Spatial-temporal evolution characteristics and prediction of land subsidence in the eastern plain of Beijing
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摘要: 地面沉降是地表高程下降的一种自然地质现象,若发生在人口密集、社会发展程度较高的城市,将对城市基础设施具有严重的破坏性,威胁着城市安全。地面沉降演化特征分析可以反映其对地面基础设施的影响程度,建立一个高效的地面沉降预测模型对于地面沉降的防治和保障城市安全有着重要意义。首先,利用永久散射体合成孔径雷达干涉测量方法(persistent scatterer interferometric synthetic aperture Radar,PS-InSAR)获取到地面沉降时空信息,且与水准验证得到较高的精度。其次,利用经验正交函数对地面沉降场整体时空特性进行分析,发现研究区域空间模态1方差贡献率很大,几乎代表研究区域空间的整体演化情况,对应时间系数线性趋势显著; 模态2有一定的方差贡献率,但占比很小,对应的时间系数季节性显著。最后,分别利用长短期记忆(long short term memory,LSTM)与嵌入注意机制的长短期记忆(Attention-LSTM)模型对区域地面沉降进行时序预测,发现Attention-LSTM模型优于LSTM模型,其均方误差损失函数(mean square error loss,MSE-loss)可低至0.01。该预测方法扩大了深度学习在地面沉降研究方面的应用。
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关键词:
- 地面沉降 /
- 经验正交函数 /
- 演化特征 /
- Attention-LSTM /
- 时序预测
Abstract: Land subsidence is a natural geological phenomenon in which the surface elevation drops. It can severely destroy urban infrastructure and threaten urban safety if it occurs in densely populated cities with a high social development degree. The analysis of the evolution characteristics of land subsidence can reflect the degree of the influence of land subsidence on the ground infrastructures, and building an efficient land subsidence prediction model is of great significance for preventing and controlling land subsidence and protecting urban safety. This study obtained the spatial-temporal information on land subsidence using the persistent scatterer interferometric synthetic aperture Radar (PS-InSAR) method first and then verified the information using leveling to get high precision. Then, this study analyzed the general spatial-temporal characteristics of the land subsidence field using an empirical orthogonal function. The analysis results are as follows. Spatial modal No. 1 had a high variance contribution rate, almost representing the general spatial evolution of the study area. Its corresponding time coefficient showed a significant linear trend. By contrast, spatial mode No. 2 had a low variance contribution rate and a seasonally significant time coefficient. Finally, the time series of the regional land subsidence were predicted using both long short-term memory (LSTM) and Attention-LSTM models. The prediction results indicate that the Attention-LSTM model was superior to the LSTM model, with the mean square error loss (MSE-loss) of as low as 0.01. This prediction method expands the application of deep learning in the study of land subsidence. -
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