Research progress of aftershock forecasting in the early stage after the mainshock
-
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.
-
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
-
[1] Aki K, Chouet B. 1975. Origin of coda waves: Source, attenuation, and scattering effects[J]. Journal of Geophysical Research, 80: 3322-3342. doi: 10.1029/JB080i023p03322 [2] 毕金孟, 蒋长胜. 2019. 华北地区地震序列参数的分布特征[J]. 地球物理学报, 62(11): 4300-4312 doi: 10.6038/cjg2019M0453Bi J M, Jiang C S. 2019. Distribution characteristics of earthquake sequence parameters in North China[J]. Chinese Journal of Geophysics, 62(11): 4300-4312 (in Chinese). doi: 10.6038/cjg2019M0453 [3] Bi J M, Jiang C S. 2020a. Comparison of early aftershock forecasting for the 2008 Wenchuan MS8.0 earthquake[J]. Pure and Applied Geophysics, 177: 9-25. doi: 10.1007/s00024-019-02192-6 [4] Bi J M, Jiang C S. 2020b. Research on the forecasting strategy of early aftershocks in North China[J]. Annals of Geophysics, 63(4): SE441-SE441. [5] 毕金孟, 蒋长胜, 来贵娟, 等. 2022a. 中国大陆强震的早期余震概率预测效能评估与制约因素[J]. 地球物理学报, 65(7): 2532-2545Bi J M, Jiang C S, Lai G J, et al. 2022a. Effectiveness evaluation and constraints of early aftershock probability forecasting for strong earthquakes in continental China[J]. Chinese Journal of Geophysics, 65(7): 2532-2545 (in Chinese). [6] 毕金孟, 宋程, 马永. 2023. 芦山两次强震序列活动特征及余震预测效能对比分析[J]. 地震研究, 46(2): 204-215. DOI: 10.20015/j.cnki.ISSN1000-0666.2023.0021.Bi J M, Song C, Ma Y. 2023. Comparative analysis of activity characteristics and aftershock forecasting efficiency of two Lushan earthquake sequences[J]. Journal of Seismological Research, 46(2): 204-215 (in Chinese). DOI: 10.20015/j.cnki.ISSN1000-0666.2023.0021. [7] Cattania C, Khalid F. 2016. A parallel code to calculate rate-state seismicity evolution induced by time dependent, heterogeneous Coulomb stress changes[J]. Computers and Geosciences, 94: 48-55. doi: 10.1016/j.cageo.2016.06.007 [8] Cattania C, Werner M J, Marzocchi W, et al, 2018. The forecasting skill of physics-based seismicity models during the 2010-2012 Canterbury, New Zealand, earthquake sequence[J]. Seismological Research Letters, 89(4): 1238-1250. doi: 10.1785/0220180033 [9] Enescu B, Mori J, Miyazawa M. 2007. Quantifying early aftershock activity of the 2004 mid-Niigata Prefecture earthquake (MW6.6)[J]. Journal of Geophysical Research Solid Earth, 112: B04310. [10] Enescu B, Mori J, Miyazawa M, et al, 2009. Omori-utsu law c-values associated with recent moderate earthquakes in Japan[J]. Bulletin of the Seismological Society of America, 99 (2A): 884-891. doi: 10.1785/0120080211 [11] ERC (Earthquake Research Committee Report). 2016. Information on earthquake forecasting after a large earthquake[J/OL]. Available at. https://www.jishin.go.jp/main/yosoku_info/honpen.pdf (last accessed June 2022). [12] Gentili S, Di Giovambattista R. 2017. Pattern recognition approach to the subsequent event of damaging earthquakes in Italy[J]. Physics of the Earth and Planetary Interiors, 266: 1-17. doi: 10.1016/j.pepi.2017.02.011 [13] Gentili S, Di Giovambattista R. 2020. Forecasting strong aftershocks in earthquake clusters from northeastern Italy and western Slovenia[J]. Physics of the Earth and Planetary Interiors, 303: 106483. doi: 10.1016/j.pepi.2020.106483 [14] Gerstenberger M C, Rhoades D A, McVerry G H. 2016. A hybrid time-dependent probabilistic seismic-hazard model for Canterbury, New Zealand[J]. Seismological