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。     

3种远震P波到时拾取方法的比较及其参数优化

分析了自回归赤池信息准则（AR-AIC）、高阶统计量（HOS）和累积和（CUSUM）等3种到时拾取方法中参数对远震P波到时估计的影响,以450个远震P波信号为样本集,参考人工拾取到时,以网格搜索方式确定了每种方法的最优参数。之后重新选取信噪比处于[2,20]区间的100个远震P波信号,用确定最优参数后的3种到时拾取方法估计其P波初至时间,并比较了3种方法对低信噪比远震P波的拾取准确度。结果表明,AR-AIC方法和CUSUM方法对低信噪比远震P波的拾取准确度要优于HOS方法,CUSUM方法的计算速度最快,HOS方法由于其原理的限制更适用于信噪比较大、初动较尖锐的信号。      

STA/LTA—AIC算法对地震P波震相拾取稳定性影响

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。     

基于最大顺序统计量的P波初动极性自动读取方法的小震震源机制反演及在云南小江地区的应用研究

地震P波震相到时与初动极性的精确读取是地震资料分析处理的重要步骤之一.本研究采用了一种新的高精度自动确定地震波P波震相到时与初动极性的概率分布的方法(POI方法,后同),在此基础上研究开发了一套自动化小震震源机制解的反演流程,并将其应用在云南小江断裂带周边区域的小震震源机制和构造应力场反演中.得到的震源机制与应力场反演结果与前人的研究一致,且与区域GPS观测结果和构造背景吻合度较好,显示该方法具有良好的应用前景,为高效分析密集台阵地震观测资料,开展小震震源参数测量与区域应力场反演提供了新的研究思路和技术手段.     

基于最大顺序统计量的P波初动极性自动读取方法的小震震源机制反演及在云南小江地区的应用研究

地震P波震相到时与初动极性的精确读取是地震资料分析处理的重要步骤之一.本研究采用了一种新的高精度自动确定地震波P波震相到时与初动极性的概率分布的方法(POI方法,后同),在此基础上研究开发了一套自动化小震震源机制解的反演流程,并将其应用在云南小江断裂带周边区域的小震震源机制和构造应力场反演中.得到的震源机制与应力场反演结果与前人的研究一致,且与区域GPS观测结果和构造背景吻合度较好,显示该方法具有良好的应用前景,为高效分析密集台阵地震观测资料,开展小震震源参数测量与区域应力场反演提供了新的研究思路和技术手段.     

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。     

P波自动识别算法及其在四川地震台网中的应用

采用了三种P波自动识别算法对四川地区单台记录的单个地震事件和连续波形进行了测试,结果表明:(1)STA/LTA算法简单高效,无论单个地震事件还是连续波形都能对P波到时有较好的识别效果,但需要挑选时窗长度及阈值以权衡虚报率和漏报率;(2)MER和AIC算法对单个地震P波到时识别精度高,但无法从连续波形中识别单个地震事件;(3)无论哪种方法都无法做到不经过任何其他处理而直接从单一算法中获得准确的S波到时数据;(4)利用多台P波震相的自动识别数据,完全可以实现地震的自动定位。     

三分向数据震相的自动识别

利用三分向地震数据,提出了一种自动检测Ｐ波和Ｓ波初至的算法。此算法组合使用了短时窗检测器ＤＳｐ和长时窗ＤＢｐ检测器,具有短时窗的较高精度和长时窗较低的误触发率。应用广东、福建和云南省的地震数据进行测试,结果表明：自动检测具有可靠的精度,地方震和近震Ｐ波到时误差小于０．２ｓ的超过８４％;Ｓ波到时误差小于０．５ｓ的超过７４％。     

一种适用于地方震事件的S波到时自动拾取方法

基于特征值分解方法,本文讨论了一种适用于地方震事件S波震相到时拾取的自动处理算法.该算法计算参数少、简便快捷、易于实现,通过选用七个不同长度的时间窗,有效地减小了窗长选择不合理所引起的震相拾取误差.利用福建地震台网记录的9855条三分向波形记录进行测试,结果表明:本文方法的S波平均拾取偏差为(0.003±1.34)s,...     

