共查询到20条相似文献，搜索用时 31 毫秒
1.
地震P波、S波初至时间的拾取是地震波分析的一项基础性工作.本文提出了一种新的地震波初至时间自动拾取的方法:首先,把地震波的三分量时程曲线变换为一组空间向的能量变化率时程曲线;然后对能量变化率时程曲线进行STA/LTA(Short Time Average/Long Time Average,短时间的均值/长时间的均值)处理,拾取地震P波和S波的大致初至时间;最后提出采用一种二次方自回归模型对初至附近的能量变化率曲线进行二次方自回归处理,精确拾取出P波和S波的初至时间.本文采用了10组芦山地震的记录数据和150组汶川地震的记录数据对此方法的可靠性进行了检验.以人工拾取结果为参考,此方法具有很高的准确率和稳定性,同时,相比于常用的STA/LTA方法和AIC(Akaike Information Criterion,Akaike信息准则)方法,此方法在计算时间效率方面稍微逊色,但是对S波初至时间的拾取精度和可靠性更高.此方法丰富了地震P波、S波初至时间的自动拾取方法. 相似文献
2.
基于小波包和峰度赤池信息量准则的P波震相自动识别方法 总被引：2，自引：2，他引：0
基于小波包变换和峰度赤池信息量准则(AIC), 提出了一种新的自动识别P波震相的综合方法, 即小波包-峰度AIC方法． 首先对由加权长短时窗平均比(STA/LTA)法粗略确定的P波到时前后3 s的记录进行小波包三尺度的分解与重构, 分别计算每个尺度重构信号的峰度AIC曲线并将其叠加, 叠加曲线的最小值则为P波震相到时; 然后对原始地震记录进行有限冲激响应自适应滤波以提高信噪比和识别精度; 最后将小波包-峰度AIC方法应用到合成理论地震图及实际地震记录的P波初至自动识别中. 结果表明: 初至清晰度对识别精度的影响比信噪比对其影响更大; 与单独使用加权STA/LTA方法和峰度AIC法相比, 小波包-峰度AIC法具有更强的抗噪能力, 识别精度更高; 当初至清晰时, 小波包-峰度AIC法自动识别与人工识别的P波到时平均绝对差值为(0.077±0.075) s． 相似文献
3.
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. 相似文献
4.
针对低信噪比条件下微震初至拾取准确度低的问题,基于信号幅度变化引入权重因子,对传统长短时窗比值（STA/LTA）算法进行改进,提高初次拾取精度。为了进一步降低拾取误差,对变分模态分解（VMD）算法进行优化,基于互相关系数和排列熵准则自适应确定VMD分解层数,对初次拾取结果前后2—3 s的记录进行优化VMD,并计算分解后各本征模函数（IMF）的峰度赤池信息准则值,得到各IMF的到时,以各IMF的拾取结果及能量比综合加权得到二次拾取到时。仿真实验表明:改进后的STA/LTA在较低信噪比下可降低初次拾取误差约0.01 s以上;相比经验模态分解（EMD)和小波包分解,自适应VMD分解后能再次降低误差,最终与人工拾取结果平均误差在0.023 s以内。实际微震信号初至拾取结果表明,本算法能快速有效地识别初至P波,与人工拾取结果相比误差小,准确率高。 相似文献
5.
Active Source Seismic Identification and Automatic Picking of the P-wave First Arrival Using a Convolutional Neural Network 总被引：1，自引：1，他引：0
XU Zhen WANG Tao XU Shanhui WANG Baoshan FENG Xuping SHI Jing YANG Minghan 《中国地震研究》2019,33(2):288-304
In seismic data processing, picking of the P-wave first arrivals takes up plenty of time and labor, and its accuracy plays a key role in imaging seismic structures. Based on the convolution neural network (CNN), we propose a new method to pick up the P-wave first arrivals automatically. Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment, the vertical component of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually picking up the first arrivals (a total of 7242). Based on these arrivals, we establish the training and testing sets, including 25,290 event samples and 710,616 noise samples (length of each sample:2s). After 3,000 steps of training, we obtain a convergent CNN model, which can automatically classify seismic events and noise samples with high accuracy (> 99%). With the trained CNN model, we scan continuous seismic records and take the maximum output (probability of a seismic event) as the P-wave first arrival time. Compared with STA/LTA (short time average/long time average), our method shows higher precision and stronger anti-noise ability, especially with the low SNR seismic data. This CNN method is of great significance for promoting the intellectualization of seismic data processing, improving the resolution of seismic imaging, and promoting the joint inversion of active and passive sources. 相似文献
6.
