Time-varying background field and anomaly analysis of Swarm satellite magnetic field data before the 2020 Jamaica MW7.7 earthquake
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摘要: 蜂群卫星磁场数据受地磁活动和地方时的影响，本文先通过变分模态分解去除地磁活动对数据的影响，再建立时变背景场以消除地方时对数据的影响。基于建立的时变背景场，我们利用蜂群卫星磁场数据对2020年牙买加MW7.7地震进行震前异常分析，计算地震影响区域内轨道数据的能量值，利用时变背景场设置阈值提取异常。结果表明：牙买加地震影响区域内的异常轨道累计数量在震前50天至震前43天出现加速增长的现象。此外，基于时变背景场与昼、夜侧背景场提取异常的结果对比显示：由于昼、夜侧背景场的建立混合了多个当地时间的磁场数据，高值背景场会被低值背景场拉低，导致部分非异常的轨道被错误地识别为异常轨道；而低值背景场会被高值背景场抬高，导致部分异常轨道不能被识别。而时变背景场针对每一个地方时建立了更为准确的背景，其时间分辨率高，能凸显出不同地方时卫星磁场数据的背景值差异，这对异常轨道的准确提取十分重要。进一步对岩石层、大气层和电离层多圈层的参量进行了震前异常分析，并对三个圈层的异常出现时间进行解释，证明了这些异常可能与牙买加地震的孕育有关。Abstract: The magnetic field data of Swarm satellite are influenced by geomagnetic activity and local time. In this paper, the influence of geomagnetic activity was firstly removed by variational mode decomposition, and then we built a time-varying background field to eliminate the influence of local time on the data. Based on the established time-varying background field, we used the Swarm satellite magnetic field data to conduct a pre-earthquake anomaly analysis of the 2020 Jamaica earthquake. We calculated the amount of energy of the magnetic data inside the earthquake affected area, then compared with the threshold value of the time-varying background field. If the difference between the amount of energy of a track inside the earthquake affected area and the corresponding background value is over the threshold value, the track was considered to be pre-earthquake anomalies. The results show that the cumulative number of anomalous tracks in function of the time shows accelerated growth from 50 days to 43 days before the earthquake. In addition, we also built day-side and night-side background fields to compare with the time-varying background field. By analyzing the differences between the anomalous orbits extracted based on the two methods, we find that the high-value background fields will be pulled down by the low-value background fields, which leads to some nonanomalous orbits being wrongly identified as anomalous orbits. For the same reason, the low-value background fields will be pulled up by the high-value background fields, which leads to some abnormal orbits cannot be recognized. It is because that the day-side and night-side background fields were built by the data of multiple local times magnetic field data, which cannot identify the great variations of magnetic field at different local time. However, the time resolution of time-varying background field is high, so it can highlight the great variations of magnetic field at different local time. As a result, the establishment of time-varying background field is crucial for the accurate detection of anomalous orbits. Further, we analyzed the cumulative number of anomalies in lithosphere, atmosphere and ionosphere, and explained the time correlation of these anomalies, which proved that these anomalies may be related to the seismogeny of the Jamaica earthquake.
