时频域相位滤波在远震接收函数噪声压制中的应用

宽频带地震观测数据中有效信号和干扰噪声经常发生混频效应,常规的频率域滤波方法很难将二者分离.地震波信号属于时变非平稳信号,时频分析方法能够同时得到地震波信号随着时间和频率变化的振幅和相位特征,S变换是其中较为高效的时频分析工具之一.本文以S变换为例,提出了基于相位叠加的时频域相位滤波方法.与传统叠加方法相比,相位叠加方法对强振幅不敏感,对波形一致性相当敏感,更加利于有效弱信号信息的检测.时频域相位滤波方法滤除与有效信号不相干的背景噪声,保留了相位一致的有效信号成分,显著提高了信噪比.运用理论合成的远震接收函数数据和实际的宽频带地震观测数据检验结果显示该方法较传统的带通滤波方法相比,即使在信噪较低且混频严重条件下,时频域相位滤波方法的滤波效果依然很明显,有助于识别能量较弱的有效信号.

     

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．      

基于小波分频叠前相干噪声压制方法

基于小波分析的去噪方法在地震资料叠前处理中得到了广泛应用.本文主要介绍利用小波变换的分频特性来压制相干噪声.通过小波分频技术将叠前地震信号分解为不同频带,然后利用有效波和相干干扰波的频谱差异来区分有效信号和噪声,最后利用加权方法去掉不需要的噪声信息来达到去除相干噪声的目的.实际资料的处理结果表明：基于小波分频方法能很好地压制相干噪声,从而提高地震资料信噪比和分辨率.     

基于非稳态多项式拟合的地震噪声衰减方法研究(英文)

基于非稳态多项式拟合理论,针对地震数据中同相轴振幅变化这一特征,我们提出了一种地震噪声衰减的新方法。非稳态多项式拟合系数是时变的,通过整形正则化约束多项式拟和系数的光滑性,自适应的估计地震数据的相干分量。基于动校正后的共中心点道集(CMP)中地震信号的相干性,利用非稳态多项式拟合估计有效信号,从而衰减随机噪声。对于线性相干噪声,如地滚波,首先利用径向道变换(RadialTraceTransform,RTT)将地震数据变换到时间一视速度域,在时间—视速度域利用非稳态多项式拟合估计出相干噪声,然后减去相干噪声。该方法可以有效的估计振幅变化的相干分量,不需要相干分量振幅为常量的假设。模拟和实际资料处理结果表明,与传统的稳态多项式拟合和低切滤波相比,该方法可以更为有效的衰减地震噪声,同时保真了地震有效信号。     

二氧化碳相变技术应用于新型震源研发的可行性研究

为了研究二氧化碳物理相变技术应用于新型震源研发的可行性,在地下成层性较好的某煤田地震测区,开展了利用二氧化碳相变技术激发地震波的野外人工震源激发-接收实验.并与传统炸药震源进行了对比.地震数据利用Aries2.66型垂直分量反射地震仪和PDS-2型三分量地震仪接收.根据实测地震数据,从野外地震记录震相识别,初至波传播距离分析,震源近场地震信号时频分析,CO₂相变激发震源子波提取和基于CO₂震源子波的地震初至波波形反演实验等多个方面,进行了关于CO₂相变激发技术能否产生地震波信号以及能否将其应用于新型震源研发的可行性研究.研究结果表明CO₂物理相变膨胀能够产生能量集中的地震波信号;在实验区地质条件和激发参量下地震记录中初至波的可识别的传播距离约为1 km;震源近场地震信号的主频集中在8~13 Hz;利用震源近场数据提取了CO₂震源子波;通过地震初至波波形反演实验认为这种震源子波能够应用于波形反演等方面的研究.因为CO₂相变激发具有绿色、环保、安全等方面的优点,若能进一步在激发能量、激发—延迟时间一致性等方面加以改进,该技术有望在城市隐伏活动断层探测、城市地下空间探测、煤矿高瓦斯环境人工地震勘探等领域发挥重要的作用.

