基于深度卷积神经网络的地震震相拾取方法研究

地震震相拾取是地震数据自动处理的首要环节,包括了信号检测、到时估计和震相识别等过程,震相拾取的准确性直接影响到后续事件关联处理的性能,影响观测报告的质量.为了提高震相拾取的准确性,进而提高观测报告质量,本文采用深度卷积神经网络方法来解决震相拾取问题,构建了多任务卷积神经网络模型,设计了分类和回归的联合损失函数,定义了基于加权的分类损失函数,以三分量地震台站的波形数据作为输入,同时实现对震相的检测识别和到时的精确估计.利用美国南加州地震台网的200万条震相和噪声数据对模型进行训练、验证和测试,对于测试集中直达波P、S震相识别的查全率达到98%以上,到时估计的标准偏差分别为0.067s,0.082s.利用迁移学习和数据增强,将模型用于对我国东北地区台网的6个台站13000条数据的训练、验证和测试中,对该数据集P、S震相查全率分别达到91.21%、85.65%.基于迁移训练后的模型,设计了用于连续数据的震相拾取方法,利用连续的地震数据对该算法进行了实际应用测试,并与国家数据中心和中国地震局的观测报告进行比对,该方法的震相检测识别率平均可达84.5%,验证了该方法在实际应用中的有效性.本文所提出的方法展示了深度神经网络在地震震相拾取中的优异性能,为地震震相和事件的检测识别提供了新的思路.     

基于深度学习技术的地震实时流数据自动处理辅助工具开发

以JOPENS系统实时流接收为基础,应用Redis共享内存技术和近年来发展较快的深度学习震相自动识别技术,设计一套可7×24小时不间断稳定接收并实时识别连续地震流数据中P、S震相的系统,为地震台网实时数据处理提供一套辅助工具,并在福建省地震局测震台网128个台站的实时数据流上进行测试。该工具由Redis实时数据流共享模块与深度学习震相到时自动拾取、MSDP震相格式转换3个模块组成,可以实时接收并自动识别台网地震连续波形,生成P、S震相报告,并可导入MSDP人机交互工具进一步处理,在一定程度上可以减轻人工处理工作量。     

区域地震信号自动识别方法及应用(英文)

地震信号的实时、自动、准确识别对于地震自动速报和地震预警十分重要。仿真信号试验分析表明,观测数据的四阶统计量函数(BKCF)对信号与噪声在能量和(或)频率方面的微弱差异变化具有较高的分辨能力。以此为基础,本文提出了一种新的自动探测区域地震事件的方法和测定直达波震相到时的BKCF-AIC方法。为了进一步提高波震相到时测定的精度,本文首先对指定时段的P-波记录进行偏振特性分析,其次对含有P波的S波记录进行偏振滤波处理,再次应用上述方法测定震相到时。与传统算法相比,基于山东测震台网记录的区域地震震例分析结果表明,使用本文提出的方法能够大幅度降低地震事件误检、漏检率,进一步提高了震相识别精度。     

峰度和AIC方法在区域地震事件和直达P波初动自动识别方面的应用

提出了一种基于直达P波信号的峰度和Kurtosis-AIC方法进行区域地震事件实时检测和直达P波初动精细识别的新方法,并应用于山东地震台网记录的地震波资料处理.结果表明:(1)应用峰度方法能够有效识别出地震事件,可有效减低地震事件的错误报警率和漏报率;(2)与人工识别震相到时的结果相比,根据Kurtosis-AIC震相自动识别方法得到的震相到时的平均绝对值误差为(0.09±0.08)s。     

地震自动定位的综合解决方案

针对已有两类地震自动定位方法各自的优点和限制，提出了综合利用这两类方法以形成一个统一系统的方案，希望以此提高地震自动定位能力。该方案的基本流程是：利用波形相关方法在连续地震波形上检测出地震事件并初定其震源位置和发震时刻；利用初定的震源位置和发震时刻，预测各台站各震相的到时；以预测的各震相到时为参考点，利用单台波形(单道或三分向)在该参考点附近精确测定震相到时；利用修定后的震相到时测定震源位置和发震时刻；根据震中距判定地震类型(地方震、近震、远震)，在预期的波形段自动测定波形最大振幅和相应周期，进而测定相应类型的震级(ML、Ms或mb)。     

读取地震目录自动截取波形程序

“九五”期间各个数字测震台网利用北京港震机电技术有限公司提供的EDSPRTP实时数据处理系统接收各个数字地震台站数据并将数据保存为EDAS格式的连续波形文件.本文介绍EDAS格式波形文件的结构以及利用C语言编写程序,从而实现通过读取地震目录文件得到地震事件的发震时刻和震级,然后自动截取地震波形并且保存该地震事件.     