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图 1 2020年牙买加 MW7.7地震的地理位置和地震影响区（USGS，2020a）
Figure 1. Location of the 2020 Jamaica MW7.7 earthquake and related earthquake affected areas （USGS，2020a）
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图 2 两个模态的平均能量与对应的$a_p $指数
（a） 轨道第一个模态的能量；（b） 轨道第二个模态的能量；（c） 轨道对应的$a_p $指数
Figure 2. Results of average energy of the two modes and their corresponding $a_p $ index
（a） The average energy of the first mode of all the tracks；（b） The average energy of the second mode of all the tracks； （c） The $a_p $ index at the moment corresponding to the time of the track
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图 3 模态的平均能量与对应的$\overline a_p $的相关系数
Figure 3. The correlation coefficients between $\overline \varphi_{\rm{IMF1}} $ and $\overline a_p $ and those between $\overline \varphi_{\rm{IMF2}} $ and $\overline a_p $
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图 4 牙买加地震影响区域在不同地方时（LT）的时变背景场
Figure 4. The time-varying background field at different local time inside the earthquake affected areas of the Jamaica earthquake
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图 5 牙买加地震影响区域内24个地方时的平均背景值
Figure 5. The average background values of 24 local times inside the earthquake-affected area of the Jamaica earthquake
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图 6 基于时变背景场（a）与昼侧、夜侧背景场（b）分别提取的异常轨道累计结果
Figure 6. The cumulative number of anomalous tracks over time based on the time-varying background field （a） and the day-side and the night-side background field （b）
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图 7 地方时为白天（a）和夜晚（b）时昼侧、夜侧的两个背景场对比
Figure 7. Comparison between dayside and night side background field in the day time （a） and the night time （b）
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图 8 时变背景场与昼侧、夜侧背景场提取异常的阈值（a）及其对比图（b）
Figure 8. The threshold （a） of the time-varying background field and the day-side and the night-side background field used to extract anomalous tracks and its comparison （b）
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安张辉,谭大诚,陈军营,范莹莹,刘君,解涛. 2011. DEMETER卫星磁场波形数据分析方法初探[J]. 华南地震,31(1):55–65. doi: 10.3969/j.issn.1001-8662.2011.01.007 An Z H,Tan D C,Chen J Y,Fan Y Y,Liu J,Xie T. 2011. Discussion on the analysis method of magnetic field waveform data recorded by the DEMETER satellite[J]. South China Journal of Seismology,31(1):55–65 (in Chinese).
池成全. 2020. 钻孔应变前兆观测数据分析与异常提取研究[D]. 长春: 吉林大学: 81–82. Chi C Q. 2020. Analysis of Borehole Strain Precursory Observation Data and Research on Anomaly Extraction[D]. Changchun: Jilin University: 81–82 （in Chinese）.
丁鉴海, 卢振业, 余素荣. 2011. 地震地磁学概论[M]. 合肥: 中国科学技术大学出版社: 59–63. Ding J H, Lu Z Y, Yu S R. 2011. A Brief Treatise on Seismomagnetism[M]. Hefei: University of Science and Technology of China Press: 59–63 （in Chinese）.
金巍,张效信,宋燕,何飞,李丽光,于超,吕景天,肖子牛. 2017. 地磁活动对气候要素影响的研究进展[J]. 地球物理学报,60(4):1276–1283. doi: 10.6038/cjg20170404 Jin W,Zhang X X,Song Y,He F,Li L G,Yu C,Lü J T,Xiao Z N. 2017. Progress of research on the effect of geomagnetic activity on climatic elements[J]. Chinese Journal of Geophysics,60(4):1276–1283 (in Chinese).
李凯艳. 2020. Swarm卫星磁场数据震前异常提取与统计分析[D]. 长春: 吉林大学: 31–32. Li K Y. 2020. Pre-Earthquake Anomaly Detection and Statistical Analysis of Swarm Satellite Magnetic Field Data[D]. Changchun: Jilin University: 31–32 （in Chinese）.
马瑾,郭彦双. 2014. 失稳前断层加速协同化的实验室证据和地震实例[J]. 地震地质,36(3):547–561. doi: 10.3969/j.issn.0253-4967.2014.03.001 Ma J,Guo Y S. 2014. Accelerated synergism prior to fault instability:Evidence from laboratory experiments and an earthquake case[J]. Seismology and Geology,36(3):547–561 (in Chinese).
万剑华,王飞飞,单新建,闫相相. 2012. DEMETER电磁卫星高能粒子在汶川地震中的初步应用[J]. 地球物理学进展,27(6):2279–2288. doi: 10.6038/j.issn.1004-2903.2012.06.002 Wan J H,Wang F F,Shan X J,Yan X X. 2012. Preliminary application of energetic particle on DEMETER satellite in Wenchuan earthquake[J]. Progress in Geophysics,27(6):2279–2288 (in Chinese).
王楚钦,杜丹,张效信,赵明现,蒋勇. 2015. 基于多台站观测的中国区域地磁指数[J]. 空间科学学报,35(1):26–39. Wang C Q,Du D,Zhang X X,Zhao M X,Jiang Y. 2015. New regional geomagnetic activity index based on multi-station observation[J]. Chinese Journal of Space Science,35(1):26–39 (in Chinese).
