<p>本文讨论了用于微地震信号到时自动拾取的几种方法的原理及特点,包括长短时均值比(STA/LTA)方法、AIC方法、基于高阶统计量偏斜度和峰度的PAI-S/K方法等,提出了移动时窗峰度的快速算法和改进的峰度拾取初至方法.对我国西部某地观测到的13359个微地震记录,采用两种时窗进行了初至到时拾取,并与人工拾取的结果进行了对比.为使所研究的方法达到最佳效果,采用DE全局搜索方法,以人工拾取的初至作为参照,以时差在0.3 s以内的记录所占百分比作为目标函数,自动搜索最佳的拾取参数.结果显示,在拾取时窗选为P波初至前3 s至S波初至位置时,AIC方法的结果最佳,时差在0.3 s以内的记录占比达到93.6%;在拾取时窗选为包含S波到时的时窗时,改进的峰度法效果最佳,时差在0.3 s以内的记录占比83.8%.</p>

传统的微地震数据震源定位算法依赖于波至时间的拾取。由于微地震数据量较大且信噪比较低,手动拾取纵波和横波的波至时间很耗时,且引入的人为误差不易控制。笔者在分析已有方法特点的基础上,给出一种较快捷、准确的两步法微地震初至自动拾取方法。该方法首先使用时窗能量比法识别微震事件并大致确定波至时间,然后使用局部AIC精确拾取波至时间。与常规时窗能量比法相比,该方法减弱了时窗大小对拾取精度的影响;与常规的AIC法相比,由于只在局部使用AIC,避免了在低信噪比情况下AIC会出现多个局部极小从而难以准确拾取的问题,同时也提高了拾取效率。最后通过野外实际微地震数据进行了测试,分析了该方法的性能,验证了该方法的有效性和实用性。

Identification of P- and S-phase arrivals is the primary work in Microseismic signal analysis. In this study, a new multi-step AIC algorithm is recommended. The algorithm consists of P- and S-phase arrival pickers (P-picker and S-picker). The P-picker has three steps: in step 1, a preliminary P-phase arrival window is determined by the waveform peak. Then a preliminary P-pick is identified using the AIC algorithm. Finally, the P-phase arrival window is narrowed based on the above P-pick. Thus the P-phase arrival can be identified accurately by using the AIC algorithm again. The S-picker has five steps: in step 1, a narrow S-phase arrival window is determined based on the P-pick and the AIC curve of amplitude biquadratic time-series. In step 2, the S-picker automatically judges whether the S-phase arrival is clear to identify. In step 3 and 4, the AIC extreme points are extracted, and the relationship between the local minimum and the S-phase arrival is researched. In step 5, the S-phase arrival is picked based on the maximum probability criterion. The field data tests show that the P- and S-pickers have a high picking accuracy in comparison with the manual P- and S-picks. Furthermore, the technique is independent of the kind of SNR. Even in the poor-quality signal group which the SNRs are below 5, the effective picking (the picking residual is <30 sample points) rates of P- and S-phase arrivals are still up to 81% and 84% respectively.

The first-break picking accuracy greatly affects the seismic events discriminant and source localization.At present，none of the first-break picking algorithms proposed is ideal to collect the low SNR seismic data.In order to solve the noise problem (improve noise resistance performance)，an improved first arrival picking method of micro-seismic event based on power ration and wavelet transform is proposed.Firstly，the characteristics function is introduced to the traditional energy ratio method wavelet transform method so as to search the range of the P-wave first break by adopting the improved energy ratio method.Then，the first break time of P-wave is positioned precisely by using wavelet transform.Finally，the seismic model data and actual measured seismic data are used to conduct experiment to analyze the performance of the method proposed in this paper.The results show that，the method based on energy ratio and wavelet transform is the first-break automatic picking method with the characteristics of noise resistance and stability.Besides that，it can identify the P-wave first arrival time from the data with a low signal to noise ratio accurately.

微地震事件初至拾取是微地震数据处理的关键步骤之一.实际微地震监测资料中存在大量低信噪比事件,而传统方法对这些事件的应用效果并不理想.为了克服传统方法抗噪性弱的缺点,本文通过综合地震信号与环境噪声在振幅、偏振以及统计特征等方面的存在的差异,设计了一种针对低信噪比微地震事件的初至拾取方法——SLPEA算法.为了检验本文方法的可行性和有效性,分别对模型数据和实际资料进行了处理,并将处理结果与传统方法及手工拾取的结果进行了对比.分析表明,利用本文方法得到的初至到时与手工拾取结果的绝对误差平均值仅为1.33×10^-3s,小于3个采样点;方差为3.21×10^-6s^2;初至到时在手工拾取结果±0.005s误差范围内的个数占总数的95.8%.这些参数值均优于传统方法的同类参数,证明了本文方法的可靠性.