     

A COMPARISON OF STATISTICAL AND DETERMINISTIC WIENER DECONVOLUTION OF DEEP-TOW SEISMIC DATA*

Wiener ‘spiking’ deconvolution of seismic traces in the absence of a known source wavelet relies upon the use of digital filters, which are optimum in a least-squares error sense only if the wavelet to be deconvolved is minimum phase. In the marine environment in particular this condition is frequently violated, since bubble pulse oscillations result in source signatures which deviate significantly from minimum phase. The degree to which the deconvolution is impaired by such violation is generally difficult to assess, since without a measured source signature there is no optimally deconvolved trace with which the spiked trace may be compared. A recently developed near-bottom seismic profiler used in conjunction with a surface air gun source produces traces which contain the far-field source signature as the first arrival. Knowledge of this characteristic wavelet permits the design of two-sided Wiener spiking and shaping filters which can be used to accurately deconvolve the remainder of the trace. In this paper the performance of such optimum-lag filters is compared with that of the zero-lag (one-sided) operators which can be evaluated from the reflected arrival sequence alone by assuming a minimum phase source wavelet. Results indicate that the use of zero-lag operators on traces containing non-minimum phase wavelets introduces significant quantities of noise energy into the seismic record. Signal to noise ratios may however be preserved or even increased during deconvolution by the use of optimum-lag spiking or shaping filters. A debubbling technique involving matched filtering of the trace with the source wavelet followed by optimum-lag Wiener deconvolution did not give a higher quality result than can be obtained simply by the application of a suitably chosen Wiener shaping filter. However, cross correlation of an optimum-lag spike filtered trace with the known ‘actual output’ of the filter when presented with the source signature is found to enhance signal-to-noise ratio whilst maintaining improved resolution.     

PARTIAL COHERENCE MATCHING OF SYNTHETIC SEISMOGRAMS WITH SEISMIC TRACES*

A crucial step in the use of synthetic seismograms is the estimation of the filtering needed to convert the synthetic reflection spike sequence into a clearly recognizable approximation of a given seismic trace. In the past the filtering has been effected by a single wavelet, usually found by trial and error, and evaluated by eye. Matching can be made more precise than this by using spectral estimation procedures to determine the contribution of primaries and other reflection components to the seismic trace. The wavelet or wavelets that give the least squares best fit to the trace can be found, the errors of fit estimated, and statistics developed for testing whether a valid match can be made. If the composition of the seismogram is assumed to be known (e.g. that it consists solely of primaries and internal multiples) the frequency response of the best fit wavelet is simply the ratio of the cross spectrum between the synthetic spike sequence and the seismic trace to the power spectrum of the synthetic spike sequence, and the statistics of the match are related to the ordinary coherence function. Usually the composition cannot be assumed to be known (e.g. multiples of unknown relative amplitude may be present), and the synthetic sequence has to be split into components that contribute in different ways to the seismic trace. The matching problem is then to determine what filters should be applied to these components, regarded as inputs to a multichannel filter, in order to best fit the seismic trace, regarded as a noisy output. Partial coherence analysis is intended for just this problem. It provides fundamental statistics for the match, and it cannot be properly applied without interpreting these statistics. A useful and concise statistic is the ratio of the power in the total filtered synthetic trace to the power in the errors of fit. This measures the overall goodness-of-fit of the least squares match. It corresponds to a coherent (signal) to incoherent (noise) power ratio. Two limits can be set on it: an upper one equal to the signal-to-noise ratio estimated from the seismic data themselves, and a lower one defined from the distribution of the goodness-of-fit ratios yielded by matching with random noise of the same bandwidth and duration as the seismic trace segment. A match can be considered completely successful if its goodness-of-fit reaches the upper limit; it is rejected if the goodness-of-fit falls below the lower one.     

利用标准时频变换方法在强噪声环境下无偏拾取地震P波、S波到时

地震P波、S波到时是精确分析地震水平位置、深度与速度结构等的重要参数,如何准确拾取P波和S波到时是地震学的一项重要的基础工作.大数据量与强噪声环境给地震到时的自动拾取带来了很大挑战.在频率域中可将信号与噪声分离,但会造成震相的偏移.针对上述问题,本文在STA/LTA、AIC方法的基础上,引入了标准时频变换（Normal Time-Frequency Transform,NTFT）,结合信号时间域与频率域特征,提出了基于NTFT的STA/LTA方法,以及基于NTFT的AIC方法来拾取P波和S波的到时.基于NTFT的STA/LTA方法通过构建即时频率约束的特征函数,以增强地震信号振幅响应的变化特征.基于NTFT的AIC方法则根据NTFT的变换系数定位即时频率-时间基准点,通过滑动窗口直接对标准时频谱进行AIC处理拾取最佳到时.本文采用了不同强度噪声的60组合成数据和105组实测地震数据对方法的可靠性进行检验.以人工拾取到时为参考,实测数据中NTFT-STA/LTA方法拾取P波、S波到时的均方根误差分别为0.36 s和0.56 s;NTFT-AIC方法拾取P波、S波到时的均方根误差分别为0.25 s和0.35 s.相比于STA/LTA、AIC方法,NTFT改进后的方法提高了P波和S波到时的拾取准确率,为强噪声环境下的地震波形到时拾取提供了新思路.

