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Article(id=1322917048434701205, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322916945741348911, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2025.10.351, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1760889600000, receivedDateStr=2025-10-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793337602956, onlineDateStr=2026-10-30, pubDate=1789401600000, pubDateStr=2026-09-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793337602956, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793337602956, creator=13701087609, updateTime=1793337602956, updator=13701087609, issue=Issue{id=1322916945741348911, tenantId=1146029695717560320, journalId=1281212831689347082, year='2026', volume='46', issue='9', pageStart='1039', pageEnd='1170', issueExtLink='null', onlineDate='null', pubDate='1789401600000', pubDateStr='2026-09-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793337578473, creator='13701087609', updateTime=1793337578473, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1088, endPage=1094, ext={EN=ArticleExt(id=1322917048870908822, articleId=1322917048434701205, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Determination of Dam Deformation Outliers Based on EEMD High-Frequency Differential Conversion and GRU Algorithm, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

We propose an outlier detection method for dam deformation based on ensemble empirical mode decomposition (EEMD) high-frequency differential conversion and gated recurrent unit (GRU) analysis. First, the EEMD technology is employed to decompose the original deformation monitoring data, yielding deformation components at different frequencies. Then, high-frequency components containing abnormal deformation information can be extracted for differential sequence conversion, and identification criteria for abnormal single-jump points are proposed. Finally, a high-frequency component time series model is established by GRU neural networks, which is then combined with the abnormal single-jump point identification pattern to recognize outliers. The results show that EEMD technology and high-frequency sequence differential conversion can effectively isolate deformation anomaly information. The proposed single-jump point detection model aligns with the characteristics of dam deformation anomalies. Combining GRU and the 3σ criterion for time series modeling analysis of differential sequences, all deformation anomalies can be accurately identified, and achieve higher accuracy than traditional methods.

, authors=Jinji XIE1, Yuan SHAO1, Peipei CAI2, Junzhuo LI1, Yongjiang WEI3, Jichi LIU2, authorsList=Jinji XIE, Yuan SHAO, Peipei CAI, Junzhuo LI, Yongjiang WEI, Jichi LIU, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., 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=1322917052771611561, articleId=1322917048434701205, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=基于EEMD高频差分转换和GRU算法的大坝变形异常值判定, columnId=1281933709121138897, journalTitle=大地测量与地球动力学, columnName=地球动力学, runingTitle=null, highlight=null, articleAbstract=

提出基于集合经验模态分解(EEMD)高频差分转换和门控递归单元(GRU)算法的大坝变形异常值识别方法。首先，利用EEMD技术对原始变形监测数据进行分解变换，获得不同频率下的变形分量。然后，提取包含异常变形信息的高频分量进行差分序列转换，并提出异常单跳点判定准则。最后，通过GRU神经网络建立高频分量时序模型，并结合异常单跳点判定模式进行异常值的识别。结果表明，EEMD技术和高频序列差分转换能够有效分离变形异常信息，所提的单跳点判定模式符合大坝变形异常值特征，进一步结合GRU和3σ准则对差分序列进行时序建模分析能够准确识别所有变形异常值，识别准确度高于传统方法。

, authors=谢金记1, 邵元1, 蔡培培2, 李俊卓1, 韦永江3, 刘骥驰2, authorsList=谢金记, 邵元, 蔡培培, 李俊卓, 韦永江, 刘骥驰, authorCompany=null, correspAuthors=null, authorNote=

谢金记，高级工程师，主要研究方向为大坝安全管理、水库调度和数智化，E-mail:

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谢金记，高级工程师，主要研究方向为大坝安全管理、水库调度和数智化，E-mail:

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谢金记，高级工程师，主要研究方向为大坝安全管理、水库调度和数智化，E-mail:

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tableContent=null), ArticleFig(id=1322917061156025327, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322917048434701205, language=EN, label=Tab. 1, caption=

