Automatic detection,location and quantification of earthquakes: Application to tsunami warning

We describe a fully automated seismic event detection and location system, providing for real-time estimates of the epicentral parameters of both local and distant earthquakes. The system uses 12 telemetered short-period stations, with a regional aperture of 350 km, as well as two 3-component broad-band stations. Detection and location of teleseismic events is achieved independently and concurrently on the short-period and long-period channels. The long-period data is then used to obtain an estimate of the seismic momentM ₀ of the earthquake through the mantle magnitudeM _m, as introduced byOkal andTalandier (1989). In turn, this estimate ofM ₀ is used to infer the expected tsunami amplitude at Papeete, within 15 minutes of the recording of Rayleigh waves. The performance of the method is discussed in terms of the accuracy of the epicentral parameters and seismic moment obtained in real time, as compared to the values later published by the reporting agencies. Our estimates are usually within 3 degrees of the reported epicenter, and the standard deviation on the seismic moment only 0.19 unit of magnitude for a population of 154 teleseismic events.     

基于模板匹配定位法的江苏盐城附近微震检测和构造分析

利用基于GPU加速的匹配定位法和双差定位法,对江苏盐城及邻区18个台站记录的2009~2018年共10年的连续地震资料进行分析。首先从台网目录中挑选211个地震事件作为模板事件,使用匹配定位技术对江苏盐城附近连续10年的地震进行检测和识别,共识别出1349个地震事件,约为台网目录地震事件的3倍,最小完备震级由台网目录的M_L1.9降为M_L1.2。然后利用双差定位法对检测到的地震事件进行精定位,精定位的结果揭示：建湖地区的地震密集带与洪泽-沟墩断裂有关,震源深度优势分布为5~20km,断裂两侧震源深度有显著差异,断裂带倾向NW;射阳震群震源深度比建湖震群有所加深,优势分布为10~25km,震源深度由南东向西北逐渐变浅;宝应地区地震丛集分布;东台地区由于模板事件相对较少,扫描定位后,地震事件在陈家堡-小海断裂带附近零星分布。研究结果为研究盐城地区的地震活动性、发震断层的深部构造提供了基础数据支撑。     

地震预警连续定位方法研究

本文首先总结了地震预警系统中采用的一些实时定位方法,随后结合我国地震监测台网实际运行情况,提出了一套从单个台站触发开始逐渐过渡到利用前四台触发信息的完整地震预警连续定位方法.利用福建省地震监测台网记录的68个M3.0级以上地震观测记录,对本文方法的验证结果表明,对于网内地震,采用本文方法的单台定位结果误差均小于为50 km,双台定位结果误差均小于35 km,三台定位结果误差约为15 km,四台定位结果误差约为6 km;网外地震的三台、四台定位结果误差均小于30 km.推导了采用本文三、四台预警定位方法的误差公式.利用福建地区现有及"十一五"完成后的地震观测台网分别计算得到了定位误差分布图.根据误差分析结果即可对定位结果的可靠性预先做出判断,有利于提高地震预警系统运行的可靠性.     

单纯形定位方法在新疆数字地震台网的测定精度分析

地震定位是地震学中最基本的问题之一,现阶段大部分采用基于走时的方法。单纯形定位方法作为一种直接搜索类的方法,在新疆测震台网日常地震目录产出中发挥了重要作用。本文采用数值模拟的方法,利用新疆测震台网现有布局,结合“3400”走时表,分析单纯形定位方法在新疆测震台网的测定精度。研究表明,对于新疆测震台网网内浅源地震,单纯形定位方法能够得出较为精确的震中位置,而得出的网外地震震中位置存在一定偏差;初至折射波对震源深度有一定的控制能力,速度模型的准确性对震源深度和发震时刻的测定影响较大。     

