南水北调工程作为世界上最大的跨流域调水工程，有效缓解了我国南北水资源分配差异引起的水资源短缺问题，有利于我国经济发展、社会稳定以及可持续发展，直接受益人口超过1.2亿。南水北调干渠线路复杂，运行环境多变，在工程运行过程中应竭力保证工程安全。为获取南水北调中线辉县段干渠及其周围地表的时空形变信息并对地表形变的敏感性展开评价，本文首先以Sentinel-1和RadarSat-2为数据源，采用时序InSAR技术获取了干渠及其周围地表的时空形变信息；其次，以时序形变结果为基础，进行干渠及其周围地表活动变形区域的自动识别和评估；最后，在活动变形区域识别结果的基础上，选择合适的敏感性评价因子，利用超参数寻优和集成学习算法，进行干渠及其周围地表的形变敏感性评价。主要研究内容和成果如下：

（1）本文利用StaMPS InSAR和TCP InSAR技术处理了覆盖南水北调中线辉县段及其周围地表的23景RadarSat-2数据和138景Sentinel-1数据，获取了2015年至2020年间干渠及其周围地表的年平均形变速率和时序累积形变量。此外，TCP InSAR技术成功提取到加装在低相干区域的角反射器，结合角反射器点位水准监测数据分析得出两种监测结果的单期累积沉降量偏差在±4mm以内，表明了TCP InSAR技术和角反射器技术在南水北调中线干渠及其周围地表形变监测应用中具有可靠性，能够为工程安全运营管理提供数据支撑。

（2）南水北调辉县段干渠存在着高填方、深挖方、弱膨胀性岩土、湿陷性黄土以及液化砂层等特殊地质渠段，为进一步掌握特殊地质渠段的形变规律，本文结合时序累积形变量、降雨量和土壤湿度数据展开分析。相关性分析结果表明，五种特殊地质渠段的累积形变量与降雨量和土壤湿度间的相关性均处于中等及以上水平；时滞互相关分析结果表明，累积形变量变化均滞后于降雨量和土壤湿度变化1~2个月。

（3）为了调查和管理南水北调中线辉县段干渠及其周围地表形变，本文以时序InSAR技术结果为基础，进行了地表活动变形区域的识别与评估，共识别得到41个活动变形区域，变形区域内的大部分目标点的形变梯度适中，累积形变呈现指数加速型，不稳定程度处于中等水平。表明了该方法结合时序InSAR结果能够快速识别和评价南水北调中线干渠及其周围发生的地表形变，支持干渠的安全运营管理。

（4）为计算干渠及其周围特定的地表形变风险，本文利用递归特征消除算法选择了10种地表形变敏感性评价因子，针对传统麻雀搜索算法收敛速度慢且易陷入局部最优这一问题，改进麻雀搜索算法的初始化种群方式、发现者更新位置方式以及最优解扰动方式，建立了参数寻优后的地表形变敏感性评价模型。评价结果表明，大部分区域的敏感性程度较低，在气象因子等级分布重合程度适中的区域敏感性程度较高。评价模型精度评定结果表明，优化后的极限提升树模型的评价精度高于未优化的极限提升树模型的评价精度，对敏感性评价精度有一定提升，为干渠安全运营管理提供了数据支撑。

As the largest inter-basin water transfer project in the world, the South-to-North Water Transportation has effectively alleviated the water shortage problem caused by the difference in the distribution of water resources between the north and the south of my country and has a significant impact on China's future economic development, social stability, and sustainable development, with a direct beneficiary population of more than 120 million. The main canal route of the South-to-North Water Transportation is complex, and the operating environment is changeable. We should try our best to ensure the safety of the project during the operation of the project. In order to obtain the deformation information of the main canal and its surrounding surface in Huixian section of the Middle Route of the South-to-North Water Transportation and evaluate its deformation sensitivity, firstly, the spatiotemporal deformation information of the main canal and its surrounding surface is obtained by using time series InSAR technology with Sentinel-1 and RadarSat-2 as data sources; Secondly, based on the time series deformation results, the automatic identification and evaluation of the active deformation area of the main canal and its surrounding surface are carried out; Finally, based on the recognition results of active deformation area, the appropriate sensitivity evaluation factors are selected, and the deformation sensitivity of canal and its surrounding surface is evaluated by using parameter optimization and ensemble learning algorithm. The main research contents and results are as follows:

