## Message Board

Respected readers, authors and reviewers, you can add comments to this page on any questions about the contribution, review,        editing and publication of this journal. We will give you an answer as soon as possible. Thank you for your support!

Volume 47 Issue 9
Sep.  2022
Turn off MathJax
Article Contents

ZHAO Yali, WANG Yanbing, WANG Xinyu, TIAN Xiuxiu, LI Xiaojuan, YU Jie. Temporal and Spatial Analysis of Land Subsidence in Beijing Plain Based on TPCA[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1498-1506. doi: 10.13203/j.whugis20200721
 Citation: ZHAO Yali, WANG Yanbing, WANG Xinyu, TIAN Xiuxiu, LI Xiaojuan, YU Jie. Temporal and Spatial Analysis of Land Subsidence in Beijing Plain Based on TPCA[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1498-1506.

# Temporal and Spatial Analysis of Land Subsidence in Beijing Plain Based on TPCA

##### doi: 10.13203/j.whugis20200721
Funds:

The Beijing Natural Science Foundation 8202009

the Open Project Program of the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China 01117220010020

More Information
• Author Bio:

ZHAO Yali, master, specializes in geographic information science and land subsidence monitoring and analysis research based on InSAR. E-mail: zhaoyali@cnu.edu.cn

• Corresponding author: WANG Yanbing, PhD, associate professor. E-mail: wyb@cnu.edu.cn
• Received Date: 2021-03-04
• Publish Date: 2022-09-05
•   Objectives  Most of the characteristics of land subsidence are analyzed separately from the perspective of temporal or spatial, and the hidden information and possible laws in the data cannot be discov‍ered simultaneously. Temporal principal component analysis (TPCA) can be used to extract temporal and spatial characteristics of temporal and spatial data in the field of geosciences. The land subsidence in the Beijing plain has typical temporal and spatial characteristics.  Methods  (1) Permanent scatterer interferometric synthetic aperture radartechnique provide a convenient method to measure land subsidence in sub-centimeter precision. 51 Envisat ASAR data acquired from 2003 to 2010 in the Beijing plain were used to produce 50 interferograms and obtain time-series deformation with nonlinear model.(2)Based on the land subsid‍ence of about 100 000 permanent scatterer (PS) points and 51-time series, we construct the original data matrix X100 000×51, calculate the correlation coefficient matrix, and use the TPCA method to analyze the temporal and spatial evolution characteristics of land subsidence in the Beijing plain.  Results  It is found that: (1) The first principal component obtained by TPCA analysis represents the long-term development trend of the spatial distribution of land subsidence. (2) The area that the second principal component that is positive has a correlation in spatial distribution with the area of compressible layer thickness above 130 m. (3) ‍The PS points where the first principal component scores are negative and the second principal component scores are positive are distributed in the severe subsidence area above 30 mm/a. There is an obvious classification of land subsidence and seasonal variation between north and south area in the severe subsid‍ence area. Specifically, in the northern subsidence area, the amount of subsidence in spring and summer is larger than that of in autumn and winter, it is an opposite variation in the southern subsidence area.  Conclusions  In general, the temporal and spatial variation of land subsidence could be studied for urban safety monitor‍ing by TPCA. It also can identify the main characteristics of the space and the law of temporal and spatial evolution. Since TPCA is a linear combination, by finding the direction with the largest variance for projection, the variables obtained are just uncorrelated and not independent of each other. Principal component analysis(PCA) only uses the second‍-‍order statistical information of the original data and ignores its high‍-‍order statistical information. Therefore, it is necessary to optimize by rotating principal components to find more physical meanings of principal components.
