Project plan and research on data analysis and processing technology of geophysical exploration satellite and application research of earthquake prediction
-
摘要: 地震监测预警和预测预报是当前地球科学及相关学科所面临的最艰巨的问题之一，是关系到人类社会安全与国计民生的亟待攻克的科学难题。为进一步提高地震预测科学研究水平，推进地震监测预测能力建设，我国于世纪之交提出了建立地震立体观测体系的战略发展思路，并希望突破三维地球物理场获取能力瓶颈，发展地球多圈层耦合模型，通过卫星观测获取全球大地震的震例信息，以有效推进地震监测预测科学探索。地球物理卫星探测作为天基平台成为地震立体监测预测科学探索的新的重要方向。2018年2月我国首颗地球物理场卫星电磁监测试验卫星ZH1（01）成功发射入轨，并顺利通过在轨测试。针对我国电磁卫星数据处理技术接近国际并跑状态，重力卫星数据处理尚处于跟踪研究阶段，全球地磁场建模方面处于起步阶段，电离层建模技术达到国际并跑水平的问题，以及多圈层耦合的地球物理场多参量综合分析面临的科学问题。本项目开展了星载电磁场、电离层数据处理与定标校验技术、星载重力场数据处理技术、全球/区域地球物理场精细建模技术、地球物理场多参量综合分析与地震异常识别技术的研究，实现了相应算法和模块的研发，建立了全球地球物理场模型和主要地震震例特征库及样本库，构建了卫星地震监测预测应用平台，完成全球及中国强震震例积累，并在川滇地区开展地震监测预测应用示范和震情跟踪检验，示范应用取得明显成效。主要成果体现在：取得新技术3个：①高精度三频信标电离层反演技术；②VLF电波FDTD传播模型；③星载重力梯度数据的精细处理技术。新方法3个：①高精度磁场数据的优化处理方法；②卫星磁场扰动信号提取算法；③卫星多参量综合分析方法。新产品3个：①全球主磁场模型，成功纳入新一代全球地磁场参考模型IGRF2020.0，为全球地球物理场建模百年来首个中国模型；②全球三维电离层电子密度模型；③全球时变重力场模型。新理论1项：多源异质地震异常信息融合与异常识别。新平台1个：卫星地震监测预测应用平台。Abstract: Earthquake monitoring and prediction is one of the most difficult problems in earth science and related disciplines, and it is a scientific problem that concerns the safety of human society, national economy and people’s livelihood. In order to further improve the scientific research level of earthquake prediction and promote the construction of earthquake monitoring and prediction ability, the strategic development idea of three-dimensional seismic observation system was put forward in China at the turn of the century. It is hoped to break through the bottleneck of 3D geophysical field acquisition ability, develop the earth multi-layer coupling model, and obtain the earthquake case information of global large earthquakes through satellite observation, so as to effectively promote the scientific exploration of earthquake monitoring and prediction. As a space-based platform, geophysical satellite exploration has become a new and important direction in the scientific exploration of three-dimensional seismic monitoring and prediction. In February 2018, China’s first geophysical field satellite, electromagnetic monitoring and experimental satellite ZH1(01) was successfully launched into orbit and passed the in-orbit test. At present, the electromagnetic satellite data processing technology and the ionospheric modeling technology in China is close to the international parallel state. However, the gravity satellite data processing is still in the tracking research stage, the global geomagnetic field modeling is in the initial stage, and the multi-parameter comprehensive analysis of geophysical field coupled by multiple layers needs to be further studied. In view of the above scientific problems, the project has carried out researches on spaceborne electromagnetic field, ionospheric data processing and calibration technology, satellite load field data processing technology, global/regional geophysical field fine modeling technology, geophysical field multi-parameter comprehensive analysis and seismic anomaly identification technology. Through research, this project implements the corresponding algorithm and the module of research and development and the global geophysical field model is established. The main characteristics of earthquake cases library and sample library construct the satellite monitoring earthquake prediction application platform, complete the global and China’s earthquake cases accumulation, and predicting earthquake monitoring application demonstration in the Sichuan-Yunnan region and earthquake track inspection, demonstrate significant results were obtained in the application. The main achievements are as follows: three new technologies have been obtained: ① high-precision three-frequency beacon ionospheric inversion technology; ② VLF wave FDTD propagation model; ③ fine processing technology of satellite gravity gradiometry data. Three new methods were proposed: ① optimization processing method of high-precision magnetic field data; ② satellite magnetic disturbance signal extraction algorithm; ③ satellite multi-parameter comprehensive analysis method. Three new products were produced: ① global main magnetic field model. This model was successfully incorporated into IGRF2020.0, which is the first model for global geophysical field modeling in China; ② global three-dimensional ionospheric electron density model; ③ global time-varying gravity field model. One theory have been proposed: multi-source heterogeneous seismic anomaly information fusion and anomaly recognition. A new platform is developed: satellite seismic monitoring and prediction application platform.