Research Letters, 87 (6): 1311-1318. doi: 10.1785/0220160084 [15] Gibbons S J, Ringdal F. 2006. The detection of low magnitude seismic events using array-based waveform correlation[J]. Geophysical Journal International, 165: 149-166. doi: 10.1111/j.1365-246X.2006.02865.x [16] Gibbons S J, Bøttger Sørensen M, Harris D B, et al. 2007. The detection and location of low magnitude earthquakes in northern Norway using multi-channel waveform correlation at regional distances[J]. Physics of the Earth and Planetary Interiors, 160: 285-309. doi: 10.1016/j.pepi.2006.11.008 [17] Grigoli F, Scarabello L, Böse M, et al. 2018. Pick-and waveform-based techniques for real-time detection of induced seismicity[J]. Geophysical Journal International, 213(2): 868-884. doi: 10.1093/gji/ggy019 [18] Gutenberg R, Richter C F. 1944. Frequency of earthquakes in California[J]. Bulletin of the Seismological Society of America, 34: 185-188. doi: 10.1785/BSSA0340040185 [19] Helmstetter A, Kagan Y Y, Jackson D D. 2005. Importance of small earthquakes for stress transfers and earthquake triggering[J]. Journal of Geophysical Research: Solid Earth, 110: B05S08. [20] Helmstetter A, Kagan Y Y, Jackson D D. 2006. Comparison of short-term and time-independent earthquake forecast models for southern California[J]. Bulletin of the Seismological Society of America, 96(1): 90-106. doi: 10.1785/0120050067 [21] Ishimoto M, Iida K. 1939. Observations sur les seismes enregistres parle microsismographe construit dernierement[J]. Bulletin of the Earthquake Research Institute 17: 443-478. [22] Iwata T. 2008. Low detection capability of global earthquakes after the occurrence of large earthquakes: Investigation of the Harvard CMT catalogue[J]. Geophysical Journal International, 174(3): 849-856. doi: 10.1111/j.1365-246X.2008.03864.x [23] Japan Meteorological Agency (JMA). 2009. The Iwate-Miyagi Nairiku Earthquake in 2008[R]. Reports Coordnated Commuication on Earthquake Prediction, 81: 101-131. [24] 蒋长胜, 吴忠良, 李宇彤. 2008. 首都圈地区“重复地震”及其在区域地震台网定位精度评价中的应用[J]. 地球物理学报, 51(3): 817-827 doi: 10.3321/j.issn:0001-5733.2008.03.022Jiang C S, Wu Z L, Li Y T. 2008. Estimatig the location accuracy of the Beijing Capital Digital Seismograph Network using repeating events[J]. Chinese Journal of Geophysics, 51(3): 817-827 (in Chinese). doi: 10.3321/j.issn:0001-5733.2008.03.022 [25] 蒋长胜, 吴忠良, 韩立波, 等. 2013. 地震序列早期参数估计和余震概率预测中截止震级Mc的影响: 以2013年甘肃岷县—漳县6.6级地震为例[J]. 地球物理学报, 56(12): 4048-4057 doi: 10.6038/cjg20131210Jiang C S, Wu Z L, Han L B, et al. 2013. Effect of cutoff magnitude Mc of earthquake catalogues on the early estimation of earthquake sequence parameters with implication for the probabilistic forecast of aftershock: the 2013 Minxian-Zhangxian, Gansu, MS6.6 earthquake sequence[J]. Chinese Journal of Geophysics, 56(12): 4048-4057 (in Chinese). doi: 10.6038/cjg20131210 [26] 蒋长胜, 毕金孟, 王福昌, 等. 2018. 利用早期余震预测的Omi-R-J方法对2017年四川九寨沟MS7.0地震的应用研究[J]. 