Outlier determination results of different methods

, figureFileSmall=null, figureFileBig=null, tableContent=
异常发生日期 本文方法 3σ准则 LSTM
2008-01-05 2008-01-05 2008-01-05 2008-01-05
2008-03-01 2008-03-01 / 2008-03-01
2008-03-21 2008-03-21 2008-03-21 2008-03-21
2008-05-21 2008-05-21 2008-05-21 2008-05-21
2008-07-21 2008-07-21 2008-07-21 2008-07-21
2008-08-23 2008-08-23 2008-08-23 2008-08-23
2008-09-16 2008-09-16 2008-09-16 2008-09-16
2008-10-07 2008-10-07 2008-10-07 2008-10-07
2008-11-09 2008-11-09 / 2008-11-09
2008-12-15 2008-12-15 2008-12-15 2008-12-15
/ / / 2008-04-06
/ / / 2008-04-08
), ArticleFig(id=1322917061248300016, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322917048434701205, language=CN, label=表1, caption=

各方法异常值判定结果

, figureFileSmall=null, figureFileBig=null, tableContent=
异常发生日期 本文方法 3σ准则 LSTM
2008-01-05 2008-01-05 2008-01-05 2008-01-05
2008-03-01 2008-03-01 / 2008-03-01
2008-03-21 2008-03-21 2008-03-21 2008-03-21
2008-05-21 2008-05-21 2008-05-21 2008-05-21
2008-07-21 2008-07-21 2008-07-21 2008-07-21
2008-08-23 2008-08-23 2008-08-23 2008-08-23
2008-09-16 2008-09-16 2008-09-16 2008-09-16
2008-10-07 2008-10-07 2008-10-07 2008-10-07
2008-11-09 2008-11-09 / 2008-11-09
2008-12-15 2008-12-15 2008-12-15 2008-12-15
/ / / 2008-04-06
/ / / 2008-04-08
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基于EEMD高频差分转换和GRU算法的大坝变形异常值判定
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谢金记 1 , 邵元 1 , 蔡培培 2 , 李俊卓 1 , 韦永江 3 , 刘骥驰 2
大地测量与地球动力学 | 地球动力学 2026,46(9): 1088-1094
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大地测量与地球动力学 |地球动力学 2026 , 46 (9) : 1088 -1094
基于EEMD高频差分转换和GRU算法的大坝变形异常值判定
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谢金记1 , 邵元1, 蔡培培2, 李俊卓1, 韦永江3, 刘骥驰2
作者信息
  • 1 广西桂冠电力股份有限公司(红水河)集控中心, 南宁, 530029
  • 2 杭州华辰电力控制工程有限公司, 杭州, 311121
  • 3 龙滩水电开发有限公司龙滩水力发电厂, 南宁, 530029
作者简介:

谢金记，高级工程师，主要研究方向为大坝安全管理、水库调度和数智化，E-mail:

Determination of Dam Deformation Outliers Based on EEMD High-Frequency Differential Conversion and GRU Algorithm
Jinji XIE1 , Yuan SHAO1, Peipei CAI2, Junzhuo LI1, Yongjiang WEI3, Jichi LIU2
Affiliations
  • 1 Centralized Control Center (Hongshui River) of Guangxi Guiguan Electric Power Co Ltd, Nanning 530029, China
  • 2 Hangzhou Huachen Electric Power Control Co Ltd, Hangzhou 311121, China
  • 3 Longtan Hydropower Plant of Longtan Hydropower Development Co Ltd, Nanning 530029, China
出版时间: 2026-09-15 doi: 10.14075/j.jgg.2025.10.351
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摘要
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提出基于集合经验模态分解(EEMD)高频差分转换和门控递归单元(GRU)算法的大坝变形异常值识别方法。首先，利用EEMD技术对原始变形监测数据进行分解变换，获得不同频率下的变形分量。然后，提取包含异常变形信息的高频分量进行差分序列转换，并提出异常单跳点判定准则。最后，通过GRU神经网络建立高频分量时序模型，并结合异常单跳点判定模式进行异常值的识别。结果表明，EEMD技术和高频序列差分转换能够有效分离变形异常信息，所提的单跳点判定模式符合大坝变形异常值特征，进一步结合GRU和3σ准则对差分序列进行时序建模分析能够准确识别所有变形异常值，识别准确度高于传统方法。

