整合SBAS-InSAR和LSTM在香港国际机场进行沉降监测和预测

Xianlin Shi , Jiahong Zhong , Yong Yin , Youdong Chen , Hao Zhou , Min Wang , Keren Dai
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摘要

香港国际机场(HKIA)是世界上最繁忙的机场之一,其大部分土地都是填海造地,容易出现地面不均匀沉降。监测和预测其表面的沉降对于确保机场的运营安全至关重要。本文首先应用小基线子集干涉合成孔径雷达(SBAS InSAR)技术获得了香港国际机场的地面沉降特征,然后使用标准偏差椭圆(SDE)方法分析了其时空演化。此外,利用长短期记忆(LSTM)对HKIA进行了表面趋势预测。结果表明,香港国际机场呈现不同程度的沉降和隆起,最大平均沉降率为−64毫米/年,最大累积沉降为−199毫米。InSAR在2019年至2023年期间揭示的预测曲线与实际沉降之间的比较高度一致,平均绝对误差和均方根误差小于5 mm,决定系数大于0.99。本文所使用的LSTM模型可以在基于时间序列InSAR的沉降预测中获得可靠的结果,并为地质灾害预测提供替代手段。
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Integrating SBAS-InSAR and LSTM for subsidence monitoring and prediction at Hong Kong international airport

Hong Kong International Airport (HKIA) is one of the busiest airports in the world, and much of its land is reclaimed from the sea, making it prone to uneven subsidence of the ground. Monitoring and predicting the subsidence of its surface are crucial for ensuring the operational safety of the airport. This paper firstly obtained the surface subsidence characteristics of the HKIA through applying the Small Baseline Subset Interferometry Synthetic Aperture Radar (SBAS-InSAR) technology, and then the spatial–temporal evolution was analyzed by using the Standard Deviational Ellipse (SDE) method. Moreover, the Long Short-Term Memory (LSTM) was employed to perform surface trend prediction of HKIA. The results show that the HKIA presents different levels of subsidence and uplift, with a maximum average subsidence rate of −64 mm/year and a maximum cumulative subsidence of −199 mm. The comparison between predicted curves and the actual subsidence revealed by InSAR from 2019 to 2023 is highly consistent, with the average absolute error and root mean square error less than 5 mm, and a coefficient of determination greater than 0.99. The LSTM model utilized in this paper can achieve reliable results in subsidence prediction based on time-series InSAR, and provide alternative means for geohazard prediction.

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