(1) In this paper, 23 RadarSat-2 data and 138 Sentinel-1 data covering the Huixian section of the middle route of the South-to-North Water Transportation and its surrounding surface are processed by using StaMPS InSAR and TCP InSAR technology, and the annual average deformation rate and time series cumulative deformation variables of the canal and its surrounding surface from 2015 to 2020 are obtained. In addition, TCP InSAR technology has successfully extracted the corner reflectors installed in the low coherence area. Combined with the point level monitoring data analysis of the corner reflectors, it is concluded that the single-phase cumulative settlement deviation of the two monitoring results is within ± 4mm, which shows that TCP InSAR technology and corner reflectors technology is reliable in the application of surface deformation monitoring in the main canal of the middle route of the South-to-North Water Transportation and its surroundings. The TCP InSAR technology and corner reflectors can provide data support for the safe operation and management of the project.

(2) There are special geological canal sections such as high fill, deep excavation, weakly expansive rock and soil, collapsible loess, and liquefied sand layer in the main canal of Huixian section of South-to-North Water Transportation. In order to grasp the deformation law of special geological canal sections, this paper analyzes them in combination with time series cumulative deformation variables, rainfall, and soil moisture data. The correlation analysis results show that the correlation between the cumulative shape variables of the five special geological canal sections and rainfall and soil moisture is at the medium level or above; The results of time lag cross-correlation analysis show that the changes of cumulative shape variables lag behind the changes of rainfall and soil moisture for 1~2 months.

(3) In order to investigate and manage the surface deformation of the main canal and its surrounding areas in the Huixian section of the middle route of the South-to-North Water Transportation, based on the results of time-series InSAR technology, this paper identifies and evaluates the active deformation areas on the surface. A total of 41 active deformation regions were identified. The deformation gradient of most target points in the deformation region is moderate, the accumulated deformations are exponentially accelerated, and the instability is at a moderate level. It is shown that the method combined with the time series InSAR results can quickly identify and evaluate the surface deformation in and around the canal of the middle route of the South-to-North Water Transportation and support the safe operation and management of the main canal.

(4) In order to calculate the surface deformation risk around the main canal and its surroundings, this paper uses the recursive feature elimination algorithm to select 10 surface deformation sensitivity evaluation factors. Aiming at the problem that the traditional sparrow search algorithm has slow convergence speed and is easy to fall into local optimization, the initial population mode, the position updating mode of the discoverer and the disturbance mode of the optimal solution of the sparrow search algorithm are improved, The evaluation results show that the susceptibility of most regions is low, and the susceptibility of regions with moderate coincidence of meteorological factor grade distribution is high. The accuracy results of the evaluation model show that the evaluation accuracy of the optimized XGBoost model is higher than that of the non-optimized XGBoost model, which improves the susceptibility evaluation accuracy to a certain extent and provides data support for the safe operation and management of the main canal of the South-to-North Water Transportation.

[1] Su B,Wu D,Zhang M, et al. Spatio-Temporal Characteristics of PM2. 5, PM10, and AOD over the Central Line Project of China’s South-North Water Diversion in Henan Province (China)[J]. Atmosphere, 2021, 12(2): 225.

[2] Lyu M,Ke Y,Guo L, et al. Change in regional land subsidence in Beijing after south-to-north water diversion project observed using satellite radar interferometry[J]. GIScience & Remote Sensing, 2020, 57(1): 140-156.

[3] Guo J,Dai Z,Li S, et al. Study on Creep Characteristics of Expansive Soil in High-Fill Channel of South-to-North Water Transfer Project[J]. Advances in Civil Engineering, 2020, 2020.

[4] Dong J,Lai S,Wang N, et al. Multi-scale deformation monitoring with Sentinel-1 InSAR analyses along the Middle Route of the South-North Water Diversion Project in China[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 100: 102324.

[5] 刘朋俊,张璐,陈元申,等. 南水北调中线湿陷性黄土区 InSAR 时序分析[J]. 人民长江, 2020, 51(6): 125.

[6] 田凡. 时序 InSAR 技术在南水北调中线形变监测中的应用研究[D]. 西安科技大学, 2020.

[7] Wu Y,Li L,Liu Z, et al. Real-Time Control of the Middle Route of South-to-North Water Diversion Project[J]. Water, 2021, 13(1): 97.

