A spatio-temporal prediction method of large-scale ground subsidence considering spatial heterogeneity
- Vol. 26, Issue 7, Pages: 1315-1325(2022)
Published： 07 July 2022
DOI： 10.11834/jrs.20211445
扫 描 看 全 文
浏览全部资源
扫码关注微信
- About Journal
- Journal Online
- Special Source
摘要
大速率、不均匀的地面沉降已经威胁到人类的生产活动，高精度的沉降预测结果对于地质灾害的精准防控具有重要意义。为掌握地面沉降的演化规律，利用现场观测数据或InSAR数据开展了多项预测研究。然而，由于空间异质性的存在，大范围地面沉降的准确预测仍然是一项挑战。在这项研究中，从数据驱动的角度提出了一种顾及空间异质性的大范围地面沉降时空预测方法STLSTM（Spatio-temporal Long Short-Term Memory）。首先，通过聚类识别地理空间中的均质子区；然后，在每个子区中，一个特别的长短期记忆LSTM（Long Short-Term Memory）网络被用来捕捉局部位置的非线性特征；最后，利用预训练的网络对未来时刻的地面沉降进行定量预测。在实验部分，哨兵1号影像数据被用来比较STLSTM与其他8种基准方法的性能，利用空间统计指标分析了模型的有效性。结果表明，STLSTM在152 s内达到了最高的预测精度（71.4%），且能够有效弱化空间异质性对大区域沉降预测任务的影响。总之，这项研究将空间异质性处理策略融合到深度学习模型中，实现了高精度、高时效的大范围地面沉降时空预测。
Abstract
The rapid and uneven ground subsidence has threatened human production activities
and high-precision subsidence prediction results are of great significance for the precise prevention and control of geological disasters. In order to grasp the evolution law of ground subsidence
a number of prediction studies have been carried out using field observation data or InSAR data. However
due to the existence of spatial heterogeneity
accurate prediction of large-scale ground subsidence is still a challenge.
In this study
a spatio-temporal prediction method considering spatial heterogeneity for large-scale ground subsidence STLSTM (Spatio-temporal Long Short-Term Memory) is proposed from a data-driven perspective. First
clustering is used to identify homogenous subregions in geographic space; then
in each subregion
a special Long Short-Term Memory (LSTM) networks are used to capture the nonlinearity features of local locations; Finally
the pre-trained network is used to quantitatively predict the ground subsidence at the future time.
In the experimental part
the sentinel-1 image data was used to compare the performance of STLSTM with the other 8 benchmark methods
and the effectiveness of STLSTM was analyzed using spatial statistical indicators. The results show that STLSTM achieves the highest prediction accuracy (71.4%) within 152 secs
and can effectively weaken the effect of spatial heterogeneity on large-scale subsidence prediction tasks.
In conclusion
this paper integrates the spatial heterogeneity processing strategy into the deep learning model
and large-scale subsidence prediction is realized with high precision and time efficiency.
关键词
遥感地面沉降时空预测异质性LSTMInSAR
Keywords
remote sensingground subsidencespatio-temporal predictionheterogeneityLSTMInSAR
references
Ali M Z, Chu H J and Burbey T J. 2020. Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations. Hydrogeology Journal, 28(8): 2865-2876 [DOI: 10.1007/s10040-020-02211-0http://dx.doi.org/10.1007/s10040-020-02211-0]
Cao L, Zhan Z B and Han Y. 2012. Deformation prediction and inversion of Shuibuya project based on artificial neural network and genetic algorithm. Applied Mechanics and Materials, 170-173: 2115-2118 [DOI: 10.4028/www.scientific.net/AMM.170-173.2115http://dx.doi.org/10.4028/www.scientific.net/AMM.170-173.2115]
Davies D L and Bouldin D W. 1979. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2): 224-227 [DOI: 10.1109/TPAMI.1979.4766909http://dx.doi.org/10.1109/TPAMI.1979.4766909]
Deng J, Dong W, Socher R, Li L J, Li K and Li F F. 2009. A large-scale hierarchical image database. Proc. of IEEE Computer Vision and Pattern Recognition, 248-255 [DOI: 10.1109/CVPR.2-009.5206848http://dx.doi.org/10.1109/CVPR.2-009.5206848]
Deng M, Liu Q L, Li G Q and Huang J B. 2011. Spatial Cluster Analysis and Application. Beijing: Science Press: 75-76
邓敏, 刘启亮, 李光强, 黄健柏. 2011. 空间聚类分析及应用. 北京: 科学出版社: 75-76
