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Article(id=1149773882929935233, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149773869357167407, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2404292, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717862400000, receivedDateStr=2024-06-09, revisedDate=1736179200000, revisedDateStr=2025-01-07, acceptedDate=null, acceptedDateStr=null, onlineDate=1752057055455, onlineDateStr=2025-07-09, pubDate=1746633600000, pubDateStr=2025-05-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752057055455, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752057055455, creator=13701087609, updateTime=1752057055455, updator=13701087609, issue=Issue{id=1149773869357167407, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='13', pageStart='5273', pageEnd='5704', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752057052207, creator=13701087609, updateTime=1768456769392, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1218559268744253990, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149773869357167407, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1218559268744253991, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149773869357167407, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=5305, endPage=5315, ext={EN=ArticleExt(id=1149773883127067525, articleId=1149773882929935233, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Surface Deformation Monitoring and Analysis of Gansu Jishishan MS6.2 Earthquake Based on InSAR Technology, columnId=1156262729351549255, journalTitle=Science Technology and Engineering, columnName=Papers·Astronomy and Geosciences, runingTitle=null, highlight=null, articleAbstract=

On December 18, 2023, a MS6.2 earthquake occurred in Jishishan County, Gansu Province, marking the largest earthquake recorded in the area since seismic observations began. To investigate the practical application of interferometry synthetic aperture radar(InSAR) technology in monitoring surface deformation in Jishishan County and its surrounding areas before and after the earthquake, 13 C-band Sentinel-1A SAR images covering the earthquake event and study area were utilized. D-InSAR and SBAS-InSAR techniques were employed to process the data, obtaining surface deformation information along the radar line of sight before and after the earthquake, and a comparative analysis of the deformation rates before and after the event was conducted. The results show that the monitoring results of the two techniques show a bulge phenomenon centered in Liugou Township (the epicenter), which gradually expands outward in an elliptical shape, and then evolves into irregular subsidence. The maximum line-in-line shape variables before and after the earthquake are 41 mm and 16 mm, respectively. The overall deformation of the whole region after the earthquake is relatively stable, which is consistent with the relevant data. The D-InSAR and SBAS-InSAR technologies can be used to monitor the long-term surface deformation process and abnormal conditions before and after earthquakes, and provide effective support for quickly obtaining the distribution range of coseismic geological disasters and guiding post-earthquake emergency rescue and post-disaster recovery and reconstruction.

, correspAuthors=Guo-lin LIU, 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, authorCompany=null, fund=null, authors=null, authorsList=Yun-guang WEI, Guo-lin LIU, Feng-yun WANG, Hu-zhen HE), CN=ArticleExt(id=1149773913388970945, articleId=1149773882929935233, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于InSAR技术的甘肃积石山MS6.2级地震地表形变监测与分析, columnId=1156262730077163858, journalTitle=科学技术与工程, columnName=论文·天文学、地球科学, runingTitle=null, highlight=null, articleAbstract=

2023年12月18日,甘肃省积石山县发生了MS6.2级地震,是该区域自有地震观测记录以来最大的一次地震。为了研究合成孔径雷达干涉测量(interferometry synthetic aperture radar, InSAR)技术在地震前后对积石山县及周围区域地表形变的实际应用情况,通过利用13景涵盖地震发生时间和覆盖研究区的C波段Sentinel-1A SAR影像数据,运用D-InSAR与SBAS-InSAR技术进行处理,获取了地震前后沿雷达视线向的地表形变信息,并对震前和震后的形变速率结果进行了对比分析。研究结果表明,两种技术的监测结果均以柳沟乡(震中)为中心呈现出隆起现象,并以椭圆状向外逐渐扩展,后又演化为不规则沉降,且地震前后的最大视线向形变量分别为41 mm和16 mm,整个区域震后整体形变情况较为稳定,与相关资料相符合。利用D-InSAR和SBAS-InSAR技术能够监测地震前后长时间的地表形变过程和异常情况,为快速获取同震地质灾害的分布范围,指导震后应急抢险和灾后恢复重建提供有效支撑。

, correspAuthors=刘国林, authorNote=null, correspAuthorsNote=
* 刘国林(1966—),男,汉族,河南焦作人,博士,教授。研究方向:现代测量数据处理。E-mail:。
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魏云光(1998—),男,汉族,山东临沂人,硕士研究生。研究方向:InSAR数据处理及应用。E-mail:。

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魏云光(1998—),男,汉族,山东临沂人,硕士研究生。研究方向:InSAR数据处理及应用。E-mail:。