闫相相,单新建,曹晋滨,汤吉. 2014. 利用DEMETER卫星数据统计分析全球MW≥7.0地震的电离层电子浓度异常[J]. 地球物理学报,57(2):364–376. doi: 10.6038/cjg20140203 Yan X X,Shan X J,Cao J B,Tang J. 2014. Statistical analysis of electron density anomalies before global MW≥7.0 earthquakes (2005−2009) using data of DEMETER satellite[J]. Chinese Journal of Geophysics,57(2):364–376 (in Chinese).
杨牧萍,黄建平,张学民,申旭辉,王兰炜,泽仁志玛,钱庚,翟丽娜. 2018. 东北亚地区电离层ELF/VLF电场的动态背景场研究[J]. 地球物理学进展,33(6):2285–2294. doi: 10.6038/pg2018BB0492 Yang M P,Huang J P,Zhang X M,Shen X H,Wang L W,Zeren Z M,Qian G,Zhai L N. 2018. Analysis on dynamic background field of ionosphere ELF/VLF electric field in northeast Asia[J]. Progress in Geophysics,33(6):2285–2294 (in Chinese).
杨桐,王希燕,吕晨阳,杨柳,周忠伟. 2013. 现阶段地震灾害及防御措施探索[J]. 产业与科技论坛,12(19):51–52. doi: 10.3969/j.issn.1673-5641.2013.19.025 Yang T,Wang X Y,Lü C Y,Yang L,Zhou Z W. 2013. Exploration of earthquake disaster and defense measures at present stage[J]. Industrial &Science Tribune,12(19):51–52 (in Chinese).
泽仁志玛,申旭辉,曹晋滨,张学民,黄建平,刘静,欧阳新艳,赵庶凡. 2012. 强震前ELF/VLF磁场的扰动特征统计研究[J]. 地球物理学报,55(11):3699–3708. doi: 10.6038/j.issn.0001-5733.2012.11.017 Zeren Z M,Shen X H,Cao J B,Zhang X M,Huang J P,Liu J,Ouyang X Y,Zhao S F. 2012. Statistical analysis of ELF/VLF magnetic field disturbances before major earthquakes[J]. Chinese Journal of Geophysics,55(11):3699–3708 (in Chinese).
赵国泽,陈小斌,蔡军涛. 2017. 电磁卫星和地震预测[J]. 地球物理学进展,22(3):667–673. Zhao G Z,Chen X B,Cai J T. 2017. Electromagnetic observation by satellite and earthquake prediction[J]. Progress in Geophysics,22(3):667–673 (in Chinese).
赵亚军,窦远明,张明杰. 2020. 基于变分模态分解的模态参数识别研究[J]. 振动与冲击,39(2):115–122. doi: 10.13465/j.cnki.jvs.2020.02.017 Zhao Y J,Dou Y M,Zhang M J. 2020. Modal parameter identification based on variational mode decomposition[J]. Journal of Vibration and Shock,39(2):115–122 (in Chinese).
Akhoondzadeh M,De Santis A D,Marchetti D,Piscini A,Cianchini G. 2018. Multi precursors analysis associated with the powerful Ecuador (MW=7.8) earthquake of 16 April 2016 using Swarm satellites data in conjunction with other multi-platform satellite and ground data[J]. Adv Space Res,61(1):248–263. doi: 10.1016/j.asr.2017.07.014
Bertello I,Piersanti M,Candidi M,Diego P,Ubertini P. 2018. Electromagnetic field observations by the DEMETER satellite in connection with the 2009 L’Aquila earthquake[J]. Ann Geophys,36(5):1483–1493. doi: 10.5194/angeo-36-1483-2018
Dobrovolsky I P,Zubkov S I,Miachkin V I. 1979. Estimation of the size of earthquake preparation zones[J]. Pure Appl Geophys,117(5):1025–1044. doi: 10.1007/BF00876083
Dragomiretskiy K,Zosso D. 2014. Variational mode decomposition[J]. IEEE Trans Signal Process,62(3):531–544. doi: 10.1109/TSP.2013.2288675
Finlay C C,Kloss C,Olsen N,Hammer M D,Tøffner-Clausen L,Grayver A,Kuvshinov A. 2020. The CHAOS-7 geomagnetic field model and observed changes in the South Atlantic anomaly[J]. Earth Planets Space,72(1):156. doi: 10.1186/s40623-020-01252-9
Friis-Christensen E,Lühr H,Hulot G. 2006. Swarm:A constellation to study the Earth’s magnetic field[J]. Earth Planets Space,58(4):351–358. doi: 10.1186/BF03351933
Kanamori H,Anderson D L. 1975. Theoretical basis of some empirical relations in seismology[J]. Bull Seismol Soc Am,65(5):1073–1095.
