Article(id=1318999833969537572, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1318999663622079298, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2025.06.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740931200000, receivedDateStr=2025-03-03, revisedDate=1751299200000, revisedDateStr=2025-07-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1792403666250, onlineDateStr=2026-10-19, pubDate=1765296000000, pubDateStr=2025-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792403666250, onlineIssueDateStr=2026-10-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792403666250, creator=13701087609, updateTime=1792403666250, updator=13701087609, issue=Issue{id=1318999663622079298, tenantId=1146029695717560320, journalId=1146123302524792850, year='2025', volume='38', issue='6', pageStart='1', pageEnd='147', issueExtLink='null', onlineDate='null', pubDate='1765296000000', pubDateStr='2025-12-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792403625636, creator='13701087609', updateTime=1792733312669, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320382471334646693, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1318999663622079298, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320382471334646694, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1318999663622079298, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=55, endPage=62, ext={EN=ArticleExt(id=1318999834183447077, articleId=1318999833969537572, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Application of SBAS-InSAR and ARIMA Models in Monitoring Subway Line Settlement: A Case Study of Urumqi, columnId=1152669336394183038, journalTitle=Urban Rapid Rail Transit, columnName=Civil Engineering Technology, runingTitle=null, highlight=null, articleAbstract=

In order to effectively identify and prevent and control the safety hazards of ground settlement along the subway lines and surrounding areas, it is crucial to carry out systematic settlement monitoring. In this study, the spatial and temporal evolution characteristics of ground settlement in Urumqi metropolitan area, including cumulative settlement and settlement time series data, are obtained based on the small baseline set interferometry (SBAS-InSAR) technique. At the same time, the autoregressive integral sliding average (ARIMA) prediction model is combined to predict the deformation trend of the key subsidence areas, and an in-depth analysis is carried out for the typical subsidence sections along the subway line. The results of the study show that: Shayibak district, Shuimogou district, Urumqi county and the new urban area have obvious subsidence and subsidence funnel areas, and the annual subsidence in some areas reaches -31.4～-7.3 mm. the subway line 1 and 2 have obvious subsidence in some sections, and the area of subsidence area is large, which needs to be paid attention to the road section. The ARIMA model is used to predict the future 12-period data of the two settlement areas of D4 and D5 with high prediction accuracy, which can be used as a reference for later monitoring.

, authors=Wenbin DENG, Liuyang DENG, Shuaizhen CAO, authorsList=Wenbin DENG, Liuyang DENG, Shuaizhen CAO, authorCompany=null, correspAuthors=Liuyang DENG, 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=1318999834460271142, articleId=1318999833969537572, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=SBAS-InSAR与ARIMA模型在地铁沿线沉降监测中的应用——以乌鲁木齐为例, columnId=1152669336603898239, journalTitle=都市快轨交通, columnName=土建技术, runingTitle=null, highlight=null, articleAbstract=

为有效识别和防控地铁沿线及周边区域的地面沉降安全隐患，开展系统化的沉降监测工作至关重要。基于小基线集干涉测量（SBAS-InSAR）技术，获取乌鲁木齐市区地面沉降的时空演化特征，包括累积沉降量和沉降时间序列数据。同时，结合自回归积分滑动平均（ARIMA）预测模型，对重点沉降区域进行形变趋势预测，并针对地铁沿线典型沉降区段开展深入分析。研究结果表明：沙依巴克区、水磨沟区、乌鲁木齐县及新市区都有明显沉降，并有出现沉降漏斗区域，部分区域年沉降量达到7.3～31.4 mm。地铁1、2号线部分路段沉降明显，沉降区域面积大，需要重点关注这些路段。利用ARIMA模型对D4和D5两处沉降区域未来的12期进行预测且预测结果精度高，可为后期监测提供参考。

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