深度学习在InSAR数据处理与地壳形变观测中的应用研究进展

doi:10.12196/j.issn.1000-3274.2023.02.014

Abstract

Abstract: Interferometric synthetic aperture radar (InSAR) technology, with its advantages of high precision, large range and all-weather monitoring, has been widely recognized in the acquisition and inversion of surface elevation and deformation information and other applications, and has gradually developed into an indispensable technical means in the field of crustal deformation observation. However, the use of InSAR technology for crustal deformation observation cannot be achieved without the support of massive data. This is bound to create new challenges in the collection and interpretation of information. In recent years, the rapid development of machine learning has made encouraging achievements in remote sensing image processing. The attempt to combine the deep learning method with InSAR technology comes into being. The outstanding data mining ability of deep learning and the classification and prediction ability of target tasks will provide a new technical means for the data processing and application of InSAR crustal deformation observation. In this paper, the data processing and application of deep learning in InSAR crustal deformation observation are introduced, and its application prospect is prospected.

Key words: Deep learning, InSAR, Crustal deformation, Research Progress

CLC Number:

P315.7

ZHANG Jing-ye, SUN Ke, ZHANG Guo-hong. Research Progress in Data Processing and Application of Deep Learning in InSAR Crustal Deformation Observation[J]. EARTHQUAKE, 2023, 43(2): 166-188.