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魏云光(1998—),男,汉族,山东临沂人,硕士研究生。研究方向:InSAR数据处理及应用。E-mail:。

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Mechanisms for the source of the MS6.2 Jishishan earthquake as published by different institutions

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研究
来源		 震中位置 深度/
km		 走向/
(°)		 倾角/
(°)		 滑动
角/(°)		 震级
MW
USGS 102.82°E, 35.74°N 10 333 62 88 5.9
GCMT 102.81°E, 35.83°N 18.9 303 52 62 6.1
CENC 102.79°E, 35.70°N 10 300 45 50 5.9
), ArticleFig(id=1175114022845300739, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773882929935233, language=CN, label=表1, caption=

不同机构公布的积石山MS6.2级地震震源机制

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研究
来源		 震中位置 深度/
km		 走向/
(°)		 倾角/
(°)		 滑动
角/(°)		 震级
MW
USGS 102.82°E, 35.74°N 10 333 62 88 5.9
GCMT 102.81°E, 35.83°N 18.9 303 52 62 6.1
CENC 102.79°E, 35.70°N 10 300 45 50 5.9
), ArticleFig(id=1175114022933381124, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773882929935233, language=EN, label=Table 2, caption=

Information of Sentinel-1A SAR images

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序号 轨道号 日期(年-月-日) 累计时间/d
1 050607 2023-10-03 0
2 050782 2023-10-15 12
3 050957 2023-10-27 24
4 051132 2023-11-08 36
5 051307 2023-11-20 48
6 051482 2023-12-02 60
7 051657 2023-12-14 72
8 051832 2023-12-26 84
9 052007 2024-01-07 96
10 052182 2024-01-19 108
11 052357 2024-01-31 120
12 052532 2024-02-12 132
13 052707 2024-02-24 144
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Sentinel-1A SAR影像信息

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序号 轨道号 日期(年-月-日) 累计时间/d
1 050607 2023-10-03 0
2 050782 2023-10-15 12
3 050957 2023-10-27 24
4 051132 2023-11-08 36
5 051307 2023-11-20 48
6 051482 2023-12-02 60
7 051657 2023-12-14 72
8 051832 2023-12-26 84
9 052007 2024-01-07 96
10 052182 2024-01-19 108
11 052357 2024-01-31 120
12 052532 2024-02-12 132
13 052707 2024-02-24 144
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Deformation values for each period on the 12 feature points

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监测时段 各时段形变量/mm
D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12
(a)10月3—15日 24 19 19 15 32 23 25 15 29 23 31 -4
(b)10月15—27日 7 3 12 6 8 6 -3 -8 -5 -2 -6 -9
(c)10月27日—11月8日 -58 -58 -61 -53 -62 -39 -28 22 59 32 11 6
(d)11月8—20日 44 39 47 46 52 51 15 -38 -76 -41 -4 -22
(e)11月20日—12月2日 -6 -4 -6 -7 -6 -7 4 6 -6 -6 -4 -1
(f)12月2—14日 -7 -8 -8 -10 -8 -9 6 5 -4 0 4 25
(g)12月14—26日 41 32 9 8 10 -13 -5 19 41 35 36 -4
(h)12月26日—次年1月7日 4 3 2 5 4 3 0 -2 -1 0 0 -3
(i)1月7—19日 -8 -6 -8 -12 -16 -13 1 -1 -7 -7 -6 11
(j)1月19—31日 3 3 4 5 5 4 6 7 4 4 5 -3
(k)1月31日—2月12日 12 10 12 9 14 14 -4 -1 6 9 5 -5
(l)2月12—24日 -21 -14 -16 -15 -19 -22 4 3 -14 -16 -6 24
), ArticleFig(id=1175114023143096327, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773882929935233, language=CN, label=表3, caption=

12个特征点的各时段形变量

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监测时段 各时段形变量/mm
D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12
(a)10月3—15日 24 19 19 15 32 23 25 15 29 23 31 -4
(b)10月15—27日 7 3 12 6 8 6 -3 -8 -5 -2 -6 -9
(c)10月27日—11月8日 -58 -58 -61 -53 -62 -39 -28 22 59 32 11 6
(d)11月8—20日 44 39 47 46 52 51 15 -38 -76 -41 -4 -22
(e)11月20日—12月2日 -6 -4 -6 -7 -6 -7 4 6 -6 -6 -4 -1
(f)12月2—14日 -7 -8 -8 -10 -8 -9 6 5 -4 0 4 25
(g)12月14—26日 41 32 9 8 10 -13 -5 19 41 35 36 -4
(h)12月26日—次年1月7日 4 3 2 5 4 3 0 -2 -1 0 0 -3
(i)1月7—19日 -8 -6 -8 -12 -16 -13 1 -1 -7 -7 -6 11
(j)1月19—31日 3 3 4 5 5 4 6 7 4 4 5 -3
(k)1月31日—2月12日 12 10 12 9 14 14 -4 -1 6 9 5 -5
(l)2月12—24日 -21 -14 -16 -15 -19 -22 4 3 -14 -16 -6 24
), ArticleFig(id=1175114023214399496, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773882929935233, language=EN, label=Table 4, caption=