Rodrigo Chi-Durán Diana Comte Marcos Díaz Jorge F. Silva 《Journal of Seismology》2017,21(5):1171-1184
In this work, new strategies for automatic identification of P- and S-wave arrival times from digital recorded local seismograms are proposed and analyzed. The database of arrival times previously identified by a human reader was compared with automatic identification techniques based on the Fourier transformation in reduced time (spectrograms), fractal analysis, and the basic matching pursuit algorithm. The first two techniques were used to identify the P-wave arrival times, while the third was used for the identification of the S-wave. For validation, the results were compared with the short-time average over long-time average (STA/LTA) of Rietbrock et al., Geophys Res Lett 39(8), (2012) for the database of aftershocks of the 2010 Maule M w = 8.8 earthquake. The identifiers proposed in this work exhibit good results that outperform the STA/LTA identifier in many scenarios. The average difference from the reference picks (times obtained by the human reader) in P- and S-wave arrival times is ～ 1 s. 相似文献
7.
针对现有地震直达P波到时拾取网络精度低、误差大等问题,结合UNet++编码、解码器,融入特征过滤器设计一种具有地震震相特征分析与融合能力的轻量级P波到时拾取网络PPNet,实现对地震P波的高精度、低误差拾取。首先,该网络在编码器模块采用大卷积核、低通道数的卷积层,对输入的地震信号进行深度特征提取;其次,在解码器模块的特征还原过程中加入特征融合机制,补全特征信息,避免序列特征污染问题;最后,仅对编码器后三个下采样模块添加特征过滤器,深入挖掘特征序列,通过细化P波到时特征,提升到时拾取精度。实验结果表明,提出的网络在0.1 s、0.2 s、0.3 s误差阈值下P波拾取率分别为80.73%、94.01%、97.81%,平均绝对误差0.078 s,均方误差0.021,与现有P波拾取传统方法和深度学习算法相比性能更优。 相似文献
8.
9.
Automatic phase picking is a critical procedure for seismic data processing, especially for a huge amount of seismic data recorded by a large-scale portable seismic array. In this study is presented a new method used for automatic accurate onset phase picking based on the proporty of dense seismic array observations. In our method, the Akaike＇s information criterion （AIC） for the single channel observation and the least-squares cross-correlation for the multi-channel observation are combined together. The tests by the seismic array observation data after triggering with the short-term average/long-term average （STA/LTA） technique show that the phase picking error is less than 0.3 s for local events by using the single channel AIC algorithm. In terms of multi-channel least-squares cross-correlation technique, the clear teleseismic P onset can be detected reliably. Even for the teleseismic records with high noise level, our algorithm is also able to effectually avoid manual misdetections. 相似文献
10.
大数据量、强噪声环境给地震P波到时的自动提取带来很大挑战.针对此问题,本文通过构建特殊的特征函数,建立SNR与STA/LTA的内在联系,提出两种基于SNR的地震P波到时自动提取方法,即基于SNR的STA/LTA方法与基于SNR的综合方法.这两种方法分别是运用SNR概念对传统STA/LTA方法和STA/LTA与AIC综合方法的改进.仿真分析结果表明:对于弱噪声环境(10dB)和一般噪声环境(6dB),本文方法较传统STA/LTA方法对地震P波到时提取的准确度更高;而对于强噪声环境(3dB),本文方法仍能准确提取地震P波到时,而传统STA/LTA方法则出现了较大的误判率(10%)与漏判率(65%).本文方法为STA/LTA赋予了明确的物理意义,使其阈值的选取建立在严密的数学推导之上.另外,本文方法在进行地震P波到时自动提取的同时,兼具数据预处理功能,无需额外的基线校正或高通滤波,因而具有较好的实时性. 相似文献
11.
精确获取震相到时是地震定位和地震走时成像等研究的重要基础.近年来,随着地震台站的不断加密,地震台网监测到的地震数量成倍增长,发展快速、准确、适用性强的震相到时自动拾取算法是地震行业的迫切需求.本文在前人工作基础上,发展了Pg、Sg震相自动识别与到时拾取的U网络算法（Unet_cea）,使用汶川余震和首都圈地震台网记录的89344个不同震级、不同信噪比的样本进行训练和测试.研究表明,U网络能够较好地识别Pg、Sg震相类型和拾取到时,Pg、Sg震相的正确识别率分别为81%和79.1%,与人工标注到时的均方根误差分别为0.41 s和0.54 s.U网络在命中率、均方根误差等性能指标上均明显优于STA/LTA和峰度分析自动拾取方法.研究获得的最优模型可以为区域地震台网的自动处理提供辅助. 相似文献
12.
This study is an application of a Real Time Recurrent Neural Network (RTRN) in the detection of small natural seismic events in Poland. Most of the events studied are from the Podhale region with a magnitude of 0.4 to 2.5. The population distribution of the region required that seismic signals be recorded using temporary stations deployed in populated areas. As a consequence, the high level of seismic noise that cannot be removed by filtration made it impossible to detect small events by STA/LTA based algorithms. The presence of high noise requires an alternate method of seismic detection capable of recognizing small seismic events. We applied the RTRN, which potentially can detect seismic signals in the frequency domain as well as in the phase arrival times. Data results of small local seismic events showed that the RTRN has the ability to correctly detect most of the events with fewer false detections than STA/LTA methods. 相似文献
13.
