SBAS技术结合BP神经网络在银银市银西工业园区沉降中的应用

Hui Zhang, Xing-hai Dang, Liqi Jia, Jianyun Zhao, Ming Lu
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引用次数: 0

摘要

近年来,白银银西工业园区由于地面沉降等现象明显,给当地发展带来了诸多隐患,对该区域进行长时间序列的变形监测具有重要的现实意义。利用SBAS技术对Sentinel-1A数据进行处理,获得2018年6月- 2021年4月银西工业园区地面变形场,并利用BP神经网络对高相干点D1进行预测分析。结果表明:银西工业园区多处出现沉降,年平均沉降率在-19.28 ~ 5.08 mm之间,严重沉降区域地理分布明显,主要集中在道路和建筑区域,其他区域地基较稳定;BP神经网络预测结果的均方误差为2.56 mm,平均相对误差为6.06%,预测精度较高。预测2023年D1点的累积沉降值为45 mm,沉降有加剧的趋势。预测结果对研究区地面沉降的早期识别和防治具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of SBAS Technique Combined with BP Neural Network in the Settlement of the Yinxi Industrial Park in Baiyin
In recent years, due to the obvious ground settlement and other phenomena of the Yinxi Industrial Park in Baiyin, it has brought many hidden dangers to the local development, it is of great practical significance to monitor the deformation of the area for a long time series. The ground deformation field of Yinxi Industrial Park from June 2018 to April 2021 was obtained by processing Sentinel-1A data using SBAS technology, and the high coherence point D1 was predicted and analyzed by BP neural network. The results show that subsidence occurs in several places in the Yinxi Industrial Park, and the average annual subsidence rate ranges from -19.28 mm to 5.08 mm, the areas of severe settlement have a clear geographical distribution, mainly concentrated in road and building areas, other areas have a more stable ground base; the mean square error in the BP neural network prediction result is 2.56 mm, and the average relative error is 6.06%, which is a high prediction accuracy. The predicted cumulative settlement value at point D1 in 2023 is 45 mm, and there is a tendency for the settlement to intensify. The prediction results are of great significance for the early identification and prevention of ground settlement in the study area.
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