Bolvadin Subsidence Analysis with Multi-Temporal InSAR Technique and Sentinel-1 Data

M. Imamoglu, F. Kahraman, Z. Çakır, F. B. Sanli
{"title":"Bolvadin Subsidence Analysis with Multi-Temporal InSAR Technique and Sentinel-1 Data","authors":"M. Imamoglu, F. Kahraman, Z. Çakır, F. B. Sanli","doi":"10.1109/SIU49456.2020.9302459","DOIUrl":null,"url":null,"abstract":"Surface deformations in Bolvadin town without any devastating earthquakes have been observed in the last 10 years. In this study, ground deformation analysis of Bolvadin region was performed by Sentinel-1 synthetic aperture radar (SAR) data and multi-temporal SAR interferometry (InSAR) method. Sentinel-1 images obtained between October 2014 and October 2018 in ascending and descending orbits were processed with SNAP and StaMPS softwares. Deformation velocity maps and vertical displacement time series were produced and compared with geology and groundwater level of the region. Deformation velocity maps show significant subsidence in the region. The most severe subsidence, up to 35 mm/year, was found in the southern part of Bolvadin which is characterized by the presence of soft alluvial deposits. Both in long and short term, there was a strong correlation between the subsidence and the groundwater level. As a result, the high correlation of the vertical deformation velocity with lithological units and groundwater level indicates that subsidence in the region is probably due to the excessive use of groundwater.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 28th Signal Processing and Communications Applications Conference (SIU)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU49456.2020.9302459","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Surface deformations in Bolvadin town without any devastating earthquakes have been observed in the last 10 years. In this study, ground deformation analysis of Bolvadin region was performed by Sentinel-1 synthetic aperture radar (SAR) data and multi-temporal SAR interferometry (InSAR) method. Sentinel-1 images obtained between October 2014 and October 2018 in ascending and descending orbits were processed with SNAP and StaMPS softwares. Deformation velocity maps and vertical displacement time series were produced and compared with geology and groundwater level of the region. Deformation velocity maps show significant subsidence in the region. The most severe subsidence, up to 35 mm/year, was found in the southern part of Bolvadin which is characterized by the presence of soft alluvial deposits. Both in long and short term, there was a strong correlation between the subsidence and the groundwater level. As a result, the high correlation of the vertical deformation velocity with lithological units and groundwater level indicates that subsidence in the region is probably due to the excessive use of groundwater.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于多时相InSAR技术和Sentinel-1数据的Bolvadin沉降分析
Bolvadin镇的地表变形在过去的10年里没有任何破坏性的地震。本研究采用Sentinel-1合成孔径雷达(SAR)数据和多时相SAR干涉测量(InSAR)方法对Bolvadin地区进行地面变形分析。2014年10月至2018年10月在上升和下降轨道上获得的Sentinel-1图像使用SNAP和StaMPS软件进行处理。制作了变形速度图和垂直位移时间序列,并与该地区的地质和地下水位进行了对比。变形速度图显示该地区有明显的沉降。在Bolvadin南部发现了最严重的沉降,高达35毫米/年,其特征是存在软冲积矿床。在长期和短期内,沉降与地下水位之间都有很强的相关性。因此,垂直变形速度与岩性单元和地下水位的高度相关性表明,该地区的沉降可能是由于地下水的过度使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Skin Lesion Classification With Deep CNN Ensembles Design of a New System for Upper Extremity Movement Ability Assessment Stock Market Prediction with Stacked Autoencoder Based Feature Reduction Segmentation networks reinforced with attribute profiles for large scale land-cover map production Encoded Deep Features for Visual Place Recognition
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1