大坝变形  /  异常值  /  单跳点判定模式  /  EEMD  /  差分序列  /  GRU

We propose an outlier detection method for dam deformation based on ensemble empirical mode decomposition (EEMD) high-frequency differential conversion and gated recurrent unit (GRU) analysis. First, the EEMD technology is employed to decompose the original deformation monitoring data, yielding deformation components at different frequencies. Then, high-frequency components containing abnormal deformation information can be extracted for differential sequence conversion, and identification criteria for abnormal single-jump points are proposed. Finally, a high-frequency component time series model is established by GRU neural networks, which is then combined with the abnormal single-jump point identification pattern to recognize outliers. The results show that EEMD technology and high-frequency sequence differential conversion can effectively isolate deformation anomaly information. The proposed single-jump point detection model aligns with the characteristics of dam deformation anomalies. Combining GRU and the 3σ criterion for time series modeling analysis of differential sequences, all deformation anomalies can be accurately identified, and achieve higher accuracy than traditional methods.

dam deformation  /  outliers  /  single-jump point determination pattern  /  EEMD  /  differential sequence  /  GRU
谢金记, 邵元, 蔡培培, 李俊卓, 韦永江, 刘骥驰. 基于EEMD高频差分转换和GRU算法的大坝变形异常值判定. 大地测量与地球动力学, 2026 , 46 (9) : 1088 -1094 . DOI: 10.14075/j.jgg.2025.10.351
Jinji XIE, Yuan SHAO, Peipei CAI, Junzhuo LI, Yongjiang WEI, Jichi LIU. Determination of Dam Deformation Outliers Based on EEMD High-Frequency Differential Conversion and GRU Algorithm[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (9) : 1088 -1094 . DOI: 10.14075/j.jgg.2025.10.351
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大坝的安全运行是流域经济发展的重要保证，对大坝进行安全监控是确保其正常运行的关键[1-2]。变形能够直接反映大坝的结构性态，对变形开展预测、预警等工作是及时了解大坝运行状态和消除安全隐患的重点[3]。为确保变形分析的可靠性与准确性，有效的变形监测数据是前提。然而，垂线系统收集到的变形数据经常包含一定数量的异常值，异常变形大多以单跳点形式存在，这些异常值通常由仪器缺陷或人为失误所致。因此，对异常值进行有效判定和剔除具有重要意义。