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

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．      

应用单台垂向记录进行区域地震事件实时检测和直达犘波初动自动识别

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．      

试用累积求和方法研究地震活动平静现象及大震概率预报

以表征区域地震活动强度背景的震级期望值作为单个地震事件的目标值,利用震级累积和C值随时间的变化分析地震活动相对平静现象,并给出其显著性检验. 文中还定量分析了平静异常与大震的关系,提出了利用核函数对大震发生时间进行概率外推的方法. 用上述方法对华北区的山西、张家口-渤海地震带的部分地区及新疆区域进行计算,显示该方法能够描述地震活动平静现象,并可合理地对未来大震发生时间进行概率外推估计.     

Non‐stationarity in rainfall and temperature in the Murray Darling Basin

Two analyses, one based on multiple regression and the other using the Holt–Winters algorithm, for investigating non‐stationarity in environmental time series are presented. They are applied to monthly rainfall and average maximum temperature time series of lengths between 38 and 108 years, from six stations in the Murray Darling Basin and four cities in eastern Australia. The first analysis focuses on the residuals after fitting regression models which allow for seasonal variation, the Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI). The models provided evidence that rainfall is reduced during periods of negative SOI, and that the interaction between PDO and SOI pronounces this effect during periods of negative PDO. Following this, there was no evidence of any trend in either the PDO or SOI time series. The residuals from this regression were analysed with a cumulative sum (CUSUM) technique, and the statistical significance was assessed using a Monte Carlo method. The residuals were also analysed for volatility, autocorrelation, long‐range dependence and spatial correlation. For all ten rainfall and temperature time series, CUSUM plots of the residuals provided evidence of non‐stationarity for both temperature and rainfall, after removing seasonal effects and the effects of PDO and SOI. Rainfall was generally lower in the first half of the twentieth century and higher during the second half. However, it decreased again over the last 10 years. This pattern was highlighted with 5‐year moving average plots. The residuals for temperature showed a complementary pattern with increases in temperature corresponding to decreased rainfall. The second analysis decomposed the rainfall and temperature time series into random variation about an underlying level, trend and additive seasonal effects and changes in the level; trend and seasonal effects were tracked using a Holt–Winters algorithm. The results of this analysis were qualitatively similar to those of the regression analysis. Copyright © 2010 John Wiley & Sons, Ltd.     

Restoring force correction based on online discrete tangent stiffness estimation method for real-time hybrid simulation

In real-time hybrid simulation (RTHS), it is difficult if not impossible to completely erase the error in restoring force due to actuator response delay using existing displacement-based compensation methods. This paper proposes a new force correction method based on online discrete tangent stiffness estimation (online DTSE) to provide accurate online estimation of the instantaneous stiffness of the physical substructure. Following the discrete curve parameter recognition theory, the online DTSE method estimates the instantaneous stiffness mainly through adaptively building a fuzzy segment with the latest measurements, constructing several strict bounding lines of the segment and calculating the slope of the strict bounding lines, which significantly improves the calculation efficiency and accuracy for the instantaneous stiffness estimation. The results of both computational simulation and real-time hybrid simulation show that: (1) the online DTSE method has high calculation efficiency, of which the relatively short computation time will not interrupt RTHS; and (2) the online DTSE method provides better estimation for the instantaneous stiffness, compared with other existing estimation methods. Due to the quick and accurate estimation of instantaneous stiffness, the online DTSE method therefore provides a promising technique to correct restoring forces in RTHS.     

Study of techniques of identifying the earthquake precursory anomalies in terms of mathematical modeling

IntroductionGenerallyspeaking,thedataofprecursorymonitoringwhichmightbeassociatedwiththepreparationofthefutureearthquakes,couldberepresentedbyatimeseries.There,however,wouldbenotonlyanomalouschangesassociatedwiththeearthquakeoccurrences,butalsosomechangeswhichwouldberelatedtosourcesotherthantheearthquakepreparation,eitherknownorunknowntotheinvestigatorsandbecalledas(thechangesfromnon-earthquakeeffects(,inthetimeseries,inpractice,tillnow.ItistosaythatthetimeseriesF(t)showingthetemporalchangesof…     

Application of a cascading filter implemented using morphological filtering and time–frequency peak filtering for seismic signal enhancement

