流动地震台阵观测初至震相的自动检测

震相到时的自动精确检测对实现海量波形数据自动处理有重要意义. 针对流动地震台阵观测,本文综合利用单台Akaike信息准则（AIC）和多台最小二乘互相关方法,发展了震相自动精确检测技术. 检测结果表明,在长短时平均比值方法（STA/LTA）检测地震事件的基础上,利用单台AIC方法,近震初至震相检测精度小于0.3 s;利用多台最小二乘互相关方法,能够可靠地检测高信噪比地震的初至震相到时,当信噪比较低时,能够有效地避免初至震相的错误判别.     

中国地震科学台阵流动观测现状及进展

地震科学台阵是由一定数量的地震仪组成,并根据具体研究目的布设成一定几何形状的地震观测系统。随着地震观测技术的发展与进步,探测能力的提高,大规模流动地震科学台阵观测将成为高分辨率深部结构成像的重要手段和发展方向。本文主要阐述了开展流动地震科学台阵观测的重要性,介绍了国内外流动地震科学台阵观测现状以及差距,并针对我国目前流动地震观测技术,提出了要加强我国流动地震科学台阵观测平台和人才队伍建设,提高深部结构探测成像的精度,增强对深部孕震环境和地震发生机理的认识水平,为社会防震减灾事业服务。     

CRPN: A cascaded classification and regression DNN framework for seismic phase picking*

Current deep neural networks (DNN) used for seismic phase picking are becoming more complex, which consumes much computing time without significant accuracy improvement. In this study, we introduce a cascaded classification and regression framework for seismic phase picking, named as the classification and regression phase net (CRPN), which contains two convolutional neural network (CNN) models with different complexity to meet the requirements of accuracy and efficiency. The first stage of the CRPN are shallow CNNs used for rapid detection of seismic phase and picking P and S arrival times for earthquakes with magnitude larger than 2.0, respectively. The second stage of CRPN is used for high precision classification and regression. The regression is designed to reduce the time difference between the probability maximum and the real arrival time. After being trained using 500,000 P and S phases, the CRPN can process 400 hours’ seismic data per second, whose sampling rate is 1 Hz and 25 Hz for the two stages, respectively, on a Nvidia K2200 GPU, and pick 93% P and 89% S phases with the error being reduced by 0.1s after regression correction.     

基于机器学习和台阵相关性的水力压裂微地震事件自动识别及到时拾取

利用密集台阵对水力压裂微地震进行监测将有助于优化储层压裂、揭示断层活化.为满足密集台阵海量采集数据的处理需求, 本文建立了一种综合运用多种机器学习方法和台阵相关性的、无需人工干预的自动处理流程, 从而能够快速得到高质量的密集台阵震相到时目录.该综合策略包括: (1)利用迁移学习在连续波形中快速检测地震事件; (2)利用U型神经网络PhaseNet自动拾取P波、S波震相; (3)利用三重线性剔除法, 结合密集台阵到时相关性剔除异常到时数据和地震事件; (4)利用K-means和SVM两类机器学习算法, 进一步区分发震时刻接近的多个地震事件, 减小事件漏拾率.通过将该流程应用于四川盆地长宁—昭通页岩气开发区微地震监测数据, 并将自动处理结果与人工拾取结果进行比对发现, 二者在震级测定、定位以及走时成像结果等方面具有很好的一致性, 表明本文处理流程结果精度可达到手动处理精度.本文结果为密集台阵地震监测数据的高效、高精度处理提供了新思路.

     

数字地震观测中的线性和最小相位滤波

线性相位滤波和最小相位滤波是数字地震观测中经常都要遇到的两类滤波器。现代地震数据采集器中就预置有这两种滤波系数供用户选用。本文以EDAS—C24地震数据采集器为例,通过比较两者滤波特性的差异后得出：为什么在以震相判读分析为主要用途的数字地震观测中必须选用最小相位滤波器,最好不要使用线性相位滤波器。同时还指出,最小相位滤波对优势频谱较高的地方微震波形影响较大,且振荡尾巴较长,分析研究时应注意其影响。     

