煤炭资源的大规模或过度开采引发了矿区一系列生态环境、地表沉陷以及人民安全等问题，加之小型煤矿的增多又加剧了这些问题的严重性。因此，亟须对矿区开采沉陷进行及时、高效的全面监测及分析，从而掌握开采沉陷规律，为矿区开采及治理提供可靠依据。合成孔径雷达干涉测量（Interferometry Synthetic Aperture Radar，InSAR）技术作为一种先进的对地观测方法，因其全天时、全天候、大范围等特点得到了迅速发展，被广泛应用于矿区地表开采沉降监测。尤其在小量级且缓慢的矿区地表沉陷监测中得到了良好的结果。但是矿区开采存在量级大、速度快等特点，易造成失相干、大气延迟及DEM精度低等问题，使得传统的InSAR技术在矿区地表监测应用中受到限制。针对该问题，本文采用时序InSAR技术对陕西省黄陵矿区进行开采沉陷监测研究，主要研究工作和结论如下：

（1）以陕西省黄陵矿区为研究区域，选取覆盖研究区域的12景ALOS PALSAR（2007年-2009年）和92景Sentinel-1A（2017年-2020年）数据。采用干涉图叠加技术（Stacking-InSAR）分别对两个不同时期的数据进行大范围沉降探测，成功获得了黄陵矿区的年平均沉降速率。然后结合搜集到的黄陵矿区工作面开采的相关资料，利用Arcgis软件对以上两种监测结果进行对比分析，得到Stacking-InSAR技术监测结果与矿区工作面分布相一致，表明了该技术可以对矿区大范围地表沉降进行监测，且监测结果较为可靠。

（2）选取覆盖803工作面完整开采时间的20景Sentinel-1A数据，首先采用组合模式的小基线集和相干点目标分析技术（SBAS-IPTA）获得了年平均沉降速率和时序累积沉降量，结合矿区相关资料，对监测结果进行剖线、等值线及特征点分析，得到沉降区域与矿区工作面分布、开采情况具有良好的一致性。然后采用多主影像相干点目标分析技术（多主影像IPTA）对20景Sentinel-1A进行处理，提取了年平均沉降速率及时序累积沉降量，并对监测结果进行对比分析，发现监测的沉降速率和最大累积沉降量与SBAS-IPTA监测结果相差约15 mm，且沉降区域与工作面分布一致，表明多主影像IPTA技术也可以应用于矿区地表微小、缓慢沉降监测。

（3）将SBAS-IPTA监测的时序累积沉降量与支持向量回归算法相结合进行矿区开采沉陷预计。首先将时序累积沉降量划分为训练集和测试集；然后利用训练集构建模型；最后利用平均绝对误差、均方根误差、决定系数三个精度评价指标参数对测试集的预计结果进行精度评估，得到支持向量回归预计结果与SBAS-IPTA监测结果较一致。结果表明本文所构建的开采沉陷预计模型精度高，满足矿区工程需求，为矿区开采沉陷预计提供一种方法。