[8] Goldstein RM,Werner CL. Radar interferogram filtering for geophysical applications[J]. Geophysical research letters, 1998, 25(21): 4035-4038.

[9] Wu S,Yang Z,Ding X, et al. Two decades of settlement of Hong Kong International Airport measured with multi-temporal InSAR[J]. Remote Sensing of Environment, 2020, 248: 111976.

[10] Ferretti A,Prati C,Rocca F. Measuring subsidence with SAR interferometry applications of the permanent scatterers technique[C]//Workshop on Advanced Techniques for the Assessment of Natural Hazards in Mountain Areas: Citeseer, 2000: 1-3.

[11] Ferretti A,Prati C,Rocca F. Permanent scatterers in SAR interferometry[C]//IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No. 99CH36293): IEEE, 1999: 1528-1530.

[12] Adam N,Parizzi A,Eineder M, et al. Practical persistent scatterer processing validation in the course of the Terrafirma project[J]. Journal of Applied Geophysics, 2009, 69(1): 59-65.

[13] Berardino P,Fornaro G,Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on geoscience and remote sensing, 2002, 40(11): 2375-2383.

[14] Hooper A. A multi‐temporal InSAR method incorporating both persistent scatterer and small baseline approaches[J]. Geophysical Research Letters, 2008, 35(16).

[15] Kampes BM,Hanssen RF. Ambiguity resolution for permanent scatterer interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(11): 2446-2453.

[16] Mora O,Mallorqui JJ,Broquetas A. Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2243-2253.

[17] Werner C,Wegmuller U,Strozzi T, et al. Interferometric point target analysis for deformation mapping[C]//IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477): IEEE, 2003: 4362-4364.

[18] Sun Q,Zhang L,Ding X, et al. Investigation of slow-moving landslides from ALOS/PALSAR images with TCPInSAR: A case study of Oso, USA[J]. Remote Sensing, 2015, 7(1): 72-88.

[19] Ding X,Zhang L. Development of a multi-temporal insar processing algorithm-TCP-InSAR[C]//Workshop on Emerging Sensors and Surveying Technologies, 2014.

[20] Zhang L,Sun Q,Hu J. Potential of TCPInSAR in Monitoring Linear Infrastructure with a Small Dataset of SAR Images: Application of the Donghai Bridge, China[J]. Applied Sciences, 2018, 8(3): 425.

[21] Yang C,Hu J,Cheng Z, et al. CRInSAR Using Two-Step LAMBDA Algorithm for Nonlinear Deformation Estimation: Case Study of Monitoring Xiangtan Converter Station, China[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(6): 963-967.

[22] Luo S,Feng G,Xiong Z, et al. An Improved Method for Automatic Identification and Assessment of Potential Geohazards Based on MT-InSAR Measurements[J]. Remote Sensing, 2021, 13(17): 3490.

[23] Barra A,Solari L,Béjar-pizarro M, et al. A methodology to detect and update active deformation areas based on sentinel-1 SAR images[J]. Remote sensing, 2017, 9(10): 1002.

[24] Shi L,Gong H,Chen B, et al. Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method[J]. Remote Sensing, 2020, 12(24): 4044.

[25] Ghorbanzadeh O,Blaschke T,Aryal J, et al. A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping[J]. Journal of Spatial Science, 2020, 65(3): 401-418.

[26] 霍志涛,张业明,金维群,等. 三峡库区滑坡监测中的新技术和新方法[J]. 华南地质与矿产, 2006, 4(4): 69-74.

[27] Lee S,Ryu J,Min K, et al. Landslide susceptibility analysis using GIS and artificial neural network[J]. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 2003, 28(12): 1361-1376.

[28] Devara M,Tiwari A,Dwivedi R. Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis[J]. null, 2021, 12(1): 675-693.

[29] Kim K,Lee S,Oh H, et al. Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS[J]. Environmental Geology, 2006, 50(8): 1183-1191.

[30] Oh H,Lee S. Assessment of ground subsidence using GIS and the weights-of-evidence model[J]. Engineering Geology, 2010, 115(1): 36-48.

[31] Zhi-xiang T,Pei-xian L,Li-li Y, et al. Study of the method to calculate subsidence coefficient based on SVM[J]. Procedia Earth and Planetary Science, 2009, 1(1): 970-976.

[32] Choi J,Kim K,Lee S, et al. Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea[J]. Environmental Earth Sciences, 2010, 59(5): 1009-1022.

