Article(id=1322559879538127252, 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=1737475200000, receivedDateStr=2025-01-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252447255, onlineDateStr=2026-10-29, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252447255, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252447255, creator=13701087609, updateTime=1793252447255, 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=167, endPage=174, ext={EN=ArticleExt(id=1322559879718482325, articleId=1322559879538127252, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on Source Identification and Migration Law of Slag-Type Debris Flow Based on Deep Learning, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the limitations of the current debris flow source detection algorithm, a YOLOv5s-GCE model was proposed. This model introduces CBAM attention mechanism, Ghost convolution and EIOU bounding box loss function to improve its identification accuracy and efficiency. In order to verify the detection effect of YOLOv5s-GCE model, it was compared with YOLOv4 and YOLOv5s models. The results show that the average accuracy of the YOLOv5s-GCE model is 95.4%, which is 2.5 and 8.6 percentage points higher than that of the YOLOv4 and YOLOv5s models, respectively. The detection accuracy of YOLOv5s-GCE model for debris flow source is as high as 96%, and the detection effect is better. The particle flow numerical simulation method was used to study the migration law of debris flow. The simulation results show that the front end of the source body is destroyed and migrated in the initial stage, and the infiltration of water particles leads to the increase of porosity and structural instability of the source body. The mid-section of the source body is subjected to compression from the rear end and restraint from the front end, leading to a significant reduction in porosity, structural densification and the occurrence of localized bulging. In the later stage of migration, a large number of material sources accumulated at the gully mouth. The threat of debris flow to downstream construction facilities is significant, and the soil on the left side of the mid-section of the source area is the most threatening in extreme weather. The research results are of great significance to the prevention and control of slag-type debris flow.

, authors=Lei ZHANG^{1, 2, 3}, Ao ZHENG¹, Shangkai HAO⁴, Jiayuan WANG¹, Qianru YAN¹, Zhipeng SUN¹, authorsList=Lei ZHANG, Ao ZHENG, Shangkai HAO, Jiayuan WANG, Qianru YAN, Zhipeng SUN, authorCompany=null, correspAuthors=Ao ZHENG, 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=1322559879953363350, articleId=1322559879538127252, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于深度学习的矿渣型泥石流物源识别及运移规律研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

针对当前泥石流物源检测算法的局限性，提出了YOLOv5s-GCE模型，该模型引入了CBAM注意力机制、Ghost卷积和EIOU边界框损失函数，以提高其识别精度和效率。为验证YOLOv5s-GCE模型的检测效果，将其与YOLOv4、YOLOv5s模型进行对比分析。结果表明：YOLOv5s-GCE模型的平均精度均值为95.4%，与YOLOv4、YOLOv5s模型相比，分别提升了2.5和8.6个百分点；YOLOv5s-GCE模型对泥石流物源的检测准确率高达96%，检测效果更优。采用颗粒流数值模拟方法对泥石流的运移规律进行研究，模拟结果表明，初始阶段物源体前端破坏并发生运移，水颗粒渗透导致物源体孔隙率增大、结构失稳；物源体中端受到后端挤压与前端约束，孔隙率显著降低、结构致密化且局部出现隆起现象；运移后期大量物源堆积在沟口。泥石流对下游建筑设施的威胁显著，极端天气下物源区中端左侧土体的威胁程度最高。研究结果对矿渣型泥石流的防治工作具有重要意义。

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张磊（1984—），男，山西大同人，博士，教授，研究方向为智能采矿。E-mail:dtblack84@163.com

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