Research progress of aftershock forecasting in the early stage after the mainshock
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摘要: 震后早期快速、准确的余震预测对震后灾害风险应对和采取有效的处置措施十分重要. 震后早期阶段地震目录不完整性是影响现有余震预测方法快速、准确预测的关键因素. 近年来，随着技术和模型的发展，使得震后早期数据缺失阶段的余震预测成为可能. 本文针对震后早期数据缺失阶段难以开展有效的余震预测问题，分别从提升余震检测率角度阐述了匹配滤波技术和深度学习技术，从统计地震学的余震补齐角度阐述了双尺度变换技术，从最大限度利用余震信息实时预测角度阐述了Omi模型和Lippiello模型等研究进展，分析了各类方法的优劣势，并提出了综合解决震后早期数据缺失阶段余震预测“瓶颈期”问题的技术路线，为从事地震检测、余震预测以及震后趋势研判等相关工作的科研人员提供科学参考.
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关键词:
- 快速、准确的震后早期余震预测 /
- 匹配滤波技术 /
- 深度学习 /
- 双尺度变换 /
- Omi 模型 /
- Lippiello 模型
Abstract: Rapid, accurate, and nearly real-time aftershock forecasting has attracted increasing public and social attention in dealing with disaster risk and taking effective disposal measures after the mainshock. Many aftershock forecasting methods are seriously affected by catalogue incompleteness in the early stage after the mainshock, which makes it difficult to carry out aftershock forecasting with a disaster reduction effect in time. In recent years, with the development of technology and models, the forecasting of early aftershocks has become possible. In this study, aiming at the "bottleneck period" of aftershock forecasting in the early stage after the mainshock, we elaborated the matched filtering technology and deep learning technology from the perspective of improving aftershock detection rate, the bi-scale empirical transformation technology from the perspective of statistical seismology, and the research progress of the Omi and Lippiello models from the perspective of maximizing the use of aftershock information for real-time forecasting. We analyzed the advantages and disadvantages of various methods and proposed a technical route to comprehensively solve the "bottleneck period" of aftershock forecasting in the early stage after the mainshock. This study provides a scientific reference for researchers to engage in microearthquake detection, aftershock forecasting, and post-earthquake trend research. -
图 1 震后早期阶段余震序列完备震级动态变化的示例. （a）2022年1月8日青海门源MS6.9地震序列；（b）2021年5月22日青海玛多MS7.4地震序列；（c）2021年5月21日云南漾濞MS6.4地震序列. 图中各颜色的实线和虚线分别标出了µ(t)以及μ(t)+σ、μ(t)+2σ地震检测率的位置
Figure 1. Example of catalogue completeness analysis of aftershock sequence in the early stage after the mainshock. (a) The MS6.9 earthquake sequence in Menyuan, Qinghai on January 8, 2022. (b) The MS7.4 earthquake sequence in Maduo, Qinghai on May 22, 2021. (c) The MS6.4 earthquake sequence in Yangbi, Yunnan on May 21, 2021. The solid line and dashed line of each color indicate the results of the μ(t), μ(t)+σ, μ(t)+2σ detection rate calculated from 0 to 1.00 day after the mainshock
图 2 利用双尺度变换补充技术对2021年青海玛多MS7.4地震的余震数据补充结果. （a）震级与地震发生时间图；（b）双尺度变换技术后震级与地震事件发生时间的经验分布；（c）缺失事件范围外的震级与地震事件发生时间的经验分布；（d）实际发生的地震事件与补充地震事件震级和发生时间的经验分布；（e）实际地震事件和补充事件的震级与发震时间图；（f）原始数据集（灰色曲线）和补充数据集（蓝色曲线）的累积地震发生次数. （a-d）中的蓝色多边形是缺失事件所在的区域及其对应的映射，（d）和（e）中的绿点是补充事件
Figure 2. Results of the application of replenishing algorithm to the earthquake data from the 2021 Maduo MS7.4 earthquake in Qinghai. (a) Magnitudes versus occurrence times of earthquake events. (b) Rescaled magnitudes versus empirical distribution of occurrence times of recorded events transformed using the bi-scale empirical transformation. (c) Rescaled magnitudes versus rescaled occurrence times of the combination of the observed events, in which rescaling was based on the empirical distribution that was estimated based on the events outside. (d) Rescaled magnitudes versus rescaled occurrence times of observed events and replenished events. (e) Magnitudes versus occurrence times of observed synthetic events and replenished events. (f) Cumulative numbers of events against occurrence times for the original dataset (gray curve) and replenished dataset (black curve). The blue polygons in (a-d) are the area and its corresponding mappings in which the missing events fall. Green dots in (d) and (e) are the replenished events
图 3 利用Omi-R-J模型预测未来一天的余震发生率. （a, d）2022年1月8日青海门源MS6.9地震；（b, e）2021年5月22日青海玛多MS7.4级地震；（c, f）2021年5月21日云南漾濞MS6.4级地震. 图（a-c）为预测结果与实际观测结果在震级-累积频次上的比较，黑色圆点为实际观测结果，红色线段为预测结果，粉色区域为预测结果的95%置信区间. 图（d-f）为M>2.95的预测结果与实际观测结果在时间-累积频次上的比较，黑色曲线为实际观测结果，红色曲线为预测结果，红色虚线标出了预测结果的95%置信区间，黑色垂直虚线标出了预测起始时刻
Figure 3. Future 1-day aftershock forecasting using the Omi-R-J model. (a, d) The MS6.9 earthquake sequence in Menyuan, Qinghai on January 8, 2022. (b, e) The MS7.4 earthquake sequence in Maduo, Qinghai on May 22, 2021. (c, f) The MS6.4 earthquake sequence in Yangbi, Yunnan on May 21, 2021. (a-c) Comparison of magnitude-cumulative frequency between predictions (red lines) and observed aftershocks (black dots) and their 95% confidence interval (pink area). (d-f) Show the comparison of magnitude-cumulative frequency between predictions (red curves) and observed aftershocks (black curves) when M>2.95, their 95% confidence interval (red dashed lines), and the starting and ending times of forecasts (black vertical dashed lines), respectively
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