摘要
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 optimization,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(complementary 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.
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.
作者
郑灿广
郑辉
谢世成
朱明非
韩雨辰
杨旭
ZHENG Canguang;ZHENG Hui;XIE Shicheng;ZHU Mingfei;HAN Yuchen;YANG Xu(Yankuang Energy Group Co.,Ltd.,zoucheng 273500,China;School of Geomatics,Anhui University of Science and Technology,Huainan 232001,China;Urban 3D Real Scene and Intelligent Security Monitoring Joint Laboratory of Anhui Province,Huainan 232001,China;Coal Industry Engineering Research Center of Mining Area Environmental And Disaster Cooperative Monitoring,Anhui University of Science and Technology,Huainan 232001,China;Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes,Anhui University of Science and Technology,KLAHEI(KLAHEI18015),Huainan 232001,China;School of Earth and Environment,Anhui University of Science and Technology,Huainan 232001,China)
出处
《全球定位系统》
CSCD
2024年第3期28-37,共10页
Gnss World of China
基金
国家自然科学基金(42304050)
安徽省科技重大专项(202103a05020026)
安徽省重点研究与开发计划(202104 a07020014)
矿区沉降变形智能化监测预警项目(1000B2023000043,ZMXJ-BJ-JS-2021-8)。
