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1.
The advent of signal energy on a VSP or check-shot trace may be defined as the first break. An accurate pick of this first break would be possible in the absence of noise. However, real data traces are inevitably corrupted by noise and this leads to difficulty in identifying a break because the signal-to-noise ratio is low in its neighbourhood. Under such conditions, an obvious alternative is to pick “troughs” where the local signal-to-noise ratio is likely to be much higher. Although trough picking is an effective way to minimize the noise problem, it is sensitive to signal properties (such as absorption and multiple reflections) which have no effect upon the accuracy of break picks. Thus, trough picking is signal-sensitive and break picking is noise-sensitive. Clearly, an ideal first-arrival picking scheme would combine the noise-tolerant features of trough picking with the signal-tolerant features of break picking. This ideal may be approached by exploiting known properties of the VSP trace using conventional signal processing techniques. The result of such processing is to reduce the problem to that of picking a trough correctly centered about the true break time. 相似文献
2.
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9 855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,其中79.6%的记录拾取偏差小于0.5 s,4.1%的记录拾取偏差超过2.0 s,说明本文方法能够满足日常工作基本需求。综上分析认为,波形记录质量是影响拾取算法结果精度的最主要因素,信噪比较高的记录,其S波到时拾取偏差显著优于信噪比较低的记录,对信噪比较低的部分记录进行带通滤波预处理后,S波震相拾取精度也有所提升。 相似文献
3.
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
4.
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
5.
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
6.
First-break picking of microseismic data is a significant step in microseismic monitoring. There is a great error in conventional first-break picking methods based on time domain analysis in low signal to noise ratio. S-transform may provide a novel approach, it can extract the time–frequency features of the signal and reduce the picking error because of its high time–frequency resolution and good time–frequency clustering; however, the S-transform is not well suited for microseismic data with high noise. For applications to array data where the weak signal has spatial coherency as well as some distinct temporal characteristics, we propose to combine the shearlet transform with a time–frequency transform. In the proposed method, the shearlet transform is used to capture spatial coherency features of the signal. The information of the signal and noise in shearlet domain is represented by shearlet coefficients. We use the correlation of signal coefficients at adjacent fine scales to give prominence to signal features to accurately discriminate the signal from noise. The prominent signal coefficients make the signal better gathered in time–frequency spectrum of the S-transform. Finally, we can get reliable and accurate first breaks based on the change of energy. The performance of the proposed method was tested on synthetic and field microseismic data. The experimental results indicated that our method is outstanding in terms of both picking precision and adaptability to noise. 相似文献
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8.
A back-propagation neural network is successfully applied to pick first arrivals (first breaks) in a background of noise. Network output is a decision whether each half-cycle on the trace is a first or not. 3D plots of the input attributes allow evaluation of the attributes for use in a neural network. Clustering and separation of first break from non-break data on the plots indicate that a neural network solution is possible, and therefore the attributes are suitable as network input. Application of the trained network to actual seismic data (Vibroseis and Poulter sources) demonstrates successful automated first-break selection for the following four attributes used as neural network input: (1) peak amplitude of a half-cycle; (2) amplitude difference between the peak value of the half-cycle and the previous (or following) half-cycle; (3) rms amplitude ratio for a data window (0.3 s) before and after the half-cycle; (4) rms amplitude ratio for a data window (0.06 s) on adjacent traces. The contribution of the attributes based on adjacent traces (4) was considered significant and future work will emphasize this aspect. 相似文献
9.
Seismic phase picking is the preliminary work of earthquake location and body-wave travel time tomography. Manual picking is considered as the most accurate way to access the arrival times but time consuming. Many automatic picking methods were proposed in the past decades, but their precisions are not as high as human experts especially for events with low ratio of signal to noise and later arrivals. As the increasing deployment of large seismic array, the existing methods can not meet the requirements of quick and accurate phase picking. In this study, we applied a phase picking algorithm developed on the base of deep convolutional neuron network (PickNet) to pick seismic phase arrivals in ChinArray-Phase III. The comparison of picking error of PickNet and the traditional method shows that PickNet is capable of picking more precise phases and can be applied in a large dense array. The raw picked travel-time data shows a large variation deviated from the traveltime curves. The absolute location residual is a key criteria for travel-time data selection. Besides, we proposed a flowchart to determine the accurate location of the single-station earthquake via dense seismic array and phase arrival picked by PickNet. This research expands the phase arrival dataset and improves the location accuracy of single-station earthquake. 相似文献
10.