<p>由于微地震事件本身能量弱、资料信噪比低的特点,初至快速精确的拾取成为其关键而又亟待解决的问题。针对微地震事件初至人工拾取效率低、时窗能量比方法拾取精度低的特点,从信息熵和互信息量的角度开始研究,根据互信息量是随机变量间统计依存性与关联程度的量度特点,研究了一种时窗能量比与互信息量准则的微地震初至拾取方法,首先以时窗能量比算法来粗略估计初至的到达时刻,然后再利用互信息量算法来准确的拾取初至,通过模型验证与实际数据的测试,并与常规方法对比分析,验证了方法的有效性与可行性,能够较为准确与快速地实现微地震初至的自动拾取。</p>

A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data.

初至拾取是影响微震事件分析精度的重要因素之一。本文结合微震事件偏振特性和最小信息准则（akaike information criterion,AIC）函数特性,提出了一种利用偏振约束实现AIC初至拾取的改进方法：可以将偏振特征值拾取微震初至的应用扩展至单分量的微震数据,该方法将单分量微震数据视为三分量微震数据的一种特殊形式,利用三分量微震数据协方差矩阵的最大值序列对AIC方法进行约束,从而快速准确的拾取到微震数据的初至。文中应用该方法对不同信噪比的合成数据和实测数据进行了验证,同时与长短时平均（short time average/long time average,STA/LTA）、Maeda-AIC和偏振特征值方法进行了对比,结果显示该算法速度略低于上述3种方法,但精度和可靠性优于其他三种方法,同时与其他改进算法对比,不用设置阈值,并且选取时窗的长短对拾取结果几乎没有影响,可极大地提高算法的自动化程度。

微地震震源的定位要求精确确定初至，人工拾取微地震有效事件需要很大的工作量。首先讨论了 Akaike信息准则（AIC）初至拾取方法；然后根据微地震信号在相邻小波尺度上连续的特点，将基于AIC的初至拾取方法与小波多尺度分析方法相结合， 对微地震资料进行多尺度分析；最后利用AIC拾取初至，并根据初至的分布特点确定地震记录中是否存在有效的微地震事件。克服了传统AIC法由于噪声影响使 初至点模糊而难以准确拾取的缺点。模型与实际资料的应用表明，基于小波分解与AIC相结合的初至拾取方法能够从信噪比低的资料中较准确地识别出有效微地震 事件。

<p>小波变换多尺度分析是重力资料处理中一种常用的方法,其分析的结果与最优小波基的选取有关。笔者结合重力数据特征,首先从理论层面上分析最优小波基的选取方法,再根据理论模型进行不同小波基的对比实验,最后对华北地区实测重力数据进行小波基对比试验,认为db11小波基为重力数据多尺度分析的最优小波基。</p>

We consider the coorbit theory associated to a square-integrable, irreducible quasi-regular representation of a semidirect product groupG=Rd . The existence of coorbit spaces for this very general setting has been recently established, together with concrete vanishing moment criteria for analyzing vectors and atoms that can be used in the coorbit scheme. These criteria depend on fairly technical assumptions on the dual action of the dilation group, and it is one of the chief purposes of this paper to considerably simplify these assumptions. We then proceed to verify the assumptions for large classes of dilation groups, in particular for all abelian dilation groups in arbitrary dimensions, as well as a class calledgeneralized shearlet dilation groups, containing and extending all known examples of shearlet dilation groups employed in dimensions two and higher. We explain how these groups can be systematically constructed from certain commutative associative algebras of the same dimension, and give a full list, up to conjugacy, of shearing groups in dimensions three and four. In the latter case, three previously unknown groups are found. As a result, the existence of Banach frames consisting of compactly supported wavelets, with simultaneous convergence in a whole range of coorbit spaces, is established for all groups involved.

Synthetic aperture radar (SAR) tomography is a 3-D imaging modality that is commonly tackled by spectral estimation techniques. Thus, the backscattered power along the cross-range direction can be readily obtained by computing the Fourier spectrum of a stack of multibaseline measurements. In addition, recent work has addressed the tomographic inversion under the framework of compressed sensing, thereby recovering sparse cross-range profiles from a reduced set of measurements. This paper differs from previous publications, in that it focuses on sparse expansions in the wavelet domain while working with the second-order statistics of the corresponding multibaseline measurements. In this regard, we elaborate on the conditions under which this perspective is applicable to forested areas and discuss the possibility of optimizing the acquisition geometry. Finally, we compare this approach with traditional nonparametric ones and validate it by using fully polarimetric L-band data acquired by the Experimental SAR (E-SAR) sensor of the German Aerospace Center (DLR).