地球物理学报, 61(5): 2099-2110 doi: 10.6038/cjg2018M0113Jiang C S, Bi J M, Wang F C, et al. 2018. Application of the Omi-R-J method for forecast of early aftershocks to the 2017 Jiuzhaigou, Sichuan, MS7.0 earthquake[J]. Chinese Journal of Geophysics, 61(5): 2099-2110 (in Chinese). doi: 10.6038/cjg2018M0113 [27] Kato M. 2013. Revisiting the Ishimoto-Iida law for strong-motion seismograms: A case study at CEORKA network[J]. Bulletin of the Seismological Society of America, 104 (1): 497-502. [28] Kato A, Nakagawa S. 2014a. Multiple slow-slip events during a foreshock sequence of the 2014 Iquique, Chile MW8.1 earthquake[J]. Geophysical Research Letters, 41(15): 5420-5427. doi: 10.1002/2014GL061138 [29] Kato A, Obara K. 2014b. Step-like migration of early aftershocks following the 2007 MW6.7 Noto-Hanto earthquake, Japan[J]. Geophysical Research Letters, 41(11): 3864-3869. doi: 10.1002/2014GL060427 [30] Kong Q, Trugman D T, Ross Z E, et al. 2018. Machine learning in seismology: Turning data into insights[J]. Seismological Research Letters, 90(1): 3-14. [31] Lengliné O, Enescu B, Peng Z G, et al. 2012. Decay and expansion of the early aftershock activity following the 2011, MW 9.0 Tohoku earthquake[J]. Geophysical Research Letters, 39: L18309. [32] Lippiello E, Cirillo A, Godano G, et al. 2016. Real-time forecast of aftershocks from a single seismic station signal[J]. Geophysical Research Letters, 43: 6252-6258. doi: 10.1002/2016GL069748 [33] Lippiello E, Petrillo C, Godano C, et al. 2019a. Forecasting of the first hour aftershocks by means of the perceived magnitude[J]. Nature Communication, 10: 2953. doi: 10.1038/s41467-019-10763-3 [34] Lippiello E, Cirillo A, Godano C, et al. 2019b. Post seismic catalog incompleteness and aftershock forecasting[J]. Geosciences, 9: 355. doi: 10.3390/geosciences9080355 [35] Liu M, Zhang M, Zhu W, et al. 2020a. Rapid characterization of the July 2019 Ridgecrest, California, earthquake sequence from raw seismic data using machine-learning phase picker[J]. Geophysical Research Letters, 47(4): e2019GL086189. [36] Liu M, Li H Y, Zhang M, et al. 2020b. Graphics processing unit-based Match and Locate (GPU-M&L): An improved Match and Locate method and its application[J]. Seismological Research Letters, 91 (2A): 1019-1029. doi: 10.1785/0220190241 [37] Lu W, Zhou Y, Zhao Z, et al. 2021. Aftershock sequence of the 2017 MW6.5 Jiuzhaigou, China earthquake monitored by an AsA network and its implication to fault structures and strength[J]. Geophysical Journal International, 228(3): 1763-1779. doi: 10.1093/gji/ggab443 [38] Mancini S, Segou M, Werner M, et al. 2019. Improving physics-based aftershock forecasts during the 2016-2017 central Italy Earthquake Cascade[J]. Journal of Geophysical Research: Solid Earth, 124(8): 8626-8643. doi: 10.1029/2019JB017874 [39] Marsan D, Enescu B. 2012. Modeling the foreshock sequence prior to the 2011, MW9.0 Tohoku, Japan, earthquake[J]. Journal of Geophysical Research, 117: B06316. [40] Marzocchi W, Lombardi A M, Casarotti E. 2014. The establishment of an operational earthquake forecasting system in Italy[J]. Seismological Research Letters, 85 (5): 961-969. doi: 10.1785/0220130219 [41] Meng X, Peng Z, Hardebeck J L. 2013. Seismicity around Parkfield correlates with static shear stress changes following the 2003 MW6.5 San Simeon earthquake[J]. Journal of Geophysical Research: Solid Earth, 118: 3576-3591. doi: 10.1002/jgrb.50271 [42] Meng X, Peng Z. 2014. Seismicity rate changes in the Salton Sea Geothermal Field and the San Jacinto Fault Zone after the 2010 MW7.2 El Mayor-Cucapah earthquake[J]. Geophysical Journal International, 197(3): 1750-1762 doi: 10.1093/gji/ggu085 [43] Michael A J, McBride S K, Hardebeck J L, et al. 2020. Statistical seismology and communication of the USGS operational aftershock forecasts for the 30 November 2018 MW7.1 Anchorage, Alaska, earthquake[J]. Seismological Research Letters, 91 (1): 153-173. doi: 