The cumulative deformation values on the 12 feature points

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监测时段 累计形变量/mm
D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12
(a)10月3—15日 3 0 0 0 1 -6 -5 -9 -2 -3 1 6
(b)10月3—27日 -1 -6 -1 -2 -2 -8 -17 -16 1 -1 3 9
(c)10月3日—11月8日 -5 -11 -2 -3 -4 -10 -20 -20 4 -1 1 4
(d)10月3日—11月20日 1 -3 -2 -3 -2 -10 -30 -38 -1 -1 2 17
(e)10月3日—12月2日 8 5 -1 -1 2 -11 -26 -44 -3 -4 3 21
(f)10月3日—12月14日 18 12 0 2 2 -13 -26 -43 -3 -3 3 23
(g)10月3日—12月26日 34 22 3 12 11 -16 -32 -46 -2 -3 2 5
(h)10月3日—次年1月7日 40 26 4 15 13 -16 -29 -40 0 -1 1 3
(i)10月3日—次年1月19日 39 26 5 15 12 -16 -19 -34 2 -2 1 6
(j)10月3日—次年1月31日 41 27 6 14 11 -18 -26 -41 2 -1 2 6
(k)10月3日—次年2月12日 42 29 6 12 11 -19 -30 -46 0 -1 2 9
(l)10月3日—次年2月24日 43 33 7 14 8 -20 -20 -38 1 -5 1 14
), ArticleFig(id=1175114023306674185, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149773882929935233, language=CN, label=表4, caption=

12个特征点的累计形变量

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监测时段 累计形变量/mm
D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12
(a)10月3—15日 3 0 0 0 1 -6 -5 -9 -2 -3 1 6
(b)10月3—27日 -1 -6 -1 -2 -2 -8 -17 -16 1 -1 3 9
(c)10月3日—11月8日 -5 -11 -2 -3 -4 -10 -20 -20 4 -1 1 4
(d)10月3日—11月20日 1 -3 -2 -3 -2 -10 -30 -38 -1 -1 2 17
(e)10月3日—12月2日 8 5 -1 -1 2 -11 -26 -44 -3 -4 3 21
(f)10月3日—12月14日 18 12 0 2 2 -13 -26 -43 -3 -3 3 23
(g)10月3日—12月26日 34 22 3 12 11 -16 -32 -46 -2 -3 2 5
(h)10月3日—次年1月7日 40 26 4 15 13 -16 -29 -40 0 -1 1 3
(i)10月3日—次年1月19日 39 26 5 15 12 -16 -19 -34 2 -2 1 6
(j)10月3日—次年1月31日 41 27 6 14 11 -18 -26 -41 2 -1 2 6
(k)10月3日—次年2月12日 42 29 6 12 11 -19 -30 -46 0 -1 2 9
(l)10月3日—次年2月24日 43 33 7 14 8 -20 -20 -38 1 -5 1 14
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基于InSAR技术的甘肃积石山MS6.2级地震地表形变监测与分析
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魏云光 , 刘国林 * , 王凤云 , 何虎振
科学技术与工程 | 论文·天文学、地球科学 2025,25(13): 5305-5315
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科学技术与工程 | 论文·天文学、地球科学 2025, 25(13): 5305-5315
基于InSAR技术的甘肃积石山MS6.2级地震地表形变监测与分析
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魏云光 , 刘国林* , 王凤云, 何虎振
作者信息
  • 山东科技大学测绘与空间信息学院, 青岛 266590

    • 魏云光(1998—),男,汉族,山东临沂人,硕士研究生。研究方向:InSAR数据处理及应用。E-mail:。

通讯作者:

* 刘国林(1966—),男,汉族,河南焦作人,博士,教授。研究方向:现代测量数据处理。E-mail:。
Surface Deformation Monitoring and Analysis of Gansu Jishishan MS6.2 Earthquake Based on InSAR Technology
Yun-guang WEI , Guo-lin LIU* , Feng-yun WANG, Hu-zhen HE
Affiliations
  • College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
出版时间: 2025-05-08 doi: 10.12404/j.issn.1671-1815.2404292
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摘要
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2023年12月18日,甘肃省积石山县发生了MS6.2级地震,是该区域自有地震观测记录以来最大的一次地震。为了研究合成孔径雷达干涉测量(interferometry synthetic aperture radar, InSAR)技术在地震前后对积石山县及周围区域地表形变的实际应用情况,通过利用13景涵盖地震发生时间和覆盖研究区的C波段Sentinel-1A SAR影像数据,运用D-InSAR与SBAS-InSAR技术进行处理,获取了地震前后沿雷达视线向的地表形变信息,并对震前和震后的形变速率结果进行了对比分析。研究结果表明,两种技术的监测结果均以柳沟乡(震中)为中心呈现出隆起现象,并以椭圆状向外逐渐扩展,后又演化为不规则沉降,且地震前后的最大视线向形变量分别为41 mm和16 mm,整个区域震后整体形变情况较为稳定,与相关资料相符合。利用D-InSAR和SBAS-InSAR技术能够监测地震前后长时间的地表形变过程和异常情况,为快速获取同震地质灾害的分布范围,指导震后应急抢险和灾后恢复重建提供有效支撑。

D-InSAR  /  SBAS-InSAR  /  积石山地震  /  地表形变

On December 18, 2023, a MS6.2 earthquake occurred in Jishishan County, Gansu Province, marking the largest earthquake recorded in the area since seismic observations began. To investigate the practical application of interferometry synthetic aperture radar(InSAR) technology in monitoring surface deformation in Jishishan County and its surrounding areas before and after the earthquake, 13 C-band Sentinel-1A SAR images covering the earthquake event and study area were utilized. D-InSAR and SBAS-InSAR techniques were employed to process the data, obtaining surface deformation information along the radar line of sight before and after the earthquake, and a comparative analysis of the deformation rates before and after the event was conducted. The results show that the monitoring results of the two techniques show a bulge phenomenon centered in Liugou Township (the epicenter), which gradually expands outward in an elliptical shape, and then evolves into irregular subsidence. The maximum line-in-line shape variables before and after the earthquake are 41 mm and 16 mm, respectively. The overall deformation of the whole region after the earthquake is relatively stable, which is consistent with the relevant data. The D-InSAR and SBAS-InSAR technologies can be used to monitor the long-term surface deformation process and abnormal conditions before and after earthquakes, and provide effective support for quickly obtaining the distribution range of coseismic geological disasters and guiding post-earthquake emergency rescue and post-disaster recovery and reconstruction.