地震自动速报综合触发系统产出结果评估

收集2015年1月—2020年8月地震自动速报综合触发系统的全部产出结果,选取国内天然地震事件结果共计1863条,与正式速报目录结果进行对比研究。研究结果显示,自动速报系统所产出的地震参数较正式速报目录发震时刻平均偏差约2s,震中位置平均偏差约8km,震源深度平均偏差约9km,震级绝对平均偏差约0.25,偏差均值为自动速报结果较正式速报结果偏大约0.1。到达产出震级标准的漏报地震事件176个,其主要原因为区域台站稀疏、台站分布不均匀、大震或前震尾波干扰等,未发生误报事件。发震时刻、震中位置和震源深度偏差较大事件的分布与漏报事件类似,多发生于台站密度较低、台站分布不均匀和速度模型与实际差异较大的地区。震级偏差主要与地震事件震级大小和震中所在位置有关,对于3.0≤M<5.0的地震事件,产出震级最为稳定,而对于M≥6.0的地震,震级结果可靠性较低;此外,震级偏差的大小与区域分布并无明显关系,但偏差方向具有区域特征。通过对自动速报系统运行情况的统计分析,提出了关于自动速报系统改进与完善的建议,有助于提高自动速报系统的应用效果。     

大震地震序列自动地震速报结果分析——以云南漾濞6.4级和青海玛多7.4级地震为例

根据国家台网中心自动地震速报系统在云南漾濞和青海玛多2个地区的台网监测能力,选取2个地区2021年5月18—29日的地震序列,对自动地震速报系统进行性能分析。国家台网中心自动地震速报系统基本实现1min内的单路自动地震速报信息初次产出,根据地震台网密度的不同,产出时间从30～60s不等。与地震编目结果相比,云南漾濞地震序列震中位置偏差较小,青海玛多地震序列震中位置偏差较大,2个地震序列震级偏差不大。青海玛多地震序列震中位置偏差较大的原因是该区域台站稀少且空隙角较大。自动地震速报系统存在少量的漏报地震,与系统定位时信噪比较低、台站空隙角较大及多个地震混叠在一起有关。     

2009年青海大柴旦6.4级地震序列的双差法重新定位研究

采用双差地震定位算法对2009年青海大柴旦6.4级地震和3个及以上台站记录到的余震进行了重新定位,获得了873次地震的重新定位结果。主震的震中位置为37.56°N,95.90°E,震源深度6.5 km,发震在大柴旦宗务隆山断裂带。精确定位结果与原始数据进行比较,定位残差明显减小,定位后的地震分布更加集中,主要分布在大柴旦宗务隆山断裂带周围,震源深度优势分布在2～11 km,与主震震中位置和震源深度相符。     

东祁连山北缘断裂带基于深度学习的密集台阵地震事件快速检测与定位研究

为监测东祁连山北缘断裂带附近的地震活动性,布设包含240台短周期地震仪的面状密集台阵,进行约30 d的连续观测。首先使用基于深度学习的多台站地震事件检测算法（CNNDetector）进行地震事件检测,然后使用震相拾取网络（PhaseNet）对地震事件进行P波和S波到时拾取,其次使用震相关联算法（REAL）进行震相关联及初定位,最后使用双差定位（hypoDD）进行地震重定位,最终的精定位地震目录中共有517个地震。在密集台阵观测期间,中国地震台网正式地震目录中共有39个位于台阵内的地震事件,相比而言,密集台阵检测到大量小于0级的地震。因此通过布设密集台阵,可提高活动断裂微地震活动性的监测能力。与历史地震空间分布相比,密集台阵地震精定位分布具有较好的一致性,表现出更明显的线性分布特征。基于地震分布,发现研究区域存在与地表断层迹线走向不同的隐伏活跃断裂。     

2013年8月新疆乌鲁木齐市5.1级地震强震动记录及特征分析

2013年8月30日乌鲁木齐市发生M_S5.1地震,乌鲁木齐烈度速报台网有32个强震动台触发获得了主震加速度记录。选取23条强震动记录进行常规处理,统计强震动记录数量随震中距分布,对比分析此次地震峰值加速度(PGA)与新疆土层加速度衰减关系;并利用强震动数据对此次地震进行定位,定位结果对应台站震中距与到时线性度较好;最后分析了典型强震动台站记录特性与建筑物震害及工程震害相关性。     