10.1785/0220190196 [44] Mizrahi L, Nandan S, Wiemer, S. 2021. Embracing data incompleteness for better earthquake forecasting[J]. Journal of Geophysical Research: Solid Earth, 126: e2021JB022379. [45] Nanjo K Z, Tsuruoka H, Yokoi S, et al. 2012. Predictability study on the aftershock sequence following the 2011 Tohoku-Oki, Japan, earthquake: First results[J]. Geophysical Journal International, 191: 653-658. doi: 10.1111/j.1365-246X.2012.05626.x [46] Ogata Y. 1989. Statistical model for standard seismicity and detection of anomalies by residual analysis[J]. Tectonophysics, 169(1/2/3): 159-174. [47] Ogata Y, Katsura K. 1993. Analysis of temporal and spatial heterogeneity of magnitude frequency distribution inferred from earthquake catalogues[J]. Geophysical Research Letters, 113(3): 727-738. [48] Ogata Y. 2001. Increased probability of large earthquakes near aftershock regions with relative quiescence[J]. Journal of Geophysical Research: Solid Earth, 106(B5): 8729-8744. doi: 10.1029/2000JB900400 [49] Ogata Y, Zhuang J C. 2006. Space-time ETAS models and an improved extension[J]. Tectonophysics, 413(1-2): 13-23. doi: 10.1016/j.tecto.2005.10.016 [50] Ogata Y, Katsura K, Falcone G, et al. 2013. Comprehensive and topical evaluations of earthquake forecasts in terms of number, time, space, and magnitude[J]. Bulletin of the Seismological Society of America, 103(3): 1692-1708. doi: 10.1785/0120120063 [51] Omi T, Ogata Y, Hirata Y, et al. 2013. Forecasting large aftershocks within one day after the main shock[J]. Scientific Reports. 3: 2218. doi: 10.1038/srep02218 [52] Omi T, Ogata Y, Hirata Y, et al. 2014. Estimating the ETAS model from an early aftershock sequence[J]. Geophysical Research Letters, 41: 850-857. doi: 10.1002/2013GL058958 [53] Omi T, Ogata Y, Hirata Y, et al. 2015. Intermediate-term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches[J]. Journal of Geophysical Research: Solid Earth, 120: 2561-2578. doi: 10.1002/2014JB011456 [54] Omi T, Ogata Y, Shiomi K, et al. 2016. Automatic aftershock forecasting: A test using real-time seismicity data in Japan[J]. Bulletin of the Seismological Society of America, 106(6): 2450-2458. doi: 10.1785/0120160100 [55] Omi T, Ogata Y, Shiomi K. 2019. Implementation of a real-time system for automatic aftershock forecasting in Japan[J]. Seismological Research Letters, 90(1): 242-250. doi: 10.1785/0220180213 [56] Omori F. 1894. On aftershocks of earthquakes[J]. Journal of the College of Science. Imperial University of Tokyo, 7: 111-200. [57] Page M T, van der Elst N, Hardebeck J, et al. 2016. Three Ingredients for improved global aftershock forecasts: Tectonic region, time-dependent catalog incompleteness, and intersequence variability[J]. Bulletin of the Seismological Society of America, 106 (5): 2290-2301. doi: 10.1785/0120160073 [58] Peng Z, Vidale J, Houston H. 2006. Anomalous early aftershock decay rate of the 2004 MW6.0 Parkfield, California, earthquake[J]. Seismological Research Letters, 33: L17307. [59] Peng Z, Vidale J, Ishii M, et al. 2007. Seismicity rate immediately before and after main shock rupture from high-frequency waveforms in Japan[J]. Journal of Geophysical Research, 112: B03306. [60] Peng Z, Zhao P. 2009. Migration of early aftershocks following the 2004 Parkfield earthquake[J]. Nature Geoscience, 2: 877-881. doi: 10.1038/ngeo697 [61] Perol T, Gharbi M, Denolle M. 2018. Convolutional neural network for earthquake