现有的大坝变形异常值判定方法主要分为统计方法和数学模型两大类[4]。其中，统计方法包括拉依达准则、格拉布斯准则、罗曼诺夫准则、肖维勒准则、狄克逊准则等[5-8]，上述方法能够识别包含异常值较少的数据，而当变形监测数据中异常值较多时，容易导致误判，并且要求变形数据满足某种特定分布，应用条件较难满足。因此，部分学者提出基于数学模型的异常值判定方法，如王军玉[9]提出基于支持向量机和最小协方差矩阵相结合的大坝变形异常值判定方法；何金平等[10]提出通过监测数据模型获得变形数学期望值计算中误差的方法；郑磊等[11]和徐文博[12]提出基于孤立森林算法的大坝施工期变形异常检测模型；张鸣之等[13]提出将稳健随机分割森林算法应用于位移数据粗差检测中。数学模型方法较统计方法具有更优的异常值判定结果，但模型精度受模型参数的选取和拟合精度的影响较大，判定结果的可信度难以辨别。同时，现有方法并未从异常单跳点本身特点出发，而是对所有采集的变形数据统一建模，导致判定过程复杂化且无统一判定准则，确定的异常变形点并无强有力的理论支撑。
为了解决现有模型存在的缺陷，本文从变形实测数据出发，提出基于信号分解算法和高频差分序列转换的大坝变形异常单跳点判定模式。首先通过集合经验模态分解(EEMD)[14-16]对原始变形监测数据进行分解，获得包含异常变形信息的高频分量，然后通过对高频分量进行差分序列转换确定异常值辨别模式，并根据3σ准则确定判定界限。
1 大坝变形异常值判定模式及实现流程
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1.1 EEMD基本理论
采集到的变形监测数据是多种复杂因素共同影响的直观表现，因此提出采用信号分解方法解决原始数据的非线性和非平稳性问题，从而将异常变形信息集中在高频分量中。经验模态分解(EMD)[17]可以将错综复杂的信号分解为一系列相对简单的本征模态函数(IMF)，但其存在模态混叠问题，难以完全分离高频和低频成分，导致分解结果的准确性降低。为了解决EMD的局限性，本文采用EEMD对其进行改进。EEMD通过引入噪声辅助分解，解决了模态混叠的问题，可以将高频分量完全分离出来。EEMD[15-16]的实现算法如下。
首先将白噪声引入原始变形信号x(t)：
$s_i(t)=x(t)+n_i(t)$
式中，ni(t)表示符合标准正态分布的白噪声，i=1, 2, 3, …, M；si(t)表示新生成的变形序列。
然后对si(t)进行EMD分解，得到J个IMF的求和结果，表达式为：
$x_i(t)=\sum\limits_{j=1}^J c_{i, j}(t)+r_{i, j}(t)$
式中，ci, j(t)表示在第i次添加白噪声的情况下进行分解而获得的第j个IMF分量，j=1, 2, 3, …, J；ri, j(t)表示分解的残差项。
最后计算EMD分解结果的集合平均值，以获得EEMD分解的最终IMF：
$c_j(t)=\frac{1}{M} \sum\limits_{i=1}^M c_{i, j}(t)$
式中，i=1, 2, 3, …, M，j=1, 2, 3, …, J；cj(t)表示第j个IMF的总体平均值。
经过上述计算过程，可以得到基于EEMD分解的变形分量，表达式为：
$x(t)=\mathrm{IMF}_1+\mathrm{IMF}_2+\mathrm{IMF}_3+\cdots+\mathrm{IMF}_\beta+r$
式中，β表示IMF分量的个数；r表示分解残差项。
1.2 高频差分序列转化的异常值判定模式
基于上述分析可以得到变形监测数据对应的高频分量IMFk。由于EEMD是将不规则的原始数据分解成一组规律分量，这些分量包含不同的变形频率，而异常值的存在是不符合一般形式下的变形变化规律的，因此异常的变形信息包含在高频分量中，通过对变形高频分量的分析能够提取出大坝变形监测的异常值。
现假设大坝变形的高频分量为：
$\mathrm{IMF}_k=\left\{\mathrm{IMF}_{k_1}, \mathrm{IMF}_{k_2}, \mathrm{IMF}_{k_3}, \cdots, \mathrm{IMF}_{k_n}\right\}$
则分量对应的原始变形数据为：
$X=\left\{x_1, x_2, x_3, \cdots, x_n\right\}$
若原始变形数据中的xα为异常值，则对应高频分量中IMFkα的变幅也将异于其他时间处的变形值。但在对高频分量中的异常信息进行考察时，由于序列的波动性较强，异常值附近的变形值在分解过程中也将出现较大幅度的波动，难以直观提取其中的异常信息，此时经过EEMD分解后得到的高频分量中包含的变形信息已较为“纯粹”。因此，可通过对高频分量的差分序列转换降低分量波动性，使异常变形更为聚集而非异常变形回归正常范围内，从而凸显异常信息的显著性。
分量IMFk的差分序列IMFk-1可表示为：
$\begin{gathered}\mathrm{IMF}_k^{-1}=\left\{\mathrm{IMF}_{k_2}-\mathrm{IMF}_{k_1}, \mathrm{IMF}_{k_3}-\mathrm{IMF}_{k_2}, \right. \\\left.\mathrm{IMF}_{k_4}-\mathrm{IMF}_{k_3}, \cdots, \mathrm{IMF}_{k_n}-\mathrm{IMF}_{k_{n-1}}\right\}\end{gathered}$