D-InSAR  /  SBAS-InSAR  /  Jishishan earthquake  /  surface deformation
魏云光, 刘国林, 王凤云, 何虎振. 基于InSAR技术的甘肃积石山MS6.2级地震地表形变监测与分析. 科学技术与工程, 2025 , 25 (13) : 5305 -5315 . DOI: 10.12404/j.issn.1671-1815.2404292
Yun-guang WEI, Guo-lin LIU, Feng-yun WANG, Hu-zhen HE. Surface Deformation Monitoring and Analysis of Gansu Jishishan MS6.2 Earthquake Based on InSAR Technology[J]. Science Technology and Engineering, 2025 , 25 (13) : 5305 -5315 . DOI: 10.12404/j.issn.1671-1815.2404292
正文
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2023年12月18日23∶59,在甘肃省积石山县(35.70°N, 102.79°E)发生了MS6.2级地震,其震源深度为10 km。由于这次地震发生在夜间,大量居民来不及防范,地震造成共计151人遇难,部分区域内出现了建筑物倒塌和电力通信损坏的情况,是近年来最严重的一次地震[1-3]。中国地质灾害发生频繁且多发生于高密度植被覆盖的山区[4],传统的监测方式会受到地形条件限制,而合成孔径雷达干涉测量(interferometry synthetic aperture radar, InSAR)技术具有全天候、覆盖广、精度高、成本低的优势,能够获取连续的地表形变信息,准确地分析地表沉降、山体滑坡、断层位移和地震等灾害情况[5-7]。
自Gabriel等[8]在1989年首次成功利用差分干涉测量(differential InSAR, D-InSAR)技术获取了加利福尼亚州东南部灌溉区农田的厘米级形变信息后,InSAR技术便在地表形变监测中得到了大范围应用。2002年,Berardino等[9]提出短基线差分干涉测量(small baseline subset InSAR, SBAS-InSAR)技术,将时序InSAR技术提高了一个新的高度。2020年,Valerio等[10]基于D-InSAR技术获取了东阿塞拜疆地震的形变结果,对得到的D-InSAR同震形变场采用了分析建模两步法,以更好地确定主震源的运动学特征。2024年,Filipiak等[11]利用D-InSAR和SBAS-InSAR技术对上西里西亚煤田进行地表形变的评估,结果表明,两种方法均在一定程度上适用于该区域的监测,相比之下,D-InSAR更擅长捕获大梯度的变形,而SBAS-InSAR技术更适合低速率形变监测。
随着InSAR技术在地表形变方面的广泛应用,学者们将其引入地震地表形变监测。2023年,单新建等[12]通过D-InSAR技术获取了四川泸定地震的同震形变场,表明了泸定地震的震源机制为左旋走滑,进一步实验并得出安宁河断裂北端是未来需要重点关注的结论。同年,屈春燕等[13]收集了2015—2020年的SAR影像数据,基于时序InSAR技术分析得出昆仑山地震震后形变加载的高应变积累可能促进了玛多地震的发生。2024年,白希选等[14]为了深入研究泸定地震的形变及构造,以InSAR同震形变场为约束,基于弹性半空间位错模型,反演断层几何参数和滑动分布,进一步获取了震中区域库伦应力变化,对分析区域危险性具有重要意义。由此可见,InSAR技术对理解地震机制和防灾减灾具有重要的意义[15-17]。
由于地震等地质灾害具有时效性,及时地获取震区的形变信息就显得尤为重要,为了论证D-InSAR与SBAS-InSAR技术在识别震区破坏程度以及辅助震后救灾工作及时开展的可行性,现分别利用两种InSAR技术对从欧空局获取的13景Sentinel-1A SAR影像数
据进行处理,获取了2023年10月—2024年2月甘肃积石山县及其周边地震区域沿雷达视线向(line of sight, LOS)的地表形变信息,进一步筛选并提取形变区域内高相干特征点的地表形变值,对震区进行稳定性分析,研究InSAR技术在地震地表形变监测中的应用。
1 研究区及数据源
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1.1 研究区概况
甘肃省积石山地震震中(柳沟乡)地处青藏高原北缘,地势西南高、东北低,距离最近的拉脊山北缘断裂约8 km,其断裂主要由左旋走滑断层组成,局部有逆冲断层,并伴有褶皱、冲断层和正断层等构造形迹。而此次发震断裂为拉脊山北缘断裂东段,为一条走向NNW、倾向SWW的挤压逆冲型断层,断层两侧地貌高差显著。虽然积石山县MS6.2级地震的强度只达到了中等,但其破坏力不低于高等级的地震冲击力,且此次地震能量释放的过程较为缓慢,导致地震持续时间较长,余震较多。根据甘肃省地震局公布(https://www.gsdzj.gov.cn/)的信息,截至2023年12月21日,共记录到余震512次,其中3.0级及以上余震11次,最大余震为4.1级。不同机构公布的发震断层参数如表1所示,积石山MS6.2级地震研究区如图1所示。