Rapid report of the 8 January 2022 ​MS 6.9 Menyuan earthquake,Qinghai, China

The M_S 6.9 Menyuan earthquake in Qinghai Province, west China is the largest earthquake by far in 2022. The earthquake occurs in a tectonically active region, with a background b-value of 0.87 within 100 ?km of the epicenter that we derived from the unified catalog produced by China Earthquake Networks Center since late 2008. Field surveys have revealed surface ruptures extending 22 ?km along strike, with a maximum ground displacement of 2.1 ?m. We construct a finite fault model with constraints from InSAR observations, which showed multiple fault segments during the Menyuan earthquake. The major slip asperity is confined within 10 ?km at depth, with the maximum slip of 3.5 ?m. Near real-time back-projection results of coseismic radiation indicate a northwest propagating rupture that lasted for ～10 ?s. Intensity estimates from the back-projection results show up to a Mercalli scale of IX near the ruptured area, consistent with instrumental measurements and the observations from the field surveys. Aftershock locations (up to January 21, 2022) exhibit two segments, extending to ～20 ?km in depth. The largest one reaches M_S 5.3, locating near the eastern end of the aftershock zone. Although the location and the approximate magnitude of the mainshock had been indicated by previous studies based on paleoearthquake records and seismic gap, as well as estimated stressing rate on faults, significant surface-breaching rupture leads to severe damage of the high-speed railway system, which poses a challenge in accurately assessing earthquake hazards and risks, and thus demands further investigations of the rupture behaviors for crustal earthquakes.     

联合概率法在合并相邻台网地震目录中的应用:以2014年鲁甸序列为例

中国目前实行的区域地震台网独立运行机制,使得在相邻不同台网的交界地区可能存在多个版本的地震目录和震相观测报告,影响了地震活动性分析与研究.为此,本文提出了一种基于联合概率的方法,可标明两个或多个相邻台网目录中相同的事件,合并它们的震相数据开展重新定位,并重构不同台网交界地区的统一地震目录.该方法的思路与分析步骤是：首先,计算获得不同台网之间具有最小发震时刻差异的两两地震的时空强差异分布,查找并剔除独立地震,计算事件合并的联合概率;其次,基于联合概率分析合并不同台网的地震目录和震相观测报告,对合并事件进行重新定位和定位误差分析,并基于G-R关系检验重构目录的完整性.本文以2014年鲁甸地震序列为例的初步应用结果显示,震相合并之后的地震定位精度相比之前单个台网的结果,特别是相比四川台网的目录,定位精度提高非常显著,合并后的目录与之前相对完整的云南目录接近,但相比由两个台网目录简单拼凑而成的目录更加准确.此外,研究还发现在目录合并过程中,对于4级以上的中强震,应选择M_S而不是以M_L震级标度;震相合并后被复用台站记录的到时信息可用于检测不同台网间的震相拾取是否存在系统偏差.本文提出的方法使得在相邻不同台网的过渡区形成一个统一且尽可能准确可靠的地震目录成为可能.     

近台资料对近震相对定位算法的影响

从地震定位的基本原理出发，分析认为近台资料可用于双差定位算法，但不能用于其他相对定位方法.在此基础上，通过分析和模拟计算表明，应用近台资料时，双差法有可能给出一定精度的地震绝对位置.但仅采用近台资料时，相对位置的误差会比使用远台资料时有所增加.近台和远台资料的联合使用，有利于得到较为精确的定位结果.当震源深度远小于震中距时，如果没有深度震相的参与，只能得到误差较小的震中相对分布，深度的相对位置仍有较大的误差.对2003年新疆伽师地震余震序列中部分余震的重新定位试验，验证了近台资料对双差定位算法的上述影响.     

四川地震预警网外玛多7.4级地震预警分析研究

2021年5月22日2时4分青海省玛多县发生M7.4地震。四川地震预警台网成功地处理并产出了本次地震预警结果。本文针对四川地震预警系统产出的8次地震预警处理结果及预警减灾效能进行了分析。此次地震震中距四川地震预警网55km,首台触发后12.19s发布首次预警处理结果,前5次预警处理结果用时距离首台触发时间均小于35s。与正式地震目录对比,第1次预警处理结果震中位置偏差为17.1km,震级偏差为-1.8,盲区半径为121km。随着参与计算台站数量增多,震中位置偏差和震级偏差不断减小,盲区半径不断增大。由于地震破裂时间长和台站限幅等原因,8次预警震级均偏小。本次地震预警的有效获益区为震中距121～324km的环形区域,此区域内减灾效能显著。本次地震预警结果表明,四川地震预警系统在本次地震中预警产出效果较好,对于网外大地震预警有重要参考意义。     