变形数据无异常值时，IMFk-1取值趋于规律性变化; 而当xα发生异常变化时，IMFkα-IMFkα-1与IMFkα+1-IMFkα的取值将明显异于其他数值。根据IMFkα-IMFkα-1以及IMFkα+1-IMFkα的符号，异常值判定可分为2种模式(图 1)。
从图 1可以看出，由于高频分量也具有一定的平稳性，因此对于不包含异常变形的时间段，其差分序列取值一般位于正常差分区间内，而若某点发生异常，此时对应的差分结果显著异于正常时段，如图中红点所示。由于变形监测数据中部分正常变化数据也可能存在较小波动，此时若仅依靠图 1中的判定模式进行异常值确定，容易将正常变形归为异常值。为解决该问题，对变换后的差分序列建立GRU神经网络模型，并结合3σ准则确定异常值判定的边界条件，从而减小异常值判定时的误差。
1.3 基于GRU神经网络的差分序列异常值判定
门控递归单元(GRU)和长短期记忆神经网络(LSTM)是递归神经网络(RNN)的2种变体，用于处理时间序列数据的建模和分析任务。与LSTM相比，GRU通过引入重置门zt+1和更新门rt+1来取代LSTM中原有的“门”结构，从而简化了其内部结构，提高了模型训练效率，降低了模型过拟合风险。GRU内部结构如图 2所示，采用更新门rt+1来定义当前时刻保留先前数据状态的程度，重置门zt+1控制前一状态ht输入当前候选状态$ \widetilde{h}_{t+1}$的比重。
GRU神经网络的更新原理如下。
1) 计算重置门和更新门：
$ r_{t+1}=\sigma\left(\boldsymbol{W}_r *\left[h_t, x_{t+1}\right]\right)$
$z_{t+1}=\sigma\left(\boldsymbol{W}_z *\left[h_t, x_{t+1}\right]\right)$
式中，xt+1表示t+1时间步隐含层的输入；ht表示t时间步单元输出；σ表示Sigmoid激活函数，取值范围为[0, 1]；Wr和Wz分别表示重置门和更新门的权重矩阵。
2) 计算隐含层输出$ \tilde{h}_{t+1}$：
$\tilde{h}_{t+1}=\tanh \left(\boldsymbol{W}_{\tilde{h}} *\left[r_{t+1} * h_t, x_{t+1}\right]\right)$
式中，tanh表示双曲正切函数；$ \boldsymbol{W}_{\tilde{h}}$表示$ \tilde{h}_{t+1}$对应的权重矩阵。
3) 计算GRU单元的输出：
$h_{t+1}=\left(1-z_{t+1}\right) * h_t+z_{t+1} * \tilde{h}_{t+1}$
GRU神经网络中的门控机制使其能够有效挖掘时间序列的时序特性，捕捉不同周期内的特征和趋势，适用于对大坝变形时间序列的建模分析。本文采用GRU神经网络对大坝变形高频分量差分序列进行建模，综合利用分析残差和变形异常值判定模式，结合3σ准则实现变形异常值的判定。
1.4 大坝变形异常值判定的实现流程
本文基于高频差分序列转化的大坝变形异常值判定方法主要分为3个部分，第1部分为信号分解，第2部分为高频差分判定，第3部分为对比分析。各部分具体实现流程如下。
步骤1：提取包含异常变形的大坝变形时间序列。
步骤2：利用EEMD算法分解原始变形时间序列，并提取包含异常变形信息的高频分量。
步骤3：对高频分量进行差分序列转换，并确定相应的变形单跳点异常值判定模式。
步骤4：对变形分量中的高频分量建立GRU神经网络，通过数据集的划分得到性能较好的模型结构，提取对应的时序分析残差时间序列。
步骤5：结合3σ准则和变形异常值判定模式进行变形异常值的判别。
步骤6：将提出的异常值判定方法与传统异常值判定方法进行对比，验证所提方法的合理性。
综上所述，所提大坝变形异常值判定流程如图 3所示。
2 工程实例
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2.1 工程概况
以某混凝土连拱坝为例说明所提方法的合理性。该坝水平位移采用正倒垂线系统进行监测，选取PL4变形监测点2008年变形数据作为建模样本。为验证本文所提方法的有效性，在选取的时间段中预设10个异常变形值，对应日期分别为2008-01-05、2008-03-01、2008-03-21、2008-05-21、2008-07-21、2008-08-23、2008-09-16、2008-10-07、2008-11-09、2008-12-15，包含异常值的变形序列如图 4所示。
从图 4可以看出，原始变形序列波动性较强，且难以准确判定其中是否包含异常值或哪些时间点的变形属于异常值。因此，基于所提方法对案例中的变形异常值进行建模提取。
