Accurate and efficient shot-gather dip moveout processing in the log-stretch domain1

An accurate analytical expression for shot-gather dip-moveout (DMO) in the timespace log-stretch domain has until now not been published. We present a simpler, alternative derivation of the exact DMO relationships of Black et al. which correctly take account of the repositioning of the midpoint. A new computationally efficient frequency-wavenumber (F-K) DMO operator for shot profiles is then derived, based on these DMO relationships in the time-space log-stretch domain. The newly derived DMO operator is, unlike most other log-stretch DMO operators) accurate for the full range of reflector dips. Along with other schemes which are performed in the log-stretch domain, it offers considerable time savings over conventional DMO processing. We have compared numerically the impulse response of the new operator with those of a number of other shot-gather DMO operators, and found it to be superior and well match to the theoretical elliptical DMO response.     

A FREQUENCY DOMAIN MAPPING APPROXIMATION OF MOVEOUT FILTERS WITH APPLICATIONS*

Wavenumber aliasing is the main limitation of conventional optimum least-squares linear moveout filters: it prevents adequate reject domain weighting for efficient coherent noise rejection. A general frequency domain multichannel filter design technique based on a one-to-one mapping method between two-dimensional (2D) space and one-dimensional (1D) space is presented. The 2D desired response is mapped to the 1D frequency axis after a suitable sorting of the coefficients. A min-max or Tchebycheff approximation to the desired response is obtained in the 1D frequency domain and mapped back to the 2D frequency domain. The algorithm is suitable for multiband 2D filter design. No aliasing damage is inherent in the linear moveout filters designed using this technique because the approximation is done in the frequency-wavenumber (f, k)-domain. Linear moveout filters designed by using the present coefficient mapping technique achieve better pass domain approximations than the corresponding conventional least-squares filters. Compatible reject domain approximations can be obtained from suitable mappings of the origin coefficient of the desired (f k)-response to the 1D frequency axis. The (fk)-responses of linear moveout filters designed by using the new technique show equi-ripple behavior. Synthetic and real data applications show that the present technique is superior to the optimum least-squares filters and straight stacking in recovering and enhancing the signal events with relatively high residual statics. Their outputs also show higher resolution than those of the optimum least-squares filters.     

3D F–K and finite-difference dip moveout in cross-spreads

A 3D F–K dip-moveout (DMO) is developed, which is applicable to data acquired in an elementary single-fold cross-spread. The key idea is that a 3D log-stretch transform and the inherent regularity of the cross-spread geometry make it possible to transform 3D Fourier DMO. The derived theory generalizes the 2D Fourier shot-gather DMO in the log-stretch domain; 2D turns out to be a special case. Similarly to 2D, the cross-spread DMO becomes convolutional after multidimensional logarithmic stretch. The proposed method works for orthogonal and slanted acquisition geometries; the cross-spread DMO relationships are found to be independent of the intersection angle of the shot and receiver lines. In contrast to integral (Kirchhoff-style) methods, the cross-spread F–K DMO does not degrade from the inevitable irregularity in 3D sampling of offsets in a CMP gather. The newly derived F–K DMO operator can be approximated by finite-difference (FD) schemes; the low-order FD cross-spread DMO equation is shown to be the 3D extension of the Bolondi and Rocca offset continuation. It is shown that F–K and low-order FD operators are effective in a synthetic case.     

PLANE-WAVE CONSTRAINTS IN 2D FILTER DESIGN1

A method is presented for developing and/or evaluating 2D filters applied to seismic data. The approach used is to express linear 2D filtering operations in the space-frequency (x–ω) domain. Correction filters are then determined using plane-wave constraints. For example, requiring a vertically propagating plane wave to be unaffected by migration necessitates application of a half-derivative correction in Kirchhoff migration. The same approach allows determination of the region of time-offset space where half-derivative corrections are correct in x–t domain dip moveout. Finally, an x–ω domain dip filter is derived using the constraint that a plane wave be attenuated as its dip increases. This filter has the advantage that it is significantly faster than f–k domain dip filtering and can be used on irregularly spaced data. This latter property also allows the filter to be used for interpolation of irregular data onto a regular grid.     

