Spatial-temporal evolution characteristics and prediction of land subsidence in the eastern plain of Beijing

YU Wen; GONG Huili; CHEN Beibei; ZHOU Chaofan

doi:10.6046/zrzyyg.2021390

2022 Vol. 34, No. 4

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Article navigation > Remote Sensing for Natural Resources > 2022 > 34(4): 183-193

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YU Wen, GONG Huili, CHEN Beibei, ZHOU Chaofan. 2022. Spatial-temporal evolution characteristics and prediction of land subsidence in the eastern plain of Beijing. Remote Sensing for Natural Resources, 34(4): 183-193. doi: 10.6046/zrzyyg.2021390

Citation:

YU Wen, GONG Huili, CHEN Beibei, ZHOU Chaofan. 2022. Spatial-temporal evolution characteristics and prediction of land subsidence in the eastern plain of Beijing. Remote Sensing for Natural Resources, 34(4): 183-193. doi: 10.6046/zrzyyg.2021390

Spatial-temporal evolution characteristics and prediction of land subsidence in the eastern plain of Beijing

1. Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China；
2. Key Laboratory of Land Subsidence Mechanism and Control, Ministry of Education, Capital Normal University, Beijing 100048, China；
3. Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China；
4. National Field Scientific Observation and Research Station of Groundwater and Land Subsidence in the Beijing-Tianjin-Hebei Plain, Capital Normal University, Beijing 100048, China

Received Date: 16 November 2021

Revised Date: 15 December 2022

Publish Date: 27 December 2022

Abstract

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.
- land subsidence /
- empirical orthogonal function /
- evolution characteristics /
- Attention-LSTM /
- time series prediction