单台地震实时监测与应用软件设计

详细介绍单台地震实时监测与应用软件的设计与实现,以及各模块的功能特点.该软件系统采用了基于Morlet小波理论进行震相识别,并且将标准长短时平均能量比方法和MCWT-AIC方法相结合,实现了地震事件实时监测报警,在数据分析处理中将时频分析技术应用其中,该软件很适合在测震台站与流动台站的单台地震监测与研究工作中使用.     

地震检测与震相自动拾取研究

针对微震事件易受噪声干扰等特点,本文将STA/LTA方法和基于方差的AIC方法（var-AIC）相结合,在震相到时初步拾取的基础上,使用台站的德洛内（Delaunay）三角剖分及台站间最大走时差约束来减少噪声干扰的影响. 利用到时进行地震定位之后,根据台站预测到时,在设定的时间窗内对地震震相进行更精细的分析. 特别是针对微震事件信噪比低的特点,设计了基于偏振分析的拾取函数,根据窗内STA/LTA方法和var-AIC方法的拾取结果自动选择合适的值作为震相到时. 最后,对西昌流动地震台阵2013年304个单事件波形数据的分析处理和检验结果表明,本文方法较传统方法具有更高的地震事件检测能力和更高的震相拾取精度.      

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.      

高阶统计量及AIC方法在区域地震事件和直达P波初动识别中的应用

利用高阶统计量（偏斜度和峰度）与赤池信息量准则（简称AIC）相结合,进行区域地震事件实时检测和P波初至精细识别的新方法研究,通过处理山东地震台网记录的地震波资料,结果表明：应用高阶统计量（偏斜度和峰度,尤其是峰度）能够有效识别地震事件,降低地震事件的错误报警率和漏报率;与人工识别震相到时结果相比,根据Ske-AIC、Kur-AIC震相自动识别方法得到的震相到时的平均绝对值误差小.     

基于物联网的大型地震区域监控系统设计

当前大型地震区域监控系统采用有线网络对人工指挥台进行数据传输,易导致灾害条件下受损无法及时有效地传通监控结果,存在地震实时监测信息准确性低、系统稳定性差的问题。因此设计基于物联网的大型地震区域监控系统。该设计系统进行数据采集交换的网络拓扑采用ZIGBEE实现通信。基于VS.NET基础设计系统架构,通过地震的现场指挥部建设支持可视化的调度指挥台,实现综合调度和集中指挥的目的。系统中智能地震救灾控制中心采用服务器进行数据存储和智能运算,实现对地震检测区的精准、高效检测和工作指导。通过数据采集交换与应急调度平台,监测地震实时监控与设备使用情况。采用地震救灾数据采集分析软件对物联网中地震区域监测数据进行全面、准确分析,系统采用多源信息融合技术实现地震救灾信息多视角的全面处理。实验结果说明,所设计系统测算结果实时性高、功耗低,具有较高的准确度和稳定性。     

Hypocenter location of mixed events in real-time processing systems

Automatic pickings in earthquake real-time monitoring systems often contain noise bursts and/or phases of different event(s) occurring almost simultaneously. Typically, a locator uses these picks as P and S waves arrival times coming from a single event and, therefore, should be complemented by a distinctive phase association logic. The method we propose manages to automatically associate data related to different events and eliminates the influence of spoiled data from single events. The method is based on “network beamforming”, a robust and stable algorithm, which utilizes a hypocenter grid search for the stack maximum of a set of complex exponents applied to the P phase readings. The algorithm separates the residual outliers and then uses them for location. If successful, a hypocenter is established for the interfering event. The solutions obtained are overall robust and independent from the estimate of origin times. The preliminary epicenter for the grid search is provided by the intersection of perpendicular bisectors in the modified “arrival order algorithm” or by the modified “T^now” algorithm, which uses non-arrival information. We applied this method to automatic first arrival phase readings of 915 events registered by the Hi-net Japan seismic network and our results are statistically promising. Here, we present two interesting and complicated examples.     

