福建地区地震波非弹性衰减Q值、场地响应及震源参数研究

利用Atkinson方法结合福建数字地震台网28个数字地震台记录的70个地震事件波形资料,对福建地区的非弹性衰减系数进行研究,得出福建地区介质平均Q值与频率f的关系式。利用Moya方法分别计算了福建台网"九五"和"十五"系统的台站场地响应,共获得37个地震台站的场地响应。最后根据Brune模型计算了2000年1月1日至2011年9月30日福建台网记录的省内及临近海域ML2.5以上157次地震的震源参数,这些地震ML震级与地震矩M0在单对数坐标下成线性关系,与震源尺度及应力降没有明显相关性。     

云南地区近震震级与面波震级转换关系研究

利用云南地区数字地震台站记录的云南及周边地区2000～2011年4.0级以上可同时测定近震震级ML和面波震级MS的433个地震波形数据,将速度数据进行仿真,即近震震级ML在仿真短周期地震仪DD-1记录上测定,面波震级MS在仿真中长周期地震仪SK记录上测定,对全部人工重新测定的ML和MS,采用线性回归和正交回归方法,得到了它们之间的转换关系式。结果表明,自20世纪70年代使用至今的公式MS = 1.13ML － 1.08与云南地区实际情况存在系统偏差,已不适用于云南地区。考虑到区域差异,认为采用关系式MS = 1.13ML － 0.86作为新的转换关系更合理适用。同时,对云南地震台网和国家地震台网都有记录的地震数据进行对比,发现云南台网和国家台网测定的绝大多数地震ML差值为－ 0.2～ 0.4,差值为0.2的地震数量最多;MS差值为－0.4～0.2,差值为－0.2的地震数量最多。     

利用Aki模型对山西地区尾波Q值的研究

选取2001年1月至2011年12月山西地震台网记录的ML≥2．0地震的数字地震波资料,运用单次散射Aki模型,计算得到山西地区尾波Q值,拟合Q值对频率的依赖关系。其中,右玉、代县、夏县、东山4个地震台站属于低Q值、高n值,为特质的构造运动活跃地区的尾波性质。     

新疆测震台网记录的远距离工业爆破分析

对新疆测震台网数字化记录中的宁夏ML4．4爆破记录进行了分析,新疆台网大部分台站都能记录到上千公里ML4.0以上的工业爆破。又对新疆巴里坤地震台记录的约1200km的宁夏ML3．8、内蒙ML4．2地震和宁夏ML4．4的工业爆破记录进行了分析,发现近距离爆破记录的特征大部分可以在远距离爆破中找到。     

新疆数字地震台网地方性震级量规函数的初步研究

基于2009～2014年新疆测震台网数字地震记录资料,运用震级残差统计方法,选择16269个2.0≤ML≤5.4地震事件,共计获得了179561个单台记录;利用最大地动位移衰减特性方法,选用3.6≤ML≤4.5的746个地震事件作为研究对象,对新疆数字台网地方性震级量规函数进行了研究。将得出的量规函数值进行对比后认为,现阶段使用的量规函数值在0～200km的震中距范围内偏小,导致此范围内台站测定的震级偏小;在200～800km的范围内,测算的震级值较稳定,偏差范围在±0.1的范围内;在400～800km震中距范围内,由最大地动位移衰减特性方法得出的量规函数偏小。由于目前各地震台站使用的地震计仪器响应不准确,造成量规函数偏小的原因有待用计量更加准确的台站仪器响应参数进行验证,以便得到更加精确的新疆台网地方性震级量规函数。     

地震台站台基噪声功率谱概率密度函数Matlab实现

选取2015年四川数字测震台网中筠连和华蓥山地震台记录的垂直分向连续波形数据,利用Matlab软件,计算地震台站台基噪声功率谱概率密度函数,分析地震台站环境噪声特征。结果表明,台站环境噪声功率谱密度概率密度分布对地震事件波形（体波、面波）、人为噪声（台站周围人为活动、车辆及机器噪声等高频干扰）、系统瞬变（数据丢失、地震计小故障）以及仪器标定信号等反映较好。使用台基噪声功率谱概率密度函数方法,有利于监测地震台站数据记录,提高观测数据质量。     

新疆测震台网常用地震定位方法对比

选取2009年新疆测震台网记录的49个ML≥3.0地震,选用相同地震台及相同震相到时数据,分别采用单纯形、Loc Sat和Hypo Sat定位方法重新定位,从定位残差和震中位置对各定位效果进行对比,结果表明:对地震台站包围好的浅源地震,3种定位方法均适合;对地震台站包围不好的浅源地震,Loc Sat定位方法效果较好;对深源地震,Hypo Sat定位方法效果较好。     

紫坪铺水库区地震重新定位与汶川地震震源区结构分析

研究2008年5月12日汶川Ms8.0特大地震的论文非常多,许多论文使用的资料来源为四川地震台网目录和紫坪铺水库台网的目录.四川台网因台站不够密集,ML1.9以下的地震许多仅有单台或双台记录,紫坪铺台网网缘地震精度不够,许多地震没给出震源深度.     

四川地震编目自动处理系统产出结果分析

2019年1月至2020年12月,四川地震编目自动处理系统共触发和分析地震144844条.通过将编目自动处理系统产出结果与四川台网人机交互分析正式目录结果进行对比分析,得出几点结论:编目自动处理系统分析地震事件与台网正式目录地震事件匹配率为32.15％,ML≥3.0地震触发率可达79.18％.匹配的地震事件中80.57...     