detection and location[J]. Science Advances, 4: e1700578. doi: 10.1126/sciadv.1700578 [62] Pesicek J D, Child D, Artman B, et al. 2014. Picking versus stacking in a modern microearthquake location: Comparison of results from a surface passive seismic monitoring array in Oklahoma[J]. Geophysics, 79(6): KS61-KS68. doi: 10.1190/geo2013-0404.1 [63] Reasenberg P A, Jones L M. 1989. Earthquake hazard after a mainshock in California[J]. Science, 243: 1173-1176. doi: 10.1126/science.243.4895.1173 [64] Reverso T, Steacy S, Marsan D. 2018. A hybrid ETAS-Coulomb approach to forecast spatiotemporal aftershock rates[J]. Journal of Geophysical Research: Solid Earth, 123: 9750-9763. doi: 10.1029/2017JB015108 [65] Ross Z E, Trugman D T, Hauksson E, et al. 2019. Searching for hidden earthquakes in Southern California[J]. Science, 364(6442): 767-771. doi: 10.1126/science.aaw6888 [66] Sawazaki K, Enescu B. 2014. Imaging the high-frequency energy radiation process of a main shock and its early aftershock sequence: The case of the 2008 Iwate-Miyagi Nairiku earthquake, Japan[J]. Journal of Geophysical Research: Solid Earth, 119: 4729-4746. doi: 10.1002/2013JB010539 [67] Seif S, Mignan A, Zechar J D, et al. 2017. Estimating ETAS: The effects of truncation, missing data, and model assumptions[J]. Journal of Geophysical Research: Solid Earth, 122: 449-469. doi: 10.1002/2016JB012809 [68] Shelly D R, Beroza G C, Ide S. 2007. Non-volcanic tremor and low-frequency earthquake swarms[J]. Nature, 446: 305-307. doi: 10.1038/nature05666 [69] Shelly D R. 2020. A high-resolution seismic catalog for the initial 2019 Ridgecrest earthquake sequence: foreshocks, aftershocks, and faulting complexity[J]. Seismological Research Letters, 91 (4): 1971-1978. doi: 10.1785/0220190309 [70] Shcherbakov R, Turcotte D L, Rundle J B. 2004. A generalized Omori’s law for earthquake aftershock decay[J]. Geophysical Research Letters, 31: L11613. [71] Shcherbakov R, Zhuang J, Ogata Y. 2018. Constraining the magnitude of the largest event in a foreshock-mainshock-aftershock sequence[J]. Geophysical Journal International, 212: 1-13. doi: 10.1093/gji/ggx407 [72] Steacy S, Gerstenberger M C, Williams C A, et al, 2014. A new hybrid Coulomb/statistical model for forecasting aftershock rates[J]. Geophysical Journal International, 196(2): 918-923. doi: 10.1093/gji/ggt404 [73] 苏金波, 刘敏, 张云鹏, 等. 2021. 基于深度学习构建2021年5月21日云南漾濞MS6.4地震序列高分辨率地震目录[J]. 地球物理学报, 64(8): 2647-2656 doi: 10.6038/cjg2021O0530Su J B, Liu M, Zhang Y P, et al. 2021. High resolution earthquake catalog building for the 21 May 2021 Yangbi, Yunnan, MS6.4 earthquake sequence using deep-learning phase picker[J]. Chinese Journal of Geophysics, 64(8): 2647-2656 (in Chinese). doi: 10.6038/cjg2021O0530 [74] Tan Y, Waldhauser F, Ellsworth W L, et al. 2021. Machine-learning-based high-resolution earthquake catalog reveals how complex fault structures were activated during the 2016-2017 central Italy sequence[J]. The Seismic Record, 1 (1): 11-19. doi: 10.1785/0320210001 [75] Toda S, Enescu B. 2011. Rate/state Coulomb stress transfer model for the CSEP Japan seismicity forecast[J]. Earth, Planets and Space, 63(3): 171-185. [76] Utkucua M, Süleyman S. Nalbant B, et al. 2021. The June 12, 2017 M6.3 Karaburun-Lesvos earthquake of the Northern Aegean Sea: Aftershock forecasting and stress transfer[J]. Tectonophysics, 814: 228945. doi: 10.1016/j.tecto.2021.228945 [77] Utsu T. 1961. A statistical