北京市人大办公楼的结构地震反应观测台阵研究

概述了国内外强震动观测的研究现状和结构地震反应观测台阵的研究情况。并对北京市人大办公楼的结构地震反应观测台阵的研究实例作了分析,该观测台阵在没有强震的情况下,依然取得了有用的地震记录。文中对该台阵得到的两次地震记录分析、处理,结果的对比分析用于验证该结构反应观测台阵布设的合理性。最后阐述了该台阵布设的不足,针对不足给出几点结构反应观测台阵布设的合理建议,并展望了结构地震反应观测台阵布设研究的美好前景。     

横跨天山的宽频带流动地震台阵观测

中国地震局地质研究所台阵地震学实验室于2003年4月—2004年9月,横跨天山布设了由60台仪器组成,总长度超过500 km,宽频带流动地震观测剖面.野外观测记录地震事件1434个.横跨天山的接收函数剖面表明,沿测线地壳结构复杂,介质存在明显的横向非均匀.同时,我们的初步结果还揭示了天山与两侧盆地的耦合关系,天山下方的壳幔界面较为模糊.     

Automatic picking of P and S phases using a neural tree

The large amount of digital data recorded by permanent and temporary seismic networks makes automatic analysis of seismograms and automatic wave onset time picking schemes of great importance for timely and accurate event locations. We propose a fast and efficient P- and S-wave onset time, automatic detection method based on neural networks. The neural networks adopted here are particular neural trees, called IUANT2, characterized by a high generalization capability. Comparison between neural network automatic onset picking and standard, manual methods, shows that the technique presented here is generally robust and that it is capable to correctly identify phase-types while providing estimates of their accuracies. In addition, the automatic post processing method applied here can remove the ambiguity deriving from the incorrect association of events occurring closely in time. We have tested the methodology against standard STA/LTA phase picks and found that this neural approach performs better especially for low signal-to-noise ratios. We adopt the recall, precision and accuracy estimators to appraise objectively the results and compare them with those obtained with other methodologies.Tests of the proposed method are presented for 342 earthquakes recorded by 23 different stations (about 5000 traces). Our results show that the distribution of the differences between manual and automatic picking has a standard deviation of 0.064 s and 0.11 s for the P and the S waves, respectively. Our results show also that the number of false alarms deriving from incorrect detection is small and, thus, that the method is inherently robust.This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its submission to Journal of Seismology.     

上海市环球金融中心结构地震反应台阵

"十五"期间,中国地震局在上海市环球金融中心建立我国第一个超高层结构地震反应专用台阵,随后上海市地震局对其进行升级改造。本文详细介绍环球金融中心超高层建筑结构地震反应台阵的测点布设,改造后台阵观测系统技术组成、远程通信方式、台网中心数据处理系统以及获得的强震记录。本台阵建设可以为高层建筑的抗震抗风研究提供数据支撑,为今后结构台阵建设积累宝贵经验。     

强震现场流动无线遥测地震台网观测系统的研制

描述了强震现场流动无线遥测地震台网观测系统的技术设计，系统构成，工作原理及应用。     

On some problems of seismic crustal phase

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Statistical tests for regional seismic phase characterizations

In seismic analysis some assumptions are often made aboutthe data, e.g. stationarity and Gaussianity. This is not obvious for all realseismic data. Here, we use statistical tests for characterization of regionalseismic data. We apply tests for stationarity, symmetry, linearity, andtime-reversibility. In the analysis we use twelve regional seismic events inFennoscandia recorded with the seismic small-aperture arrays NORESS,ARCESS and FINESA at hypocentral distances in the range from 160 to1580 km. For the tests we use the regional phases Pn, secondary P, Sn and Lg-phases and the preceding noise. Two of the eventsare explosions, two are rockbursts and eight are earthquakes. Theperformance and possibilities of using statistical tests based on bispectra, asa complementary tool for conventional analysis of seismic phases isdemonstrated. The preceding noise recorded before the first onset of theP-wave for the twelve events is tested to be spatially stationarybetween each channel within each array and temporal stationary in 21consecutive time windows of 3.2 sec each. Also, the preceding noise issymmetric and linear. The seismic phases defined by the symmetry test asnon-Gaussian (not symmetric) are all linear. This means a linear model canbe used to characterize both the noise and the phases. The first P-phase for the two explosions is characterized as non-Gaussian at thethree arrays. For all 36 possibly first P-phase arrivals at the three arraystations, 23 are non-Gaussian. The second P-phase is non-Gaussian at13 of 36 data records, the S-phase at eleven of 36 and the Lg-phase at nine of 36. For all the four phases more than 32 of possible36 are time-reversible.     