[33] Park I,Choi J,Lee MJ, et al. Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping[J]. Computers & Geosciences, 2012, 48: 228-238.

[34] Freund Y,Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of computer and system sciences, 1997, 55(1): 119-139.

[35] Breiman L. Bagging predictors (technical report 421)[J]. University of California, Berkeley, 1994.

[36] Wolpert DH. Stacked generalization[J]. Neural networks, 1992, 5(2): 241-259.

[37] 汪云甲. 矿区生态扰动监测研究进展与展望[J]. 测绘学报, 2017, 46(10): 1705-1716.

[38] Rogers A,Ingalls R. Venus: Mapping the surface reflectivity by radar interferometry[J]. Science, 1969, 165(3895): 797-799.

[39] Gabriel AK,Goldstein RM,Zebker HA. Mapping small elevation changes over large areas: Differential radar interferometry[J]. Journal of Geophysical Research: Solid Earth, 1989, 94(B7): 9183-9191.

[40] 朱建军,李志伟,胡俊. InSAR 变形监测方法与研究进展[J]. 测绘学报, 2017, 46(10): 1717-1733.

[41] Sandwell DT,Price EJ. Phase gradient approach to stacking interferograms[J]. Journal of Geophysical Research: Solid Earth, 1998, 103(B12): 30183-30204.

[42] Adam N,Eineder M,Yague-martinez N, et al. High resolution interferometric stacking with TerraSAR-X[C]//IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium: IEEE, 2008: II-117.

[43] Fan J,Guo H,Guo X, et al. Monitoring subsidence in Tianjin area using interferogram stacking based on coherent targets[J]. JOURNAL OF REMOTE SENSING-BEIJING-, 2008, 12(1): 117.

[44] Short N,Brisco B,Couture N, et al. A comparison of TerraSAR-X, RADARSAT-2 and ALOS-PALSAR interferometry for monitoring permafrost environments, case study from Herschel Island, Canada[J]. Remote Sensing of Environment, 2011, 115(12): 3491-3506.

[45] 何敏,何秀凤. 利用时间序列干涉图叠加法监测江苏盐城地区地面沉降[J]. 武汉大学学报(信息科学版), 2011, 36(12): 1461-1465.

[46] 刘斌,葛大庆,王珊珊,等. TOPS和ScanSAR模式InSAR在广域地灾隐患识别中的联合应用[J]. 武汉大学学报(信息科学版), 2020, 45(11): 1756-1762.

[47] 张成龙,李振洪,余琛,等. 利用GACOS辅助下InSAR Stacking对金沙江流域进行滑坡监测[J]. 武汉大学学报(信息科学版), 2021, 46(11): 1649-1657.

[48] 熊国华,杨成生,朱赛楠,等. 基于MSBAS技术的金沙江上游色拉滑坡形变分析[J]. 中国地质灾害与防治学报, 2021, 32(5): 1-9.

[49] 杨成生,董继红,朱赛楠,等. 金沙江结合带巴塘段滑坡群InSAR探测识别与形变特征[J]. 地球科学与环境学报, 2021, 43(2): 398-408.

[50] Kim J,Kim D,Kim S, et al. Monitoring of urban land surface subsidence using PSInSAR[J]. Geosciences Journal, 2007, 11(1): 59.

[51] Perissin D,Prati C,Rocca F, et al. PSInSAR analysis over the Three Gorges Dam and urban areas in China[C]//2009 Joint Urban Remote Sensing Event: IEEE, 2009: 1-5.

[52] Liu G,Jia H,Zhang R, et al. Exploration of subsidence estimation by persistent scatterer InSAR on time series of high resolution TerraSAR-X images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 4(1): 159-170.

[53] 王茹,杨天亮,杨梦诗,等. PS-InSAR技术对上海高架路的沉降监测与归因分析[J]. 武汉大学学报(信息科学版), 2018, 43(12): 2050-2057.

[54] 张双成,司锦钊,徐永福,等. 时序InSAR用于安康膨胀土机场稳定性监测[J]. 武汉大学学报(信息科学版), 2021, 46(10): 1519-1528.

[55] Tizzani P,Berardino P,Casu F, et al. Surface deformation of Long Valley caldera and Mono Basin, California, investigated with the SBAS-InSAR approach[J]. Remote Sensing of Environment, 2007, 108(3): 277-289.