Dunn J C. 1974. Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1): 95-104 [DOI: 10.1080/01969727408546059http://dx.doi.org/10.1080/01969727408546059]
Estivill-Castro V and Lee I. 2002. Multi-level clustering and its visualization for exploratory spatial analysis. GeoInformatica, 6(2): 123-152 [DOI: 10.1023/A:1015279009755http://dx.doi.org/10.1023/A:1015279009755]
Fan Z L and Zhang Y H. 2019. Research progress on intelligent algorithms based ground subsidence prediction. Geomatics and Spatial Information Technology, 42(5): 183-188
范泽琳, 张永红. 2019. 智能算法在地面沉降预测中的应用综述. 测绘与空间地理信息, 42(5): 183-188 [DOI: 10.3969/j.issn.1672-5867.2019.05.054http://dx.doi.org/10.3969/j.issn.1672-5867.2019.05.054]
Kingma D P and Ba J. 2014. Adam: A Method for Stochastic Optimization Computer Science [DOI: 10.48550/arXiv. 1412.6980http://dx.doi.org/10.48550/arXiv.1412.6980]
Li L. 2014. Study on Forcasting Model of Land Subsidence and Its Application. Xi’an: Chang’an University
李丽. 2014. 地面沉降预测模型及其应用研究. 西安: 长安大学
Li S P. 2014. Deformation Analysis Based on Space-Time Series Model. Xi’an: Chang’an University
李世鹏. 2014. 基于时空序列模型的变形分析研究. 西安: 长安大学
Liao M S and Lin H. 2003. Synthetic Aperture Radar Interferometry: Principle and Signal Processing. Beijing: Surveying and Mapping Publishing House: 1-2
廖明生, 林珲. 2003. 雷达干涉测量: 原理与信号处理基础. 北京: 测绘出版社: 1-2
Liu Q H, Zhang Y H, Deng M, Wu H A, Kang Y H and Wei J J. 2021. Time series prediction method of large-scale surface subsidence based on deep learning. Acta Geodaetica et Cartographica Sinica, 50(3): 396-404
刘青豪, 张永红, 邓敏, 吴宏安, 康永辉, 魏钜杰. 2021. 大范围地表沉降时序深度学习预测法. 测绘学报, 50(3): 396-404 [DOI: 10.11947/j.AGCS.2021.20200038http://dx.doi.org/10.11947/j.AGCS.2021.20200038]
Murakami S, Yasuhara K and Suzuki K. 2005. Reliable land subsidence mapping by a geostatistical spatial interpolation procedure: Proceedings of the 16th International Conference on Soil Mechanics and Geotechnical Engineering. IOS Press: 2829-2832 [DOI: 10.3233/978-1-61499-656-9-2829http://dx.doi.org/10.3233/978-1-61499-656-9-2829]
Ocak I and Seker S E. 2013. Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes. Environmental Earth Sciences, 70(3): 1263-1276 [DOI: 10.1007/s12665-012-2214-xhttp://dx.doi.org/10.1007/s12665-012-2214-x]
Pesaran M H. 2015. Spatial panel econometrics//Pesaran M H, ed. Time Series and Panel Data Econometrics. Oxford: Oxford University Press [DOI: 10.1093/acprof:oso/9780198736912.001.0001http://dx.doi.org/10.1093/acprof:oso/9780198736912.001.0001]
Qin T C, Cheng G M and Wang H G. 2018. The latest progress of research on land subsidence abroad and its inspiration to China. Geological Bulletin of China, 37(2/3): 503-509
秦同春, 程国明, 王海刚. 2018. 国际地面沉降研究进展的启示. 地质通报, 37(2/3): 503-509 [DOI: 10.3969/j.issn.1671-2552.2018.02.028http://dx.doi.org/10.3969/j.issn.1671-2552.2018.02.028]
Shahin M A, Maier H R and Jaksa M B. 2003. Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models. Computers and Geotechnics, 30(8): 637-647 [DOI: 10.1016/j.compgeo.2003.09.004http://dx.doi.org/10.1016/j.compgeo.2003.09.004]
Sun Y, Chen Y H, Wang X G and Tang X O. 2014. Deep learning face representation by joint identification-verification//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal: MIT Press: 1988-1996
Wang J F, Zhang T L and Fu B J. 2016. A measure of spatial stratified heterogeneity. Ecological Indicators, 67: 250-256 [DOI: 10.1016/j.ecolind.2016.02.052http://dx.doi.org/10.1016/j.ecolind.2016.02.052]
Wang Z S and Deng K Z. 2011. Richards model of surface dynamic subsidence prediction in mining area. Rock and Soil Mechanics, 32(6): 1664-1668
王正帅, 邓喀中. 2011. 采动区地表动态沉降预测的Richards模型. 岩土力学, 32(6): 1664-1668 [DOI: 10.3969/j.issn.1000-7598.2011.06.012http://dx.doi.org/10.3969/j.issn.1000-7598.2011.06.012]