时序数据库在海量地震波形数据分布式存储与处理中的应用初探

通过分析时序数据库的技术特点和海量地震数据存储和处理的需求,设计和实现了基于时序数据库的地震波形数据存储和处理系统。经过开发、测试和应用比较,新的地震时序数据库系统具有高可用、实时存储吞吐量大、历史数据压缩率高、分布式处理速度快等优势,能够较好地解决地震波形数据体量激增带来的存储和处理困难问题。     

基于加权K均值聚类的多属性初至拾取方法

为提高初至拾取方法的准确性和自适应能力,将变异系数加权K均值聚类算法引入初至拾取中。首先提取均方根振幅、相邻道相关性、线积分、振幅谱主频等多种地震属性;然后针对地震属性进行加权K均值聚类,自动识别初至所在时窗;最后结合相位校正法,实现时窗内初至波起跳时间的拾取。在此基础上通过实际数据测试,并与长短时窗能量比法、反向传播神经网络方法对比,验证了本文方法的有效性与可行性。结果表明,基于加权K均值聚类的多属性初至拾取方法能较快速、准确地拾取低信噪比数据的初至,并且无需人为判断时窗,从而提高了拾取的自适应能力。      

Passive (Micro-) Seismic Event Detection by Identifying Embedded “Event” Anomalies Within Statistically Describable Background Noise

Among engineers there is considerable interest in the real-time identification of “events” within time series data with a low signal to noise ratio. This is especially true for acoustic emission analysis, which is utilized to assess the integrity and safety of many structures and is also applied in the field of passive seismic monitoring (PSM). Here an array of seismic receivers are used to acquire acoustic signals to monitor locations where seismic activity is expected: underground excavations, deep open pits and quarries, reservoirs into which fluids are injected or from which fluids are produced, permeable subsurface formations, or sites of large underground explosions. The most important element of PSM is event detection: the monitoring of seismic acoustic emissions is a continuous, real-time process which typically runs 24?h a day, 7?days a week, and therefore a PSM system with poor event detection can easily acquire terabytes of useless data as it does not identify crucial acoustic events. This paper outlines a new algorithm developed for this application, the so-called SEED? (Signal Enhancement and Event Detection) algorithm. The SEED? algorithm uses real-time Bayesian recursive estimation digital filtering techniques for PSM signal enhancement and event detection.     

Improvements in mining induced microseismic source locations at the Lucky Friday mine using an automated whole-waveform analysis system

For years, severe rockburst problems at the Lucky Friday mine in northern Idaho have been a persistent safety hazard and an impediment to production. An MP250 based microseismic monitoring system, which uses simple voltage threshold picking of first arrivals, has been used in this mine since 1973 to provide source locations and energy estimates of seismic events. Recently, interest has been expressed in developing a whole waveform microseismic monitoring system for the mine to provide more accurate source locations and information about source characteristics. For this study, we have developed a prototype whole-waveform microseismic monitoring system based on a 80386 computer equipped with a 50 kHz analog-digital convertor board. The software developed includes a data collection program, a data analysis program, and an event detection program. Whole-waveform data collected and analyzed using this system during a three-day test have been employed to investigate sources of error in the hypocenter location process and to develop an automatic phase picker appropriate for microseismic events.Comparison of hypocenter estimates produced by the MP250 system to those produced by the whole-waveform system shows that significant timing errors are common in the MP250 system and that these errors caused a large part of the scatter evident in the daily activity plots produced at the mine. Simulations and analysis of blast data show that analytical control over the solutions is strongly influenced by the array geometry. Within the geophone array, large errors in the velocity model or moderate timing errors may result in small changes in the solution, but outside the array, the solution is very sensitive to small changes in the data.Our whole-waveform detection program picks event onset times and determines event durations by analysis of a segmented envelope function (SEF) derived from the microseismic signal. The detection program has been tested by comparing its arrival time picks to those generated by human analysis of the data set. The program picked 87% of the channels that were picked by hand with a standard error of 0.75 milliseconds. Source locations calculated using times provided by our entire waveform detection program were similar to those calculated using hand-picked arrival times. In particular, they show far less scatter than source locations calculated using arrival times based on simple voltage threshold picking of first arrivals.     

基于深度学习的地震检测模型在区域台网的泛化性研究

将识别地震的深度学习算法PhaseNet应用于四川台网和首都圈台网,对该模型的泛化能力进行了测试和评估.首先利用2010年1月至2018年10月首都圈台网199个地震台站记录的29 328个事件(ML0～ML4)所对应的126761段事件波形,以及2019年4-9月四川及邻省部分台网227个地震台站记录的16595个事...