[56] Qu F,Zhang Q,Lu Z, et al. Land subsidence and ground fissures in Xi'an, China 2005–2012 revealed by multi-band InSAR time-series analysis[J]. Remote Sensing of Environment, 2014, 155: 366-376.

[57] 杨成生,张勤,赵超英,等. 短基线集InSAR技术用于大同盆地地面沉降、地裂缝及断裂活动监测[J]. 武汉大学学报(信息科学版), 2014, 39(8): 945-950.

[58] 敖萌,张路,廖明生,等. 基于方差分量估计的多源InSAR数据自适应融合形变测量[J]. 地球物理学报, 2020, 63(8): 2901-2911.

[59] 张永光,田凡,李迎春,等. 时序 InSAR 在南水北调中线形变监测中的应用[J]. 长江科学院院报, 2021, 38(8): 72.

[60] 许强,蒲川豪,赵宽耀,等. 延安新区地面沉降时空演化特征时序InSAR监测与分析[J]. 武汉大学学报(信息科学版), 2021, 46(7): 957-969.

[61] Kiseleva Е,Mikhailov V,Smolyaninova E, et al. PS-InSAR monitoring of landslide activity in the Black Sea coast of the Caucasus[J]. Procedia Technology, 2014, 16: 404-413.

[62] Nee TS,Tien TL,Lateh H. A comparison between stamps technique with TerraSAR-X data and theodolite surveying on land deformation monitoring (Case study: Gunung pass)[C]//2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): IEEE, 2015: 3544-3547.

[63] 陈德良,陆燕燕,贾东振. 基于 StaMPS-InSAR 的常州地区地表形变监测研究[J]. 人民长江, 2018, 12.

[64] 杨崇,刘国祥,于冰,等. 基于InSAR形变的辽河油田曙光采油厂储层参数反演[J]. 国土资源遥感, 2020, 32(1): 209-215.

[65] 何秀凤,高壮,肖儒雅,等. 多时相Sentinel-1A InSAR的连盐高铁沉降监测分析[J]. 测绘学报, 2021, 50(5): 600-611.

[66] Ferretti A,Fumagalli A,Novali F, et al. A new algorithm for processing interferometric data-stacks: SqueeSAR[J]. IEEE transactions on geoscience and remote sensing, 2011, 49(9): 3460-3470.

[67] Bellotti F,Bianchi M,Colombo D, et al. Mathematics of planet earth[M]: Springer, 2014: 287-290.

[68] 蒋弥,丁晓利,何秀凤,等. 基于快速分布式目标探测的时序雷达干涉测量方法:以Lost Hills油藏区为例[J]. 地球物理学报, 2016, 59(10): 3592-3603.

[69] Liu Y,Ma P,Lin H, et al. Distributed scatterer InSAR reveals surface motion of the ancient Chaoshan residence cluster in the Lianjiang Plain, China[J]. Remote Sensing, 2019, 11(2): 166.

[70] 李毅,蒋金雄,杜玉玲,等. 融合分布式目标的矿区采动地表时序InSAR监测[J]. 中国矿业大学学报, 2020, 49(6): 1199-1206, 1232.

[71] 杨嘉威,谭志祥,邓喀中. 基于DS-InSAR的矿区铁路线沉陷监测与规律分析[J]. 煤炭工程, 2021, 53(9): 143-148.

[72] Zhang L. Temporarily coherent point SAR interferometry[J], 2012.

[73] Zhang L,Lu Z,Ding X, et al. Mapping ground surface deformation using temporarily coherent point SAR interferometry: Application to Los Angeles Basin[J]. Remote Sensing of Environment, 2012, 117: 429-439.

[74] Chen R,Liu SH,Hsu Y, et al. Real-time monitoring of Deep-seated gravitational slope deformation by TCP-InSAR technology and GPS time series analysis in the Taiwan mountain area[C], 2014.

[75] 鹿璐,范洪冬,郑美楠,等. 基于 TCP-InSAR 的采动区铁路动态沉降监测与时序分析[J]. 大地测量与地球动力学, 2019, 39(2): 164-168.

[76] Li Z,Wang Q,Zhou F, et al. Integrating an interferometric synthetic aperture radar technique and numerical simulation to investigate the Tongmai old deposit along the Sichuan-Tibet Railway[J]. Geomorphology, 2021, 377: 107586.