2013年山东莱州M4.6地震序列发震构造初探

采用双差定位法对山东莱州地震序列重新定位,通过CAP方法反演M4.6地震震源机制,在此基础上初步探讨莱州地震序列发震构造。结果显示:精确定位震中位置主要位于柞村—仙夼断裂的NW方向,深度剖面显示从SE方向到NW方向断层深度呈由浅逐渐变深的趋势,这均与柞村—仙夼断裂位置、走向、倾向特征较为吻合;M4.6地震震源机制解的节面Ⅰ与柞村—仙夼断裂走向、倾角较为接近。综合精确定位震中位置、剖面深度分布特征、M4.6地震震源机制解及宏观调查烈度分布等结果与柞村-仙夼断裂产状之间的关系,初步推测柞村—仙夼断裂可能为莱州地震序列的发震断层。     

Earthquake detection in the Jiangsu region,China using graphics-processing-unit-based Match & Locate and rapid earthquake association and location*

Earthquake detection and location are essential in earthquake studies, which generally consists of two main classes: waveform-based and pick-based methods. To evaluate the ability of two different methods, a graphics-processing-unit-based Match & Locate (GPU-M&L) method and a rapid earthquake association and location (REAL) method are applied to continuous seismic data recorded by 24 digital seismic stations from Jiangsu Seismic Network during 2013 for comparison. GPU-M&L is one of waveform-based methods by waveform cross-correlations while REAL is one of pick-based method to associate arrivals of different seismic phases and locate events through counting the number of P and S picks and travel time residuals. Twenty-six templates are selected from the Jiangsu Seismic Network local catalog by using the GPU-M&L. The number of newly detected and located events is about 2.8 times more than those listed in the local catalog. We both utilize a deep-neural-network-based arrival-time picking method called PhaseNet and a short-term/long-term average (STA/LTA) trigger algorithm for seismic phase detection and picking by applying the REAL. We then refine seismic locations using a least-squares location method (VELEST) and a high-precision relative location method (hypoDD). By applying STA/LTA and PhaseNet, 1006 and 1893 events are associated and located, respectively. The newly detected events are mainly clustered and show steeply dipping fault planes. By analyzing the performance of these methods based on long-term continuous seismic data, the detected catalogs by the GPU-M&L and REAL show that the magnitudes of completeness are 1.4 and 0.8, respectively, which are smaller than 2.6 given by the local catalog. Although REAL provides improvement compared with GPU-M&L, REAL is highly dependent on phase detection and picking which is strongly affected by signal-noise ratio (SNR). Stations at southeast of the study region with low SNR may lead to few detections in the same area.     

Machine learning-based automatic construction of earthquake catalog for reservoir areas in multiple river basins of Guizhou province,China

Large reservoirs have the risk of reservoir induced seismicity. Accurately detecting and locating microseismic events are crucial when studying reservoir earthquakes. Automatic earthquake monitoring in reservoir areas is one of the effective measures for earthquake disaster prevention and mitigation. In this study, we first applied the automatic location workflow (named LOC-FLOW) to process 14-day continuous waveform data from several reservoir areas in different river basins of Guizhou province. Compared with the manual seismic catalog, the recall rate of seismic event detection using the workflow was 83.9%. Of the detected earthquakes, 88.9% had an onset time difference below 1 s, 81.8% has a deviation in epicenter location within 5 km, and 77.8% had a focal depth difference of less than 5 km, indicating that the workflow has good generalization capacity in reservoir areas. We further applied the workflow to retrospectively process continuous waveform data recorded from 2020 to the first half of 2021 in reservoir areas in multiple river basins of western Guizhou province and identified five times the number of seismic events obtained through manual processing. Compared with manual processing of seismic catalog, the completeness magnitude had decreased from 1.3 to 0.8, and a b-value of 1.25 was calculated for seismicity in western Guizhou province, consistent with the b-values obtained for the reservoir area in previous studies. Our results show that seismicity levels were relatively low around large reservoirs that were impounded over 15 years ago, and there is no significant correlation between the seismicity in these areas and reservoir impoundment. Seismicity patterns were notably different around two large reservoirs that were only impounded about 12 years ago, which may be explained by differences in reservoir storage capacity, the geologic and tectonic settings, hydrogeological characteristics, and active fault the reservoir areas. Prominent seismicity persisted around two large reservoirs that have been impounded for less than 10 years. These events were clustered and had relatively shallow focal depths. The impoundment of the Jiayan Reservoir had not officially begun during this study period, but earthquake location results suggested a high seismicity level in this reservoir area. Therefore, any seismicity in this reservoir area after the official impoundment deserves special attention.     