Large-scale or over-exploitation of coal resources has led to a series of ecological environment, surface subsidence and people's safety problems in mining areas, and the increase of small coal mines has aggravated the seriousness of these problems. Therefore, it is urgent to carry out timely and efficient comprehensive monitoring and analysis of mining subsidence, so as to master the law of mining subsidence and provide a reliable basis for mining and governance of mining areas. As an advanced earth observation method, Interferometry Synthetic Aperture Radar (InSAR) technology has developed rapidly due to its all-weather, all-weather, and large-scale features, and has been widely used in surface mining subsidence monitoring in mining areas. Especially, good results are obtained in the monitoring of surface subsidence in small and slow mining areas. However, the mining area has the characteristics of large magnitude and fast speed, which can easily cause problems such as decoherence, atmospheric delay and low precision of DEM, so that the traditional InSAR technology is limited in the application of surface subsidence monitoring in mining areas. Aiming at this problem, this thesis adopts time-series InSAR technology to monitor mining subsidence in Huangling mining area of Shaanxi Province. The main research work and conclusions are as follows:
(1) Taking Huangling mining area of Shaanxi Province as the study area, the 12-scene ALOS PALSAR (2007-2009) and 92-scene Sentinel-1A (2017-2020) data covering the study area were selected. Stacking-InSAR technique was used to detect the subsidence in two different periods, and the annual average subsidence rate of Huangling mining area was obtained successfully. Then, combined with the relevant data of working face mining in Huangling mining area, ArcGIS software was used to compare and analyze the above two monitoring results. The results show that the monitoring results of the Stacking-InSAR technology are consistent with the distribution of the working face in the mining area, which indicates that the Stacking-InSAR technology can be Monitoring of large-scale surface subsidence in the mining area, and the monitoring results are relatively reliable.
(2) The Sentinel-1A data with 20-scene covering the full mining time of 803 working face were selected. Firstly, the combined model of Small Baseline Subsat and Interferometric Point Target Analysis technology (SBAS-IPTA) was used to obtain the annual average subsidence rate and time-series cumulative subsidence. Combined with the relevant mining data, the profile, contour and feature points of the monitoring results were analyzed. And it is concluded that the subsidence area is in good agreement with the distribution of working face and mining situation. Then, the 20-scene Sentinel-1A was processed by multi-main image coherence point target analysis (IPTA) technology, and the average annual subsidence rate and time-series cumulative subsidence were extracted. The monitoring results were compared and analyzed, and it was found that the monitored subsidence rate and maximum cumulative subsidence were about 15 mm different from the SBAS-IPTA monitoring results. And the subsidence area is consistent with the distribution of the working face, which indicates that the multi-master image IPTA technology can also be applied to the small and slow surface subsidence of the mining area.
(3) The time-series cumulative subsidence monitored by SBAS-IPTA was combined with the Support Vector Regression (SVR) algorithm to predict mining subsidence. Firstly, the cumulative subsidence of time-series was divided into training set and test set.Then the training set was used to construct the model. Finally, three precision evaluation index parameters of mean absolute error, root mean square error and determination coefficient were used to evaluate the accuracy of the predicted results of the test set. The predicted results of SVR are consistent with the monitoring results of SBAS-IPTA. The results show that the mining subsidence prediction model constructed in this thesis has high accuracy, meets the engineering requirements of mining area, and provides a method for mining subsidence prediction.

[1]何国清, 杨伦, 凌赓娣, 等. 矿山开采沉陷学[M]. 徐州:中国矿业大学出版社, 1991.

[2]陈炳乾. 面向矿区沉降监测的InSAR技术及应用研究[D]. 中国矿业大学, 2015.

[3]卫勇锋. 黄陵矿区导水裂隙带探测及冒裂安全性分区预测[D]. 西安科技大学, 2019.

[4]种可. 黄陵矿区地质灾害危险性评价研究[D]. 西安科技大学, 2009.

[5]刘茜茜. 基于多源星载SAR数据的矿区地面沉降监测研究[D]. 中国矿业大学, 2018.

[6]Rogers A E, Ingalls R P. Venus : mapping the surface reflectivity by radar interferometry[J]. Science, 1969,165(3895):797.

[7]Graham L C . Synthetic interferometer radar for topographic mapping[J]. Proceedings of the IEEE, 1974,62(6):763-768.

[8]Zebker H A, Goldstein R M. Topographic mapping from interferometric synthetic aperture rader observations[J]. Journal of Geophysical Research: Solid Earth (1978-2012), 1986, 91(B5);4993-4999.

[9]Gabriel A K , Goldstein R M , Zebker H A . Mapping small elevation changes over large areas: differential radar interferometry. J Geophys Res 94(B7):9183-9191[J]. Journal of Geophysical Research Atmospheres, 1989, 94(B7):9183-9191.

[10]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.

[11]David T. Sandwell, Evelyn J. Price. Phase gradient approach to stacking interferograms[J]. John Wiley & Sons, Ltd, 1998,103(B12).

[12]Ferretti A, Prati C, Rocca F. Permanent scatterers in SAR interferometry[J]. IEEE Transa-ctions on Geoscience and Remote Sensing, 2001,39(1):8-20.

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

[14]Werner C, Wegmuller U, Strozzi T, et al. Interferometric point target analysis for deformation mapping[C]// IGARSS 2003. (IEEE Cat. No.03CH37477). IEEE, 2004.

[15]Booth C J, Kurpis G P. IEEE Standard Dictionary of Electrical and Electronics Terms[M]. IEEE New York,USA, 1993.

[16]Wiley, C A. Synthetic Aperture Radars-A Paradigm for Technology Evolution[J]. IEEE Trans. Aerospace Elec. Sys., AES-21, 1985,440-443.

[17]Sandwell D T. Biharmonic spline interpolation of GEOS-3 and SEASAT altimeter data[J]. Geophysical research letters, 1987,14(2):139-142.

