Article(id=1322560013927825837, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1746633600000, receivedDateStr=2025-05-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252479297, onlineDateStr=2026-10-29, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252479297, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252479297, creator=13701087609, updateTime=1793252479297, updator=13701087609, issue=Issue{id=1322559951067792299, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='4', pageStart='1', pageEnd='294', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252464310, creator='13701087609', updateTime=1793252763065, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322561204200001631, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322561204200001632, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559951067792299, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=222, endPage=228, ext={EN=ArticleExt(id=1322560014087209390, articleId=1322560013927825837, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Study on Microseismic Signal Denoising Based on the ICEEMDAN-IWT Combined Algorithm, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the challenge that existing denoising methods struggle to effectively extract microseismic signals from fractured rock masses in mines, a combined algorithm based on improved adaptive noise ensemble empirical mode decomposition (ICEEMDAN) and improved wavelet thresholding (IWT) was proposed. Firstly, the algorithm decomposed the noisy microseismic signal using ICEEMDAN to obtain the instrumental modal functions (IMF_s). Secondly, noise-containing components were filtered based on correlation coefficients, and the IWT algorithm was used for denoising. Finally, the denoised microseismic signal was obtained through wavelet transformation. At the same time, the accuracy of the algorithm was verified using both microseismic simulation data and real-world data. The denoising performance of the ICEEMDAN-IWT combined algorithm was quantitatively compared with that of other mainstream denoising algorithms, using the signal-to-noise ratio (SNR), normalized cross-correlation coefficient (NCC), and mean square error (MSE) as evaluation indicators. The results show that the ICEEMDAN-IWT combined algorithm achieves the best denoising performance, with an SNR of 28.06 dB, an NCC of 0.999 2, and an MSE of 0.014 45. After processing with the ICEEMDAN-IWT combined algorithm, the denoised waveform exhibits significantly improved characteristics while fully preserving the original phase information. However, there is still room for improvement in the denoising performance for waveform data with high amplitude ratios. Future research can further optimize the algorithm performance by incorporating intelligent methods such as deep learning.

, authors=Zhiyong FU^{1, 2}, Yanliang WEN^{1, 2}, Liexian TANG^{1, 2}, authorsList=Zhiyong FU, Yanliang WEN, Liexian TANG, authorCompany=null, correspAuthors=Yanliang WEN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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=1322560014317896111, articleId=1322560013927825837, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于ICEEMDAN-IWT联合算法的微震信号去噪研究, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对现有去噪方法难以有效提取矿山岩体破裂微震信号的问题，提出了基于改进型自适应噪声集合经验模态分解（ICEEMDAN）和改进小波阈值（IWT）的联合算法。首先，该算法通过ICEEMDAN分解含噪微震信号，获得模态分量（IMF_s）；其次，基于相关系数筛选含噪分量，并采用IWT算法去噪；最后，经小波重构得到去噪微震信号。同时，采用微震仿真数据和真实数据验证算法的准确性，并以信噪比（SNR）、归一化互相关系数（NCC）及均方误差（MSE）为评价指标，定量对比分析ICEEMDAN-IWT联合算法与其他主流去噪算法的去噪效果。结果表明，ICEEMDAN-IWT联合算法去噪效果最好，SNR为28.06 dB，NCC为0.999 2，MSE为0.014 45；ICEEMDAN-IWT联合算法处理后，去噪波形特征明显改善，完整保留了原相位特征，但对大振幅比波形数据的去噪效果仍有提升空间，后续研究可结合深度学习等智能化方法进一步优化算法性能。

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付志勇（1998—），男，河南驻马店人，硕士研究生，主要从事微震信号处理及矿井岩爆预警研究。E-mail:844118357@qq.com

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付志勇（1998—），男，河南驻马店人，硕士研究生，主要从事微震信号处理及矿井岩爆预警研究。E-mail:844118357@qq.com

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