[77] Tomás R,Pagán JI,Navarro JA, et al. Semi-automatic identification and pre-screening of geological–geotechnical deformational processes using persistent scatterer interferometry datasets[J]. Remote Sensing, 2019, 11(14): 1675.

[78] Navarro JA,Cuevas M,Barra A, et al. Detection of Active Deformation Areas based on Sentinel-1 imagery: An efficient, fast and flexible implementation[C]//Proceedings of the 18th International Scientific and Technical Conference, Crete, Greece, 2018: 24-27.

[79] Solari L,Barra A,Herrera G, et al. Fast detection of ground motions on vulnerable elements using Sentinel-1 InSAR data[J]. Geomatics, Natural Hazards and Risk, 2018, 9(1): 152-174.

[80] Merad M,Verdel T,Roy B, et al. Use of multi-criteria decision-aids for risk zoning and management of large area subjected to mining-induced hazards[J]. Tunnelling and underground space technology, 2004, 19(2): 125-138.

[81] Tzampoglou P,Loupasakis C. Mining geohazards susceptibility and risk mapping: The case of the Amyntaio open-pit coal mine, West Macedonia, Greece[J]. Environmental Earth Sciences, 2017, 76(15): 1-16.

[82] Tien bui D,Shahabi H,Shirzadi A, et al. Land subsidence susceptibility mapping in south korea using machine learning algorithms[J]. Sensors, 2018, 18(8): 2464.

[83] Mohammady M,Pourghasemi HR,Amiri M. Assessment of land subsidence susceptibility in Semnan plain (Iran): A comparison of support vector machine and weights of evidence data mining algorithms[J]. Natural Hazards, 2019, 99(2): 951-971.

[84] Rezaei M,Yazdani noori Z,Dashti barmaki M. Land subsidence susceptibility mapping using analytical hierarchy process (AHP) and Certain Factor (CF) models at Neyshabur plain, Iran[J]. Geocarto International, 2020: 1-17.

[85] Hakim WL,Achmad AR,Lee C. Land subsidence susceptibility mapping in jakarta using functional and meta-ensemble machine learning algorithm based on time-series InSAR data[J]. Remote Sensing, 2020, 12(21): 3627.

[86] Ranjgar B,Razavi-termeh SV,Foroughnia F, et al. Land subsidence susceptibility mapping using persistent scatterer sar interferometry technique and optimized hybrid machine learning algorithms[J]. Remote Sensing, 2021, 13(7): 1326.

[87] 伍洲云. 苏锡常地区地裂缝灾害危险性评价与预测[J]. 水文地质工程地质, 2004, (1): 28-31, 51.

[88] 李伟,杨旭东,马学军,等. 模糊评判法在沧州市地面沉降灾害危险性评价中的应用[J]. 勘察科学技术, 2006, (6): 39-42, 58.

[89] 朱锦旗,焦珣,于军,等. 基于GA-ANN的苏锡常地裂缝危险性评价[J]. 水文地质工程地质, 2008, (4): 106-110.

[90] 刘凯斯,宫辉力,陈蓓蓓. 基于地面沉降监测的地铁运营危险性评价——以北京地铁6号线为例[J]. 地理与地理信息科学, 2018, 34(3): 68-73.

[91] 石鹏远,余洁,朱琳,等. 应用地理探测器改进地面沉降危险性评估模型的研究[J]. 中国地质灾害与防治学报, 2019, 30(3): 101-112.

[92] 郑渊茂,王翠平,王豪伟,等. 厦门市地面沉降影响分析与风险评价[J]. 生态学报, 2021, 41(1): 388-400.

[93] 何理,焦蒙蒙,王喻宣,等. 基于组合模型的天津市地面沉降预测及危险性评价[J]. 水利水电技术(中英文), 2022, 53(1): 178-189.

[94] Rosen PA,Hensley S,Joughin IR, et al. Synthetic aperture radar interferometry[J]. Proceedings of the IEEE, 2000, 88(3): 333-382.

[95] 王宏宇. 短时空基线 PS-InSAR 技术在大同地区的形变监测研究[J]. 长安大学, 2011.

[96] 舒宁. 雷达影像干涉测量原理[M]: 武汉大学出版社, 2003.

[97] 廖明生,林珲. 雷达干涉测量: 原理与信号处理基础[M]: 测绘出版社, 2003.

[98] 胡东明. 基于时间序列InSAR技术的西安市2015-2019年地表沉降监测研究[D]. 长安大学, 2020.

