Detecting, Monitoring, and Analyzing the Surface Subsidence in the Yellow River Delta (China) Combined with CenterNet Network and SBAS-InSAR

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-05-24 DOI:10.1155/2022/2672876

Zhenjin Li, Zhiyong Wang, Wei Liu, Xing Li, Maotong Zhou, Baojing Zhang

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引用次数: 0

Abstract

Long-term industrial activities tend to cause surface subsidence and damage to ground facilities and local ecological environment. Monitoring and analyzing surface subsidence is of great significance to prevent potential disasters. The surface type of the Yellow River Delta in China is complex and there are many industrial activities, so it is necessary to monitor the surface subsidence in this area. Small Baseline Subset InSAR (SBAS-InSAR) can monitor the surface subsidence with millimeter-level accuracy, but it takes a long time to process wide images (Sentinel-1) and is seriously affected by atmospheric errors. To avoid these limitations, we constructed a method combining the CenterNet network and SBAS-InSAR (CNSBAS-InSAR). Firstly, the CenterNet network is used to automatically detect the subsidence areas from the wide differential interferogram formed by two SAR satellite images and determine the location of the subsidence area. Then, the SBAS-InSAR monitoring is performed on the detected multiple subsidence areas. Finally, the small-scale subsidence results are obtained. In this study, based on 24 Sentinel-1A satellite images acquired from 10 January 2018 to 24 December 2018, nine subsidence areas in Yellow River Delta were detected. Three of them had long-term surface subsidence. They were located in Zhanhua District, Xianhe Town, and Hongguang Village, respectively. This paper focuses on analyzing these three areas. The maximum subsidence rate of Zhanhua District, Xianhe Town, and Hongguang Village were −135.21 mm/a, −330.91 mm/a, and −209.68 mm/a, respectively. In addition, the analysis showed that precipitation in the Zhanhua District could effectively slow down the subsidence rate of the area. The subsidence of Xianhe Town threatened the safety of the Shugang Expressway. The subsidence of Hongguang Village caused the safety risks of buildings. The results of this study prove that CNSBAS-InSAR method is reliable for monitoring subsidence areas and it can provide a reference for local construction and protection of Yellow River Delta.

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结合中心网和SBAS-InSAR的黄河三角洲地表沉降探测、监测与分析

长期的工业活动容易造成地表沉陷，破坏地面设施和当地生态环境。地表沉降监测与分析对预防潜在灾害具有重要意义。中国黄河三角洲地表类型复杂，工业活动较多，因此有必要对该地区地表沉降进行监测。小基线子集InSAR (SBAS-InSAR)能够以毫米级精度监测地表沉降，但处理宽图像(Sentinel-1)需要较长时间，且受大气误差影响严重。为了避免这些限制，我们构建了一种结合CenterNet网络和SBAS-InSAR (CNSBAS-InSAR)的方法。首先，利用CenterNet网络从两幅SAR卫星图像形成的宽差干涉图中自动检测塌陷区，确定塌陷区位置;然后，对探测到的多个塌陷区进行SBAS-InSAR监测。最后，得到了小尺度沉降结果。本研究基于2018年1月10日至12月24日采集的24幅Sentinel-1A卫星图像，对黄河三角洲9个沉陷区进行了探测。其中三个有长期地表沉降。它们分别位于沾化区、仙河镇和红光村。本文主要对这三个方面进行分析。沾化区、仙河镇和红光村的最大沉降速率分别为−135.21 mm/a、−330.91 mm/a和−209.68 mm/a。此外，分析表明，沾化区的降水可以有效减缓该地区的沉降速度。鲜河镇的沉陷严重威胁着蜀港高速公路的安全。红光村的下沉造成了建筑物的安全隐患。研究结果表明，CNSBAS-InSAR方法对塌陷区的监测是可靠的，可为黄河三角洲地区的局部建设和保护提供参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.