     

A new direction for shallow refraction seismology: integrating amplitudes and traveltimes with the refraction convolution section

The refraction convolution section (RCS) is a new method for imaging shallow seismic refraction data. It is a simple and efficient approach to full‐trace processing which generates a time cross‐section similar to the familiar reflection cross‐section. The RCS advances the interpretation of shallow seismic refraction data through the inclusion of time structure and amplitudes within a single presentation. The RCS is generated by the convolution of forward and reverse shot records. The convolution operation effectively adds the first‐arrival traveltimes of each pair of forward and reverse traces and produces a measure of the depth to the refracting interface in units of time which is equivalent to the time‐depth function of the generalized reciprocal method (GRM). Convolution also multiplies the amplitudes of first‐arrival signals. To a good approximation, this operation compensates for the large effects of geometrical spreading, with the result that the convolved amplitude is essentially proportional to the square of the head coefficient. The signal‐to‐noise (S/N) ratios of the RCS show much less variation than those on the original shot records. The head coefficient is approximately proportional to the ratio of the specific acoustic impedances in the upper layer and in the refractor. The convolved amplitudes or the equivalent shot amplitude products can be useful in resolving ambiguities in the determination of wave speeds. The RCS can also include a separation between each pair of forward and reverse traces in order to accommodate the offset distance in a manner similar to the XY spacing of the GRM. The use of finite XY values improves the resolution of lateral variations in both amplitudes and time‐depths. The use of amplitudes with 3D data effectively improves the spatial resolution of wave speeds by almost an order of magnitude. Amplitudes provide a measure of refractor wave speeds at each detector, whereas the analysis of traveltimes provides a measure over several detectors, commonly a minimum of six. The ratio of amplitudes obtained with different shot azimuths provides a detailed qualitative measure of azimuthal anisotropy and, in turn, of rock fabric. The RCS facilitates the stacking of refraction data in a manner similar to the common‐midpoint methods of reflection seismology. It can significantly improve S/N ratios.Most of the data processing with the RCS, as with the GRM, is carried out in the time domain, rather than in the depth domain. This is a significant advantage because the realities of undetected layers, incomplete sampling of the detected layers and inappropriate sampling in the horizontal rather than the vertical direction result in traveltime data that are neither a complete, an accurate nor a representative portrayal of the wave‐speed stratification. The RCS facilitates the advancement of shallow refraction seismology through the application of current seismic reflection acquisition, processing and interpretation technology.     

在地震预测研究中使用GPS和震源机制资料的问题探讨——与王秀文等商榷

针对"山西地区应力场变化与地震的关系"一文,本文指出:(1)P波初动存在误读的可能,出现矛盾符号是正常的;(2)地震仪器极性也可能反向,不校核可能会导致错误的震源机制解.还讨论如何正确地表示震源机制随时间的变化.分析认为GPS观测结果若不认真考虑观测误差、模型误差以及应变信息的层次,所得结果可能会与震源机制解反演的区域构造应力场相矛盾.比较了地震与GPS资料存在的差别和二者所提供信息的优劣后,认为二者恰好可以互补.利用GPS和地震两种资料联合反演、解释、相互约束,则可增加反演结果的可靠性.地震学和GPS观测两学科的交叉、融合必将有力地推动地学研究的深入.     

区域震相初至估计

本在地震数据自动化处理中，给出一种基于自回归模型的Akaike information criteria(AIC)算法和信号平均幅值比的混合方法来估计地震信号的初至．用信号的AIC曲线和平均幅值比曲线构造一种叠加曲线，再进行类似于坐标旋转的校正，可以准确估计低信噪比记录中信号的初至，尤其对于震相类型比较复杂的后续震相(如S波、Lg波)的初至估计结果很好．通过对中国数字地震台网乌鲁木齐台记录到的23次天然地震中P波、S波和Lg波的初至估计，与人工分析结果相比，P波初至估计的均方误差为0．71s，后续震相(S波、Lg波)的均方误差为1．64s，优于传统AIC算法的估计结果．     