-
图 3 张衡1号的磁场数据中不规则变化点的全球分布图及已确认的干扰源
Figure 3. Global distribution of irregularity points in ZH1（01） magnetic field data and confirmed disturbance sources
图 4 三频信标工作对磁场数据影响及处理效果
（a） 三频信标发射机工作区；（b） 基于三频信标干扰模型对磁场数据的校正结果
Figure 4. Influence of three-frequency beacon on magnetic field data and processing effect
（a） Three-frequency beacon transmitter working area；（b） Correction results of magnetic field data based on three-frequency beacon interference model
图 5 地面VLF辐射源NWC顶空电离层重访轨道电磁场观测结果
Figure 5. Observation results of orbital electromagnetic field revisited by NWC headspace ionosphere, a ground-based VLF emitter
图 6 基于多路径组合序列滑动窗口与中位数定标的张衡1号掩星数据质量控制
Figure 6. Occultation data quality control of ZH1（01） based on multi-path combination sequence sliding window and median calibration
图 7 三频信标k值计算流程及三频TEC反演精度统计
Figure 7. Calculation flow of k value of three-frequency beacon and accuracy statistics of three-frequency TEC inversion
图 12 重力梯度测量卫星数据产品的分类分级体系
Figure 12. Classification system of gravity gradient measurement
图 14 重力梯度数据精细处理技术的方案与流程
Figure 14. Scheme and flow of fine processing technology for gravity gradient data
图 15 各种滤波器处理得到的重力梯度分量误差功率谱
（a） MA滤波器；（b） CPR滤波器；（c） MA+ARMA组合滤波器；（d） CPR+ARMA组合滤波器
Figure 15. Error power spectrum of gravity gradient components processed by various filters
（a） MA filter；（b） CPR filter；（c） MA+ARMA cascaded filter；（d） CPR+ARMA cascaded filter
图 17 相位中心结果与JPL1B结果的残差序列
Figure 17. Discrepancies of antenna-center correction between JPL1B and our results
图 19 全球三维电子密度模型网格及输出结果切片示意图
Figure 19. 3D global electron density model grid and output slice diagram
图 20 CSES 全球地磁场模型（CGGM）与其他磁场模型的对比
（a） 主磁场；(b)长期变化
Figure 20. Comparison of the CSES Global Geomagnetic Field Model （CGGM） with other magnetic field models
（a） Main magnetic field；（b） Secular variation
图 21 基于张衡1号卫星磁测数据建立的全球主磁场模型CGGM 2020计算得到的地磁场Bx，By，Bz分量及磁倾角和磁偏角全球分布
Figure 21. The global distribution of geomagnetic Bx, By and Bz components, magnetic inclination and magnetic declination angle calculated by the global main magnetic field model CGGM 2020 based on the magnetic survey data of ZH1（01） Satellite
图 22 110阶全球岩石圈磁场模型各地磁要素分布图
Figure 22. Distribution of magnetic elements in 110th order global lithospheric magnetic field model
图 23 中国区域的卫星磁异常： （a） 张衡1号卫星数据结果； （b） CHAOS-7模型计算结果
TMA：塔里木高值磁异常；SCMA：四川盆地高值磁异常；SGMA：大兴安岭和松辽盆地高值磁异常；HMLA：青藏高原南部低值磁异常
Figure 23. Satellite magnetic anomalies in China：（a） ZH1（01） satellite data; （b） CHAOS-7 model
TMA：Tarim high-value magnetic anomaly；SCMA：Sichuan Basin high-value magnetic anomaly；SGMA：Greater Khingan Mountains and Songliao Basin high-value magnetic anomaly；HMLA：Southern Tibetan Plateau low-value magnetic anomaly
图 25 采用马尔科夫链—蒙特卡洛方法得到的局部重力场结果（华北地下水从2003年到2014年的变化速率）
Figure 25. Local gravity field results using Markov chain-Monte Carlo method (groundwater change rate in North China from 2003 to 2014)
图 26 2019和2020年月均值背景逐月变化
（a） 2019白天数据；（b） 2019夜间数据；（c） 2020白天数据；（d）2020夜间数据
Figure 26. Variations of monthly ionospheric background in 2019 and 2020
（a） Daytime data in 2019；（b） Nighttime data in 2019；（c） Daytime data in 2020；（d） Nighttime data in 2020
图 27 夜间冬季异常现象
（a） 北半球冬季异常分布区；（b） 南半球冬季异常分布区
Figure 27. Winter anomaly phenomenon at night
（a） This phenomenon in the Northern Hemisphere；（b） This phenomenon in the Southern Hemisphere.