2.2 EEMD分解
在进行异常值判定前，首先对变形时间序列进行EEMD分解，各项参数分别为：Nstd取值为0.2，NR取值为5 000，最大迭代次数取值为5 000。文中所取时间序列对应分解个数为6个，各变形分量结果如图 5所示。从图中可以看出，原始非稳定的变形时间序列被分解为一组变形分量，各变形分量具有不同频率且稳定性得到提升。IMF2~IMF5以及r呈现一定的周期规律，这些分量中并不包含异常变形信息，而高频分量IMF1在部分区间内对应的变形发生明显突变，说明以高频分量作为异常值判定依据是合理的。然而，IMF1中突变部分对应的时间点较多，因此无法准确判定哪些点为异常变形点。下文将根据理论部分所述，对高频分量进行差分序列转换，以期进一步进行异常分析。
2.3 差分序列转换和异常值判定
首先提取基于EEMD分解得到的高频变形分量IMF1，对其进行差分序列转换，结果如图 6所示。
对比IMF1与差分转换结果(图 7)可以发现，差分序列中异常信息聚集能力更为显著。从图中方框部分可以看出，高频分量的差分变换能够使异常信息聚集到特定点，而非异常变形在差分变换后，其分量幅值被削弱，从而更容易判定异常变形点。
将高频分量差分序列按照7 ∶ 3划分为训练集和测试集，建立GRU神经网络，网络参数取值为：input_size取值12，hidden_size取值64，单层GRU，dropout取值0.2，迭代次数取值200。结合3σ准则求取对应分析边界区间，本案例中求得的差分序列对应的3σ=0.218 mm，异常值边界如图 8所示。
根据图 8中异常边界可得12个变形异常值，即除了预设的10个异常值以外，2008-04-06和2008-04-08对应的变形值也被判定为异常值。结合大坝变形单跳点异常判定模式，可以进一步从差分序列中提取如图 9所示的变形异常值。10个异常点定位的变形监测日期分别为2008-01-05、2008-03-01、2008-03-21、2008-05-21、2008-07-21、2008-08-23、2008-09-16、2008-10-07、2008-11-09、2008-12-15，分析结果与预设异常信息吻合，大坝变形异常值判定精度达到100%。由此可见，在对大坝变形高频分量差分序列进行异常值分析时，结合所提的异常判定模式可以得到更为准确的异常值判定结果。
传统大坝变形异常值判定方法多采用3σ准则或直接采用数学模型法，为了验证本文所提方法的变形异常单跳点判定性能，分别采用3σ准则以及基于GRU的数学模型法进行异常值判定，3种方法异常值判定结果如表 1所示。可以看出，本文所提方法的异常判定率高达100%；而直接对原始变形数据进行3σ准则判定，2008-03-01和2008-11-09出现了漏判现象；基于LSTM的数学模型法虽然可确定所有异常值，但在2008-04-06和2008-04-08出现了误判现象。综上可知，本文所提的异常值判定模式和方法能够很好地应用于大坝变形异常单跳点的判定。
3 结语
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本文针对大坝变形异常单跳点，提出结合EEMD高频差分转换和GRU算法的大坝变形异常值判定方法，主要结论如下：
1) 基于EEMD对包含异常变形信息的数据进行分解，能够得到包含异常信息的变形高频分量。
2) 高频分量的差分序列转换能够使异常变形信息更加集中，使无异常时间点的变形信息趋于稳定。
3) 基于差分序列提出单跳点异常值识别模式，结合GRU神经网络和3σ准则能够有效判定出所有变形异常值，判定精度高于传统方法。

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2026年第46卷第9期
27
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doi: 10.14075/j.jgg.2025.10.351
  • 接收时间：2025-10-20
  • 首发时间：2026-10-30
  • 出版时间：2026-09-15
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  • 收稿日期：2025-10-20
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    1 广西桂冠电力股份有限公司(红水河)集控中心, 南宁, 530029
    2 杭州华辰电力控制工程有限公司, 杭州, 311121
    3 龙滩水电开发有限公司龙滩水力发电厂, 南宁, 530029
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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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