基于正交时频原子的地震信号快速匹配追踪

匹配追踪能够根据地震信号自身的特点进行自适应分解,但因计算量巨大使其不能被广泛应用.本文提出了一种正交时频原子快速匹配追踪方法,该方法在迭代分解过程中,充分利用地震信号的局部性特征作为先验信息点,在先验信息点附近采用动态搜索策略寻找最佳匹配时频原子,同时对时频原子进行正交变换,消除了时频原子库中的冗余分量,最终将信号分解为一系列正交时频原子的线性叠加.测试结果表明,该方法不仅保持了匹配追踪的分解精度,而且使计算效率有了质的提高.     

The attenuated Ricker wavelet basis for seismic trace decomposition and attenuation analysis

The widely used wavelets in the context of the matching pursuit are mostly focused on the time–frequency attributes of seismic traces. We propose a new type of wavelet basis based on the classic Ricker wavelet, where the quality factor Q is introduced. We develop a new scheme for seismic trace decomposition by applying the multi-channel orthogonal matching pursuit based on the proposed wavelet basis. Compared with the decomposition by the Ricker wavelets, the proposed method could use fewer wavelets to represent the seismic signal with fewer iterations. Besides, the quality factor of the subsurface media could be extracted from the decomposition results, and the seismic attenuation could be compensated expediently. We test the availability of the proposed methods on both synthetic seismic record and field post-stack data.     

Time-frequency decomposition of seismic signals via quantum swarm evolutionary matching pursuit

Matching pursuit belongs to the category of spectral decomposition approaches that use a pre-defined discrete wavelet dictionary in order to decompose a signal adaptively. Although disengaged from windowing issues, matching point demands high computational costs as extraction of all local structure of signal requires a large size dictionary. Thus in order to find the best match wavelet, it is required to search the whole space. To reduce the computational cost of greedy matching pursuit, two artificial intelligence methods, (1) quantum inspired evolutionary algorithm and (2) particle swarm optimization, are introduced for two successive steps: (a) initial estimation and (b) optimization of wavelet parameters. We call this algorithm quantum swarm evolutionary matching pursuit. Quantum swarm evolutionary matching pursuit starts with a small colony of population at which each individual, is potentially a transformed form of a time-frequency atom. To attain maximum pursuit of the potential candidate wavelets with the residual, the colony members are adjusted in an evolutionary way. In addition, the quantum computing concepts such as quantum bit, quantum gate, and superposition of states are introduced into the method. The algorithm parameters such as social and cognitive learning factors, population size and global migration period are optimized using seismic signals. In applying matching pursuit to geophysical data, typically complex trace attributes are used for initial estimation of wavelet parameters, however, in this study it was shown that using complex trace attributes are sensitive to noisy data and would have lower rate of convergence. The algorithm performance over noisy signals, using non-orthogonal dictionaries are investigated and compared with other methods such as orthogonal matching pursuit. The results illustrate that quantum swarm evolutionary matching pursuit has the least sensitivity to noise and higher rate of convergence. Finally, the algorithm is applied to both modelled seismograms and real data for detection of low frequency anomalies to validate the findings.     

AVO信息约束匹配追踪技术在富煤储层刻画中的应用

针对西湖凹陷富煤环境下储层刻画精度低问题,本文结合煤层AVO截距、梯度特征,提出一种基于AVO信息约束的匹配追踪技术,压制煤层强反射引起的岩性假象,凸显储层真实、有效信号。该方法首先利用煤层4类AVO负强截距P、正强梯度G特点,构建煤层地震敏感因子P - G,放大煤层地震响应,并压制非煤层强振幅影响,实现煤层位置精细定位;在此基础上,将该煤层地震信息作为匹配追踪需要分解、重构的原始信号,利用复地震道分析技术提高信号快速匹配分解的效率,完成煤层强反射解耦。模型试算及实际资料应用表明:匹配追踪技术在精细定位煤层地震响应基础上,提高了匹配追踪算法去煤层强振幅效率;煤层解耦后地震数据较好地凸显储层横向展布变化,提高主力气层的纵向刻画精度。      