References

[1] 武楠, 冯大政, 刘宝泉. 一种基于枝切法和有限元法的干涉SAR合成相位展开方法[J]. 电子与信息学报, 2007(4): 846-850.
WU Nan, FENG Da-zheng, LIU Bao-quan. A phase unwrapping approach based on the branch-cut method and finite element method for interferometric SAR[J]. Journal of Electronics & Information Technology, 2007(4):846-850 (in Chinese).
[2] 杨帆, 王道顺, 张磊, 等. 一种改进的InSAR城市地面沉降监测精度评价方法[J]. 测绘通报, 2019(2): 54-57+85.
YANG Fan, WANG Dao-shun, ZHANG Lei, et al. An improved accuracy evaluation method of urban ground subsidence monitoring in InSAR[J]. Bulletin of Surveying and Mapping, 2019(2): 54-57+85 (in Chinese).
[3] Carnec C, Massonnet D, King C. Two examples of the use of SAR interferometry on displacement fields of small spatial extent[J]. Geophysical Research Letters, 1996, 23(24): 3579-3582.
[4] Ng A H, Ge L, Zhang K, et al. Deformation mapping in three dimensions for underground mining using InSAR -Southern highland coalfield in New South Wales, Australia[J]. International Journal of Remote Sensing, 2011, 32(22): 7227-7256.
[5] 李承涛, 李琦, 谭凯, 等. 2020年西藏尼玛M_W6.3地震InSAR同震形变特征与破裂滑动分布[J]. 地球物理学报, 2021, 64(7): 2297-2310.
LI Cheng-tao, LI Qi, TAN Kai, et al. Coseismic deformation characteristics of the 2020 Nima, Xizang M_W6.3 earthquake from Sentinel-1A/B InSAR data and rupture slip distribution[J]. Chinese Journal of Geophysics, 2021, 64(7): 2297-2310 (in Chinese).
[6] Feng G. An improved quadtree sampling method for InSAR seismic deformation inversion[J]. Remote Sensing, 2021, 13(9): 1678.
[7] Abir I A, Khan S D, Ghulam A, et al. Active tectonics of western Potwar Plateau-Salt Range, northern Pakistan from InSAR observations and seismic imaging[J]. Remote Sensing of Environment, 2015,168.
SHAN Xin-jian, QU Chun-yan, ZHANG Guo-hong, et al. InSAR crustal deformation observation and seismogenic fault characteristics[M]. Beijing: Science Press, 2021 (in Chinese).
[8] Sandwell D T, Price E J. Phase gradient approach to stacking interferograms[J]. Journal of Geophysical Research: Solid Earth, 1998, 103(B12): 30183-30204.
[9] Ferretti A, Prati C, Rocca F. Permanent scatterers in SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1): 8-20.
[10] 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.
[11] Chen S Z, Wang H P. Sar target recognition based on deep learning[C]. Shanghai: IEEE International Conference on Data Science & Advanced Analytics, 2014: 541-547.
[12] Ding J, Chen B, Liu H, et al. Convolutional neural network with data augmentation for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 364-368.
[13] Ševo I, Avramovic′ A. Convolutional neural network based automatic object detection on aerial images[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(5): 740-744.
[14] Zhu H, Chen X, Dai W, et al. Orientation robust object detection in aerial images using deep convolutional neural network[C]. Quebec: IEEE International Conference on Image Processing (ICIP), 2015: 3735-3739.
[15] Jiao L, Liu F. Wishart deep stacking network for fast POLSAR image classification[J]. IEEE Transactions on Image Processing, 2016, 25(7): 3273-3286.
[16] Yang W, Yin X, Xia G. Learning high-level features for satellite image classification with limited labeled samples[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(8): 4472-4482.
[17] Zhao Z, Jiao L, Zhao J, et al. Discriminant deep belief network for high-resolution SAR image classification[J]. Pattern Recognition, 2017, 61: 686-701.
[18] Mukherjee S, Zimmer A, Sun X, et al. An unsupervised generative neural approach for InSAR phase filtering and coherence estimation[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(11): 1971-1975.
[19] Valade S, Ley A, Massimetti F, et al. Towards global volcano monitoring using multisensor sentinel missions and artificial intelligence: The mounts monitoring system[J]. Remote Sensing, 2019, 11(13): 1528.
[20] Sun X Y, Zimmer A, Mukherjee S, et al. Deep InSAR: A deep learning framework for sar interferometric phase restoration and coherence estimation[J]. Remote Sensing, 2020, 12(14): 2340.
[21] Mukherjee S, Zimmer A, Kottayil N K, et al. CNN-based InSAR denoising and coherence metric[C]. New Delhi: 2018 IEEE sensors, 2018.
[22] Wang H, Hu J, Fu H, et al. A novel quality-guided two-dimensional InSAR phase unwrapping method via GAUNet[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 7840-7856.
[23] Zhou L, Yu H, Lan Y. Deep convolutional neural network-based robust phase gradient estimation for two-dimensional phase unwrapping using SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4653-4665.