图 28 2019年地磁赤道附近闪烁的季节演化规律
Figure 28. Seasonal evolutions of the equatorial scintillation in 2019
图 29 不同地震分类的地震发生前后扰动的分布特征
Figure 29. Distribution characteristics of disturbance before and after earthquake occurrence of different earthquake classification
图 30 地震微波辐射异常的地应力变化响应链条示意图
Figure 30. Response chain of seismic and microwave radiation anomalies
图 31 基于人工智能地震预测技术的全球震例回溯验证
Figure 31. Retrospective verification of global earthquake cases based on artificial intelligence earthquake prediction technology
图 32 地震区域2018年8月4日电离层底部z=90 km 的水平异常电场分布
（a） 总电场强度E；（b） 磁南北向电场强度ESN；（c） 磁东西向电场强度EEW
Figure 32. Distribution of the horizontal abnormal electric field on August 4，2018 at the bottom of the ionosphere （z=90 km）
（a） Total electric field intensity E；（b） The electric field intensity ESN in magnetic north-south direction； （c） The electric field intensity EEW in magnetic east-west direction
-
[1] Yan R,Zhima Z,Xiong C,et al. Comparison of electron density and temperature from the CSES satellite with other space-borne and ground-based observations[J]. Journal of Geophysical Research:Space Physics,2020,125(10):e2019JA027747 doi: 10.1029/2019JA027747 [2] Diego P, Huang J P, Piersanti M, et al. The electric field detector on board the China Seismo Electromagnetic Satellite-in-orbit results and validation[J]. Instruments, 2021, 5(1). https://dx. doi.org/10.3390/instruments5010001 [3] Liu J, Guan Y B, Zhang X M, et al. The data comparison of electron density between CSES and DEMETER satellite, swarm constellation and IRI model[J]. Earth and Space Science, 2021, 8(2): e2020EA001475 [4] 郭泽华,吴云龙,肖云,等. 联合星象仪四元数的卫星重力梯度测量角速度重建方法[J]. 武汉大学学报:信息科学版,2021,46(9):1336-1344Guo Z H,Wu Y L,Xiao Y,et al. Reconstruction method of satellite gravity gradient measurement angular velocity by combining star tracker Quaternion[J]. Geomatics and Information Science of Wuhan University,2021,46(9):1336-1344 [5] Zhou H,Zhou Z B,Luo Z C. A new hybrid processing strategy to improve temporal gravity field solution[J]. Journal of Geophysical Research:Solid Earth,2019,124(8):9415-9432 [6] Zhou H, Zhou Z B, Luo Z C. HUST-Grace2020: Monthly gravity field model derived from GRACE mission[DB]. GFZ Data Services, 2021. https://doi.org/10.5880/ICGEM.2021.001 [7] Yang Y Y, Hulot G, Vigneron P, et al. The CSES global geomagnetic field model （CGGM）: An IGRF-type global geomagnetic field model based on data from the China Seismo-Electromagnetic Satellite[J]. Earth, Planets and Space, 2021, 73. https://doi.org/10.1186/s40623-020-01316-w [8] Wang J,Shen X H,Yang Y Y,et al. Initial scalar lithospheric magnetic anomaly map of China and surrounding regions derived from CSES satellite data[J]. Science China Technological Sciences,2021,64:1118-1126 doi: 10.1007/s11431-020-1727-0 [9] Zhou H,Luo Z C,Zhou Z B,et al. What can we expect from the inclined satellite formation for temporal gravity field dete rmination?