多尺度快速匹配追踪多域联合地震反演方法

多尺度快速匹配追踪多域联合地震反演是一种通过地震数据多尺度分解的迭代反演方法.与此同时,在快速匹配追踪算法中引入低频模型约束,有效提高了收敛精度,使反演结果具有丰富的高低频信息.首先通过对大尺度地震资料进行反演得到低频背景.在此基础上,采用中尺度与小尺度地震数据进行逐级迭代用以获得高频数据,因而有效缓解了常规反演方法对于初始模型精度的依赖.最后利用理论模型与实际地震数据进行测试,通过与常规时间域反演方法的反演结果进行对比可以看出,本文方法在地层连续变化处依然可以对变化地层进行精确刻画,且在纵向分辨率提升的同时保持了较好的横向连续性.

     

多尺度快速匹配追踪多域联合地震反演方法

多尺度快速匹配追踪多域联合地震反演是一种通过地震数据多尺度分解的迭代反演方法.与此同时,在快速匹配追踪算法中引入低频模型约束,有效提高了收敛精度,使反演结果具有丰富的高低频信息.首先通过对大尺度地震资料进行反演得到低频背景.在此基础上,采用中尺度与小尺度地震数据进行逐级迭代用以获得高频数据,因而有效缓解了常规反演方法对于初始模型精度的依赖.最后利用理论模型与实际地震数据进行测试,通过与常规时间域反演方法的反演结果进行对比可以看出,本文方法在地层连续变化处依然可以对变化地层进行精确刻画,且在纵向分辨率提升的同时保持了较好的横向连续性.     

谱图重排的谱分解理论及其在储层探测中的应用

谱分解理论是把单道地震记录分解为连续的时频谱平面,是地震资料处理和解释的重要技术之一.由于谱分解方法的多解性,所以同一道地震记录因为分解方法不同,得到的时频谱是不一样的.短时傅里叶变换,小波变换,S变换和匹配追踪算法都是对信号开窗分析,这些方法都受到不确定性原理的限制.Wigner-Ville变换避开了不确定性原理的限制,但是交叉项的存在限制了本方法的使用.本文利用谱图重排的时频分析方法(RSPWV)对合成的单道地震记录和实际的地震资料进行了分析.与短时傅里叶变换和匹配追踪算法的比较得出:此方法具有较高的时频分辨率,能够很好地识别气层.     

基于匹配追踪和遗传算法的大地电磁噪声压制

针对匹配追踪计算量大、大地电磁数据处理效率低的问题,提出基于匹配追踪和遗传算法的大地电磁噪声压制方法.首先,利用Gabor原子构建过完备原子库,并对过完备原子库集合进行划分.然后,借助遗传算法的自适应性,快速搜寻最优匹配原子及所在位置.最后,运用最优匹配原子对待处理信号进行稀疏分解,重构有用信号.通过对计算机模拟的典型强干扰和矿集区实测大地电磁数据进行分析处理,实验结果表明,相对于匹配追踪和正交匹配追踪,文中所提方法能从过完备原子库中快速、自适应地选取最优匹配原子与不同噪声干扰类型高精度的匹配,极大地提升了计算效率;大地电磁时间域序列中的大尺度强干扰被有效剔除,视电阻率曲线更为光滑、连续,低频段的数据质量得到明显改善.

     

Application of the continuous wavelet transform on seismic data for mapping of channel deposits and gas detection at the CO2SINK site, Ketzin, Germany

Conventional seismic data are band limited and therefore, provide limited geological information. Every method that can push the limits is desirable for seismic data analysis. Recently, time‐frequency decomposition methods are being used to quickly extract geological information from seismic data and, especially, for revealing frequency‐dependent amplitude anomalies. Higher frequency resolution at lower frequencies and higher temporal resolution at higher frequencies are the objectives for different time‐frequency decomposition methods. Continuous wavelet transform techniques, which are the same as narrow‐band spectral analysis methods, provide frequency spectra with high temporal resolution without the windowing process associated with other techniques. Therefore, this technique can be used for analysing geological information associated with low and high frequencies that normally cannot be observed in conventional seismic data. In particular, the continuous wavelet transform is being used to detect thin sand bodies and also as a direct hydrocarbon indicator. This paper presents an application of the continuous wavelet transform method for the mapping of potential channel deposits, as well as remnant natural gas detection by mapping low‐frequency anomalies associated with the gas. The study was carried out at the experimental CO₂ storage site at Ketzin, Germany (CO₂SINK). Given that reservoir heterogeneity and faulting will have significant impact on the movement and storage of the injected CO₂, our results are encouraging for monitoring the migration of CO₂ at the site. Our study confirms the efficiency of the continuous wavelet transform decomposition method for the detection of frequency‐dependent anomalies that may be due to gas migration during and after the injection phase and in this way, it can be used for real‐time monitoring of the injected CO₂ from both surface and borehole seismics.     