[24] Wang K, Li Y, Kemao Q. et al. One-step robust deep learning phase unwrapping[J]. Optics Express, 2019, 27(10): 15100-15115.
[25] Zhang T, Jiang S, Zhao Z, et al. Rapid and robust two-dimensional phase unwrapping via deep learning[J]. Optics Express, 2019, 27(16): 23173-23185.
[26] Ma Y L, Li Y H. Millimeter-wave InSAR target recognition with deep convolutional neural network[J]. Ieice Transactions on Information and Systems, 2019, E102D(3): 655-658.
[27] Li S, Xu H, Gao S, et al. An interferometric phase noise reduction method based on modified denoising convolutional neural network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 4947-4959.
[28] Sica F, Calvanese F, Scarpa G, et al. A CNN-based coherence-driven approach for InSAR phase unwrapping[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5.
[29] Zhao Z, Wu Z, Zheng Y, et al. Recurrent neural networks for atmospheric noise removal from InSAR time series with missing values[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 180: 227-237.
[30] Pu L M, Zhang X L, Zhou Z N, et al. A phase filtering method with scale recurrent networks for InSAR[J]. Remote Sensing, 2020, 12(20): 3453.
[31] Liu Q, Zhang Y, Wei J, et al. HLSTM: Heterogeneous long short-term memory network for large-scale InSAR ground subsidence prediction[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 8679-8688.
[32] Radman A, Akhoondzadeh M, Hosseiny B. Integrating InSAR and deep-learning for modeling and predicting subsidence over the adjacent area of Lake Urmia, Iran[J]. GIScience & Remote Sensing, 2021, 58(8): 1413-1433.
[33] Wang S B, Zhuang J Q, Mu J Q, et al. Evaluation of landslide susceptibility of the Ya'an-Linzhi section of the Sichuan-Tibet Railway based on deep learning[J]. Environmental Earth Sciences, 2022, 81: 250.
[34] Panahi M, Jaafari A, Shirzadi A, et al. Deep learning neural networks for spatially explicit prediction of flash flood probability[J]. Geoscience Frontiers, 2021, 12(3): 101076.
[35] Yang W, He Y, Yao S, et al. An InSAR interferogram filtering method based on multi-level feature fusion CNN[J]. Sensors, 2022, 22: 5956.
[36] Zeyada H H, Mostafa M S, Ezz M M, et al. Resolving phase unwrapping in interferometric synthetic aperture radar using deep recurrent residual U-Net[J]. Egyptian Journal of Remote Sensing and Space Sciences, 2022, 25(1): 1-10.
[37] Zhao X, Wang C, Zhang H, et al. Inversion of seismic source parameters from satellite InSAR data based on deep learning[J]. Tectonophysics, 2021, 821: 229140.
[38] Chen Y, He Y, Zhang L, et al. Prediction of InSAR deformation time-series using a long short-term memory neural network[J]. International Journal of Remote Sensing, 2021, 42(18): 6921-6944.
[39] Ma P, Zhang F, Lin H. Prediction of InSAR time-series deformation using deep convolutional neural networks[J]. Remote Sensing Letters, 2020, 11(2): 137-145.
[40] Ge S J, Antropov O, Su W M, et al. Deep recurrent neural networks for land-cover classification using sentinel-1 insar time series[C]. Yokohama: IEEE international geoscience and remote sensing symposium (IGARSS 2019), 2019: 473-476.
[41] Chen K, Chen K, Wang Q, et al. Short-term load forecasting with deep residual networks[J]. IEEE Transactions on Smart Grid, 2019, 10(4): 3943-3952.
[42] 曹州. 一种改进的基于RNN的遥感影像变化检测方法研究[D]. 北京: 中国科学院大学, 2020.
CAO Zhou. Research on a modified remote sensing image change detection basedon RNN[D]. Beijing: University of Chinese Academy of Sciences, 2020 (in Chinese).
[43] 康秦瑀. 基于DNN的遥感图像海面目标检测技术研究[D]. 西安: 西安工业大学, 2020.
KANG Qin-yu. Research on remote sensing image of sea surface object detection based on DNN[D]. Xi'an: Xi'an Technological University, 2020 (in Chinese).
[44] 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.
LIU Qing-hao, ZHANG Yong-hong, DENG Min, 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 (in Chinese).
[45] 王俊, 郑彤, 雷鹏, 等. 深度学习在雷达中的研究综述[J]. 雷达学报, 2018, 7(4): 395-411.
WANG Jun, ZHENG Tong, LEI Peng, et al. Study on deep learning in radar[J]. Journal of Radars, 2018, 7(4): 395-411 (in Chinese).
[46] 周金国. InSAR数据处理方法与应用研究[D]. 重庆: 重庆大学, 2008.
ZHOU Jin-guo. Study of InSAR data processing methods and application[D]. Chongqing: Chongqing university, 2008 (in Chinese).
[47] 黄惠宁, 覃辉. InSAR技术基本原理及其数据处理流程[J]. 地理空间信息, 2012, 10(2): 93-95.
HUANG Hui-ning, TAN Hui. Basic principles and data processing of InSAR technology[J]. Geospatial Information, 2012, 10(2): 93-95 (in Chinese).
