回顾了中国石化十年来油气资源储量和产量增长情况以及油气勘探方面取得的重大成果与认识，展望了中国石化未来十年油气资源勘探前景。

Seismic data are usually contaminated with both random and coherent noise, even when the data have been properly migrated and are multiple-free. Seismic attributes are particularly effective at extracting subtle features from relatively noise-free data. Certain types of noise can be addressed by the interpreter through careful structure-oriented filtering or postmigration footprint suppression. However, if the data are contaminated by multiples or are poorly focused and imaged due to inaccurate velocities, the data need to go back to the processing team.

针对<em>f-x</em>域预测技术的局限性和实际地下构造的复杂性，本文提出了<em>f-x</em>域预测与二维滤波并用的办法，使反射信号畸变尽可能小。考虑到<em>f-x</em>域预测技术对随机干扰的压制程度不太理想，本文根据信号的可预测性和随机干扰的不可预测性，应用信噪比的模对预测值作自适应加权处理，提高<em>f-x</em>域预测技术压制随机干扰的能力。这两项对<em>f-x</em>域预测技术的改进在理论数据和实际地震记录上均见到了明显的效果。

The conventional method of f filtering for random noise reduction suffers from three drawbacks. Firstly, the wavenumber response of the filter does not peak exactly at the wavenumbers of the signal components. Secondly, the amplitude of the filter response is less than one at the signal component wavenumbers, causing attenuation of the signal. Finally, sidelobes in the filter response cause noise at wavenumbers well separated from the signal components to leak into the filtered output. Singular value decomposition (SVD) of the data matrix shows that the problems may be reduced by using a transient-free formulation of the data matrix; that is, minimizing the squared errors over a finite data length rather than minimizing the expected value of the squared errors under the assumption of an infinite length of available data. Using the transient-free formulation, noise-free signal can be predicted perfectly, unlike the conventional method. The SVD analysis of the transient-free case shows that the noise-reduction performance may be improved further at all signal-to-noise ratios (SNRs). This is achieved because in the noise-free case, the correlation matrix is rank-deficient. For noisy data, an estimate of the correlation matrix is made by selecting appropriate eigenvectors to construct the filter. The use of selected eigenvectors ensures stability, thus permitting the use of much longer filters than the usual methods, with a consequent improvement in SNR gain. Conventional techniques for estimating the effective rank of the correlation matrix focus on variations in the size of the eigenvalues, selecting only the largest. It was found that these methods severely overestimate the effective rank. Furthermore, in synthetic tests it was found that some eigenvectors corresponding to noise may have eigenvalues larger than some of the signal eigenvectors. The eigenvector selection is therefore based on the observation that the phase of a noise eigenvector has a random walk appearance, whereas the phase of the signal eigenvectors varies in a smooth manner. Statistical criteria permit the selection of the signal eigenvectors in a robust way. Tests on synthetic data show that the SNR gain may typically be 10 dB for the selected eigenvector method, as opposed to 5 dB to 0 dB at different input signal-to-noise ratios for the other methods. The optimal filter lengths were about twice the optimal lengths found for the conventional method. Tests on real stacked data also show considerable improvement in performance. Care must be taken in areas of complicated structure, particularly when strongly curved events are present, to select sufficient eigenvectors, but this may be achieved at the price of a slight loss in noise reduction.

ABSTRACT The Karhunen-Lo ve transform, which optimally extracts coherent information from multichannel input data in a least-squares sense, is used for two specific problems in seismic data processing. The first is the enhancement of stacked seismic sections by a reconstruction procedure which increases the signal-to-noise ratio by removing from the data that information which is incoherent trace-to-trace. The technique is demonstrated on synthetic data examples and works well on real data. The Karhunen-Lo ve transform is useful for data compression for the transmission and storage of stacked seismic data. The second problem is the suppression of multiples in CMP or CDP gathers. After moveout correction with the velocity associated with the multiples, the gather is reconstructed using the Karhunen-Lo ve procedure, and the information associated with the multiples omitted. Examples of this technique for synthetic and real data are presented.

ABSTRACT Singular value decomposition (SVD) is a coherency-based technique that provides both signal enhancement and noise suppression. It has been implemented in a variety of seismic applications - mostly on a global scale. In this paper, we use SVD to improve the signal-to-noise ratio of unstacked and stacked seismic sections, but apply it locally to cope with coherent events that vary with both time and offset. The local SVD technique is compared with f-x deconvolution and median filtering on a set of synthetic and real-data sections. Local SVD is better than f-x deconvolution and median filtering in removing background noise, but it performs less well in enhancing weak events or events with conflicting dips. Combining f-x deconvolution or median filtering with local SVD overcomes the main weaknesses associated with each individual method and leads to the best results.

