再与这个时间段内GNSS站点监测的垂直方向的表面位移年形变速率P作比较，计算其绝对误差R，绝对误差的计算式为：
R = H - P
GNSS测量值与InSAR监测值及其误差统计见表1。由表1可知：GNSS测量值与InSAR监测值的绝对误差R的范围在-1.41~2.55 mm/a。由于GNSS提供的是点位测量，能捕捉坝体小范围内的显著变化，精度更高。而InSAR技术易受植被、大气条件等的影响，其监测的沉降值较GNSS测量值而言普遍较小，但其绝对误差相对较小。因此，SBAS-InSAR能作为监测铀尾矿坝的有效技术手段。
3.3 铀尾矿库形变分析
研究区的累积形变量及年均形变速率如图5所示，图例中正值代表上升，负值代表下沉。从监测结果看，铀尾矿库的整体形变是不均匀的，形变速率在-60.06~34.94 mm/a。铀尾矿坝体整体都处于下沉状态，形变速率范围为-23.34~-2.96mm/a，其中，战斗坝和松林坝的形变最严重，累积沉降量最大，分别为-46.18和-46.67mm;雷公塘坝和南坡横坝最为稳定，最大累积沉降量只有-12.17和-19.65mm。
滩面内部高低不平是由于滩面内的尾矿分布不均匀，西南部较低，东北部较高，其中，大部分处于下沉状态，最大累积沉降量为-124.69mm，在铀尾矿库东南角有一小部分滩面是上升状态，最大累积上升量为60.31mm，上升原因主要是因为雨水期间细颗粒尾矿会被挟带至南坡坝前的低洼处沉积。
为进一步研究铀尾矿坝2年内的形变特征，分别在雷公塘坝A、南坡横坝B、战斗坝C和松林坝D等4个坝段随机选取一个特征点，4个特征点位置如图5a所示。结合2020年12月—2022年12月的降雨量绘制时间序列累积沉降图，分析其沉降成因，如图6所示。从图6可以看出，C点和D点的沉降速率比A点和B点的沉降速率较大，雷公塘坝和南坡横坝靠近丘陵，植被密度高，植被根系维持地表稳定性，累积沉降量较少且沉降速率低。战斗坝和松林坝受雨季的影响，形变幅度较大。战斗坝和松林坝是由黏土初期坝和尾矿堆积坝组成，矿泥与尾砂的含量不一，其中，尾矿堆积坝含有大量的尾粉砂和尾细砂，其渗透系数较大，排水速度较快，因此，松林坝沉降速率较大。
图6中，在2021—2022年的5月份雨季期间以及2022年11月份，4个特征点处略有抬升，当雨季结束后长时间不降雨，坝体便会持续沉降。此外，当降雨量突然大幅增加时，坝体也可能表现出地表的“抬升”现象。这是由于雨季期间的大量雨水回灌导致地下水位上升，地表吸水膨胀，从而触发周期性的“抬升”现象。但随着雨季的结束，降雨量急剧减小，地表水分被蒸发后大量流失，地表便会出现明显地骤降。
3.4 铀尾矿库形变预测
由于参数的选择和样本的选取会影响BPNN的精度，因此，为提高模型预测的准确性，利用SARscape软件随机并均匀地从9个坝段提取2020年12月—2022年12月100个沉降点的累计沉降量，其中，80个作为样本进行BPNN训练，后20个作为预测样本。将2020年12月—2022年11月每8个月的累计沉降值作为输入层，即输入层节点数为3，2022年12月的沉降值作为输出层，即输出层节点数为1，同时，设置隐藏层节点数为9。BPNN模型预测值与监测值对比如图7所示，BPNN模型预测结果如图8所示。
从图7可以看出，BPNN模型的预测值与实际监测值整体保持较高的一致性，其中，平均绝对误差为0.586mm，均方误差为0.624mm。
从图8可以看出，训练集样本的相关性达到0.998，验证集、测试集以及整体的相关性也都超过0.99，证明该模型效果较好。
4 结论
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通过SBAS-InSAR技术对铀尾矿库进行形变智能监测，获取铀尾矿库2020年12月—2022年12月的累积形变量和年均形变速率，并利用BPNN模型预测铀尾矿坝沉降，结论如下：
1) 铀尾矿库的沉降速率范围为-60.06~34.94 mm/a，其中，9个坝段都处于沉降状态，形变速率范围为-23.34~-2.96mm/a。用第一拦水坝的7个GNSS监测站验证InSAR监测值的精度，结果表明：SBAS-InSAR监测技术监测铀尾矿库的形变具有可行性。
2) 降雨量对铀尾矿坝形变具有显著的影响，在每年5月份左右的雨季期间，大量的降雨会导致地下水位上升，地表吸水膨胀，坝体都会出现3~7mm的抬升。
3) 利用BPNN模型预测分析随机提取铀尾矿坝100个沉降点的累积沉降数据的形变，结果表明：监测值与预测值整体保持很高的一致性。可以利用构建的 BPNN模型预测铀尾矿库未来形变。
基金
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  • 国家自然科学基金资助(42377076)
  • 湖南省自然科学基金资助(2023JJ50129)
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2024年第34卷第4期
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doi: 10.16265/j.cnki.issn1003-3033.2024.04.1693
  • 接收时间：2023-12-15
  • 首发时间：2025-07-09
  • 出版时间：2024-04-28
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  • 收稿日期：2023-12-15
  • 修回日期：2024-01-18
基金
国家自然科学基金资助(42377076)
湖南省自然科学基金资助(2023JJ50129)
作者信息
    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

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：。
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https://castjournals.cast.org.cn/joweb/zgaqkxxb/CN/10.16265/j.cnki.issn1003-3033.2024.04.1693
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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