1.2 实验数据
欧空局(European Space Agency, ESA)于2014年4月发射Sentinel-1卫星,运行轨道的高度为693 km,因其具有高重访率、高覆盖能力、高时效性而被广泛关注,使用覆盖积石山地震研究区域的2023年10月3日—2024年2月24日期间的13景Sentinel-1A干涉宽幅(interferometric wide swath, IW),垂直极化(V-vertical, VV)模式的SAR影像降轨数据,其搭载C波段合成孔径雷达,空间分辨率为5 m×20 m(距离向×方位向),重访周期为12 d。引入对应时间的精密轨道数据,并采用来自地理空间数据云90 m分辨率数字高程模型(digital elevation model,DEM)数据作为地形参考数据进行计算,以消除地形相位影响。表2为SAR影像的基本信息。
2 原理与方法
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2.1 D-InSAR技术原理
D-InSAR是在InSAR的基础上演变而来,理论上干涉相位Δφ主要包括平地相位φflat、地形相位φtopo、形变相位φdef、大气相位φatm和噪声相位φnoise,即
Δφ=φflat+φtopo+φdef+φatm+φnoise
φdef=- 4 π d λ
式中:λ为雷达波长;d为雷达视线向(LOS)形变。
根据干涉模式,D-InSAR技术可分为二轨、三轨和四轨差分干涉测量,其中二轨法应用最为广泛。主要技术要点和流程如下。
(1)影像配准:为了减少入射角差异带来的随机误差,将同一研究区的不同时段的两景影像进行配准。
(2)生成干涉图:将对应像元作复共轭相乘获取差分干涉图,得到干涉相位。干涉相位是主、辅影像对同一地面点距离差的相位反映。
(3)地形相位消除:干涉相位滤波后,借助外部90 m DEM数据消除地形影响,获取形变相位。
(4)相位解缠:利用最小费用流法(minimum cost flow, MCF)对形变缠绕相位进行解缠,解算出连续的相位场。
(5)地理编码:以上处理基于SAR系统坐标系完成,最终需要转换到地理坐标系中。
主要数据处理流程如图2所示。
2.2 SBAS-InSAR技术原理
在研究区获取N+1景SAR影像,其时间分别为t0,t1,…,tn,选取其中一景影像作为主影像,假设每景SAR影像都能与其他至少一景影像进行干涉,可生成M景差分干涉图,M满足关系式为
N + 1 2≤M≤ N ( N + 1 ) 2
利用在tA和tB时刻(tA<tB)获取的两景SAR影像,生成去除地形相位的第f个干涉对,则像元(r,x)的差分干涉相位为
δφf(x,r)=φ(tB,r,x)-φ(tA,r,x)≈ 4 π λ[d(tB,r,x)-d(tA,r,x)]
式(4)中:φ为干涉相位;f为影像编号;λ为信号的中心波长;d(tA,r,x)、d(tB,r,x)分别为tA、tB两个时刻相对于t0的LOS向累计形变量,d(t0,r,x)≡0,则表达式为
φ(tA,r,x)= 4 π λ(tA,r,x)
令IE=[IE1,IE2,…,IEM]为生成干涉图的主影像的时间序列,IS=[IS1,IS2,…,ISM]为辅影像的时间序列,且满足
IEf>ISf, f=1,2,…,M
则差分干涉图相位为
δφf=φ( t I E f)-φ( t I S f)
用矩阵形式表示为
Aφ=δφ
式(8)中:系数矩阵A为M×N维矩阵,当SAR影像属于同一个小基线子集时,M≥N,则矩阵A的秩为N+1。
利用奇异值分解法求其最小二乘最小范数解,获取地表累计形变量和平均形变速率值。
SBAS-InSAR方法的技术要点及主要流程为如下。
(1)影像排序和配准:首先将时间和空间基线阈值分别设置为36 d和276 m,按照时间对13景影像进行排序,然后进行配准。
(2)主影像和干涉对形成:选取2023年11月8日的SAR影像作为超级主影像,其余12景影像作为从影像,进行两两自由配对,形成33个干涉对。
(3)差分干涉处理:结合外部90 m DEM数据进行差分干涉处理,得到差分干涉图,其包含了地表形变信息和干涉噪声,选用Goldstein滤波方法进行相位滤波来减少噪声。
(4)相位解缠:差分干涉图通常包含了相位不连续性,需要利用MCF法进行解缠以获取准确的形变信息,以便获得一致的相位信息。
(5)形变参数估计:获得了解缠后的相位信息,使用奇异值分解法估计地面形变的参数。这些参数包括LOS向的平均形变速率和累计形变量。主要数据处理流程如图3所示。
3 实验结果分析
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3.1 D-InSAR沉降结果分析
利用D-InSAR技术获取的LOS向地表形变信息,筛选出明显形变趋势的12个高相干点(D1~D12)作为特征点,提取其形变信息并进行具体分析。其中,将地震区域大致分为4个震区:A震区(D1~D6)、B震区(D7~D8)、C震区(D9~D10)、D震区(D11~D12)。高相干点分别位于甘肃省的柳沟乡(震中)、刘集乡、关家川乡、石塬镇、大河家镇,青海省的官亭镇、杏儿藏族乡、塔加藏族乡、积石镇、清水乡、白庄镇及太子山天然林保护区。图4标明了选取的高相干点位置分布及震区分布情况。
