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）
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
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 $ 
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
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
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）
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）
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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