首页 >  2022, Vol. 26, Issue (7) : 1302-1314

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全文摘要次数: 353 全文下载次数: 357
引用本文:

DOI:

10.11834/jrs.20211297

收稿日期:

2021-05-12

修改日期:

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AM-LSTM网络的北京平原东部地面沉降模拟
曹鑫宇1,2,3,朱琳1,2,3,4,7,宫辉力1,2,3,4,7,郭琳1,2,3,4,7,尉毓姣1,2,3,郭涛5,陈蓓蓓1,2,3,4,王海刚6,7,李蕙君1,2,3
1.首都师范大学 资源环境与旅游学院, 北京 100048;2.首都师范大学 水资源安全北京实验室, 北京 100048;3.首都师范大学 城市环境过程与数字模拟国家重点实验室培育基地, 北京 100048;4.首都师范大学 地面沉降机理与防控教育部重点实验室, 北京 100048;5.四川省农业科学院 遥感与数字农业研究所, 成都 610066;6.中国地质环境监测院, 北京 100081;7.自然资源部京津冀平原地下水与地面沉降野外科学观测研究站, 北京 100081
摘要:

基于传统数值方法构建的地面沉降模拟预测模型需要大量的水文地质数据和实测数据,对于地质条件复杂地区的形变模拟预测难度大。本文基于PS-InSAR技术获取的北京平原东部地区的地面沉降信息,综合考虑不同层位地下水水位对沉降的影响,采用基于注意力机制的长短时记忆网络(AM-LSTM)对不同沉降发育地区典型位置处的地面沉降进行模拟。结果表明:(1)研究区地面沉降空间差异性明显,2010年11月—2016年8月最大沉降速率约153 mm/a,累计沉降量达到1063 mm,位于朝阳区三间房乡附近;(2)基于AM-LSTM模型的模拟精度优于传统LSTM模型,本次模拟精度最高提升了22%;(3)AM-LSTM模型注意力权重表明,第二承压含水层水位对地面沉降贡献最大。本次研究能够为地面沉降防控提供可靠的技术支撑。

Land subsidence simulation in the east of Beijing plain based on the AM-LSTM Network
Abstract:

The simulation and prediction model of land subsidence based on traditional numerical methods requires a large amount of hydrogeological and measured data, and predicting the deformation in areas with complex geological conditions is difficult. In this study, on the basis of land subsidence information obtained by permanent scatterers–interferometry synthetic aperture radar (PS-InSAR) technology in the east of the Beijing plain and in consideration of the influence of groundwater level in different layers on subsidence, the long-term and short-term memory network (AM-LSTM) based on an attention mechanism is used to simulate the land subsidence at typical locations in different subsidence areas. Results show the following points. (1) The spatial difference of land subsidence in the study area is obvious. From October 2010 to August 2016, the maximum subsidence rate is about 153 mm/a, and the cumulative subsidence is 1063 mm. The area is located near Sanjianfang Township in Chaoyang District. (2) The simulation accuracy of the AM-LSTM model is better than that of the traditional LSTM model, and the accuracy of this simulation reaches 22%. (3) The attention weight of the AM-LSTM model indicates that the water level of the second confined aquifer contributes the most to land subsidence. These research findings can provide a reliable model for the prevention and control of land subsidence.

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