[48] Li L, Zhang H, Tang Y, et al. InSAR phase unwrapping by deep learning based on gradient information fusion[J]. IEEE Geoscience and Remote Sensing Letters, 2022(19): 1-5.
[49] Lee J S, Papathanassiou K P, Ainsworth T L, et al. A new technique for noise filtering of SAR interferometric phase images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5): 1456-1465.
[50] Goldstein R M, Zebker H A, Werner C L. Satellite radar interferometry: Two-dimensional phase unwrapping[J]. Radio Science, 1988, 23(4): 713-720.
[51] Deledalle C, Denis L, Tupin F. NL-InSAR: Nonlocal interferogram estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(4): 1441-1452.
[52] Sica F, Cozzolino D, Zhu X X, et al. InSAR-BM3D: A nonlocal filter for SAR interferometric phase restoration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6): 3456-3467.
[53] Ferraiuolo G, Poggi G. A Bayesian filtering technique for SAR interferometric phase fields[J]. IEEE Transactions on Image Processing, 2004, 13(10): 1368-1378.
[54] Fu S, Long X, Yang X, et al. Directionally adaptive filter for synthetic aperture radar interferometric phase images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1): 552-559.
[55] Chao C, Chen K, Lee J, et al. Refined filtering of interferometric phase from InSAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(12): 5315-5323.
[56] Shi X, Zhang Y. Improving goldstein filter by image entropy for InSAR interferogram filtering: 2011 3rd international Asia-Pacific conference on synthetic aperture radar (APSAR)[C]. Seoul, Korea (South), 2011.
[57] Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095.
[58] Wang P Y, Zhang H, Patel V M. SAR Image despeckling using a convolutional neural network[J]. IEEE Signal Processing Letters, 2017, 24(12): 1763-1767.
[59] Cozzolino D, Verdoliva L, Scarpa G, et al. Nonlocal CNN SAR image despeckling[J]. Remote Sensing, 2020, 12(6): 1006.
[60] Zhong H, Tang J, Zhang S, et al. An improved quality-guided phase-unwrapping algorithm based on priority queue (Vol 8, pg 364, 2011)[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(5): 364-368.
[61] Chao C F, Chen K S, Lee J S, et al. Refined filtering of interferometric phase from Insar data[C]. Munich: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2012:1821-1824.
[62] 张诗玉, 李陶, 夏耶. 基于InSAR技术的城市地面沉降灾害监测研究[J]. 武汉大学学报(信息科学版), 2008(8): 850-853.
ZHANG Shi-yu, LI Tao, XIA Ye. Study on urban area subsidence monitoring based on Insar technique[J]. Geomatics and Information Science of Wuhan University, 2008(8): 850-853 (in Chinese).
[63] Flynn T J. Consistent 2-D phase unwrapping guided by a quality map[C]. 1996 International Geoscience and Remote Sensing Symposium (IGARSS 96)-Remote Sensing for a Sustainable, 1996: 2057-2059.
[64] Herráez M A, Burton D R, Lalor M J, et al. Fast two-dimensional phase-unwrapping algorithm based on sorting by reliability following a noncontinuous path[J]. Applied Optics, 2002, 41(35): 7437-7444.
[65] Pritt M D. Phase unwrapping by means of multigrid techniques for interferometric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(3): 728-738.
[66] Costantini M. A novel phase unwrapping method based on network programming[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 813-821.
[67] Chen C W, Zebker H A. Phase unwrapping for large SAR interferograms: statistical segmentation and generalized network models[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(8): 1709-1719.
[68] Bioucas-Dias J M, Valadao G. Phase unwrapping via graph cuts[J]. IEEE Transactions on Image Processing, 2007, 16(3): 698-709.
[69] 曲小宁. 合成孔径雷达干涉测量及若干关键技术研究[D]. 西安: 西安电子科技大学, 2012.
QU Xiao-ning. Research on key technique for interferometric synthetic aperture radar measurement[D]. Xi'an: Xidian University, 2012 (in Chinese).
[70] 范洪冬. InSAR若干关键算法及其在地表沉降监测中的应用研究[D]. 江苏: 中国矿业大学, 2010.
FAN Hong-dong. Study on several key algorithms of InSAR technique and its application in land subsidence monitoring[D]. Jiangsu: China University of Mining and Technology, 2010 (in Chinese).
[71] Zhou L, Yu H, Pascazio V, et al. PU-GAN: A One-Step 2-D InSAR phase unwrapping based on conditional generative adversarial network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022: 60: 1-10.
[72] Isola P, Zhu J Y, Zhou T H, et al. Image-to-image translation with conditional adversarial networks[C]. 30th IEEE conference on computer vision and pattern recognition (CVPR 2017). 2017: 5967-5976.
