{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T15:51:10Z","timestamp":1774972270422,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020T130757"],"award-info":[{"award-number":["2020T130757"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Scientific Research Operating Expenses of Central Universities","award":["2020CDJQY-A047"],"award-info":[{"award-number":["2020CDJQY-A047"]}]},{"name":"Chognqing Postdoctoral Science Foundation","award":["cstc2020jcyj-bshX0106"],"award-info":[{"award-number":["cstc2020jcyj-bshX0106"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Signal denoising is one of the most important issues in signal processing, and various techniques have been proposed to address this issue. A combined method involving wavelet decomposition and multiscale principal component analysis (MSPCA) has been proposed and exhibits a strong signal denoising performance. This technique takes advantage of several signals that have similar noises to conduct denoising; however, noises are usually quite different between signals, and wavelet decomposition has limited adaptive decomposition abilities for complex signals. To address this issue, we propose a signal denoising method based on ensemble empirical mode decomposition (EEMD) and MSPCA. The proposed method can conduct MSPCA-based denoising for a single signal compared with the former MSPCA-based denoising methods. The main steps of the proposed denoising method are as follows: First, EEMD is used for adaptive decomposition of a signal, and the variance contribution rate is selected to remove components with high-frequency noises. Subsequently, the Hankel matrix is constructed on each component to obtain a higher order matrix, and the main score and load vectors of the PCA are adopted to denoise the Hankel matrix. Next, the PCA-denoised component is denoised using soft thresholding. Finally, the stacking of PCA- and soft thresholding-denoised components is treated as the final denoised signal. Synthetic tests demonstrate that the EEMD-MSPCA-based method can provide good signal denoising results and is superior to the low-pass filter, wavelet reconstruction, EEMD reconstruction, Hankel\u2013SVD, EEMD-Hankel\u2013SVD, and wavelet-MSPCA-based denoising methods. Moreover, the proposed method in combination with the AIC picking method shows good prospects for processing microseismic waves.<\/jats:p>","DOI":"10.3390\/s21165271","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T08:47:52Z","timestamp":1628066872000},"page":"5271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking"],"prefix":"10.3390","volume":"21","author":[{"given":"Kang","family":"Peng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Coal Mine Disaster Dynamics and Control, School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China"},{"name":"School of Resources and Safety Engineering, Central South University, Changsha 410083, China"},{"name":"State Key Laboratory of Coal Resources in Western China, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Hongyang","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Coal Mine Disaster Dynamics and Control, School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5013-7652","authenticated-orcid":false,"given":"Xueyi","family":"Shang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Coal Mine Disaster Dynamics and Control, School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1049\/iet-spr.2020.0104","article-title":"Review of noise removal techniques in ECG signals","volume":"14","author":"Chatterjee","year":"2020","journal-title":"IET Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107631","DOI":"10.1016\/j.apacoust.2020.107631","article-title":"Improving deep speech denoising by noisy2noisy signal mapping","volume":"172","author":"Alamdari","year":"2021","journal-title":"Appl. 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