This paper evaluates different characteristics for earthquake early warning.The scaling relationships between magnitude,epicenter distance and calculated parameters are derived from earthquake event data from USGS.The standard STA/LTA method is modified by adding two new parameters to eliminate the effects of the spike-type noise and small pulsetype noise ahead of the onset of the P-wave.After the detection of the P-wave,the algorithm extracts 12 kinds of parameters from the first 3 seconds of the P-wave.Then stepwise regression analysis of these parameters is performed to estimate the epicentral distance and magnitude.Six different parameters are selected to estimate the epicentral distance,and the median error for all 419 estimates is 16.5 km.Four parameters are optimally combined to estimate the magnitude,and the mean error for all events is 0.0 magnitude units,with a standard deviation of 0.5.Finally,based on the estimation results,additional work is proposed to improve the accuracy of the results. 相似文献
14.
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。 相似文献
15.
STA/LTA—AIC算法对地震P波震相拾取稳定性影响 总被引：1，自引：1，他引：0
选取区域地震台网记录的地震波形数据,使用STA/LTA算法与STA/LTA—AIC算法,进行地震P波震相初至到时自动拾取,对地方震及震中距较大的震相进行P波震相拾取效果分析,发现:STA/LTA算法对于地方震P波震相识别精度较高,与STA/LTA—AIC算法拾取的P波震相初至到时相差不大;震中距变大后,STA/LTA算法对P波拾取位置相对于最佳位置向后延迟,STA/LTA—AIC算法有效矫正了STA/LTA算法拾取位置的延迟问题,与人工拾取位置差别可忽略不计。 相似文献
16.
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。 相似文献
17.
Tong Shen Xianguo Tuo Huailiang Li Yong Liu Wenzheng Rong 《Journal of Seismology》2018,22(6):1613-1627
First arrival picking is a key factor which affects the precision of microseismic data analysis. Here, we propose a new method, which employs the maximum eigenvalue to constraint the Maeda-Akaike Information Criterion (Maeda-AIC) algorithm. First, aims at addressing the pick result affected by signal-to-noise ratio (SNR) of microseismic data, maximum eigenvalue method based on polarization analysis is applied, and the maximum eigenvalue is calculated firstly, as for three component (3C) microseismic data, the maximum eigenvalue is calculated with corresponding covariance matrix, a time window need to be set in the process of building the covariance matrix, and it is the only time window set in the method proposed in this paper, so the method is called single window Maeda-AIC (SWM-AIC), to the single component (1C) microseismic data, the variance of the data is taken as the maximum eigenvalue. Then, to reduce the effect of time window and increase the automation of the algorithm, Maeda-AIC method which is a non-window-based first arrival picking method is applied. Maeda-AIC values in preliminary window are calculated, and the preliminary window is the sequence before the largest eigenvalue of the 3C or 1C data. We validate the developed method with both synthetic and field microseismic data, using a range of signal-to-noise ratios. The developed method is compared with some basic methods, specifically STA/LTA, Maeda-AIC, and the maximum eigenvalue method. The results demonstrate that the new method is much better at identifying first arrival times than basic methods when the data have a low signal-to-noise ratio, and is even faster than the STA/LTA method with 1C data. In contrast to other improved methods, threshold value is not required for this method, and the only time window used in this method is just for maximum eigenvalue calculation, through test in the paper, its length has almost no effect on the first arrival picking. 相似文献
18.
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. 相似文献
19.
Vladimir Sokolov Takashi Furumura Friedemann Wenzel 《Bulletin of Earthquake Engineering》2010,8(4):767-786
The estimation of strength of shaking at a site from the initial P-wave portion of ground motion is the key problems for shortening
the alert time of the earthquake Early Warning (EEW). The most of the techniques proposed for the purpose utilize (a) ground
motion models based on the estimated magnitude and hypocentral distance, or (b) the interim proxies, such as initial vertical
displacement P
d
. We suggest the instrumental Japan Meteorological Agency (JMA) intensity (JMAI) as a characteristic for fast estimation of damage potential in the EEW systems. We investigated the scaling relations between
JMAI measured using the whole earthquake recordings (overall intensity) and using particular time intervals of various duration
(2.0–8.0 s) starting from the P-wave arrival (preliminary intensity). The dataset included 3,660 records (K-NET and the KiK-net
networks) from 55 events (M
W 4.1–7.4) occurred in 1999–2008 in Japan. We showed that the time interval of 4–5 s from the P-wave arrival can be used for
reliable estimations of the overall intensity with the average standard error of about 0.5 JMA units. The uncertainty in the
prediction may be reduced by consideration of local site conditions or by development of the station-specific models. 相似文献
20.
Earthquake detection in the Jiangsu region,China using graphics-processing-unit-based Match & Locate and rapid earthquake association and location* 
下载免费PDF全文
下载免费PDF全文 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. 相似文献