[73] Spoorthi G E, Gorthi S, Gorthi R K S S. PhaseNet: A deep convolutional neural network for two-dimensional phase unwrapping[J]. IEEE Signal Processing Letters, 2019, 26(1): 54-58.
[74] Spoorthi G E, Gorthi R K S S, Gorthi S. PhaseNet 2.0: Phase unwrapping of noisy data based on deep learning approach[J]. IEEE Transactions on Image Processing, 2020, 29: 4862-4872.
[75] Wu Z P, Wang T, Wang Y J, et al. Deep-learning-based phase discontinuity prediction for 2-D phase unwrapping of SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-16.
[76] Zhou L F, Yu H W, Lan Y, et al. Deep learning-based branch-cut method for InSAR two-dimensional phase unwrapping[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-15.
[77] 张国宏, 屈春燕, 宋小刚, 等. 基于InSAR同震形变场反演汶川MW7.9地震断层滑动分布[J]. 地球物理学报, 2010, 53(2): 269-279.
ZHANG Guo-hong, QU Chun-yan, SONG Xiao-gang, et al. Slip distribution and source parameters inverted from co-seismic deformation derived by InSAR technology of Wenchuan M_W7.9 earthquake[J]. Chinese Journal of Geophysics, 2010, 53(2): 269-279 (in Chinese).
[78] 单新建, 屈春燕, 龚文瑜, 等. 2017年8月8日四川九寨沟7.0级地震InSAR同震形变场及断层滑动分布反演[J]. 地球物理学报, 2017, 60(12): 4527-4536.
SHAN Xin-jian, QU Chun-yan, GONG Wen-yu, et al. Coseismic deformation field of the Jiuzhaigou M_S7.0 earthquake from Sentinel-1A InSAR data and fault slip inversion[J]. Chinese Journal of Geophysics, 2017, 60(12): 4527-4536 (in Chinese).
[79] Rouet-Leduc B, Jolivet R, Dalaison M, et al. Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning[J]. Nature Communications, 2021, 12(1): 6480.
[80] Atzori S, Antonioli A, Tolomei C, et al. InSAR full-resolution analysis of the 2017—2018 M>6 earthquakes in Mexico[J]. Remote Sensing of Environment, 2019, 234.
[81] Fathian A, Atzori S, Nazari H, et al. Complex co- and postseismic faulting of the 2017—2018 seismic sequence in western Iran revealed by InSAR and seismic data[J]. Remote Sensing of Environment, 2021: 253.
[82] Picchiani M, Del Frate F, Schiavon G, et al. Neural Networks for automatic seismic source analysis from DInSAR datai[C]. Prague: Conference on SAR Image Analysis, Modeling, and Techniques XI, 2011: 8179.
[83] Stramondo S, Del Frate F, Picchiani M, et al. Seismic source quantitative parameters retrieval from InSAR data and neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1): 96-104.
[84] Zhang T, Zhang W C, Cao D, et al. A new deep learning neural network model for the identification of InSAR anomalous deformation areas[J]. Remote Sensing, 2022, 14: 2690.
[85] Yuan R, Chen J. A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data[J]. Natural Hazards, 2022, 114: 1393-1426.
[86] Sameen M I, Pradhan B, Lee S. Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment[J]. Catena, 2020, 186: 104249.
[87] Pham B T, Prakash I, Dou J, et al. A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers[J]. Geocarto International, 2020, 35(12): 1267-1292.
[88] Anantrasirichai N, Biggs J, Albino F, et al. A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets[J]. Remote Sensing of Environment, 2019, 230: 111179.
[89] Anantrasirichai N, Biggs J, Albino F, et al. The application of convolutional neural networks to detect slow, sustained deformation in InSAR time series[J]. Geophysical Research Letters, 2019, 46(21): 11850-11858.
[90] Ichikawa K, Hirose A. Singular unit restoration in InSAR using complex-valued neural networks in the spectral domain[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1717-1723.
[91] 孙志军, 薛磊, 许阳明. 基于深度学习的边际Fisher分析特征提取算法[J]. 电子与信息学报, 2013, 35(4): 805-811.
SUN Zhi-jun, XUE Lei, XU Yang-ming. Marginal fisher feature extraction algorithm based on deep learning[J]. Journal of Electronics & Information Technology, 2013, 35(4): 805-811 (in Chinese).
[92] 李腾飞, 秦永彬. 基于迭代深度学习的缺陷检测[J]. 计算机与数字工程, 2017, 45(6): 1133-1137.
LI Teng-fei, QIN Yong-bin. Feature detection base on iterative deep learning[J]. Computer and Digital Engineering, 2017, 45(6): 1133-1137 (in Chinese).
[93] 熊红凯, 高星, 李劭辉, 等. 可解释化、结构化、多模态化的深度神经网络[J]. 模式识别与人工智能, 2018, 31(1): 1-11.
XIONG Hong-kai, GAO Xing, LI Shao-hui, et al. Interpretable structured multi-modal deep neural network[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(1): 1-11 (in Chinese).
[94] Zeiler M D, Krishnan D, Taylor G W, et al. Deconvolutional Networks[C]. San Francisco: Proc of the IEEE international conference on computer vision and pattern recognition (CVPR), 2010: 2528-2535.

