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

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.

Time-Series Analysis of Mining-Induced Subsidence in the Arid Region of Mongolia Based on SBAS-InSAR

“…Conducting field-level surveys for data validation at the OT site and obtaining usable GNSS data is extremely challenging due to stringent policies and regulations as well as lack of instrumentation deployment such as GNSS. In the absence of direct empirical data, cross-validation methods such as PS-InSAR are frequently utilized to confirm the reliability of monitoring outcomes [35,36]. Correlation analysis was conducted on the deformation using both SBAS-InSAR and PS-InSAR techniques, which showed great correlation, as depicted in Figure 9.…”

Monitoring Analysis of Urban Subsidence in Northern Henan Province Based on TS-InSAR Technology

“…However, these conventional methodologies encounter substantial limitations when applied to urban land subsidence monitoring. The expansive research area and intricate terrain configurations in urban locales impose constraints on conventional surveying methods, including low spatial distribution rates, exorbitant costs, time-intensive processes, limited capacity to procure large-scale monitoring results, susceptibility to environmental variables, and inadequate reflection of continuous surface settlement information within the study area [19,[28][29][30][31]. In contrast, synthetic aperture radar interferometry (InSAR) technology, conceived in the 1990s and continuously refined since then, proffers a plethora of high-precision monitoring outcomes [32][33][34][35].…”