小波变换的过零点特性与地震勘探信号的信噪比和分辨率

利用小波变换研究地震勘探信号小波变换的过零点特性,本文提出了用小波变换的过零点特性和地震勘探信号相邻道的横向相关性提高信号分辨率和信噪比的新方法．该方法包括两个主要步骤：①利用相邻地震道信号具有很好相关性,而噪音相关性差的特点以及小波变换的过零点特性得到有效反射波同相轴随空间坐标的变化信息．②利用奇异值分解和最小二乘（SVD-TLS）方法沿同相轴对振幅进行多项式拟合去噪并增加信号高频提高信号分辨率．     

小波变换的过零点特性与地震勘探信号的信噪比和分辨率

利用小波变换研究地震勘探信号小波变换的过零点特性，本文提出了用小波变换的过零点特性和地震勘探信号相邻道的横向相关性提高信号分辨率和信噪比的新方法．该方法包括两个主要步骤：①利用相邻地震道信号具有很好相关性，而噪音相关性差的特点以及小波变换的过零点特性得到有效反射波同相轴随空间坐标的变化信息．②利用奇异值分解和最小二乘（SVD-TLS）方法沿同相轴对振幅进行多项式拟合去噪并增加信号高频提高信号分辨率．     

A model‐based data‐driven dictionary learning for seismic data representation

Planar waves events recorded in a seismic array can be represented as lines in the Fourier domain. However, in the real world, seismic events usually have curvature or amplitude variability, which means that their Fourier transforms are no longer strictly linear but rather occupy conic regions of the Fourier domain that are narrow at low frequencies but broaden at high frequencies where the effect of curvature becomes more pronounced. One can consider these regions as localised “signal cones”. In this work, we consider a space–time variable signal cone to model the seismic data. The variability of the signal cone is obtained through scaling, slanting, and translation of the kernel for cone‐limited (C‐limited) functions (functions whose Fourier transform lives within a cone) or C‐Gaussian function (a multivariate function whose Fourier transform decays exponentially with respect to slowness and frequency), which constitutes our dictionary. We find a discrete number of scaling, slanting, and translation parameters from a continuum by optimally matching the data. This is a non‐linear optimisation problem, which we address by a fixed‐point method that utilises a variable projection method with ?₁ constraints on the linear parameters and bound constraints on the non‐linear parameters. We observe that slow decay and oscillatory behaviour of the kernel for C‐limited functions constitute bottlenecks for the optimisation problem, which we partially overcome by the C‐Gaussian function. We demonstrate our method through an interpolation example. We present the interpolation result using the estimated parameters obtained from the proposed method and compare it with those obtained using sparsity‐promoting curvelet decomposition, matching pursuit Fourier interpolation, and sparsity‐promoting plane‐wave decomposition methods.     

Wavelet‐based cepstrum decomposition of seismic data and its application in hydrocarbon detection

How to use cepstrum analysis for reservoir characterization and hydrocarbon detection is an initial question of great interest to exploration seismologists. In this paper, wavelet‐based cepstrum decomposition is proposed as a valid technology for enhancing geophysical responses in specific frequency bands, in the same way as traditional spectrum decomposition methods do. The calculation of wavelet‐based cepstrum decomposition, which decomposes the original seismic volume into a series of common quefrency volumes, employs a sliding window to move over each seismic trace sample by sample. The key factor in wavelet‐based cepstrum decomposition is the selection of the sliding‐window length as it limits the frequency ranges of the common quefrency section. Comparison of the wavelet‐based cepstrum decomposition with traditional spectrum decomposition methods, such as short‐time Fourier transform and wavelet transform, is conducted to demonstrate the effectiveness of the wavelet‐based cepstrum decomposition and the relation between these two technologies. In hydrocarbon detection, seismic amplitude anomalies are detected using wavelet‐based cepstrum decomposition by utilizing the first and second common quefrency sections. This reduces the burden of needing dozens of seismic volumes to represent the response to different mono‐frequency sections in the interpretation of spectrum decomposition in conventional spectrum decomposition methods. The model test and the application of real data acquired from the Sulige gas field in the Ordos Basin, China, confirm the effectiveness of the seismic amplitude anomaly section using wavelet‐based cepstrum decomposition for discerning the strong amplitude anomalies at a particular quefrency buried in the broadband seismic response. Wavelet‐based cepstrum decomposition provides a new method for measuring the instantaneous cepstrum properties of a reservoir and offers a new field of processing and interpretation of seismic reflection data.     