3.1.1 D-InSAR沉降结果的定性对比分析
为了研究D-InSAR技术在地震形变监测中的实际应用情况,对13景SAR影像数据进行了差分干涉处理,获取了研究区各时段的LOS向形变信息及累计形变信息。
为了叙述方便,将2023年10月3日—2024年2月24日的影像获取区间划分为7个时间段,即a(2023年10月3—15日)、b(2023年10月15—27日)、c(2023年10月27日—11月8日)、d(2023年11月8—20日)、e(2023年11月20日—12月2日)、f(2023年12月2—14日)、g(2023年12月14—26日)。
图5为D-InSAR获取的各时段形变情况,图6为累计形变情况。
根据图5,A、B、C震区地表地貌多样,山地、丘陵、河谷交相辉映,均位于居民活跃范围,因此监测初期,a、b两个时段,地表存在大面积抬升,大部分区域抬升量在30 mm以内,基本趋于稳定;c时段,A震区沉降明显,C震区则出现明显抬升;d时段与c时段相反,A震区出现明显抬升,C震区则出现明显沉降,其中,地表最大沉降点位于D9,沉降值达-76 mm(表3),B、D震区相对稳定,最大沉降点位于D8,沉降值达-38 mm(表3);e、f两个时段,B、D两震区开始出现大面积小幅度抬升,A、C两震区则出现大面积小幅度下沉,沉降量在-30~0 mm,其中,在e时段研究区沉降面积达2 801 km2(约占总研究区域的68.91%),在f时段研究区抬升面积达2 168 km2(约占总研究区域的53.34%);g时段,于2023年12月18日23:59在积石山县发生地震,震后呈现出以柳沟乡(D1震中)为中心的大幅抬升,持续出现隆起现象,并以椭圆状逐步向四周扩散,后演化为阶梯式逐渐下沉的情况,监测区域内抬升量在30~70 mm。由图6(g)可以看出,A震区震中附近累计抬升量达70~160 mm,D震区少部分区域累计沉降量达-130~-70 mm。
综上所述,由D-InSAR技术所监测到的研究区LOS向地表形变信息可知:D-InSAR技术能够监测到震中柳沟乡(D1)是累计抬升量最大的区域;在地震发生的前两个月曾出现较大的地表形变差异,A、C两震区曾表现出先大幅度沉降和大幅度抬升后,又演化为大幅度抬升和大幅度沉降的变化情况。
3.1.2 D-InSAR沉降结果的定量对比分析
为了直观地分析此次地震LOS向形变情况及其造成的影响,提取高相干特征点的形变数据进行对比与分析,12个特征点的各时段形变量信息如表3所示。
分析表3可知,12个特征点在a、b两个时段,地表形变变化较小,范围在-9~32 mm,最大形变点位于D5;在c时段,D1-D7均发生较大沉降,下沉范围达-28~-62 mm,D8-D12均表现出抬升现象,抬升范围达6~59 mm;在d时段,则出现相反情况,D1-D7均发生抬升现象,抬升范围达15~52 mm,D8-D12出现沉降现象,沉降范围达-4~-76 mm;在e时段,D1~D6和D9~D12出现沉降现象,D7、D8出现抬升现象;在f时段,D1-D6和D9出现沉降现象,D7、D8、D11、D12出现抬升现象;在g时段,地震发生于12月18日,A震区最大抬升量为41 mm(位于D1),B震区抬升量最大值为19 mm(位于D8);C震区村庄密集且紧临黄河流域,故受影响较大,最大抬升量为41 mm(位于D9),D震区地处黄河谷南侧山谷地带,抬升量较大,最大抬升量达到36 mm(位于D11)。
3.2 SBAS-InSAR沉降结果分析
利用SBAS-InSAR技术对同一研究区进行地表形变监测,得到了2023年10月—2024年2月期间的LOS向地表形变信息。图7给出了SBAS-InSAR获取的LOS向地表累计形变信息,表4为12个高相干特征点的LOS向累计形变数值。
由图7和表4可以看出,在监测时段,A震区的震中附近地表随着时间而逐渐显现出椭圆状的大面积抬升,而后沿四周开始逐渐发生阶梯式沉降,由地表累计形变量可以看出,震中柳沟乡(D1)出现大面积抬升,最大LOS向累计抬升量达34 mm,这表明SBAS-InSAR技术所解译到的地表沉降中心,与官方发布的震中比较吻合。B震区特征点D7、D8随时间增加出现-32 mm和-46 mm的累计沉降,由应急管理部中国地震局发布的甘肃积石山6.2级地震烈度图可知,B震区的地震烈度达到Ⅷ度,由此造成较多土木、砖木房屋破损,部分没有设防砖混的房屋遭到严重破坏,甚至部分框架房屋同样受到损害;C震区由于贯穿黄河流域,且河道两旁生活着积石镇、清水乡两地居民,可明显看出沿河道两侧出现0~30 mm的累计抬升量;D震区地处山谷地带,同样表现出0~30 mm范围内的较大面积的累计抬升。
3.3 D-InSAR与SBAS-InSAR技术结果的综合分析
3.3.1 两种InSAR技术结果的定性分析
为了能够对比分析两种InSAR技术在震区沿LOS向的形变监测情况,由D-InSAR及SBAS-InSAR技术分别处理得到的甘肃积石山地震研究区的LOS向地表年平均形变速率如图8所示。