High-resolution seismic methods are needed especially in oil and gas field development. They involve the use of backscattered energy rather than that of reflected signals, and make it interesting to look for representations of seismic traces in the time-frequency domain. One such representation was introduced by D. Gabor in 1946 into signal analysis; it is based on the consideration of a family of “elementary wavelets” that can be obtained from one “basic wavelet” by shifts in time and in frequency. We present here a different representation, in which frequency shifts are replaced by dilations. The resulting “voice transform” and “cycle-octave transform” are briefly described from the mathematical point of view and illustrated by numerical examples.

We present a new, discrete undersampling scheme designed to favor wavefield reconstruction by sparsity-promoting inversion with transform elements localized in the Fourier domain. The work is motivated by empirical observations in the seismic community, corroborated by results from compressive sampling, that indicate favorable (wavefield) reconstructions from random rather than regular undersampling. Indeed, random undersampling renders coherent aliases into harmless incoherent random noise, effectively turning the interpolation problem into a much simpler denoising problem. A practical requirement of wavefield reconstruction with localized sparsifying transforms is the control on the maximum gap size. Unfortunately, random undersampling does not provide such a control. Thus, we introduce a sampling scheme, termed jittered undersampling, that shares the benefits of random sampling and controls the maximum gap size. The contribution of jittered sub-Nyquist sampling is key in formulating a versatile wavefield sparsity-promoting recovery scheme that follows the principles of compressive sampling. After the behavior of the jittered-undersampling scheme in the Fourier domain is analyzed, its performance is studied for curvelet recovery by sparsity-promoting inversion (CRSI). The findings on synthetic and real seismic data indicate an improvement of several decibels over recovery from regularly undersampled data for the same amount of data collected.

Abstract This paper discusses an effective approach to attenuate random and coherent linear noise in a 3D data set from a carbonate environment. Figure 1 illustrates a seismic inline section from a noisy 3D seismic cube. Clearly, the section in Figure 1 is corrupted by undesirable random noise and coherent noise that are linear and vertically dipping in nature.

In this paper, we develop a new and effective multiple scale and strongly directional method for identifying and suppressing ground roll based on the second generation curvelet transform. Making the best use of the curvelet transform strong local directional characteristics, seismic frequency bands are transformed into scale data with and without noise. Since surface waves and primary reflected waves have less overlap in the curvelet domain, we can effectively identify and separate noise. Applying this method to pre-stack seismic data can successfully remove surface waves and, at the same time, protect the reflected events well, particularly in the low-frequency band. This indicates that the method described in this paper is an effective and amplitude-preserving method.

We extend our earlier work on the nonequispaced fast discrete curvelet transform (NFDCT) and introduce a second generation of the transform. This new generation differs from the previous one by the approach taken to compute accurate curvelet coefficients from irregularly sampled data. The first generation relies on accurate Fourier coefficients obtained by an l

为了更好地描述断层和断裂系统,本文将三维相干体技术与第二代Curvelet变换相结合,基于Curvelet变换良好的多尺度特性和局部滤波功能.提出了多尺度相干体分析方法,并给出了简单的计算公式.该方法在曲波域中给出不同的重构系数,得到突出不同频带的地震数据体,然后再利用相干体算法得到分频相干体.将本文提出的方法应用到实际地震数据中,突出了特定频带范围的地质异常体,提高了解释精度,有助于实现地质目标的精细解释.

Given a dictionary$D = \lbrace\underline d_k\rbrace$of vectors$\underline d_k$, we seek to represent a signal S as a linear combination$\underline S = \Sigma_k\>\gamma(k)\underline d_k$, with scalar coefficients (k). In particular, we aim for the sparsest representation possible. In general, this requires a combinatorial optimization process. Previous work considered the special case where D is an overcomplete system consisting of exactly two orthobases and has shown that, under a condition of mutual incoherence of the two bases, and assuming that S has a sufficiently sparse representation, this representation is unique and can be found by solving a convex optimization problem: specifically, minimizing the 1norm of the coefficients$\underline\gamma$. In this article, we obtain parallel results in a more general setting, where the dictionary D can arise from two or several bases, frames, or even less structured systems. We sketch three applications: separating linear features from planar ones in 3D data, noncooperative multiuser encoding, and identification of over-complete independent component models.

An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of the dictionary atoms over a base dictionary, and takes the form D = A, where is a fixed base dictionary and A is sparse. The sparse dictionary provides efficient forward and adjoint operators, has a compact representation, and can be effectively trained from given example data. In this, the sparse structure bridges the gap between implicit dictionaries, which have efficient implementations yet lack adaptability, and explicit dictionaries, which are fully adaptable but non-efficient and costly to deploy. In this paper, we discuss the advantages of sparse dictionaries, and present an efficient algorithm for training them. We demonstrate the advantages of the proposed structure for 3-D image denoising.