study of on the occurrence of aftershocks[J]. Geophysical Magazine, 30: 521-605. [78] Wang R, Schmandt B, Zhang M, et al. 2020. Injection-induced earthquakes on complex fault zones of the Raton basin illuminated by machine-learning phase picker and dense nodal array[J]. Geophysical Research Letters, 47(14): e2020GL088168. [79] Werner M J, Helmstetter A, Jackson D D, et al. 2011. High-resolution long-term and short-term earthquake forecasts for California[J]. Bulletin of the Seismological Society of America, 101: 1630-1648. doi: 10.1785/0120090340 [80] Wiemer S, Wyss M. 2000. Minimum magnitude of completeness in earthquake catalogs: Examples from Alaska, the western United States, and Japan[J]. Bulletin of the Seismological Society of America, 90 (4): 859-869. doi: 10.1785/0119990114 [81] Woessner J, Wiemer S, 2005. Assessing the quality of earthquake catalogues: Estimating the magnitude of completeness and its uncertainty[J]. Bulletin of the Seismological Society of America, 95 (2): 684-698. doi: 10.1785/0120040007 [82] Woessner J, Hainzl S, Marzocchi W, et al. 2011. A retrospective comparative forecast test on the 1992 Landers sequence[J]. Journal of Geophysical Research, 116: B05305. [83] Wu J, Yao D, Meng X, et al. 2017. Spatial-temporal evolutions of early aftershocks following the 2013 MW 6.6 Lushan earthquake in Sichuan, China[J]. Journal of Geophysical Research: Solid Earth, 122(4): 2873-2889. doi: 10.1002/2016JB013706 [84] Zhang M, Wen L. 2015. An effective method for small event detection: Match and locate (M&L)[J]. Geophysical Journal International, 200(3): 1523-1537. doi: 10.1093/gji/ggu466 [85] Zhang M, Ellsworth W L, Beroza G C. 2019. Rapid earthquake association and location[J]. Seismological Research Letters, 90(6): 2276-2284. doi: 10.1785/0220190052 [86] Zhang M, Liu M, Feng T, et al. 2022. LOC-FLOW: An end-to-end machine learning-based high-precision earthquake location workflow[J]. Seismological Research Letters,DOI: 10.1785/0220220019. [87] Zheng Y, Enescu B, Zhuang J C, et al. 2021. Data replenishment of five moderate earthquake sequences in Japan, with semi-automatic cluster selection[J]. Earthquake Science, 34(4): 310-322. doi: 10.29382/eqs-2021-0030 [88] Zhou Y, Yue H, Fang L, et al. 2021. An earthquake detection and location architecture for continuous seismograms: phase picking, association, location, and matched filter (PALM)[J]. Seismological Research Letters, 93(1): 413-425. [89] Zhou Y, Yue H, Zhou S, et al. 2022. Microseismicity along Xiaojiang Fault Zone (southeastern Tibetan Plateau) and the characterization of interseismic fault behavior[J]. Tectonophysics, 833: 229364. doi: 10.1016/j.tecto.2022.229364 [90] Zhuang J, Ogata Y, Vere-Jones D. 2002. Stochastic declustering of space-time earthquake occurrences[J]. Journal of the American Statistical Association, 97(458): 369-380. doi: 10.1198/016214502760046925 [91] Zhuang J, Ogata Y, Vere-Jones D. 2004. Analyzing earthquake clustering features by using stochastic reconstruction[J]. Journal of Geophysical Research: Solid Earth, 109(B5): B05301. [92] Zhuang J, Ogata Y, Wang T. 2017. Data completeness of the Kumamoto earthquake sequence in the JMA catalog and its influence on the estimation of the ETAS parameters[J]. Earth, Planets and Space, 69: 36. [93] Zhuang J, Wang T, Kiyosugi K. 2020. Detection and replenishment of missing data in marked point processes[J]. Statistica Sinica, 30: 2105-2130. -