基于安砂水库气枪震源实验资料,采用线性叠加、频谱白化叠加和相位加权叠加三种常用叠加技术进行处理,从信噪比、波形相关性和震相走时差等方面,分析评估三种叠加方法的实际应用效果。结果表明:(1)相位加权叠加方法提高信噪比的能力最强,但是与线性叠加一样,两者都无法有效地消除强干扰,而频谱白化则可以削弱强干扰的影响,有利于信号准确识别;对低信噪比波形,频谱白化提高信噪比的效果优于线性叠加,反之亦然。(2)以线性叠加结果为参考,相位加权的相关性高,走时差基本为零,但波形中较小幅值的信号可能会被压制,影响小幅值波形信号的判别;频谱白化在台站信噪比高时,波形相关性较差,且存在一定走时差,可能出现震相到时前的波形被放大,使震相初至变得模糊,影响到时拾取精度。 相似文献
11.
地震反射走时拾取是反射走时层析成像的首要环节。本文提出一种基于共炮点域、共检波点域、共中心点域、共偏移距域的多域人机交互反射波走时拾取方法。通过分析地震记录在不同域的特征,选择最佳的域进行反射波同相轴的拾取,在人机交互的环境下采用人工和计算机相结合,提高拾取的准确度和效率。利用Qt语言编程实现了地震资料的多域显示及反射波走时多域人机交互拾取软件。合成地震记录和实际地震资料的走时拾取结果表明,该软件操作灵活方便,对复杂地震资料的反射波走时拾取取得良好效果。 相似文献
12.
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
13.
常规的地震定位方法通常需要拾取地震记录的初至,当初至不明显或被较高水平的噪声淹没时精度较低.本文采用基于三维高斯射线束的偏移成像方法对震源进行定位,较好地解决了该问题.通过三维高斯射线束对台站记录进行偏移归位,并将各台站成像结果的交点作为地震能量释放的中心位置;当各台站成像结果不能交于一点时,采用三维空间高斯滤波方法可实现震源位置的自动获取.提出的变网格计算方案极大地减少了计算量,显著地提高了成像精度和计算效率.利用首都圈地震台网数据,对涿鹿、滦县以及房山三个地震事件进行试算,结果表明:基于变网格三维高斯束偏移成像的地震定位方法自动化程度很高,而且具有较好的抗噪能力,特别适合处理低信噪比资料的地震定位问题. 相似文献
14.
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29328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个事件(ML0~ML6.0)所对应的120233段事件波形分别建立了SC和CA测试数据集, 并用预训练好的PhaseNet模型进行P、 S震相自动识别和到时拾取, 并将拾取结果与人工拾取结果在不同误差阈值下进行对比。 测试结果表明, PhaseNet在两个数据集上具有良好的震相检测能力(误差阈值为0.5 s), 其P、 S震相检测的F1值都超过0.75, 具有比较稳定的准确拾取P波到时能力(误差阈值0.1 s), 其检测F1值均超过0.6, 而S波到时拾取的F1值分别为0.33(SC)和0.53(CA)。 进一步分析了测试结果与震中距、 震级、 信噪比、 台站所处地域之间的关系, 为下一步继续训练更优化的模型指明了方向。 研究结果表明, PhaseNet算法在区域台网地震自动检测和到时拾取方面有很大的应用潜力和提升空间, 可以为区域台网的自动编目工作提供辅助。 相似文献
15.
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
16.