基于提升算法和百分位数软阈值的小波去噪技术

在地震勘探领域,随机噪声一直是影响地震信号信噪比的主要因素之一,如何从被干扰的地震信号中有效去除随机噪声并保护有用信号具有重要的意义.针对经典小波变换在计算效率方面的缺陷,本文推荐应用提升算法实现第二代小波变换的构建,分析和对比了提升算法(Lifting Scheme)下不同小波变换方法的特性,选取更加符合小波域去噪原理的CDF 9/7双正交小波变换作为基本算法,同时应用了简单、有效的百分位数(Percentiles)软阈值进行信噪分离.通过理论模型处理,本方法可以在去噪能力和保护有用信号之间找到很好的平衡点.实际剖面的处理效果表明,此方法不仅能有效的滤除随机噪声,而且很好地保护有用信号,提高地震数据分析的精确性.     

基于重构激发信号的电磁式可控震源相关检测参考信号方法研究

针对电磁式可控震源地震数据的相关检测,研究发现,在地下结构复杂、基板-大地耦合不佳时,常规方法——基于震源控制信号或基板附近信号作为参考信号检测得到的地震记录中,存在子波到时误差和虚假多次波问题.本文分析了上述问题的理论原因,并提出基于重构激发信号的相关检测参考信号方法(Correlation Detection Reference Signal Based on the Reconstructed Excitation Signal,CDRSBRES).首先,利用直达波与其他地震波到时不一致的特点,从震源基板附近信号中分离、提取直达波.然后,利用直达波重构震源激发信号并作为参考信号对地震数据进行相关检测.最后,应用谱白化技术提高检测结果质量.数值模拟研究表明,重构激发信号与理想激发信号的相关系数为0.9869,达到高度线性相关,CDRSBRES方法检测的地震记录在子波到时和波形特征上均与模型相符.随后,在某金属矿区开展了可控震源对比实验.与液压式可控震源MiniVib T15000检测结果相比,电磁式可控震源PHVS 500的检测结果中:基于震源控制信号的检测结果存在子波到时误差约0.012 s,对应垂向精度误差约11.16 m;基于基板附近信号的检测结果部分区域出现虚假多次波,信噪比降低;而CDRSBRES方法的检测结果子波到时误差约0.001 s,对应垂向精度误差约0.93 m,波形特征一致,相同区域无虚假多次波.综上,本方法适用于电磁式可控震源地震数据的高精度检测,尤其对于地下结构复杂区域的高分辨率地震勘探具有重要意义.     

叠后地震属性分析在油气田勘探开发中的应用

地震属性分析技术一直是地震特殊处理和解释的主要研究内容.随着油气勘探开发的发展,地震属性分析技术已经成为油藏地球物理研究的核心内容,是勘探地震与开发地震之间纽带.本文针对鄂尔多斯盆地的低幅度构造、低孔隙、低渗透率、致密性隐蔽油气藏的特点,综合应用相干数据体分析、地震相自动分类定性识别砂体厚度、地震振幅属性分析、频谱分解、多井约束的储层叠后反演等叠后属性分析技术,探索了一套适合该区油气特征的储层横向预测及油气识别模式.为该区油气勘探开发,储量计算提供可靠依据.同时也为隐蔽性油气藏的勘探开发积累了经验.     

Ambient noise as the new source for urban engineering seismology and earthquake engineering: a case study from Beijing metropolitan area

In highly populated urban centers, traditional seismic survey sources can no longer be properly applied due to restrictions in modern civilian life styles. The ambient vibration noise, including both microseisms and microtremor, though are generally weak but available anywhere and anytime, can be an ideal supplementary source for conducting seismic surveys for engineering seismology and earthquake engineering. This is fundamentally supported by advanced digital signal processing techniques for effectively extracting the useful information out from the noise. Thus, it can be essentially regarded as a passive seismic method. In this paper we first make a brief survey of the ambient vibration noise, followed by a quick summary of digital signal processing for passive seismic surveys. Then the applications of ambient noise in engineering seismology and earthquake engineering for urban settings are illustrated with examples from Beijing metropolitan area. For engineering seismology the example is the assessment of site effect in a large area via microtremor observations. For earthquake engineering the example is for structural characterization of a typical reinforced concrete high-rise building using background vibration noise. By showing these examples we argue that the ambient noise can be treated as a new source that is economical, practical, and particularly valuable to engineering seismology and earthquake engineering projects for seismic hazard mitigation in urban areas.     