New Data on Aftershocks of the December 7, 1988, Spitak Earthquake

The paper addresses the collection and analysis of new data on aftershocks that occurred within 20 days of the main shock of the December 7, 1988, Spitak earthquake, Mw = 6.8. The data were used to improve the location of aftershock hypocenters and magnitudes. Available data concerning this 20-day period were the least reliable in terms of completeness, representativeness, and the accuracy of hypocenter location and, in particular, estimation of energy classes and magnitudes. New data were retrieved from the records and bulletins of the seismic stations of the regional and global networks. Hypocenter parameters were determined by means of the minimization of wave travel-time residuals and subsequent double-difference hypocenter relocation. Digital records of the Obninsk and Arti seismic stations (Δ = 15°–18°) and five more distant stations (Δ = 34°–53°) were used to more accurately estimate the surface-wave magnitude of the main shock and strongest aftershock. The aftershock catalog of the Spitak earthquake was substantially revised. First, the previous hypocenter locations (Aref’ev et al., 1991) were improved using the double-difference method; second, new data were retrieved from the bulletins of Caucasian seismic stations. The minimum magnitude of completeness (M _c = 1.9) of the new catalog for the first 20 days after the main shock (when there were no epicentral observations) is the same as that for the period from December 7, 1988, to December 31, 1989. The new catalog contains information on 2090 aftershocks with magnitude M = 1.9 and more for the period from December 7, 1988, to December 31, 1989. The double-difference method allowed the location of the epicenters of clustered earthquakes to be reliably estimated with a longitude error of no more than 4.6 km, a latitude error of 4 km, and a depth error of 5 km. The new spatial distribution of the aftershock hypocenters is better correlated with the tectonic setting than the old data. The new catalog can be used to assess seismic hazard after strong earthquakes in the region.     

地震震中实时动态定位的方法研究

在地震早期预警系统中具破坏性地震震中的确定速度十分重要.本文提出了地震发生后根据1个、2个、3个台站P波到时记录进行动态、近实时确定地震发生区域、线区间和震中位置的方法.方法充分考虑了地震触发台站和非触发台站分布与地震波传播规律的一致性和差异性特征,提高了震中定位结果的精度.对由79个台站组成的山东虚拟测震台网2009-2010年期间记录的425次网内地震进行了快速定位,结果表明对发生在网内的地震可在要求时间内给出比较准确的震中位置,可满足预警地震速报时效性和精度的双重要求.     

云南地区地壳速度模型研究及应用

选取2009~2014年发生在云南地区、每个地震均在10个以上台站有记录的7412个地震数据,作走时曲线。同时为提高精度,重点对其中每个地震均在80%以上台站有记录的、ML≥3.0的83个地震数据,再作线性分析、折合走时曲线和区间稳定性分析,结合前人研究成果得到了研究区的初始地壳速度模型。选取2010~2014年云南省内M≥3.0的200次地震,采用Hyposat批处理方法迭代初始速度模型,以及对S波作分层速度拟合,得到云南地区的地壳速度模型,即2015云南模型:v_(P1)=6.01km/s,v_(P2)=6.60km/s,v_(Pn)=7.89km/s,H_1=20km,H_2=21km,v_(S1)=3.52km/s,v_(S2)=3.86km/s,v_(sn)=4.43km/s。基于新模型的地震重定位分析表明,云南地区地震事件大多发生于20km内的上地壳;对2011年3月10日盈江M5.8和2014年8月3日鲁甸M6.5典型地震进行重定位,得出震源深度分别与精定位结果和震中强震台震源距接近,表明新的一维速度模型能更好地反映研究区平均速度结构。     

2020年自动地震速报系统结果分析

选取2020年3月1日至12月31日速报地震,通过对比自动地震速报和人工地震速报结果,对自动地震速报系统产出结果进行偏差分析。该时段内自动速报系统共产出国内地震事件216次,漏报49个地震,未发生误报地震。自动速报地震震中位置平均偏差为6.7 km,震级平均偏差为0.15,平均用时119 s。震中位置偏差较大主要由台站分布不均匀、缺少近台、台站空隙角过大及系统自动拾取的震相误差较大所致。震级偏差较大主要是震级转换算法不合理等造成的。自动速报漏报地震与系统在地震定位时初至到时信噪比较低、台站分布不均匀和地震的孤立性等有关。此外,还对全国自动地震速报系统的改进提出了建议。