基于压缩感知的高分辨率平面波分解方法研究

平面波分解方法是地震资料处理中的一项关键技术,它广泛地运用在平面波偏移、各类Beam偏移等成像方法中.平面波分解不仅可以提高偏移成像的效率,而且可以压制地震资料中的随机噪音,提高地震数据的信噪比.线性Radon变换（LRT）是一种常见的实现平面波分解的方法,但常规的LRT存在以下两个缺点：（1）分辨率受测不准原理限制;（2）变换结果存在很多噪音和空间假频.为了克服LRT的上述缺点,本文提出一种基于压缩感知的高分辨率平面波分解方法,并利用加权匹配追踪（WMP）技术实现了该方法.该方法将LRT视为一个参数估计问题,并将LRT结果的稀疏性作为约束条件,在压缩感知理论的指导下利用WMP方法得到高分辨率、高信噪比的平面波分解结果.另外,该方法还可以用于提取地震数据的线性信号、压制随机噪音、实现高维地震数据规则化等地震资料处理技术.数值实验结果证明：WMP方法可以有效地提取地震数据中的线性信号,提高LRT的分辨率和信噪比,从而改善平面波分解的质量.     

自适应多道匹配追踪去强屏蔽方法研究与应用

阻抗差异较大的地层在地震剖面上呈现为强振幅同相轴,会掩盖附近储层有效信息,需要做去除强屏蔽的目标处理。针对常规匹配追踪方法在构造复杂地区匹配精度和空间连续性不佳的问题,本文提出一种基于自适应权值的多道匹配追踪去强屏蔽方法。首先利用层位构造信息对强反射层进行局部拉平处理,减弱地层空间构造对提取强反射层的影响,然后引入相邻地震道与中心道之间的相关系数作为多道平均的权值,以提高匹配结果的稳定性和横向连续性。同时使用解释得到的层位时间作为子波初始时间,有效提高运算效率。模型试算和实际地震资料应用分析表明,改进方法能有效剥离强反射并突出有效储层信息,剥离后的井震吻合度得到明显提升,相较于常规匹配追踪算法匹配精度更高,同时具有更好的空间连续性与强反射去除效果。      

Reef Reservoir Identification by Wavelet Decomposition and Reconstruction: A Case Study from Yuanba Gas Field in China

The organic reef is a special type of carbonate reservoir which always dominates the spatial distribution, reserves and accumulations of natural gas. However, it is difficult to determine the organic reef’s internal structure and gas reservoirs due to numerous adverse factors such as the low resolution of seismic data, depth of burial, strong anisotropy, irregular spatial distribution and complex internal structure. A case study of wavelet decomposition and reconstruction technology applied to elucidate the features of organic reef reservoirs in the Changxing formation from Yuanba gas field shows that the seismic record reconstructed by high frequency signal can adequately describe the internal properties of organic reef reservoirs. Furthermore, the root mean square amplitude ratio of both low and high frequency data obtained from the reconstructed seismic data clearly show spatial distribution of gas and water in reef reservoirs.     

希尔伯特-黄变换在地震资料处理中的应用

希尔伯特-黄变换是最新发展起来的处理非线性非平稳信号的时频分析方法.本文介绍了该方法的基本原理和几个关键技术,以及其优势所在,并将它应用于含随机噪音的地震信号和实际地震资料的分解.分解后的信号能够重构原始信号,重构后在信噪比和分辨率方面有了一定的提高,对地震资料的去噪有重要意义.     