SOURCES OF ERROR IN A SEISMIC DIGITAL RECORDING SYSTEM*)

The use of digital recorders and computers in seismic exploration promises major enhancement of the quality of final documents available to interpreters. The ultimate objectives of recording and processing remain what they always have been: 1 Record the reflection wavelet as a function of time; this requirement has been met with satisfactory accuracy for a number of years. 2. Record the reflection wavelets with sufficient fidelity to permit the interpreter to recognize them. Various factors affect our ability to achieve this second objective. Certain recording errors are associated with digital recording systems. However, an understanding of the sources of error will enable the operator to use his system properly and to estimate the noise level or inaccuracy of field recordings. Field operations do not require rigorous error analysis; in most cases a satisfactory approximation can be obtained from simple calculations. Three types of “noise”–seismic, instrument and power line–introduce errors. Factors which contribute to over-al recording system error include specifically input noise, power supply ripple, crosstalk, A-D conversion error, quantizing noise, aliasing, distortion. Examination of each component of a recording system, permits the determination of its ultimate effect on the over-all noise level–or error level–of the entire system. Many of the error sources produce statistically independent noise which is not correlative. Where this is true, error voltages from various sources may be combined by taking the square root of the sum of the mean square noise voltages, giving a result slightly greater than the largest single voltage if one source is much greater than any other source. This simplification can be used to estimate over-all system noise levels. Distortion and crosstalk depend on signal amplitude and should be added algebraically in each category. Each final sum should be used as a statistically independent noise source with respect to other system noise sources. Using the foregoing examples and simplified system for estimating over-all system noise, and assuming that much of the distortion (which limits signal/instrument noise ratio to 54 db) can be removed by filtering, we determine that the combined effect of all sources of error is to reduce the system S/N ratio to approximately 74 db. With proper care digital field recording systems can produce very good field records, and exotic computer processes can enhance signal and reduce various forms of noise. However, one always must recall that the level of confidence which one can place in an interpretation of seismic data must be dependent on a knowledge of the accuracy of the basic data.     

Monitoring temporal variations in instrument responses in regional broadband seismic network using ambient seismic noise

High‐quality broadband data are required to promote the development of seismology research. Instrument response errors that affect data quality are often difficult to detect from visual waveform inspection alone. Here, we propose a method that uses ambient noise data in the period range of 5?25 s to monitor instrument performance and check data quality in situ. Amplitude information of coda waves and travel time of surface waves extracted from cross‐correlations of ambient noise are used to assess temporal variations in the sensitivity and poles–zeros of instrument responses. The method is based on an analysis of amplitude and phase index parameters calculated from pairwise cross‐correlations of three stations, which provides multiple references for reliable error estimates. Index parameters calculated daily during a two‐year observation period are evaluated to identify stations with instrument response errors in real time. During data processing, initial instrument responses are used in place of available instrument responses to simulate instrument response errors, which are then used to verify our results. The coda waves of noise cross‐correlations help mitigate the effects of a non‐isotropic field and make the amplitude measurements quite stable. Additionally, effects of instrument response errors that experience pole–zero variations on monitoring temporal variations in crustal properties appear statistically significant of velocity perturbation and larger than the standard deviation. Monitoring seismic instrument performance helps eliminate data pollution before analysis begins.     

2012年印尼8.6级地震应变地震波的Hilbert-Huang时频分析

本文通过端点效应压制的Hilbert-Huang变换, 对大同及沁源台布置的四分量钻孔应变仪记录的印尼8.6级地震激发的应变地震波形进行时频分析, 结果显示印尼8.6级地震的主震和8.2级余震的应变地震波序列各个震相具有不同的时频特征: ① 地震波到达之前的所谓“环境噪声”部分, 瞬时频率低, 瞬时振幅小; ② P波初至时, 高频成分突然增加, 振幅也随即增强; ③ S波到达时, 频率有所降低而振幅剧烈上升; ④ 面波到达时, 振幅进一步剧烈上升达到整个序列的极大值; ⑤ 尾波部分振幅逐渐降低, 但与噪声部分相比频率依然偏高, 振幅依然偏大。 本文也将应变地震波与地震仪记录的地震波进行对比, 虽然应变地震波与地震波波形和Fourier谱具有极高的相关系数, 但从Hilbert-Huang变换得到的边际谱上看, 应变地震波与地震波有显著的区别, 应变地震波比地震波记录的低频成分相对更多。 通过Hilbert谱, 有助于更好地了解非平稳信号的局部特征, 对于突变信号的地震波, Hilbert-Huang变换是一个较好的时频分析工具。