•  [1] 杨艳, 贾三满, 王海刚. 北京平原区地面沉降现状及发展趋势分析[J]. 上海地质, 2010, 31(4): 23-28 Yang Yan, Jia Sanman, Wang Haigang. The Status and Development of Land Subsidence in Beijing Plain[J]. Shanghai Geology, 2010, 31(4): 23-28 [2] 刘凯斯. 北京地铁M1/M6沿线区地面沉降演化特征及风险评价[D]. 北京: 首都师范大学, 2018 Liu Kaisi. Evolution Characteristics and Risk Assessment of Land Subsidence in the Area along Beijing Subway M1/M6[D]. Beijing: Capital Normal University, 2018 [3] 段光耀, 刘欢欢, 宫辉力, 等. 京津城际铁路沿线不均匀地面沉降演化特征[J]. 武汉大学学报·信息科学版, 2017, 42(12): 1847-1853 Duan Guangyao, Liu Huanhuan, Gong Huili, et al. Evolution Characteristics of Uneven Land Subsid‍ence Along Beijing-Tianjin Inter-City Railway[J]. Geomatics and Information Science of Wuhan University, 2017, 42(12): 1847-1853 [4] 罗三明, 杜凯夫, 万文妮, 等. 利用PSInSAR方法反演大时空尺度地表沉降速率[J]. 武汉大学学报·信息科学版, 2014, 39(9): 1128-1134 Luo Sanming, Du Kaifu, Wan Wenni, et al. Ground Subsidence Rate Inversion of Large Temporal and Spatial Scales Based on Extended PSInSAR Method[J]. Geomatics and Information Science of Wuhan University, 2014, 39(9): 1128-1134 [5] 朱邦彦, 姚冯宇, 孙静雯, 等. 利用InSAR与地质数据综合分析南京河西地面沉降的演化特征和成因[J]. 武汉大学学报·信息科学版, 2020, 45(3): 442-450 Zhu Bangyan, Yao Fengyu, Sun Jingwen, et al. Attribution Analysis on Land Subsidence Feature in Hexi Area of Nanjing by InSAR and Geological Data[J]. Geomatics and Information Science of Wuhan University, 2020, 45(3): 442-450 [6] Guo L, Gong H L, Zhu F, et al. Analysis of the Spatiotemporal Variation in Land Subsidence on the Beijing Plain, China[J]. Remote Sensing, 2019, 11‍(10): 1170-1189 [7] Zhou C D, Lan H X, Gong H L, et al. Reduced Rate of Land Subsidence Since 2016 in Beijing, China: Evidence from Tomo-PSInSAR Using RadarSAT‍-‍2 and Sentinel‍-‍1 Datasets[J]. International Journal of Remote Sensing, 2020, 41(4): 1259-1285 [8] 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 [9] Richman M B. Rotation of Principal Components[J]. Journal of Climatology, 1986, 6(3): 293-335 [10] Lin Y N N, Kositsky A P, Avouac J P. PCAIM Joint Inversion of InSAR and Ground‍-‍Based Geodet‍ic Time Series: Application to Monitoring Magmatic Inflation Beneath the Long Valley Caldera[J]. Geophysical Research Letters, 2010, 37(23): 23301-23305 [11] Ji K H, Herring T A. Transient Signal Detection Using GPS Measurements: Transient Inflation at Akutan Volcano, Alaska, During Early 2008[J]. Geophysical Research Letters, 2011, 38(6): 6307-6312 [12] Zhang J P, Zhu T, Zhang Q H, et al. The Impact of Circulation Patterns on Regional Transport Pathways and Air Quality over Beijing and Its Surround‍ings[J]. Atmospheric Chemistry and Physics, 2012, 12(11): 5031-5053 [13] 朱飙, 王振会, 李春华, 等. 江苏雷暴时空变化的气候特征分析[J]. 气象科学, 2009, 29(6): 849-852 Zhu Biao, Wang Zhenhui, Li Chunhua, et al. Anal‍ysis of Climate Spatial‍-‍Temporal Character of Thunderstorm over Jiangsu Province[J]. Scientia Meteorologica Sinica, 2009, 29(6): 849-852 [14] Neeti N, Eastman J R. Novel Approaches in Extended Principal Component Analysis to Compare Spatio‍-‍Temporal Patterns Among Multiple Image Time Series[J]. Remote Sensing of Environment, 2014, 148: 84-96 [15] Rudolph M L, Shirzaei M, Manga M, et al. Evolution and Future of the Lusi Mud Eruption Inferred from Ground Deformation[J]. Geophysical Research Letters, 2013, 40(6): 1089-1092 [16] Lipovsky B. Physical and Statistical Models in Deformation Geodesy [D]. Riverside, USA: University of California, Riverside, 2011 [17] Chaussard E, Bürgmann R, Shirzaei M, et al. Predictability of Hydraulic Head Changes and Character‍ization of Aquifer‍-‍System and Fault Properties from InSAR-Derived Ground Deformation[J]. Journal of Geophysical Research: Solid Earth, 2014, 119(8): 6572-6590 [18] 吴玉苗. 基于EOF与神经网络的隧道变形监测方法研究[D]. 成都: 西南交通大学, 2014 Wu Yumiao. Investigation on Tunnel Deformation Monitoring Methods Based on the EOF and Neural Network[D]. Chengdu: Southwest Jiaotong University, 2014 [19] 邹正波, 李辉, 吴云龙, 等. 日本Mw 9.0地震震区及其周缘2002-2015年卫星重力变化时空特征[J]. 地震学报, 2016, 38(3): 417-428 https://www.cnki.com.cn/Article/CJFDTOTAL-DZXB201603009.htm Zou Zhengbo, Li Hui, Wu Yunlong, et al. Spatial and Temporal Characteristics of Long‍-‍Term Satellite Gravity Change in the Epicenter of Mw 9.0 Japan Earthquake and Its Surrounding Regions[J]. Acta Seismologica Sinica, 2016, 38(3): 417-428 https://www.cnki.com.cn/Article/CJFDTOTAL-DZXB201603009.htm [20] Jiang L, Bai L, Zhao Y, et al. Combining InSAR and Hydraulic Head Measurements to Estimate Aquifer Parameters and Storage Variations of Confined Aquifer System in Cangzhou, North China Plain[J]. Water Resources Research, 2018, 54(10): 8234-8252
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

