首页|面向矿区沉陷监测的GNSS垂向时间序列降噪方法

面向矿区沉陷监测的GNSS垂向时间序列降噪方法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
GNSS技术作为开采沉陷监测的重要手段,其时间序列中的噪声会对监测结果造成较大影响.本文提出一种混合灰狼粒子群优化算法(improved hybrid grey wolf particle swarm optim-ization,IPSOGWO)和改进自适应噪声完备集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)联合小波阈值(wavelet thresholding,WT)的降噪方法.通过IPSOGWO优化ICEEMDAN算法的超参数,对GNSS时间序列进行分解,提取本征模态函数(Intrinsic Mode Function,IMF).利用多尺度排列熵筛选出含有噪声的IMF分量,采用小波阈值对含噪分量进行二次处理,并与剩余IMF分量重构,获得降噪结果.利用仿真信号和某矿区自动化监测站的实测数据进行实验,结果表明:与小波阈值、完备集合经验模态分解(comple-mentary ensemble empirical mode decomposition,CEEMD)和 GWO-ICEEMDAN 相比,本文方法降噪性能更好,降噪后的数据可为后续工作面沉降分析提供支持.
GNSS vertical time series denoising method for mining area subsidence monitoring
The GNSS technology,as an important tool for mining subsidence monitoring,is significantly affected by the noise present in its time series.This paper proposes a denoising method that combines an Improved hybrid grey wolf particle swarm optimization(IPSOGWO)and an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN),coupled with wavelet thresholding(WT).The IPSOGWO optimizes the hyperparameters of the ICEEMDAN algorithm to decompose the GNSS time series and extract the intrinsic mode functions(IMF).The multi-scale permutation entropy is used to select the IMF components containing noise.These components are then secondarily processed using wavelet thresholding and reconstructed with the remaining IMF components to obtain the denoised results.Experiments with simulated signals and actual data from an automated monitoring station in a mining area demonstrate that the proposed method outperforms the wavelet threshold,complete ensemble empirical mode decomposition(CEEMD),and GWO-ICEEMDAN in terms of denoising performance,providing reliable data for subsequent analysis of working face subsidence.

GNSSICEEMDANwavelet thresholdingmining area monitoringtime series denoising

郑灿广、郑辉、谢世成、朱明非、韩雨辰、杨旭

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兖矿能源集团股份有限公司,山东邹城 273500

安徽理工大学空间信息与测绘工程学院,安徽淮南 232001

城市实景三维与智能安全监测安徽省联合共建学科重点实验室,安徽淮南 232001

安徽理工大学矿区环境与灾害协同监测煤炭行业工程研究中心,安徽淮南 232001

安徽理工大学矿山采动灾害空天地协同监测与预警安徽普通高校重点实验室,安徽淮南 232001

安徽理工大学地球与环境学院,安徽淮南 232001

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GNSS ICEEMDAN 小波阈值 矿区监测 时间序列降噪

国家自然科学基金安徽省科技重大专项安徽省重点研发计划矿区沉降变形智能化监测预警项目矿区沉降变形智能化监测预警项目

42304050202103a05020026202104a070200141000B2023000043ZMXJ-BJ-JS-2021-8

2024

10.12265/j.gnss.2024002

全球定位系统
中国电波传播研究所

全球定位系统

影响因子：0.462
ISSN：1008-9268
年,卷(期)：2024.49(3)
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