[18]Hounam D , Werner M . The Shuttle Radar Topography Mission[J]. Reviews of Geophysics, 1999, 45(2):361.

[19]Maurizio, Santoro, Jan, Askne, Gary, Smith, et al. Stem volume retrieval in boreal forests from ERS-1/2 interferometry[J]. Remote Sensing of Environment, 2002,81(1):19-35.

[20]Sandwell D T, Myer D, Mellors R, et al. Accuracy and Resolution of ALOS Interferometry: Vector Deformation Maps of the Father's Day Intrusion at Kilauea[J]. IEEE Transactions on Geoscience & Remote Sensing, 2008,46(11):3524-3534.

[21]Hillman A, Comi D, Branson W. Countdown for RADARSAT-2 system operations[C]. Geoscience and Remote Sensing Symposium, . Proceedings. IEEE International, 2005,70:1-4.

[22]McNaim H, Van Der Sanden J J, Brown R J. The potential of RADARSAT-2 for crop mapping and assessing crop condition[C]. Proceedings of Second International Conference on Geospatial Information in Agriculture and Forestry. 2000,45:10-12.

[23]Caltagirone F, Capuzi A, Leonard R. COSMO-SkyMed: Earth Observation Italian Constellation for Risk Management and Security[J]. Progress in Astronautics and Aeronautics, 2007,220:473-477.

[24]Covello F, Battazza F, Coletta A. COSMO- SkyMed an existing opportunity for abserving the earth[J]. Journal of Geodynamics, 2010,49(3):171-180.

[25]廖明生, 田馨, 赵卿. TerraSAR_X/TanDEM_X雷达遥感计划及其应用[J]. 测绘信息与工程, 2007,32(2):44-46.

[26]刘国祥. 合成孔径雷达遥感新技术——InSAR介绍[J]. 四川测绘, 2004(02):92-95.

[27]刘国祥, 丁晓利, 陈永奇, 等. 极具潜力的空间对地观测新技术—合成孔径雷达干涉[J]. 地球科学进展, 2000,15:734-740.

[28]Goldstein R M, Engelhardt H, Kamb B, et al. Satellite Radar Interferometry for Monitoring Ice Sheet Motion: Application to an Antarctic Ice Stream[J]. Science, 1993,262(5139):1525-1530.

[29]Massonnet D , Rossi M , Carmona, César, et al. The displacement field of the Landers earthquake mapped by radar interferometry[J]. Nature, 1993,364(6433):138-142.

[30]Carnec C, Massonnet D, King C. Two excamples of the use of SAR interferometry on displacement fields of small spatial extent[J]. Geophysical Research Letters, 2013,23(24):3579-3582.

[31]Massonnet D, Briole P, Arnaud A. Deflation of Mount Etna monitored by spaceborne radarinterferometry[J]. Nature, 1995,375(6532):567-570.

[32]何敏, 何秀凤. 利用D-InSAR技术监测盐城地区地表形变[J]. 测绘通报, 2010(11):1-3.

[33]吴立新, 高均海, 葛大庆, 等. 基于D-InSAR的煤矿区开采沉陷遥感监测技术分析[J].地理与地理信息科学, 2004(02):22-25+37.

[34]吴立新, 高均海, 葛大庆, 等. 工矿区地表沉陷D-InSAR监测试验研究[J]. 东北大学学报, 2005(08):778-782.

[35]张建龙, Vernon H. SINGHROY, 李晓春, 等. 差分干涉测量技术在四川甲居滑坡监测中应用研究[J]. 成都理工大学学报(自然科学版), 2010,37(05):554-557.

[36]Lyons, Suzanne. Fault creep along the southern San Andreas from interferometric synthetic aperture radar, permanent scatterers, and stacking[J]. Journal of Geophysical Research Solid Earth, 2003,108(B1):2047.

[37]Ioannis K, Charalabos K, Dimitrios P, et al. A Methodology to Validate the InSAR Derived Displacement Field of the September 7th, 1999 Athens Earthquake Using Terrestrial Surveying. Improvement of the Assessed Deformation Field by Interferometric Stacking[J]. Sensors, 2008,8(7):4119-4134.

[38]龙四春, 张诗玉, 冯涛, 等. 公用主影像干涉图加权叠加方法及其在地面沉降监测中的应用[J]. 测绘学报, 2012,41(06):844-850.

[39]张洋, 汪云甲, 闫世勇. 基于Stacking InSAR技术的沛北矿区沉降监测[J]. 煤炭技术, 2016,35(07):102-105.

