AM-LSTM网络的北京平原东部地面沉降模拟

曹鑫宇; 朱琳; 宫辉力; 郭琳; 尉毓姣; 郭涛; 陈蓓蓓; 王海刚; 李蕙君

doi:10.11834/jrs.20211297

区域地面沉降 | 浏览量 : 0 下载量: 875 CSCD: 8

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AM-LSTM网络的北京平原东部地面沉降模拟
Land subsidence simulation in the east of Beijing plain based on the AM-LSTM Network
2022年26卷第7期页码：1302-1314
收稿：2021-05-12，

纸质出版：2022-07-07
DOI： 10.11834/jrs.20211297
稿件说明：

移动端阅览

曹鑫宇，朱琳，宫辉力，郭琳，尉毓姣，郭涛，陈蓓蓓，王海刚，李蕙君.2022.AM-LSTM网络的北京平原东部地面沉降模拟.遥感学报，26（7）： 1302-1314 DOI： 10.11834/jrs.20211297.

Cao X Y，Zhu L，Gong H L，Guo L，Wei Y J，Guo T，Chen B B，Wang H G and Li H J. 2022. Land subsidence simulation in the east of Beijing plain based on the AM-LSTM Network. National Remote Sensing Bulletin， 26（7）：1302-1314 DOI： 10.11834/jrs.20211297.

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。

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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