2016年6月在南黄海海域实施了海底地震仪(OBS)的二维深地震探测.本文详细分析了在该次地震探测中获得的浅水水域OBS数据的特征,提出了噪声的组合压制方法.研究表明,浅海水域的OBS数据在系统时间、能量及子波等方面存在明显差异,海底多次波干扰严重、有效频段中陷波问题突出,原始台站记录信噪比低、品质差、大炮检距的有效震相难以识别和拾取.本文提出的噪声组合压制处理技术与流程,主要由基于统计子波反褶积的子波整形、基于多项式插值的t-x域线性噪声压制和采用自动搜索的海底多次波压制等三部分组成.净化处理之后,反射/折射震相的波组特征清晰,信噪比得到有效改善与较大提高,可识别震相的范围较常规处理平均扩大60%以上.本文完善了浅水区OBS数据处理的步骤与流程,将为后续地壳结构研究提供可靠的基础数据,可更好的服务于地壳深地震及油气资源的探测. 相似文献
17.
地震属性分析技术在地球物理勘探领域的广泛应用，启发研究人员将其应用于人工源宽角反射/折射深地震测深剖面的资料预处理和震相识别。采用札达-泉水沟深地震测深资料，提取振幅、信噪比、主频、瞬时带宽、瞬时高频能量等地震属性参数，分析不同参数的物理含义，挑选其中对界面变化敏感的参数，对深地震测深资料进行预处理，并利用P波和S波的联合扫描，提高震相识别的准确性。走时互换结果显示，采用地震属性参数可有效提高震相拾取的准确性，进而提高后续地壳速度结构反演结果的精度。 相似文献
18.
V. Lapenna M. Macchiato D. Patella C. Satriano C. Serio V. Tramutoli 《Geophysical Prospecting》1994,42(8):917-952
We study the geoelectrical problem of picking out the useful signal from voltage time series, monitored under conditions of a low signal-to-noise ratio and non-stationary noise. Statistical tests performed at different sites show that geoelectrical noise often belongs to the class of non-stationary phenomena with non-Gaussian probability distributions. In such cases, the application of conventional methods of geoelectrical useful signal extraction, based on the stationary white-noise assumption, gives biased estimates. For the on-line processing of geoelectrical recordings, we recommend the use of the periodogram technique combined with the Kolmogorov–Smirnov test, a suitable algorithm of which is described in detail. The suggested procedure allows data acquisition to stop as soon as the useful signal power is estimated with a relative error smaller than a predetermined value. Finally, we compare the suggested procedure with the autoregressive approach. The previously used and simpler periodogram method, applied to the solution of problems of this kind, appears to give better performances than the autoregressive analysis. 相似文献
19.
叠前逆时偏移等基于波场互相关原理的地球物理方法存在极大的计算与存储需求，因此采用合适的波场重构方法显得尤为重要.常规的随机边界法容易产生成像噪声，而有效边界法在三维情况仍难以实现，检查点技术具有内存要求小的特点，但存在较高的重算率，因此本文提出了插值原理的检查点技术波场重构方法.在满足Nyquist采样定理的前提下对相邻检查点间的波场进行规则抽样，将抽样波场作为插值节点，运用多项式插值算法重构任意时刻的波场，从而避免优化检查点技术反复递推造成的计算效率问题.数值实验表明：插值检查点重构算法能有效的恢复波场，其中三次样条插值重构精度最高，而牛顿法插值法计算代价较小适合于快速重构.经Sigsbee模型的叠前逆时偏移证明了插值算法的可行性，并且极大的提高了波场重构的计算效率.三维模型分析得出在增加少量存储的情况下插值重构法的重算率大幅度降低，存储量减少为有效边界法的7.1%，对于三维尺度的叠前逆时偏移有实际意义. 相似文献
20.
为能在搜救地震受困人员时，实时监测灾区情况，增强实时性及降低搜救误差，研究物联网技术在地震受困人员应急搜救中的应用，设计基于物联网的地震应急搜救系统，采用RFID识读器将采集的数据通过互联网传输至数据处理中心服务器上，再反馈至灾区信息处理子系统中的监测防御模块中，若出现异常情况则开启射频模块，命令现场报警装置响应报警应急搜救信号；通过蚁群算法获取最优搜救路线，及时搜救地震受困人员。实验结果表明，设计系统可有效搜救地震受困人员，且系统的吞吐率高达90%，搜救准确率均值高达97.6%，耗时均值仅为0.88 h，具有较高的搜救准确率和搜救效率。 相似文献