由图8可知,研究区均表现出震中大幅抬升,D-InSAR与SBAS-InSAR所监测到的A震区抬升面积占比分别为97.26%和53.25%,震中柳沟乡(D1)年平均抬升速率分别为160 mm/a和143 mm/a,其值大致相同,但两种InSAR技术监测到的特征点D2~D6的数值却略有差距,分别为140、178、60、149、104 mm/a和109、22、51、60、-39 mm/a。在图8(a)中,D震区太子山(D12)附近出现-120~-313 mm/a的形变数值变化。在图8(b)中,A震区以柳沟乡(D1)为震中心的抬升量以椭圆状逐渐向四周发生阶梯式递减,B震区沉降较为严重,年平均沉降速率范围达-70~-120 mm/a,这与D-InSAR技术所监测到的c时段D7、D8特征点的累计沉降量分别达到-53 mm、-71 mm的大幅度沉降情况相一致。在图8(a)和图8(b)中,C震区黄河流域两侧年平均抬升速率分别为30~70 mm/a和0~30 mm/a,同时从表3和表4的震后的数据分析来看,震后的抬升和沉降趋势总体平稳,形变数据无急剧变化情况。
3.3.2 两种InSAR技术结果的定量分析
为了进一步对比分析两种技术方法的监测能力,绘制12个高相干特征点在整个时间跨度中的日平均形变速率折线图,如图9所示。
从图9可以出,D-InSAR技术所获得的特征点的形变速率变化较为明显,而SBAS-InSAR技术所获得的特征点的形变速率曲线较为光滑,趋势也更为稳定。从形变速率曲线的趋势和形变结果来看,对于特征点D1~D5及D9~D11,D-InSAR与SBAS-InSAR两种技术均在地震时刻监测到了抬升现象,对于特征点D6、D7、D12,则均监测到了沉降现象。虽然两种技术的结果在数值上有微小差异,但总体上具有较强的一致性,形变区域大致重合。值得注意的是,与SBAS-InSAR技术相比,D-InASR技术对形变变化更为敏感,在2023年10月27日—11月20日地震前期,曾监测出大幅度沉降,后又出现抬升的情况。
4 结论
收起
基于13景Sentinel-1A SAR获得的影像数据,采用D-InSAR和SBAS-InSAR技术获取了甘肃积石山地震研究区的LOS向地表形变特征,初步分析了时空演变规律并进行了探讨,实验结论如下。
(1)D-InSAR和SBAS-InSAR技术监测到的震中(积石山县柳沟乡)均为以抬升为主,且与官方公布的震中位置一致。由D-InSAR和SBAS-InSAR技术监测到的特征点的最大LOS向地表形变分别为41 mm和16 mm,这与现有相关资料的形变数据值相似,其中D-InSAR技术的形变结果更接近相关数据[18-19]。
(2)D-InSAR和SBAS-InSAR技术监测到的A震区(震中)特征表现为抬升面积略大于沉降面积,且两种InSAR技术监测到的形变位置吻合,形变趋势基本一致,范围具有较高的相关性,均能有效监测地表形变变化。同时,两种技术在B震区获取的形变速率分别达-120~-313 mm/a和-120~-362 mm/a,表明B震区是除震中范围外发生形变现象最为严重的区域。
(3)此次积石山县发生的地震为逆冲型地震,形变场呈椭圆形,长轴沿西北-东南走向,与USGS、GCMT等机构公布的震源参数走向基本一致,与拉脊山断裂走向基本平行,该地震特点是震动幅度较大,破坏能量较强,可导致严重的地质灾害如山体滑坡、泥石流等。
基于D-InSAR与SABS-InSAR技术对震区的地表进行形变分析,可为后续制定有效的地震灾害应对策略及震后恢复工作具有重要意义。
基金
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  • 国家自然科学基金(42074009)
  • 山东省自然科学基金(ZR2020MD043)
  • 山东省自然科学基金(ZR2020MD044)
文献
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doi: 10.12404/j.issn.1671-1815.2404292
  • 接收时间：2024-06-09
  • 首发时间：2025-07-09
  • 出版时间：2025-05-08
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  • 收稿日期：2024-06-09
  • 修回日期：2025-01-07
基金
国家自然科学基金(42074009)
山东省自然科学基金(ZR2020MD043)
山东省自然科学基金(ZR2020MD044)
作者信息
    山东科技大学测绘与空间信息学院, 青岛 266590

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* 刘国林(1966—),男,汉族,河南焦作人,博士,教授。研究方向:现代测量数据处理。E-mail:。
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2种不同金属材料的力学参数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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