Figures(11)  / Tables(1)

## Article Metrics

Article views(393) PDF downloads(58) Cited by()

## Temporal and Spatial Analysis of Land Subsidence in Beijing Plain Based on TPCA

##### doi: 10.13203/j.whugis20200721
###### 1. Schools of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China2. Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China3. Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China4. Beijing Institute of Surveying and Mapping, Beijing 100038, China
Funds:

The Beijing Natural Science Foundation 8202009

the Open Project Program of the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China 01117220010020

• Author Bio:

• ###### Corresponding author:WANG Yanbing, PhD, associate professor. E-mail: wyb@cnu.edu.cn

Abstract:   Objectives  Most of the characteristics of land subsidence are analyzed separately from the perspective of temporal or spatial, and the hidden information and possible laws in the data cannot be discov‍ered simultaneously. Temporal principal component analysis (TPCA) can be used to extract temporal and spatial characteristics of temporal and spatial data in the field of geosciences. The land subsidence in the Beijing plain has typical temporal and spatial characteristics.  Methods  (1) Permanent scatterer interferometric synthetic aperture radartechnique provide a convenient method to measure land subsidence in sub-centimeter precision. 51 Envisat ASAR data acquired from 2003 to 2010 in the Beijing plain were used to produce 50 interferograms and obtain time-series deformation with nonlinear model.(2)Based on the land subsid‍ence of about 100 000 permanent scatterer (PS) points and 51-time series, we construct the original data matrix X100 000×51, calculate the correlation coefficient matrix, and use the TPCA method to analyze the temporal and spatial evolution characteristics of land subsidence in the Beijing plain.  Results  It is found that: (1) The first principal component obtained by TPCA analysis represents the long-term development trend of the spatial distribution of land subsidence. (2) The area that the second principal component that is positive has a correlation in spatial distribution with the area of compressible layer thickness above 130 m. (3) ‍The PS points where the first principal component scores are negative and the second principal component scores are positive are distributed in the severe subsidence area above 30 mm/a. There is an obvious classification of land subsidence and seasonal variation between north and south area in the severe subsid‍ence area. Specifically, in the northern subsidence area, the amount of subsidence in spring and summer is larger than that of in autumn and winter, it is an opposite variation in the southern subsidence area.  Conclusions  In general, the temporal and spatial variation of land subsidence could be studied for urban safety monitor‍ing by TPCA. It also can identify the main characteristics of the space and the law of temporal and spatial evolution. Since TPCA is a linear combination, by finding the direction with the largest variance for projection, the variables obtained are just uncorrelated and not independent of each other. Principal component analysis(PCA) only uses the second‍-‍order statistical information of the original data and ignores its high‍-‍order statistical information. Therefore, it is necessary to optimize by rotating principal components to find more physical meanings of principal components.