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References

[1]	周飞飞. 《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[J]. 中国应急管理, 2012(3):58-61. Google Scholar
[2]	Zhou F F. Interpretation of the national plan for land subsidence prevention and control (2011—2020):Interview with Tao Qingfa,deputy director of the department of geological environment,Ministry of Land and Resources[J]. China Emergency Management, 2012(3):58-61. Google Scholar
[3]	叶晓宾. 华北平原地面沉降经济损失评估[M]. 北京: 中国大地出版社, 2006. Google Scholar
[4]	Ye X B. Evaluation of economic loss of land subsidence in the North China Plain[M]. Beijing: China Land Publishing House, 2006. Google Scholar
[5]	刘国祥. InSAR系列讲座6 InSAR应用实例及其局限性分析[J]. 四川测绘, 2005, 28(3):139-143. Google Scholar
[6]	Liu G X. Application examples of InSAR and its limitation analysis[J]. Surveying and Mapping of Sichuan, 2005, 28(3):139-143. Google Scholar
[7]	Zebker H A, Goldstein R M. Topographic mapping from interferometric synthetic aperture Radar observations[J]. Journal of Geophysical Research Solid Earth, 1986, 91(b5):4993-4999. Google Scholar
[8]	Massonnet D, Rossi M, Carmona C, et al. The displacement field of the Landers earthquake mapped by Radar interferometry[J]. Nature, 1993, 364(6433):138-142. Google Scholar
[9]	宫辉力, 张有全, 李小娟. 基于永久散射体雷达干涉测量技术的北京市地面沉降研究[J]. 自然科学进展, 2009, 19(11):1261-1266. Google Scholar
[10]	Gong H L, Zhang Y Q, Li X J. Beijing land subsidence research based on permanent scatterer Radar interferometry technology[J]. Advances in Natural Science, 2009, 19(11):1261-1266. Google Scholar
[11]	Gabriel A K, Goldstein R M, Zebker H A. Mapping small elevation changes over large areas:Differential Radar interferometry[J]. Journal of Geophysical Research:Solid Earth, 1989, 94(b7):9183-9191. Google Scholar
[12]	Hanssen R F. Radar interferometry[M]. Springer Netherlands, 2001. Google Scholar
[13]	Ghiglia D C, Pritt M D. Two-dimensional phase unwrapping[M]. Wiley-Interscience, 1985. Google Scholar
[14]	Zebker H A, Rosen P A, Hensley S. Atmospheric effects in interfero-metric synthetic aperture Radar surface deformation and topographic maps[J]. Journal of Geophysical Research Solid Earth, 1997, 102(b4):7547-7563. Google Scholar
[15]	Ferretti A, Prati C. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5):2202-2212. Google Scholar
[16]	周超凡, 宫辉力, 陈蓓蓓, 等. 北京市典型地区地面沉降空间格局分析[J]. 遥感信息, 2017, 32(4):24-29. Google Scholar
[17]	Zhou C F, Gong H L, Chen B B, et al. Spatial pattern of land subsidence in Beijing typical areas[J]. Remote Sensing Information, 2017, 32(4):24-29. Google Scholar
[18]	Zuo J J, Gong H L, Chen B B, et al. Time-series evolution patterns of land subsidence in the Eastern Beijing Plain,China[J]. Remote Sensing, 2019, 11(5): 539. Google Scholar
[19]	杨翠玉, 王彦兵, 赵亚丽, 等. 北京来广营地区地面沉降时空演化特征[J]. 遥感信息, 2020, 35(5):138-143. Google Scholar
[20]	Yang C Y, Wang Y B, Zhao Y L, et al. Temporal and spatial characteristics of land subsidence in Laiguangying,Beijing[J]. Remote Sensing Information, 2020, 35(5):138-143. Google Scholar
[21]	Zhou Q H, Hu Q W, Ai M Y, et al. An improved GM (1,3) model combining terrain factors and neural network error correction for urban land subsidence prediction[J]. Geomatics Natural Hazards and Risk, 2020, 11:212-229. Google Scholar
[22]	Nie L, Wang H, Xu Y, et al. A new prediction model for mining subsidence deformation:The arc tangent function model[J]. Natural Hazards, 2015, 75(3):2185-2198. Google Scholar
[23]	杨天亮, 许言. 国际地面沉降与城市安全研究动态——第一届国际城市地质学术研讨会综述[J]. 上海国土资源, 2017, 38(2):1-3. Google Scholar
[24]	Yang T L, Xu Y. Research trends in international land subsidence and urban security:An overview of the first international symposium on urban geology[J]. Shanghai Land and Resources, 2017, 38(2):1-3. Google Scholar
[25]	Deng Z, Ke Y, Gong H, et al. Land subsidence prediction in Beijing based on PS-InSAR technique and improved Grey-Markov model[J]. Giscience and Remote Sensing, 2017, 54(6):1-22. Google Scholar
[26]	刘青豪, 张永红, 邓敏, 等. 大范围地表沉降时序深度学习预测法[J]. 测绘学报, 2021, 50(3):396-404. Google Scholar
[27]	Liu Q H, Zhang Y H, Deng M, et al. Time series prediction method of large-scale surface subsidence based on deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(3):396-404. Google Scholar
[28]	岳振华, 沈涛, 毛曦, 等. 循环神经网络的地面沉降预测方法[J]. 测绘科学, 2020, 45(12):149-156. Google Scholar
[29]	Yue Z H, Shen T, Mao X, et al. Ground subsidence prediction method based on recurrent neural network[J]. Science of Surveying and Mapping, 2020, 45(12):149-156. Google Scholar
[30]	刘予. 北京市地面沉降区含水层和压缩层组划分及地面沉降自动监测系统[D]. 长春: 吉林大学, 2004. Google Scholar
[31]	Liu Y. Divided water-bearing zones and compressible zones of Beijing land subsidence area and land subsidence automatic monitoring system[D]. Changchun: Jilin University, 2004. Google Scholar
[32]	周毅, 罗郧, 郭高轩, 等. 冲洪积平原地面沉降特征及主控因素——以北京平原为例[J]. 地质通报, 2016, 35(12):2100-2110. Google Scholar
[33]	Zhou Y, Luo Y, Guo G X, et al. A study of the characteristics of land subsidence and the main control factors in the alluvial plain:A case study of Beijing Plain[J]. Geological Bulletin of China, 2016, 35(12):2100-2110. Google Scholar
[34]	程凌鹏, 王新惠, 张琦伟, 等. 南水进京对北京地面沉降的影响及趋势分析[J]. 人民黄河, 2018, 40(5):93-97. Google Scholar
[35]	Cheng L P, Wang X H, Zhang Q W, et al. Influence of transferring Yangtze River water into Beijing on ground subsidence and trend analysis[J]. Yellow River, 2018, 40(5):93-97. Google Scholar
[36]	刘媛媛. 基于多源SAR数据的时间序列InSAR地表形变监测研究[D]. 西安: 长安大学, 2014. Google Scholar
[37]	Liu Y Y. Research on time series InSAR surface deformation monitoring based on multi-source SAR Data[D]. Xi’an: Chang’an University, 2014. Google Scholar
[38]	Lorenz E N. Empirical orthogonal functions and statistical weather prediction[M]. Cambridge: Massachusetts Institute of Technology, Department of Meteorology, 1956:1,52. Google Scholar
[39]	Asoka A, Gleeson T, Wada Y, et al. Relative contribution of monsoon precipitation and pumping to changes in groundwater storage in India[J]. Nature Geoscience, 2017, 10(2):109-117. Google Scholar
[40]	Smith T M, Reynolds R W, Livezey R E, et al. Reconstruction of historical sea surface temperatures using empirical orthogonal functions[J]. Journal of Climate, 1996, 9(6):1403-1420. Google Scholar
[41]	Bianchi F M, Maiorino E, Kampffmeyer M C, et al. Recurrent neural networks for short-term load forecasting:An overview and comparative analysis[EB/OL].(2017-05-11) [2018-07-20] https://arxiv.org/pdf/1705.04378.pdf . Google Scholar