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Surface deformation prediction based on TS-InSAR technology and long short-term memory networks

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Urban land subsidence is a geological disaster formed by natural and human factors. Cumulative land subsidence easily causes damage to buildings, infrastructure, underground engineering, and other hazards, which seriously threaten the safety of people’s lives and property and cause national economic losses. In the face of urban land subsidence, monitoring, analyzing, and predicting spatiotemporal changes in land subsidence are necessary. The prediction of land subsidence is a crucial step for the early warning of urban infrastructure damage and establishment of a timely remedy.In this study, the Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) technique was utilized to monitor the time series land subsidence at Hong Kong International Airport from 2015 to 2020 by using 152 Sentinel-1A images with an ascending orbit. The local weighted scatter smoothing (Loess) method was used to reduce and smooth the noise in the original data of surface deformation points. Given that the advantages of LSTM correspond to the results of TS-InSAR, on the basis of TS-InSAR data, a stacked LSTM neural network was used to construct a surface deformation prediction model with two LSTM layers, two dense layers, and three dropout layers. The stacked LSTM model was employed to predict the surface deformation of the airport, and its results were compared the predicted results obtained with the true InSAR findings.The average vertical deformation rate of Hong Kong International Airport’s surface for 2015—2020 was -19—5 mm/year. The surface subsidence of the airport gradually increased, and the cumulative subsidence in the vertical direction reached 116 mm in December 2020. The cross-validation of the two time-series analysis methods and the comparison of the monitoring results with the level data showed that the InSAR monitoring results in this study had high accuracy and reliability. A stacked LSTM prediction model was established based on the time-series InSAR monitoring results, and the InSAR observation results were compared with the stacked LSTM prediction results. The root-mean-square error and mean absolute error of the predicted and true values were low, namely, 0.75 and 0.61 mm, respectively, and the correlation coefficient was 0.99. The LSTM prediction model demonstrated good performance at the point level scale and could predict ground subsidence accurately on the basis of TS-InSAR data. The stacked LSTM model was employed to predict the time-series subsidence of Hong Kong International Airport in 2021 by using TS-InSAR deformation data from 2015—2020. The analysis revealed that the long-term prediction process of the stacked LSTM prediction model became invalid after six months. Therefore, the stacked LSTM prediction model is suitable for short-term predictions with a short-term prediction scale of about six months, and the maximum cumulative vertical subsidence of the airport will reach 114 mm in June 2021.In summary, the stacked LSTM prediction model proposed in this study can be used as an effective method to predict surface deformation, and although the LSTM model is only suitable for short-term predictions, its prediction results can be used to assist in decision-making, early warning, and hazard mitigation. In the future, additional data can be incorporated into the LSTM model to accurately determine if the model is suitable for long-term predictions and improve the robustness of the prediction model.

ключеви́че слова́

remote sensing;Land deformation;TS-InSAR;Surface deformation prediction;deep learning;LSTM