辽宁遥测数字地震台网的技术系统

1　台网概况辽宁遥测数字地震台网 (下称辽宁台网 )是在原沈阳遥测地震台网的基础上进行数字化改造而建成的。台网由设在沈阳的台网中心、分布在辽宁省内的 4个中继站和 1 5个遥测数字地震台站组成 ,主要采用港震公司提供的设备和软件 ,根据实际需要 ,台网又自行研制了一些硬件设备和应用软件 ,对整个系统进行了完善和补充 ,整个系统采用了先进的数字地震观测技术 ,具有宽频带、大动态、高分辨率等特点。辽宁台网承担着省内 ML≥ 2 .5、责任区内 ML≥ 5.0、国外 MS≥ 7.0地震的速报任务 ;同时承担着编制辽宁省地震目录及地震观测报告 ,并…     

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.     

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.     

Comparison of the earthquake detection abilities of PhaseNet and EQTransformer with the Yangbi and Maduo earthquakes

PhaseNet and EQTransformer are two state-of-the-art earthquake detection methods that have been increasingly applied worldwide. To evaluate the generalization ability of the two models and provide insights for the development of new models, this study took the sequences of the Yunnan Yangbi M6.4 earthquake and Qinghai Maduo M7.4 earthquake as examples to compare the earthquake detection effects of the two abovementioned models as well as their abilities to process dense seismic sequences. It has been demonstrated from the corresponding research that due to the differences in seismic waveforms found in different geographical regions, the picking performance is reduced when the two models are applied directly to the detection of the Yangbi and Maduo earthquakes. PhaseNet has a higher recall than EQTransformer, but the recall of both models is reduced by 13%–56% when compared with the results reported in the original papers. The analysis results indicate that neural networks with deeper layers and complex structures may not necessarily enhance earthquake detection performance. In designing earthquake detection models, attention should be paid to not only the balance of depth, width, and architecture but also to the quality and quantity of the training datasets. In addition, noise datasets should be incorporated during training. According to the continuous waveforms detected 21 days before the Yangbi and Maduo earthquakes, the Yangbi earthquake exhibited foreshock, while the Maduo earthquake showed no foreshock activity, indicating that the two earthquakes’ nucleation processes were different.     

机器学习在地震检测与震相识别的应用综述

在地震学研究中地震检测与震相识别是最基础的环节,其拾取速度和精度直接影响其在地震精确定位以及地震层析成像中的应用效率和精度。近年来,机器学习在地震学领域中引起广泛关注。机器学习可以改进传统地震检测和震相识别方法,使它们能达到更加准确,识别率更高的效果。把机器学习方法按照监督学习和无监督学习分类介绍,并对机器学习方法流程进行总结,并对目前在地震检测与震相识别方面应用较为广泛的机器学习方法(卷积神经网络、指纹和相似性阈值、广义相位检测、PhaseNet、模糊聚类)进行综述。结果表明:机器学习在地震事件检测和震相识别将会是主要的手段。数据驱动的机器学习在地震学中的应用和物理模型的联合运用将是未来的发展趋势。     

Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes

Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods. However, methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking. The present study establishes a deep learning network model combining a convolutional neural network (CNN) and recurrent neural network (RNN). The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time. The neural network automatically picks the P-wave arrival time, providing a strong constraint for small earthquake positioning. The model is shown to achieve an accuracy rate of 90.7% in picking P waves of microseisms in the reservoir area, with a recall rate reaching 92.6% and an error rate lower than 2%. The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes, thus providing new technical measures for subsequent microseismic monitoring in the reservoir area.     

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。     

基于深度学习卷积神经网络的地震波形自动分类与识别

发展高效、高精度、普适性强的自动波形拾取算法在地震大数据时代背景下显得越来越重要.波形自动拾取算法的主要挑战来自如何适应不同区域的不同类型地震事件的分类与筛选.本文针对地震事件-噪音分类这一问题,使用13839个汶川地震余震事件建立数据集,应用深度学习卷积神经网络(CNN)方法进行训练,并用8900个新的汶川余震事件作为检测数据集,其训练和检测准确率均达到95%以上.在对连续波形的检测中,CNN方法在精度和召回率上优于STA/LTA和Fbpicker传统方法,并能找出大量人工挑选极易遗漏的微震事件.最后,我们应用训练好的最优模型对选自全国台网的441个台站8天的连续波形数据进行了识别、到时挑取及与参考地震目录关联,CNN检出7016段波形,用自动挑选算法拾取到1380对P,S到时,并与540个地震目录事件成功关联,对1级以上事件总体识别准确率为54%,二级以上为80%,证明了CNN模型具有泛化能力,初步展示了CNN在发展兼具效率、精度、普适性算法,实时地震监测等应用上具有巨大潜力.     

基于深度学习到时拾取自动构建长宁地震前震目录

将深度学习到时拾取、震相关联技术与传统定位方法联系起来,构建一套连续波形自动化处理与地震目录自动构建流程,对于高效充分利用地震资料,提升微震检测能力具有十分重要的意义.我们应用最新发展的迁移学习震相识别技术、震相自动关联技术,对长宁Ms6.0地震震中附近21个台站震前半个月(6月1日-6月17日)的连续记录波形进行P、...     

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.     

General seismic wave and phase detection software driven by deep learning

We developed an automatic seismic wave and phase detection software based on PhaseNet, an efficient and highly generalized deep learning neural network for P- and S-wave phase picking. The software organically combines multiple modules including application terminal interface, docker container, data visualization, SSH protocol data transmission and other auxiliary modules. Characterized by a series of technologically powerful functions, the software is highly convenient for all users. To obtain the P- and S-wave picks, one only needs to prepare three-component seismic data as input and customize some parameters in the interface. In particular, the software can automatically identify complex waveforms (i.e. continuous or truncated waves) and support multiple types of input data such as SAC, MSEED, NumPy array, etc. A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software. The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data, thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.