Research Progress in Data Processing and Application of Deep Learning in InSAR Crustal Deformation Observation

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	ZHANG Xia, DONG Yan-fang, HONG Shun-ying. Research on the Three-Dimensional Deformation along the Maqin-Maqu Section of the East Kunlun Fault zone from InSAR [J]. EARTHQUAKE, 2023, 43(4): 169-184.
[2]	CHEN Qiu-yu, ZHANG Xiao-dong, ZHAO Cui-ping. Research Progress of Hydraulic Fracturing Induced Earthquake [J]. EARTHQUAKE, 2023, 43(4): 215-227.
[3]	ZHANG Yu-cheng, HUA Wei. Phase Picking and Earthquake Location based on Deep Learning in the Baihetan Reservior Area [J]. EARTHQUAKE, 2023, 43(1): 137-151.
[4]	ZOU Fang, MENG Guo-jie, SU Xiao-ning, WU Wei-wei, ZHAO Qian, SHE Ya-wen, Thant Myo. Characterstics of Tectonic Deformation before the 2021 Yangbi M_S6.4 Earthquake in Yunnan [J]. EARTHQUAKE, 2022, 42(3): 21-36.
[5]	WANG Xin, DOU Ai-xia, GUO Hong-mei, YUAN Xiao-xiang. Research on Extraction of Building Damage Information in the 2019 Changning M6.0 Earthquake Based on Deeplab V3+ [J]. EARTHQUAKE, 2022, 42(3): 124-140.
[6]	XIAO Yan-feng, HU Xiao-bin, TAN Kai. Study on the Coseismic Slip Model of the 2020 Dingri M_W5.7 Earthquake in Xizang, China Constrained by InSAR Measurements [J]. EARTHQUAKE, 2022, 42(2): 140-148.
[7]	LIU Chao-ya, DONG Yan-fang, HONG Shun-ying, MENG Guo-jie, HUANG Xing, LIU Tai. InSAR Coseismic Deformation and Fault Slip Distribution Inversion of the 2020 Yutian M_W6.3 Earthquake, Xinjiang [J]. EARTHQUAKE, 2021, 41(2): 62-79.
[8]	KANG Shuai, LIU Chuan-jin, ZHU Liang-yu, JI Ling-yun. The 2020 M_S6.4 Earthquake in Yutian Xinjiang Based on the Ascending and Descending Sentinel-1 SAR Data [J]. EARTHQUAKE, 2021, 41(2): 80-91.
[9]	YUAN Ai-jing, WANG Wei-jun, PENG Fei, YAN Kun, KOU Hua-dong. Recent Progress of Earthquake Prediction with Machine Learning [J]. EARTHQUAKE, 2021, 41(1): 51-66.
[10]	WANG Yong-zhe, CHEN Shi, CHEN Kun. Source Model and Tectonic Implications of the 2020 Dingri M_W5.7 Earthquake Constrained by InSAR Data [J]. EARTHQUAKE, 2021, 41(1): 116-128.
[11]	YU Hong-yuan, LI Wei, WANG Wen-da. The Fault Parameter and Slip Distribution of the 2017 Iraq M_W7.3 Earthquake Based on the Improved Inversion Method Considering Topography [J]. EARTHQUAKE, 2020, 40(4): 63-75.
[12]	XU Xiao-bo, MA Chao, HONG Shun-ying, LIAN Da-jun, ZHANG Ying-feng. Analysis of Coseismic Displacements due to the M_W7.8 Nepal Earthquake Using InSAR Data and Inversion of Fault Slip Distribution [J]. EARTHQUAKE, 2020, 40(4): 76-89.
[13]	SUN Kai, MENG Guo-jie, HONG Shun-ying, HUANG Xing, DONG Yan-fang. Coseismic Deformation and Fault Slip Inversion of the M_S6.0 Earthquake Changning, Sichuan Based on Sentinel-1A Data [J]. EARTHQUAKE, 2020, 40(3): 15-27.
[14]	LUO Yi, TIAN Yun-feng, ZHANG Su, ZHANG Jing-fa. Study on Characteristics of Permafrost Deformation in the Tibetan Plateau Using InSAR Technique [J]. EARTHQUAKE, 2020, 40(3): 179-188.
[15]	HUANG Xing, HONG Shun-ying, JIN Hong-lin, LIU Tai, DONG Yan-fang. Inversion of the Seismogenic Fault and Fault Slip Distribution of the 2015 Pishan M_W6.4 Earthquake [J]. EARTHQUAKE, 2020, 40(1): 84-98.