References

1.
Aobpaet A, Cuenca M C, Hooper A and Trisirisatayawong I. 2013. InSAR time-series analysis of land subsidence in Bangkok, Thailand. International Journal of Remote Sensing, 34 (8): 2969-2982
2.
Berardino P, Fornaro G, Lanari R and Sansosti E. 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40(11): 2375-2383
3.
Cao Q, Chen B B, Gong H L, Zhou C F, Luo Y, Gao M L, Wang X, Shi M, Zhao X X and Zuo J J. 2019. Monitoring of land subsidence in Beijing-Tianjin-Hebei Urban by combination of SBAS and IPTA. Journal of Nanjing University (Natural Science), 55(3): 381-391
4.
Chen Y D, Zhang L F, He Y, Wang W H and Yang W. 2021. Ground deformation monitoring and analysis of Zhongchuan International Airport based on the time series InSAR of Sentinel-1A with ascending and descending orbits. Journal of Engineering Geology
5.
Dehghan-Soraki Y, Sharifikia M and Sahebi M R. 2015. A comprehensive Interferometric process for monitoring land deformation using ASAR and PALSAR satellite Interferometric data. GIScience and Remote Sensing, 52(1): 58-77
6.
Deng Z, Ke Y H, Gong H L, Li X J and Li Z H. 2017. Land subsidence prediction in Beijing based on PS-InSAR technique and improved grey-Markov model. Giscience and Remote Sensing, 54(6): 797-818.
7.
Ferretti A, Novali F, Bürgmann R, Hilley G and Prati C. 2004. InSAR permanent scatterer analysis reveals ups and downs in San Francisco Bay Area. Eos, Transactions American Geophysical Union, 85(34): 317-324
8.
Ferretti A, Prati C and Rocca F. 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1): 8-20
9.
Galloway D L and Burbey T J. 2011. Review: regional land subsidence accompanying groundwater extraction. Hydrogeology Journal, 19(8): 1459-1486
10.
Graves A. 2012. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin: Springer
11.
He Y, Chen Y D, Wang W H, Yan H W, Zhang L F and Liu T. 2021. TS-InSAR analysis for monitoring ground deformation in Lanzhou New District, the loess Plateau of China, from 2017 to 2019. Advances in Space Research, 67(4): 1267-1283
12.
He Y, Wang W H, Yan H W, Zhang L F, Chen Y D and Yang S W. 2020. Characteristics of surface deformation in Lanzhou with sentinel-1A TOPS. Geosciences, 10(3): 99
13.
He Y, Yang T B, Chen J and Ji Q. 2015. Remote sensing detection of glacier changes in Dong Tianshan Bogda region in 1972-2013. Scientia Geographica Sinica, 35(7): 925-932
14.
Hochreiter S and Schmidhuber J. 1997. Long short-term memory. Neural Computation, 9(8): 1735-1780
15.
Lanari R, Casu F, Manzo M, Zeni G, Berardino P, Manunta M and Pepe A. 2007. An overview of the small BAseline subset algorithm: a DInSAR technique for surface deformation analysis. Pure and Applied Geophysics, 164(4): 637-661
16.
Lin H, Ma P F and Wang W X. 2017. Urban Infrastructure Health Monitoring with Spaceborne Multi-temporal Synthetic Aperture Radar Interferometry. Acta Geodaetica et Cartographica Sinica, 46(10): 1421-1433
17.
Liu G X, Chen Q, Luo X and Cai G L. 2012. Theory and Method of Permanent Scatterer Radar Interferometry. Beijing: Science Press
18.
Liu X, Zhao N, Guo J Y and Guo B. 2020. Prediction of monthly precipitation over the Tibetan Plateau based on LSTM neural network. Journal of Geo-information Science, 2020, 22(8): 1617-1629
19.
Ma P F, Wang W X, Zhang B W, Wang J L, Shi G Q, Huang G Q, Chen F L, Jiang L M and Lin H. 2019. Remotely sensing large-and small-scale ground subsidence: a case study of the Guangdong-Hong Kong-Macao Greater Bay Area of China. Remote Sensing of Environment, 232: 111282
20.
Qu F F, Lu Z, Zhang Q, Bawden G W, Kim J W, Zhao C Y and Qu W. 2015. Mapping ground deformation over Houston-Galveston, Texas using multi-temporal InSAR. Remote Sensing of Environment, 169: 290-306
21.
Schmidhuber J. 2015. Deep learning in neural networks: an overview. Neural Networks, 61: 85-117
22.
Wang H X and Li K Y. 2019. Lithium-ion battery life prediction based on modified extend Kalman Filter. Computer Measurement and Control, 27(8): 271-275
23.
Wang W H, He Y, Zhang L F, Chen Y D, Qiu L S and Pu H Y. 2020. Analysis of surface deformation and driving forces in Lanzhou. Open Geosciences, 12(1): 1127-1145
24.
Wei F Y. 2007. Modern Climate Statistical Diagnosis and Prediction Technology. 2nd ed. Beijing: China Meteorological Press: 175-181
25.
Wu S B, Yang Z F, Ding X L, Zhang B C, Zhang L and Lu Z. 2020. Two decades of settlement of Hong Kong International Airport measured with multi-temporal InSAR. Remote Sensing of Environment, 248: 111976
26.
Ye S J, Luo Y, Wu J C, Yan X X, Wang H M, Jiao X and Teatini P. 2016. Three-dimensional numerical modeling of land subsidence in Shanghai, China. Hydrogeology Journal, 24(3): 695-709
27.
Zhao Q, Lin H, Wei G, Zebker H, Chen A and Yeung K. 2011. InSAR detection of residual settlement of an ocean reclamation engineering project: a case study of Hong Kong International Airport. Journal of Oceanography, 67(4): 415-426

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Surface deformation prediction based on TS-InSAR technology and long short-term memory networks

DOI：10.11834/jrs.20221457

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