共查询到18条相似文献，搜索用时 265 毫秒
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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
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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. 相似文献
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提出一种基于直达P波信号和其它背景噪声在能量、非高斯性、非线性和偏振特性的不同而进行区域地震事件实时检测的新方法信噪综合差异特征量方法（简写为EFGLP方法）,同时对比分析了应用信号的不同统计特性来精细识别震相初至的３种有效方法,其中的TOC AIC方法是新提出的．应用山东数字地震波资料处理的结果表明:①与常规的STA/LTA地震事件触发算法相比,EFGLP方法能够有效降低地震事件的错误报警率和漏报率;②与人机交互震相识别结果相比,当信噪比比较低、震相初至比较模糊时,3种震相精细识别方法中的TOC-AIC方法识别精度最高;当信噪比比较高、震相初至比较清晰时,基于VAR-AIC 和TOC-AIC方法所测量得到的震相初至识别基本一致． 相似文献
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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
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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. 相似文献
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数字化地震记录震相自动识别的方法研究 总被引：10，自引：3，他引：7
针对目前震相自动识别方法不能自动给出震相识别区间,以及不能确定识别出震相名称的问题,运用震相的运动学特征,由平均速度模型和J-B走时表数据,自动计算近震、远震和极远震的震相走时及震中距。对多尺度小波分解进行单支重构作为识别不同震相的分析信号。先求出初至震相和最大面波到时,估算出震中距,然后找到S波或PP波到时,求出准确的震中距,即可自动给出各震相的识别区间,采用线性偏振法在给定区间中识别出震相的初至时刻。由于该方法采用的是先明确要找什么震相,再由该震相的走时确定寻找区间,所以找出的初至就是要寻找的震相,自然解决了识别出震相的名称问题,从而实现了对震相的全程自动识别。 相似文献
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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
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Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning(EEW)systems.Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise,missing P-waves and inaccurate P-wave arrival estimation.To address these issues,an automatic algorithm based on the convolution neural network(DPick)was developed,and trained with a moderate number of data sets of 17,717 accelerograms.Compared to the widely used approach of the short-term average/long-term average of signal characteristic function(STA/LTA),DPick is 1.6 times less likely to detect noise as a P-wave,and 76 times less likely to miss P-waves.In terms of estimating P-wave arrival time,when the detection task is completed within 1 s,DPick′s detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band,and 1.6 times when the error band is 0.10 s.This verified that the proposed method has the potential for wide applications in EEW. 相似文献
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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
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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
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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
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The first arrival time picking is a crucial step in microseismic data processing. When the signal-to-noise ratio (SNR) is low, however, it is difficult to get the first arrival time accurately with traditional methods. In this paper, we propose the double-sliding-window SW (DWSW) method based on the Shapiro-Wilk (SW) test. The DWSW method is used to detect the first arrival time by making full use of the differences between background noise and effective signals in the statistical properties. Specifically speaking, we obtain the moment corresponding to the maximum as the first arrival time of microseismic data when the statistic of our method reaches its maximum. Hence, in our method, there is no need to select the threshold, which makes the algorithm more facile when the SNR of microseismic data is low. To verify the reliability of the proposed method, a series of experiments is performed on both synthetic and field microseismic data. Our method is compared with the traditional short-time and long-time average (STA/LTA) method, the Akaike information criterion, and the kurtosis method. Analysis results indicate that the accuracy rate of the proposed method is superior to that of the other three methods when the SNR is as low as ??10 dB. 相似文献
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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
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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