[J]. Surveys in Geophysics,2021,42(5):699-726 [10] Cui J,Shen X H,Zhang J F,et al. Analysis of spatiotemporal variations in middle-tropospheric to upper-tropospheric methane during the Wenchuan MS=8.0 earthquake by three indices[J]. Natural Hazards and Earth System Sciences,2019,19(12):2841-2854 [11] Qi Y,Wu L X,He M,et al. Spatio-temporally weighted two-step method for retrieving seismic MBT anomaly:May 2008 Wenchuan earthquake sequence being a case[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2020,13:382-391 [12] Qi Y,Wu L X,Mao W F,et al. Discriminating possible causes of microwave brightness temperature positive anomalies related with May 2008 Wenchuan earthquake sequence[J]. IEEE Transactions on Geoscience and Remote Sensing,2021,59(3):1903-1916 [13] Qi Y,Wu L X,Ding Y F,et al. Microwave brightness temperature anomalies associated with the 2015 MW7.8 Gorkha and MW7.3 Dolakha earthquakes in Nepal[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60:1-11 [14] Ding Y F,Qi Y,Wu L X,et al. Discriminating the multi-frequency microwave brightness temperature anomalies relating to 2017 MW7.3 Sarpol Zahab (Iran-Iraq Border) earthquake[J]. Frontiers in Earth Science,2021,9:656216 [15] 何苗,吴立新,崔静,等. 汶川地震前多圈层短—临遥感异常回顾及其时空关联性[J]. 遥感学报,2020,24(6):681-700He M,Wu L X,Cui J,et al. Remote sensing anomalies of multiple geospheres before the Wenchuan earthquake and its spatiotemporal correlations[J]. Journal of Remote Sensing,2020,24(6):681-700 [16] Qi Y, Miao Z L, Wu L X, et al. Seismic microwave brightness temperature anomaly detection using multitemporal passive microwave satellite images: Ideas and limits[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 6792-6806 [17] Xiong P,Long C,Zhou H Y,et al. Identification of electromagnetic pre-earthquake perturbations from the DEMETER data by machine learning[J]. Remote Sensing,2020,12(21):3643 [18] Xiong P,Tong L,Zhang K,et al. Towards advancing the earthquake forecasting by machine learning of satellite data[J]. Science of the Total Environment,2021:145256 [19] Xiong P,Zhai D L,Long C, et al. Long short-term memory neural network for ionospheric total electron content forecasting over China[J]. Space Weather,2021,19(4):2020SW002706 doi: 10.1029/2020SW002706 [20] 张学民,刘静,熊攀,等. 2020年新疆于田6.4级地震前电离层扰动现象分析[J]. 地震,2021,41(2):145-157 doi: 10.12196/j.issn.1000-3274.2021.02.011Zhang X M,Liu J,Xiong P,et al. The seismo-ionospheric disturbances before the 2020 Yutian MS6.4 earthquake[J]. Earthquake,2021,41(2):145-157 doi: 10.12196/j.issn.1000-3274.2021.02.011 [21] Song R,Hattori K,Zhang X M,et al. Seismo-ionospheric effects prior to four earthquakes in Indonesia detected by the China seismo-electromagnetic satellite[J]. Journal of Atmospheric and Solar-Terrestrial Physics,2020,205:105291 [22] Zhang X M,Wang Y L,Boudjada M Y,et al. Multi-experiment observations of ionospheric disturbances as precursor effects of the Indonesia MS6.9 earthquake on August 05,2018[J]. Remote Sensing,2020,12(24):4050 [23] 王壮凯,刘祎,刘静,等. 2018 年 8 月 5 日印度尼西亚地震震前电离层异常特征研究[J]. 地震学报,2020,42(3):327-340Wang Z K,Liu Y,Liu J,et al. Characteristics of ionospheric anomalies before the earthquake in Indonesia on August 5,2018[J]. Acta Seismologica Sinica,2020,42(3):327-340 [24] Ouyang X Y,Bortnik J,Ren J,et al. Features of nightside ULF wave activity in the ionosphere[J]. Journal of Geophysical Research:Space Physics,2019,124(11):9203-9213 doi: 10.1029/2019ja027103 [25] Ouyang X Y, Parrot M, Bortnik J. ULF wave activity observed in the nighttime ionosphere above and some hours before strong earthquakes[J]. Journal of Geophysical Research: Space Physics, 2020, 125(9): e2020JA028396 -
下载:
点击查看大图
计量
- 文章访问数: 478
- HTML全文浏览量: 127
- PDF下载量: 79