[40]沙永莲, 刘国祥, 王晓文, 等. 基于Stacking时序InSAR的北泉矿区沉陷监测[J]. 测绘科学技术, 2020,8(2):60-67.

[41]Ferretti A, Prati C, Rocca F L. Permanent Scatterers in SAR Interferometry : Remote Sensing[J]. International Society for Optics and Photonics, 1999.

[42]Ferretti A, Prati C, Rocca F. Non-linear subsidence rate estimation using permanent scatterers in differential SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002,38(5):2202-2212.

[43]Kampes B M, Hanssen R F. Ambiguity Resolution for Permanent Scatterer Interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(11):2446-2453.

[44]Hooper A. Persistent scatter radar interferometry for crustal deformation studies and modeling of volcanic deformation[D]. Palo Alto: Stanford University, 2004.

[45]Walter D, Hoffmann J, Kampes B, et al. Radar Interferometric Analysis of Mining Induced Surface Subsidence using Permanent Scatterers[C]//Envisat & ERS Symposium. 2005,(45):572.

[46]陈强, 刘国祥, 李永树, 等. 干涉雷达永久散射体自动探测——算法与实验结果[J]. 测绘学报, 2006(02):112-117.

[47]刘国祥, 陈强, 丁晓利. 基于雷达干涉永久散射体网络探测地表形变的算法与实验结果[J]. 测绘学报, 2007(01):13-18.

[48]陶秋香. PS InSAR关键技术及其在矿区地面沉降监测中的应用研究[D]. 山东科技大学, 2010.

[49]何建国. 长时序星载InSAR技术的煤矿沉陷监测应用研究[D]. 中国矿业大学（北京）, 2010.

[50]邢学敏. CRInSAR与PSInSAR联合监测矿区时序地表形变研究[D]. 中南大学, 2011.

[51]朱武, 张勤, 丁晓利. 多参考点的PS-InSAR变形监测数据处理[J]. 测绘学报, 2012,41(06):886-890+903.

[52]邢学敏, 贺跃光, 吴凡, 等. 永久散射体雷达差分干涉反演矿区时序沉降场[J]. 遥感学报, 2016,20(03):491-501.

[53]邹昊, 邓喀中. 基于PSInSAR技术的老采空区地表沉降监测研究[J]. 煤炭技术, 2017,36(05):175-177.

[54]魏纪成, 张红霞, 白泽朝, 等. 融合D-InSAR与PS-InSAR的神东矿区开采沉陷监测方法[J]. 金属矿山, 2019(10):55-60.

[55]田文涛. 时序InSAR技术用于神东矿区塌陷的监测研究[D]. 长安大学, 2019.

[56]魏聪敏, 葛伟鹏, 邵延秀, 等. 利用Sentinel-1A合成孔径雷达干涉时间序列监测陇东地区地面沉降变形[J]. 遥感技术与应用, 2020,35(04):864-872.

[57]Lanari R, Lundgren P, Manzo M, et al. Satellite rader interferometry time series analysis of surface deformation for Los Angeles, California[J]. Geophysical Research Letters, 2004,31(23).

[58]Lauknes T R , Dehls J , Larsen Y , et al., A comparison of SBAS and PS ERS InSAR for subsidence monitoring in Oslo, Norway[J]. Fringe Workshop, 2006,25.

[59] Cigna F, Rawlins B G, Jordan C J, et al. Intermittent Small Baseline Subset(ISBAS) InSAR of rural and vegetated terrain: a new method to monitor land motion applied to peat lands in Wales, UK[J]. EGU General Assembly, 2014.

[60]葛大庆, 王艳, 郭小方, 等. 基于相干点目标的多基线D-InSAR技术与地表形变监测[J]. 遥感学报, 2007(04):574-580.

[61]尹宏杰, 朱建军, 李志伟, 等. 基于SBAS的矿区形变监测研究[J]. 测绘学报, 2011,40(01):52-58.

[62]张学东, 葛大庆, 吴立新, 等. 基于相干目标短基线InSAR的矿业城市地面沉降监测研究[J]. 煤炭学报, 2012,37(10):1606-1611.

[63]朱煜峰. 矿区地面沉降的InSAR监测及参数反演[D]. 中南大学, 2013.

[64]赵伟颖, 邓喀中, 杨俊凯, 等. 基于SBAS技术的采动区形变对建筑物的影响监测[J]. 煤矿安全, 2015,46(02):205-208.