格尔木地震台阵监测能力估计

为满足地震监测需要,在中国西部格尔木地区拟建立小孔径地震台阵,并建立临时地震台阵。新建临时台阵记录到青海地震月报目录中大部分地震,及目录中未有地震序列60多个。可见,格尔木地震台阵的建立将改善该地区地震监测能力,从而改善中国西部地区的地震监测能力。     

基于Viterbi算法的复杂地质体速度约束化自动拾取

对于复杂地质体而言,由于各种因素的影响,速度信息中往往会包含一些假的速度信息(如速度异常值)．因此,如何在速度谱能量团(由所定义目标函数——相似系数法等得到)中拾取有效的叠加速度,是地震数据处理中一个重要的环节．本文,所引入的Viterbi算法具有约束化自动搜寻并获取最优解的功能,将其应用于速度的自动拾取中,它能向前做最大“能量团”的积分并向后递归计算最优解——叠加速度,是一种实现速度自动优化拾取的便利工具．     

基于振幅和瞬时频率的震相自动识别方法

为了充分反映地震信号振幅增大和频率改变的特征,发展了一种以振幅和瞬时频率比乘积为特征参量的震相自动识别方法。谐波信号和有限实际地震记录的应用表明,本方法具有较高的识别精度。     

核幔界面衍射波Pdiff震相最大 记录震中距探讨

智利M_S8.8特大地震在甘肃数字测震台网记录的最大震中距达179.7°, 通过读取该地震初至震相的观测走时, 计算该震相的平均观测慢度为4.104 s/°, 与P_diff震相的理论慢度4.439 s/°基本一致. 研究结果表明: 由于甘肃数字测震台网和甘东南野外观测流动台阵独特的地理位置, 记录到了至今全球最大震中距(179.7°)的核幔界面衍射波P_diff震相; P_diff震相起始观测走时明显滞后于IASP91理论走时; 核幔界面复杂的形态对不同方位传播的P_diff震相观测走时产生了一定的影响.      

岩石超声检测信号初至的AIC自动拾取方法

岩石超声检测中最重要的一个环节是初至的拾取,然而该项工作往往费时费力,拾取精度受人为因素影响较大。为提高声波速度检测、声发射定位、以及超声层析成像的应用效率和精度,本研究将地震学中应用比较广泛的AIC初至自动提取技术引入到岩石超声检测中,并进行了适当改进。利用改进前后的AIC方法,自动拾取仿真信号和实际信号的初至,并利用长短时窗比方法(STA/LTA)和手动方法拾取了初至,同时分别与设定的实际初至进行对比。根据实验结果,对于信噪比较低的信号AIC方法要优于STA/LTA方法;改进前的AIC方法适用于起跳干脆、幅度变化大的信号,而改进后的AIC方法则适用于起跳较平缓的信号,且拾取到的初至与手动拾取的初至更加接近。     

USTC-Pickers: a Unified Set of seismic phase pickers Transfer learned for China

Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China. To mitigate this problem, we build a unified set of customized seismic phase pickers for different levels of use in China. We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet. This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China. Then, using different subsets of the DiTing data, we fine-tune the base picker to better adapt to different regions. In total, we provide 5 pickers for major tectonic blocks in China, 33 pickers for provincial-level administrative regions, and 2 special pickers for the Capital area and the China Seismic Experimental Site. These pickers show improved performance in respective regions which they are customized for. They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets. We anticipate that this picker set will facilitate earthquake monitoring in China.     

国家测震台站卫星传输状态自动检测

介绍了国家测震台站卫星传输链路和数据传输状态检测方法,使用VS2015开发环境的C#语言编写了自动检测程序并将其应用于台站,台站值班人员可以及时得到卫星传输不正常的报警信息,以便及时进行修复。     

一种震相拾取误差的自动判别和计算方法

本文给出了一个主要用于深地震测深数据的震相识别误差（不确定性）的判别和计算方法.该方法集中讨论从记录截面拾取震相这一过程所引起的判别误差.以震相前后一定时窗内的地震记录振幅的均方根之比为判别依据,找出误差分布范围并给出走时误差与振幅比的分级相关函数.由此,当震相确定后,计算程序将根据记录数据自动算出识别误差.实践证明该方法不仅更加客观真实、方便快捷,而且为今后震相提取工作的进一步科学规范打下了基础.