Further improvement of temporal resolution of seismic data by autoregressive (AR) spectral extrapolation

Seismic data have still no enough temporal resolution because of band-limited nature of available data even if it is deconvolved. However, lower and higher frequency information belonging to seismic data is missing and it is not directly recovered from seismic data. In this paper, a method originally applied by Honarvar et al. [Honarvar, F., Sheikhzadeh, H., Moles, M., Sinclair, A.N., 2004. Improving the time-resolution and signal–noise ratio of ultrasonic NDE signals. Ultrasonics 41, 755–763.] which is the combination of the most widely used Wiener deconvolution and AR spectral extrapolation in frequency domain is briefly reviewed and is applied to seismic data to improve temporal resolution further. The missing frequency information is optimally recovered by forward and backward extrapolation based on the selection of a high signal–noise ratio (SNR) of signal spectrum deconvolved in signal processing technique. The combination of the two methods is firstly tested on a variety of synthetic examples and then applied to a stacked real trace. The selection of necessary parameters in Wiener filtering and in extrapolation are discussed in detail. It is used an optimum frequency windows between 3 and 10 dB drops by comparing results from these drops, while frequency windows are used as standard between 2.8 and 3.2 dB drops in study of Honarvar et al. [Honarvar, F., Sheikhzadeh, H., Moles, M., Sinclair, A.N., 2004. Improving the time-resolution and signal–noise ratio of ultrasonic NDE signals. Ultrasonics 41, 755–763.]. The results obtained show that the application of the purposed signal processing technique considerably improves temporal resolution of seismic data when compared with the original seismic data. Furthermore, AR based spectral extrapolated data can be almost considered as reflectivity sequence of layered medium. Consequently, the combination of Wiener deconvolution and AR spectral extrapolation can reveal some details of seismic data that cannot be observed in raw signal or which lost during the previous processing.     

An efficient and self‐adaptive approach for Q value optimization

The time‐invariant gain‐limit‐constrained inverse Q‐filter can control the numerical instability of the inverse Q‐filter, but it often suppresses the high frequencies at later times and reduces the seismic resolution. To improve the seismic resolution and obtain high‐quality seismic data, we propose a self‐adaptive approach to optimize the Q value for the inverse Q‐filter amplitude compensation. The optimized Q value is self‐adaptive to the cutoff frequency of the effective frequency band for the seismic data, the gain limit of the inverse Q‐filter amplitude compensation, the inverse Q‐filter amplitude compensation function, and the medium quality factor. In the processing of the inverse Q‐filter amplitude compensation, the optimized Q value, corresponding gain limit, and amplitude compensation function are used simultaneously; then, the energy in the effective frequency band for the seismic data can be recovered, and the seismic resolution can be enhanced at all times. Furthermore, the small gain limit or time‐variant bandpass filter after the inverse Q‐filter amplitude compensation is considered to control the signal‐to‐noise ratio, and the time‐variant bandpass filter is based on the cutoff frequency of the effective frequency band for the seismic data. Synthetic and real data examples demonstrate that the self‐adaptive approach for Q value optimization is efficient, and the inverse Q‐filter amplitude compensation with the optimized Q value produces high‐resolution and low‐noise seismic data.     

地震复谱分解技术及其在烃类检测中的应用

谱分解技术在地震解释领域已得到广泛应用,但常用的谱分解方法存在两方面的不足.一是时间分辨率低,难以对薄层进行刻画;二是在烃类检测中多解性强,难以区分流体类型.为了改善该问题,本文提出一种基于地震复谱分解技术的烃类检测方法.复谱分解是指用一个包含多个不同频率Ricker子波的复子波库对地震道进行分解,从而得到时变子波频率和相位信息的过程.借助稀疏反演技术复谱分解可以获得高分辨率的时频能量谱和时频相位谱.本文首先通过拟合算例验证了复谱分解方法刻画薄层的能力以及求取子波频率和相位的准确性.然后利用基于Kelvin-Voigt模型的黏弹波动方程数值模拟对衰减引起子波相位改变的原因进行了分析.最后通过实际资料应用展示了本文方法在储层预测中的高时间分辨率优势,验证了利用子波相位信息识别气藏的有效性.