Article(id=1322559873187954821, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559853093048851, 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=1739721600000, receivedDateStr=2025-02-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252445741, onlineDateStr=2026-10-29, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252445741, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252445741, creator=13701087609, updateTime=1793252445741, updator=13701087609, issue=Issue{id=1322559853093048851, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='2', pageStart='1', pageEnd='326', issueExtLink='null', onlineDate='null', pubDate='1771948800000', pubDateStr='2026-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252440951, creator='13701087609', updateTime=1793252699964, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322560939526828556, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559853093048851, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322560939526828557, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559853093048851, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=158, endPage=166, ext={EN=ArticleExt(id=1322559873506721926, articleId=1322559873187954821, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Prediction Method for Surface Subsidence in Underground Mining Based on LSTM-DCNN and Transfer Learning, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

During the mining process of underground ore bodies, the collapse of goaf can disrupt the stress balance of rock strata, leading to geological disasters and endangering surface facilities. In order to improve the accuracy of surface subsidence prediction in mines without actual monitoring data during the unmined stage, as well as to ensure personnel safety and mining production, a method combining neural networks and transfer learning for underground mining surface subsidence prediction was proposed. The method combines the advantages of long-short-term memory neural networks to accurately extract time series features and deep convolutional neural networks to extract local features of data, improving the accuracy of the prediction model. According to the mining design of Zhonggangou vanadium titanium magnetite, the prediction model was used to predict the surface subsidence under different mining schedules. The results show that with the increase of ore output, the mean square error of each segment is less than 0.4.The proposed model shows a high accuracy in surface subsidence prediction under different mining schedules. This method can provide a reference for surface subsidence prediction and safety monitoring of similar mines.

, authors=Dongwei ZHANG¹, Yishan ZHANG¹, Shihui JIAO², Yong ZHAO², Wenxue DENG², Bin XUE², Xin WEN², authorsList=Dongwei ZHANG, Yishan ZHANG, Shihui JIAO, Yong ZHAO, Wenxue DENG, Bin XUE, Xin WEN, authorCompany=null, correspAuthors=Yong ZHAO, 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=1322559873745797255, articleId=1322559873187954821, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于LSTM-DCNN和迁移学习的矿山地下开采地表沉降预测方法, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

地下矿体开采过程中，采空区坍塌会破坏岩层的应力平衡，进而引发地质灾害并危及地表设施。为在矿山未开采阶段缺乏实际监测数据的情况下提高矿山地表沉降预测的精度，保障人员安全和矿区生产，提出了一种结合神经网络和迁移学习的矿山地下开采地表沉降预测方法，融合长短时记忆神经网络准确提取时间序列特征以及深度卷积神经网络提取数据的局部特征的优点，提高预测模型的准确性。根据中干沟钒钛磁铁矿的开采设计，利用预测模型开展不同开采进度下地表沉降预测。结果表明，随着出矿量的增加，各分段的均方误差均小于0.4，所提出的模型在不同开采进度下的地表沉降预测中表现出较高的准确性。该方法能够为类似矿山的地表沉降预测和安全监测提供参考。

, authors=张东炜¹, 张艺山¹, 焦诗卉², 赵永², 邓文学², 薛斌², 文鑫², authorsList=张东炜, 张艺山, 焦诗卉, 赵永, 邓文学, 薛斌, 文鑫, authorCompany=null, correspAuthors=赵永, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=LXy0NyOwzyUpk5M65nh/eA==, magXml=+smGyNbUJJ3rQBedCJ81lA==, pdfUrl=null, pdf=Uuav5uzkBPaBkJVExmu6WA==, pdfFileSize=3832013, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=wayRRnzN8eFN3rpw/iNLQw==, mapNumber=null, fund=null)}, authors=[Author(id=1322559874123284623, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559873187954821, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zdw450674870@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322559874211365009, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559873187954821, authorId=1322559874123284623, language=EN, stringName=Dongwei ZHANG, firstName=Dongwei, middleName=null, lastName=ZHANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.Hainan Mining Co., Ltd., Changjiang Li Autonomous County, Hainan 572700, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322559874286862482, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322559873187954821, authorId=1322559874123284623, language=CN, stringName=张东炜, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．海南矿业股份有限公司，海南 昌江黎族自治县　572700, bio={"content":"

张东炜（1985—），男，江西上饶人，硕士，工程师，主要从事金属矿山安全开采及岩石力学等方面的研究工作。E-mail:zdw450674870@163.com

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张东炜（1985—），男，江西上饶人，硕士，工程师，主要从事金属矿山安全开采及岩石力学等方面的研究工作。E-mail:zdw450674870@163.com

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