全球定位系统2024，Vol.49Issue(3)：28-37,10.DOI:10.12265/j.gnss.2024002
面向矿区沉陷监测的GNSS垂向时间序列降噪方法
GNSS vertical time series denoising method for mining area subsidence monitoring
摘要
Abstract
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.关键词
GNSS/ICEEMDAN/小波阈值/矿区监测/时间序列降噪Key words
GNSS/ICEEMDAN/wavelet thresholding/mining area monitoring/time series denoising分类
天文与地球科学引用本文复制引用
郑灿广,郑辉,谢世成,朱明非,韩雨辰,杨旭..面向矿区沉陷监测的GNSS垂向时间序列降噪方法[J].全球定位系统,2024,49(3):28-37,10.基金项目
国家自然科学基金(42304050) （42304050）
安徽省科技重大专项(202103a05020026) （202103a05020026）
安徽省重点研究与开发计划(202104a07020014) （202104a07020014）
矿区沉降变形智能化监测预警项目(1000B2023000043,ZMXJ-BJ-JS-2021-8) （1000B2023000043,ZMXJ-BJ-JS-2021-8）