Filter formulation and wavefield separation of cross-well seismic data

Multichannel filtering to obtain wavefield separation has been used in seismic processing for decades and has become an essential component in VSP and cross-well reflection imaging. The need for good multichannel wavefield separation filters is acute in borehole seismic imaging techniques such as VSP and cross-well reflection imaging, where strong interfering arrivals such as tube waves, shear conversions, multiples, direct arrivals and guided waves can overlap temporally with desired arrivals. We investigate the effects of preprocessing (alignment and equalization) on the quality of cross-well reflection imaging wavefield separation and we show that the choice of the multichannel filter and filter parameters is critical to the wavefield separation of cross-well data (median filters, f–k pie-slice filters, eigenvector filters). We show that spatial aliasing creates situations where the application of purely spatial filters (median filters) will create notches in the frequency spectrum of the desired reflection arrival. Eigenvector filters allow us to work past the limits of aliasing, but these kinds of filter are strongly dependent on the ratio of undesired to desired signal amplitude. On the basis of these observations, we developed a new type of multichannel filter that combined the best characteristics of spatial filters and eigenvector filters. We call this filter a ‘constrained eigenvector filter’. We use two real data sets of cross-well seismic experiments with small and large well spacing to evaluate the effects of these factors on the quality of cross-well wavefield separation. We apply median filters, f–k pie-slice filters and constrained eigenvector filters in multiple domains available for these data sets (common-source, common-receiver, common-offset and common-midpoint gathers). We show that the results of applying the constrained eigenvector filter to the entire cross-well data set are superior to both the spatial and standard eigenvector filter results.     

Noncausal f–x–y regularized nonstationary prediction filtering for random noise attenuation on 3D seismic data

Seismic noise attenuation is very important for seismic data analysis and interpretation, especially for 3D seismic data. In this paper, we propose a novel method for 3D seismic random noise attenuation by applying noncausal regularized nonstationary autoregression (NRNA) in f–x–y domain. The proposed method, 3D NRNA (f–x–y domain) is the extended version of 2D NRNA (f–x domain). f–x–y NRNA can adaptively estimate seismic events of which slopes vary in 3D space. The key idea of this paper is to consider that the central trace can be predicted by all around this trace from all directions in 3D seismic cube, while the 2D f–x NRNA just considers that the middle trace can be predicted by adjacent traces along one space direction. 3D f–x–y NRNA uses more information from circumjacent traces than 2D f–x NRNA to estimate signals. Shaping regularization technology guarantees that the nonstationary autoregression problem can be realizable in mathematics with high computational efficiency. Synthetic and field data examples demonstrate that, compared with f–x NRNA method, f–x–y NRNA can be more effective in suppressing random noise and improve trace-by-trace consistency, which are useful in conjunction with interactive interpretation and auto-picking tools such as automatic event tracking.     

An algorithm for interpolation in the pyramid domain‡

With the pyramid transform, 2D dip spectra can be characterized by 1D prediction‐error filters (pefs) and 3D dip spectra by 2D pefs. These filters, contrary to pefs estimated in the frequency‐space domain (ω, x) , are frequency independent. Therefore, one pef can be used to interpolate all frequencies. Similarly, one pef can be computed from all frequencies, thus yielding robust estimation of the filter in the presence of noise. This transform takes data from the frequency‐space domain (ω, x) to data in a frequency‐velocity domain (ω, u=ω·x) using a simple mapping procedure that leaves locations in the pyramid domain empty. Missing data in (ω, x) ‐space create even more empty bins in (ω, u) ‐space. We propose a multi‐stage least‐squares approach where both unknown pefs and missing data are estimated. This approach is tested on synthetic and field data examples where aliasing and irregular spacing are present.     

三维共偏移距叠前地震数据反假频射线束形成偏移成像

对稀疏/非规则采样或者低信噪比数据,射线束提取困难并伴随有假频产生,对叠加剖面和道集造成严重干扰.为了提升射线束偏移在稀疏和低信噪比地震数据采集中的成像效果,本文提出基于三角滤波的局部倾斜叠加波束形成偏移假频压制方法.射线束偏移首先将地震数据划分为超道集,经过部分NMO后转化为以射线束中心定义的共偏移距数据,倾斜叠加和反假频操作均在局部共中心点坐标上实现.时间域倾斜叠加是对地震数据的时移累加操作,三角低通滤波同样可以在时间域完成,在对地震数据进行因果和反因果积分后,亦为地震数据的时移累加.因此,三角低通滤波与倾斜叠加可在时间域结合同时完成,避免了频域滤波的正反傅里叶变换.本文在反假频公式中加入权重系数,用以对反假频的程度进行控制,达到分辨率和噪声压制的最佳折衷.以某海上三维实际数据为例,文中展示了反假频射线束形成对偏移叠加剖面和共成像点偏移距道集中的噪声进行了有效压制.     