[65]李达, 邓喀中, 高晓雄, 等. 基于SBAS-InSAR的矿区地表沉降监测与分析[J]. 武汉大学学报(信息科学版), 2018,43(10):1531-1537.

[66]阎跃观, 代文晨, 赵传武, 等. 基于SBAS-InSAR技术的矿区地表移动规律研究[J]. 中国矿业, 2019,28(S2):177-180.

[67]刘琪. 基于InSAR技术的峰峰矿区老采空区残余及异常沉降监测与分析[D]. 中国矿业大学, 2019.

[68]任文静, 贾洪果, 闫斌. SBAS-InSAR方法支持下的矿区地表沉降监测及参数反演[J]. 测绘通报, 2021(03):113-117+155.

[69]Stramondo S, Bozzano F, Marra F, et al. Subsidence induced by urbanisation in the city of Rome detected by advanced InSAR technique and geotechnical investigations[J]. Remote Sensing of Environment, 2008,112(6):3160-3172.

[70]Parcharidis I, Foumelis M, Kourkouli P, et al. Persistent Scatterers InSAR to detect ground deformation over Rio-Antirio area (Western Greece) for the period 1992-2000[J]. Journal of Applied Geophysics, 2009,68(3):348-355.

[71]Zhang Y H, Zhang J X, Wu H A, et al. Monitoring of urban subsidence with SAR interferometric point target analysis: A case study in Suzhou, China[J]. International Journal of Applied Earth Observation & Geoinformation, 2011,13(5):812-818.

[72]张鹏飞. 基于时序InSAR技术的山区煤矿开采沉陷监测研究[D]. 中国矿业大学, 2014.

[73]孔令淑. 基于IPTA和SBAS-InSAR的聊城市地表沉降对比分析[D]. 山东科技大学, 2018.

[74]沈佳广. 时序InSAR监测黑方台地区典型地质灾害形变及分析[D]. 中国地质大学(北京), 2019.

[75]刘雨鑫. 利用时序InSAR技术提取格尔木市及周缘地区的形变[D]. 武汉大学, 2019.

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

[77]朱建军, 杨泽发, 李志伟. InSAR矿区地表三维形变监测与预计研究进展[J]. 测绘学报, 2019,48(02):135-144.

[78]杨红磊, 彭军还, 张丁轩, 等. 轨道误差对InSAR数据处理的影响[J]. 测绘科学技术学报, 2012,29(02):118-121+126.

[79]张艳妮. 黄陵矿区煤矿隐蔽致灾因素及防治措施[J]. 陕西煤炭, 2020,39(01):172-175.

[80]牛玉芬. SAR/InSAR技术用于矿区探测与形变监测研究[D]. 长安大学, 2015.

[81]刘一霖. 矿区开采沉陷大量级形变监测与反演分析[D]. 长安大学, 2013.

[82]毛文军. 基于遗传BP神经网络模型的矿区开采沉陷预计[J]. 金属矿山, 2016(02):164-167.

[83]杨俊凯, 范洪冬, 赵伟颖, 等. 基于D-InSAR技术和灰色Verhulst模型的矿区沉降监测与预计[J]. 金属矿山, 2015(03):143-147.

[84]石晓宇, 魏祥平, 杨可明, 等. 基于D-InSAR技术和改进GM(1,1)模型的矿区沉降监测与预计[J]. 金属矿山, 2020(09):173-178.

[85]陈炳乾, 邓喀中, 范洪冬. 基于D-InSAR技术和SVR算法的开采沉陷监测与预计[J]. 中国矿业大学学报, 2014,43(05):880-886.

[86]张天. 基于SBAS-InSAR城市地表沉降的预测建模及原因分析[D]. 北京建筑大学, 2020.

[87]汪磊, 邓喀中, 薛继群, 等. 融合概率积分模型与D-InSAR的开采沉陷预计[J]. 金属矿山, 2016,476(2):160-163.

[88]Vapnik V, “The nature of statistic learning theory,” New York: Springer; 1995.

[89]Chang C C, Lin C J, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, 2011,2:27:1-27:27.

[90]Shi Y , Li Q W , Meng X , et al. On Time-Series InSAR by SA-SVR Algorithm: Prediction and Analysis of Mining Subsidence[J]. Journal of Sensors, 2020,2020(24):1-17.

[91]祁亨年. 支持向量机及其应用研究综述[J]. 计算机工程, 2004.(10):6-9.