Wu X D, Kumar V, Quinlan J R, Ghosh J, Yang Q, Motoda H, Mclachlan G J, Ng A, Liu B, Yu P S, Zhou Z H, Steinbach M, Hand D J and Steinberg D. 2008. Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1): 1-37 [DOI: 10.1007/s10115-007-0114-2http://dx.doi.org/10.1007/s10115-007-0114-2]
Yang X X, Shi Y, Deng M and Tang J B. 2016. A new method of spatial outlier detection by considering multi-level thematic attribute constraints. Geomatics and Information Science of Wuhan University, 41(6): 810-817
杨学习, 石岩, 邓敏, 唐建波. 2016. 一种基于多层次专题属性约束的空间异常探测方法. 武汉大学学报(信息科学版), 41(6): 810-817 [DOI: 10.13203/j.whugis20140425http://dx.doi.org/10.13203/j.whugis20140425]
Zhang L, Zhang Y C and Luo Y H. 1998. Theory and Practice of Geological Disaster Assessment. Wuhan: Geological Publishing House: 1-2
张梁, 张业成, 罗元华. 地质灾害灾情评估理论与实践. 武汉: 地质出版社: 1-2
Zhang Y H, Liu B, Wu H A, Cheng X and Kang Y H. 2018. Ground subsidence in Xiong’an new area from 2012 to 2016 monitored by InSAR technique. Journal of Earth Sciences and Environment, 40(5): 652-662
张永红, 刘冰, 吴宏安, 程霞, 康永辉. 2018. 雄安新区2012—2016年地面沉降InSAR监测. 地球科学与环境学报, 40(5): 652-662 [DOI: 10.3969/j.issn.1672-6561.2018.05.013http://dx.doi.org/10.3969/j.issn.1672-6561.2018.05.013]
Zhang Z W. 2017. Land Subsidence Monitoring Based on Time Series inSAR and Parameter Inversing of Groundwater-Subsidence Prediction Model. Fuxin: Liaoning Technical University
张子文. 2017. 时序InSAR地面沉降监测与地下水—地面沉降预测模型参数反演. 阜新: 辽宁工程技术大学
Zhou F F. 2012. Interpretation of “National Land Subsidence Prevention and Control Plan 2011—2020”-interview with Tao Qingfa, deputy director of the department of geological environment, ministry of land and resources. China Emergency Management, (3): 56-59
周飞飞. 2012. 《全国地面沉降防治规划(2011—2020年)》解读——访国土资源部地质环境司副司长陶庆法. 中国应急管理, (3): 56-59
Zhou H Y, Wang Y J, Yan S Y, Li Y, Liu X X and Zhang F Y. 2018. Monitoring of recent ground surface subsidence in the Cangzhou region by the use of the InSAR time-series technique with multi-orbit Sentinel-1 TOPS imagery. International Journal of Remote Sensing, 39(21/22): 8113-8128 [DOI: 10.1080/01431161.2018.1482020http://dx.doi.org/10.1080/01431161.2018.1482020]
Zhou P, Liu C, Liu Q F, Dai L R and Jiang H. 2013. A cluster-based multiple deep neural networks method for large vocabulary continuous speech recognition//Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver: IEEE [DOI: 10.1109/ICASSP.2013.6638948http://dx.doi.org/10.1109/ICASSP.2013.6638948]
Zhou Q C, Shen H H, Zhao J and Xiong X L. 2019. Tunnel settlement prediction by transfer learning. Journal of ICT Research and Applications, 13(2): 118-132 [DOI: 10.5614/itbj.ict.res.appl.2019.13.2.3http://dx.doi.org/10.5614/itbj.ict.res.appl.2019.13.2.3]
Zhu J J, Li Z W and Hu J. 2017. Research progress and methods of InSAR for deformation monitoring. Acta Geodaetica et Cartographica Sinica, 46(10): 1717-1733
朱建军, 李志伟, 胡俊. 2017. InSAR变形监测方法与研究进展. 测绘学报, 46(10): 1717-1733 [DOI: 10.11947/j.AGCS.2017.20170350http://dx.doi.org/10.11947/j.AGCS.2017.20170350]
Zhu J Y. 2014. Research on Land Subsidence Mechanism and Prediction of Settlement in Cangzhou Area. Beijing: China University of Geosciences (Beijing)
朱菊艳. 2014. 沧州地区地面沉降成因机理及沉降量预测研究. 北京: 中国地质大学(北京)
Alert me when the article has been cited
提交
相关文章
相关作者
暂无数据
相关机构
School of Geography and Information Engineering, China University of Geosciences
State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences
School of Earth and Planetary, University of Chinese Academy of Sciences
MOE Key Laboratory of Fundamental Physical Quantities Measurement, School of Physics, Huazhong University of Science and Technology
School of Geography and Information Engineering, China University of Geosciences
选择翻译语言
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0
Bulk Citation
Export to
TOP