基于时序InSAR技术的工业园区地表形变监测

doi:10.13474/j.cnki.11-2246.2024.0627

摘要/Abstract

摘要： 工业园区作为城市经济发展的核心区,对园区进行地表形变监测尤为重要。目前,对工业园区形变机制的研究较少,且传统的监测手段成本高、效率低。因此,本文提出利用时序合成孔径雷达干涉测量(InSAR)技术构建园区的综合监测模型,在提升监测效率的同时节约了成本。以白银区银西工业园为例,基于2018年6月—2021年4月34景Sentinel-1A数据,利用StaMPS-PS与SBAS-InSAR 技术获取了园区地表形变信息,并对两种技术获取的形变信息从时空分布的角度进行交叉验证。结果表明,两种技术获取的形变特征点均对应于实地勘察照片中形变位置,再利用585个相同经纬度点进行精度验证,发现两者相关性较好,决定系数R²达0.82,均方根误差RMSE为2.20 mm/a,形变速率范围也高度一致。由于StaMPS-PS技术识别出的形变点数量比SBAS-InSAR技术多47％,因此StaMPS-PS技术在园区的适用性更好。最后分析并讨论了园区地表形变的地质条件及诱因,为更好了解园区形变机制及预警灾害发生提供了参考依据。

关键词: StaMPS-PS, SBAS-InSAR, 地表形变, WOA-BP神经网络, 银西工业园区

Abstract: Industrial parks, as the core areas of urban economic development, are particularly important for monitoring surface deformation. Currently, there is limited research on the deformation mechanism of industrial parks, and traditional monitoring methods are costly and inefficient. Therefore, this study proposes the use of time-series interferometric synthetic aperture radar (InSAR) technology to construct a comprehensive monitoring model for industrial parks, which improves monitoring efficiency while reducing costs. Taking the Yinxi industrial park in Baiyin district as an example, based on 34 scenes of Sentinel-1A data from June 2018 to April 2021, the deformation information of the park’s surface was obtained using the StaMPS-PS (stanford method for persistent scatterers-permanent scatterers) and SBAS-InSAR (small baseline subsets-interferometry synthetic aperture radar) techniques. The deformation information obtained from the two techniques was cross-validated from a spatio-temporal distribution perspective. The results show that the deformation features obtained by both techniques correspond to the deformation locations in field survey photos. Additionally, using 585 identical latitude and longitude points for accuracy verification, a good correlation between the two techniques is found, with a coefficient of determination (R²) of 0.82 and a root mean square error (RMSE) of 2.20 mm/a. The deformation rates are highly consistent as well. Since the StaMPS-PS technique identifies 47% more deformation points than the SBAS-InSAR technique, it is more applicable for the industrial park. Finally, the geological conditions and factors inducing surface deformation in the industrial park are analyzed and discussed, providing reference for better understanding the deformation mechanism and early warning of disasters in the park.

Key words: StaMPS-PS, SBAS-InSAR, ground deformation, WOA-BP neural network, Yinxi industrial park

中图分类号:

P237

黄标, 张辉, 尹剑辉. 基于时序InSAR技术的工业园区地表形变监测[J]. 测绘通报, 2024, 0(6): 157-163.

HUANG Biao, ZHANG Hui, YIN Jianhui. Surface deformation monitoring of industrial parks based on temporal InSAR technology[J]. Bulletin of Surveying and Mapping, 2024, 0(6): 157-163.

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