ZHAO Yali, WANG Yanbing, WANG Xinyu, TIAN Xiuxiu, LI Xiaojuan, YU Jie. Temporal and Spatial Analysis of Land Subsidence in Beijing Plain Based on TPCA[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1498-1506. doi: 10.13203/j.whugis20200721
 Citation: ZHAO Yali, WANG Yanbing, WANG Xinyu, TIAN Xiuxiu, LI Xiaojuan, YU Jie. Temporal and Spatial Analysis of Land Subsidence in Beijing Plain Based on TPCA[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1498-1506.
• 北京地区最早发生的地面沉降出现在20世纪30年代，位于西单-东单区域。近年来，北京的快速发展需求使得地下水长期超采，导致地面沉降的幅度和范围逐年扩大。2003—2010年的最大年沉降速率达到110 mm/a，最大累计沉降量达到了723 mm，年均沉降速率达到30 mm/a的覆盖区域面积为480 000 000 m2。北京平原区形成海淀苏家坨、昌平沙河-八仙庄、顺义、朝阳来广营、东郊八里庄、大兴榆垡6大沉降区[1]。由于地面沉降严重威胁城市安全，因此需要分析该地区的地面沉降时空演化特征，预测演化趋势。

有关地面沉降时空演化特征方面的研究方法分为时序分析与空间分析两类。时序演化特征分析通常采用典型点时序图法，从原始数据中观察地面沉降在时间序列上的变化特征或者通过统计某区域的年度沉降量来分析时序特征。刘凯斯[2]采用时序排列熵法分析北京地铁1号和6号线的地面沉降时间序列演化特征。段光耀等[3]用Mann-Kendall检验对北京平原区时空变化进行分析，研究历年发生突变现象的机理。空间演化特征分析一般采用剖面分析、梯度分析来分析空间上的不均匀地面沉降特征[4-6]。Zhou等[7]用空间分析方法等扇分析探究2012—2018年北京平原区地面沉降的扩张趋势，发现地面沉降由向东扩张变为向东和向北扩张。Zuo等[8]运用标准差椭圆方法发现北京市地面沉降漏斗的移动，揭示不均匀地面沉降。上述方法分别从时间或空间的角度研究沉降演化特征，在时间和空间上相分离，不能从时空角度发现数据中隐藏的信息和可能存在的规律。本文采用高维数据分析中的主成分分析（principal component analysis，PCA）方法研究地面沉降的时空演化特征，充分利用合成孔径雷达干涉测量（interferometric synthetic aperture radar，InSAR）所得的沉降信息具有长时序、覆盖范围广的优势。

PCA常用于数据降维，本文的应用目的是通过降维挖掘地面沉降的主要时空特征。PCA应用于地学领域中，能够有效地从时空数据中提取出某信号的时间序列与空间分布，主成分分析模式分为6种，其中T模式（temporal mode）[9]已应用在GPS站点数据、电磁测距和潮汐计数据上，用来分离出瞬态形变事件[10-11]。气象领域中，可对得出的多种环流模式进行解释[12]，分析气象雷暴日的规律[13]

T模式时间序列分析能识别多个时间序列之间的相似空间模式[14]。Rudolph等[15]通过时间主成分分析（temporal principal component analysis，TPCA）从InSAR时序数据中提取主要的时间行为模式；Lipovsky[16]运用TPCA提取长时序形变的季节信号；Chaussard等[17]对小范围且量级小的InSAR监测结果进行TPCA分析，得到第一成分为沉降主趋势，第二主成分表征为季节性形变，与承压水空间覆盖范围较一致，第三主成分表现的空间特征与断裂带位置相关；吴玉苗[18]用类似于PCA的经验正交函数得到隧道两个方向的变形时空特征；邹正波等[19]基于重力场数据识别日本地震，并研究2002—2015年的重力场时空变化特征；Jiang等[20]在对沧州中部承压含水层系统的含水层参数和地下水储量变化定量研究过程中，通过多通道奇异谱分析对地表形变和地下水数据中的季节信号进行分离，推算出弹性骨架存储率，此外，分别重构出总地下水储量、可恢复地下水储量和不可逆地下水储量。

综上所述，TPCA可应用于地学领域中，在未知先验知识条件下，提取时空数据中的时间序列和空间分布特征。本文使用TPCA方法来分析2003—2010年的北京平原区地面沉降，定量提取时空特征并进行合理解释。

Reference (20)

### Catalog

/

DownLoad:  Full-Size Img  PowerPoint