Liu J Y,Chen Y I,Pulinets S A,Tsai Y B,Chuo Y J. 2000. Seismo-ionospheric signatures prior to M≥6.0 Taiwan earthquakes[J]. Geophys Res Lett,27(19):3113–3116. doi: 10.1029/2000GL011395
Marchetti D,Akhoondzadeh M. 2018. Analysis of Swarm satellites data showing seismo-ionospheric anomalies around the time of the strong Mexico (MW=8.2) earthquake of 8 September 2017[J]. Adv Space Res,62(3):614–623. doi: 10.1016/j.asr.2018.04.043
Marchetti D,De Santis A,D'arcangelo S,Poggio F,Piscini A,Campuzano S A,De Carvalho W V J O. 2019. Pre-earthquake chain processes detected from ground to satellite altitude in preparation of the 2016−2017 seismic sequence in Central Italy[J]. Remote Sens Environ,229:93–99. doi: 10.1016/j.rse.2019.04.033
Marchetti D,De Santis A,Shen X H,Campuzano S A,Perrone L,Piscini A,Di Giovambattista R,Jin S G,Ippolito A,Cianchini G,Cesaroni C,Sabbagh D,Spogli L,Zeren Z,Huang J P. 2020. Possible lithosphere-atmosphere-ionosphere coupling effects prior to the 2018 MW=7.5 Indonesia earthquake from seismic,atmospheric and ionospheric data[J]. J Asian Earth Sci,188:104097. doi: 10.1016/j.jseaes.2019.104097
Mignan A,King G C P,Bowman D. 2007. A mathematical formulation of accelerating moment release based on the stress accumulation model[J]. J Geophys Res,112(B7):B07308.
Molchanov O A,Hayakawa M. 1995. Generation of ULF electromagnetic emissions by microfracturing[J]. Geophys Res Lett,22(22):3091–3094. doi: 10.1029/95GL00781
Noda H,Nakatani M,Hori T. 2013. Large nucleation before large earthquakes is sometimes skipped due to cascade-up-implications from a rate and state simulation of faults with hierarchical asperities[J]. J Geophys Res Solid Earth,118(6):2924–2952. doi: 10.1002/jgrb.50211
Ouyang X Y,Parrot M,Bortnik J. 2020. ULF wave activity observed in the nighttime ionosphere above and some hours before strong earthquakes[J]. J Geophys Res:Space Phys,125(9):e2020JA028396.
Ouzounov D, Pulinets S, Kafatos M C, Taylor P. 2018. Thermal radiation anomalies associated with major earthquakes[G]//Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies. Washington D.C.: American Geophysical Union: 259–274.
Piscini A,Marchetti D,De Santis A. 2019. Multi-parametric climatological analysis associated with global significant volcanic eruptions during 2002−2017[J]. Pure Appl Geophys,176(8):3629–3647. doi: 10.1007/s00024-019-02147-x
Pulinets S,Ouzounov D. 2011. Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model:An unified concept for earthquake precursors validation[J]. J Asian Earth Sci,41(4/5):371–382.
USGS. 2020a. Earthquake hazards program[EB/OL]. [2020-01-28]. https://earthquake.usgs.gov/earthquakes/search.
USGS. 2020b. M7.7: 123 km NNW of Lucea, Jamaica[EB/OL]. [2020-01-28]. https://earthquake.usgs.gov/earthquakes/eventpage/pt20028001/executive.
Ventura G,Di Giovambattista R. 2013. Fluid pressure,stress field and propagation style of coalescing thrusts from the analysis of the 20 May 2012 ML5.9 Emilia earthquake (Northern Apennines,Italy)[J]. Terra Nova,25(1):72–78. doi: 10.1111/ter.12007
Zhang X,Shen X,Parrot M,Zeren Z M,Ouyang X H,Liu J,Qian J,Zhao S F,Miao Y. 2012. Phenomena of electrostatic perturbations before strong earthquakes (2005−2010) observed on DEMETER[J]. Nat Hazards Earth Syst Sci,12(1):75–83. doi: 10.5194/nhess-12-75-2012
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