Inspired by the idea of the iterative time–frequency peak filtering, which applies time–frequency peak filtering several times to improve the ability of random noise reduction, this article proposes a new cascading filter implemented using mathematic morphological filtering and the time–frequency peak filtering, which we call here morphological time–frequency peak filtering for convenience. This new method will be used mainly for seismic signal enhancement and random noise reduction in which the advantages of the morphological algorithm in processing nonlinear signals and the time–frequency peak filtering in processing nonstationary signals are utilized. Structurally, the scheme of the proposed method adopts mathematic morphological operation to first preprocess the signal and then applies the time–frequency peak filtering method to ultimately extract the valid signal. Through experiments on synthetic seismic signals and field seismic data, this paper demonstrates that the morphological time–frequency peak filtering method is superior to the time–frequency peak filtering method and its iterative form in terms of valid signal enhancement and random noise reduction.     

Unsupervised separation of seismic waves using the watershed algorithm on time-scale images

This paper illustrates the use of image processing techniques for separating seismic waves. Because of the non‐stationarity of seismic signals, the continuous wavelet transform is more suitable than the conventional Fourier transforms for the representation, and thus the analysis, of seismic processes. It provides a 2D representation, called a scalogram, of a 1D signal where the seismic events are well localized and isolated. Supervised methods based on this time‐scale representation have already been used to separate seismic events, but they require strong interactions with the geophysicist. This paper focuses on the use of the watershed algorithm to segment time‐scale representations of seismic signals, which leads to an automatic estimation of the wavelet representation of each wave separately. The computation of the inverse wavelet transform then leads to the reconstruction of the different waves. This segmentation, tracked over the different traces of the seismic profile, enables an accurate separation of the different wavefields. This method has been successfully validated on several real data sets.     

AUTOMATIC PICKING OF FIRST ARRIVALS AND PARAMETERIZATION OF TRAVELTIME CURVES*

One of the methods for studying the near-surface low-velocity zone and for subsequent determination of static corrections is the technique of employing first arrivals. During the past few years several computer algorithms, based on some simplifying assumptions, have been suggested for automatic determination of first arrivals. This paper suggests a new method for automatic picking of first arrivals, which works under quite general assumptions concerning the character of the data. The method is based on the use of correlation properties of signals and the application of a statistical criterion for the estimation of first arrivals time. A number of dimensionless parameters is used in the algorithm making it possible to regulate the level of reliability and the resolution of the picking procedure. The second stage of the algorithm is the parameterization of the traveltime curve, that is a division of the previously obtained t—x curve into separate rectilinear segments. The suggested algorithm of parameterization is based on an heuristic use of some properties of maximum likelihood estimates. This permits location of the breakpoints of the t—x curve and the estimation of the parameters of each rectilinear segment. A computer program has been written based on the picking and parameterization algorithm. This program has been tested on a large amount of field data and the results show that it works at least as well as the hand procedure.     

3-D multi-parameter type traveltime tomography in a spherical coordinate frame: comparison of double and triple class simultaneous inversions

It is now common practice to perform simultaneous traveltime inversion for the velocity field and the reflector geometry in reflection/refraction tomography, or the velocity field and the hypocenter locations in regional earthquake tomography, but seldom are all three classes of model parameters updated simultaneously. This is mainly due to the trade-off between the different types of model parameters and the lack of different seismic phases to constrain the model parameters. Using a spherical-coordinate ray tracing algorithm for first and later(primary reflected) arrival tracing algorithm in combination with a popular linearized inversion solver, it is possible to simultaneously recover the three classes of model parameters in regional or global tomographic studies. In this paper we incorporate the multistage irregular shortest-path ray tracing algorithm(in a spherical coordinate system) with a subspace inversion solver to formulate a simultaneous inversion algorithm for triple model parameters updating using direct and later arrival time information.Comparison tests for two sets of data(noise free and added noise) indicate that the new triple-class parameter inversion algorithm is capable of obtaining nearly the same results as the double-class parameter inversion scheme. Furthermore,the proposed multi-parameter type inversion method is not sensitive to a modest level of picking error in the traveltime data, and also performs well with a relatively large uncertainty in earthquake hypocentral locations. This shows it to be a feasible and promising approach in regional or global tomographic applications.