[99] 杨秋玲. 低相干雷达干涉信息相位解缠技术研究与应用[D]. 成都理工大学, 2011.

[100] 卢星宇. 基于InSAR技术的九寨沟地质灾害危险性评价研究[D]. 电子科技大学, 2021.

[101] 宁航. 基于时序InSAR技术的宁波市轨道交通一号线沿线地表沉降监测研究[D]. 长安大学, 2021.

[102] 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.

[103] 杨崇. 辽河油田地表沉降InSAR监测及储层参数反演[D]. 西南交通大学, 2019.

[104] 董雅竹. 基于 PS-InSAR 技术的地表形变过程研究[D]. 中国科学技术大学, 2020.

[105] 张帆. 运城盆地地裂缝活动时序InSAR监测与机理分析[D]. 长安大学, 2019.

[106] 郑万基,孙倩,刘小鸽. 利用Sentinel-1A数据和TCP-InSAR技术监测金沙江中游兴培当至草可都段滑坡[J]. 测绘工程, 2018, 27(9): 51-58, 63.

[107] Wu S,Zhang L,Ding X, et al. Pixel-wise MTInSAR estimator for integration of coherent point selection and unwrapped phase vector recovery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(5): 2659-2668.

[108] Zhang L,Ding X,Lu Z. Modeling PSInSAR time series without phase unwrapping[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 49(1): 547-556.

[109] Friedman JH,Bentley JL,Finkel RA. An algorithm for finding best matches in logarithmic expected time[J]. ACM Transactions on Mathematical Software (TOMS), 1977, 3(3): 209-226.

[110] Liang H,Zhang L,Lu Z, et al. Nonparametric estimation of DEM error in multitemporal InSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12): 10004-10014.

[111] Agram P,Simons M. A noise model for InSAR time series[J]. Journal of Geophysical Research: Solid Earth, 2015, 120(4): 2752-2771.

[112] Liang H,Zhang L,Ding X, et al. Toward mitigating stratified tropospheric delays in multitemporal InSAR: A quadtree aided joint model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(1): 291-303.

[113] Béjar-pizarro M,Socquet A,Armijo R, et al. Andean structural control on interseismic coupling in the North Chile subduction zone[J]. Nature Geoscience, 2013, 6(6): 462-467.

[114] 徐东彪,刘豪杰,张永光,等. PS-InSAR 技术在南水北调中线河南省辉县市膨胀土段安全监测中的应用初探[J]. 测绘与空间地理信息, 2019, 42(12): 31-34.

[115] Tax D,Duin R. Feature scaling in support vector data descriptions[J]. Learning from Imbalanced Datasets, 2000: 25-30.

[116] Pearson K. Correlation coefficient[C]//Royal Society Proceedings, 1895: 214.

[117] Farrar DE,Glauber RR. Multicollinearity in regression analysis: the problem revisited[J]. The Review of Economic and Statistics, 1967: 92-107.

[118] Liu H,Yu L. Toward integrating feature selection algorithms for classification and clustering[J]. IEEE Transactions on knowledge and data engineering, 2005, 17(4): 491-502.

[119] Guyon I,Weston J,Barnhill S, et al. Gene selection for cancer classification using support vector machines[J]. Machine learning, 2002, 46(1): 389-422.

[120] Chen T,He T,Benesty M, et al. Xgboost: extreme gradient boosting[J]. R package version 0.4-2, 2015, 1(4): 1-4.

[121] 薛建凯. 一种新型的群智能优化技术的研究与应用[D]. 东华大学, 2020.

[122] Xue J,Shen B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34.

[123] Pareek NK,Patidar V,Sud KK. Image encryption using chaotic logistic map[J]. Image and vision computing, 2006, 24(9): 926-934.

[124] Li C,Luo G,Qin K, et al. An image encryption scheme based on chaotic tent map[J]. Nonlinear Dynamics, 2017, 87(1): 127-133.

[125] Xiang T,Liao X,Wong K. An improved particle swarm optimization algorithm combined with piecewise linear chaotic map[J]. Applied Mathematics and Computation, 2007, 190(2): 1637-1645.

[126] Yang X,He X. Firefly algorithm: recent advances and applications[J]. International journal of swarm intelligence, 2013, 1(1): 36-50.

[127] Yesilnacar E,Topal T. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)[J]. Engineering Geology, 2005, 79(3): 251-266.