STACKING FILTERS AND THEIR CHARACTERIZATION IN THE (f—k) DOMAIN *

The implementation of a stacking filter involves the filtering of each trace with an individual filter and the subsequent summing of all outputs. The actual position of a trace in space as well as certain simultaneous shifts of traces and filter components in time do not influence the process. The resulting output is consequently invariant to various arbitrary coordinate transformations. For a certain useful class of ensembles of non-linear moveout arrival times for signals a particular transformation can be found which transforms a given ensemble into one consisting only of straight lines. It is thus possible to reduce, for instance, the analysis of a stacking filter designed for hyperbola-like moveout curves to the analysis of a velocity filter with linear moveout curves. As the (f—k) transform is a very useful concept to describe a velocity filter, it can consequently be applied to characterize a stacking filter in regard to its performance on input signals with non-linear moveout.     

Pattern-guided dip estimation with plane-wave destruction filters

We propose to use pattern-guided dip estimation to mitigate aliasing problem that possibly exists in structure-oriented data processing. A straightforward and effective approach of generating pattern-guided dip is presented, which generally involves three rounds of standard dip estimation with plane-wave destruction filters. The first use of plane-wave destruction filter is for generating a mask operator distinguishing aliased and non-aliased data, based on measuring the uncertainty of the first dip estimation. The second plane-wave destruction filter uses the aliasing-free portions of the input data, and the dip in the aliasing-affected area is automatically padded with the ‘pattern’ dip by smoothing regularization. The result of the second plane-wave destruction filter is used as the initial dip for the inversion of the last-pass plane-wave destruction filter, which produces a pattern-guided dip. For some specific applications, the mask operator can be easily generated through other methods, and we can skip the first dip estimation. Two numerical examples, related to picking information using predictive painting and structure-oriented interpolation, respectively, demonstrate that our pattern-guided dip can effectively mitigate the aliasing problem in structure-oriented data processing.     

A LAYER-STRIPPING TECHNIQUE FOR THE SUPPRESSION OF WATER-BOTTOM MULTIPLE REFLECTIONS*

A new method to suppress water-bottom multiples (water-bottom reverberations) uses the fact that in the domain of intercept time and ray parameter (τ–p domain) the water-bottom reverberations are strictly periodical for a horizontal flat sea bottom. Using this property a comb filter can be designed. The window of the filter should be approximately equal to the duration of a source pulse. The algorithm finds the maximum of the periodical energy throughout the τ–p domain and then designs the comb filter which eliminates the water bottom reverberations from each trace in the τ– p domain. This process can be repeated for higher order reverberations. Finally the τ–p domain with attenuated multiples is transformed back to the conventional x -- t space. The method is illustrated on a variety of synthetic data and on a set of real marine CMP data acquired in the North Sea near the Norwegian shore.     

Multiple suppression by 2D filtering in the parabolic τ–p domain: a wave-equation-based method1

The filter for wave-equation-based water-layer multiple suppression, developed by the authors in the x-t, the linear τ-p, and the f-k domains, is extended to the parabolic τ-2 domain. The multiple reject areas are determined automatically by comparing the energy on traces of the multiple model (which are generated by a wave-extrapolation method from the original data) and the original input data (multiples + primaries) in τ-p space. The advantage of applying the data-adaptive 2D demultiple filter in the parabolic τ-p domain is that the waves are well separated in this domain. The numerical examples demonstrate the effectiveness of such a dereverberation procedure. Filtering of multiples in the parabolic τ-p domain works on both the far-offset and the near-offset traces, while the filtering of multiples in the f-k domain is effective only for the far-offset traces. Tests on a synthetic common-shot-point (CSP) gather show that the demultiple filter is relatively immune to slight errors in the water velocity and water depth which cause arrival time errors of the multiples in the multiple model traces of less than the time dimension (about one quarter of the wavelet length) of the energy summation window of the filter. The multiples in the predicted multiple model traces do not have to be exact replicas of the multiples in the input data, in both a wavelet-shape and traveltime sense. The demultiple filter also works reasonably well for input data contaminated by up to 25% of random noise. A shallow water CSP seismic gather, acquired on the North West Shelf of Australia, demonstrates the effectiveness of the technique on real data.     

FREQUENCY-SELECTIVE DESIGN OF THE KIRCHHOFF MIGRATION OPERATOR1

Integral migration techniques perform a sum over an aperture of input traces to obtain output at a single point. The length of the aperture is limited by a spatial Nyquist criterion, which typically prohibits imaging very steep dips at very high frequencies without generating severe migration artifacts (migration operator aliasing). For time-domain Kirchhoff migration, this can be a fatal shortcoming. The standard way to address this problem is to interpolate traces spatially before migration. This reduces the trace spacing, thereby increasing the frequency content which can be migrated without aliasing at steep dips. An alternative remedy to the operator aliasing problem is to modify the phase response of the Kirchhoff migration operator. This operator is frequency-selective across the migration aperture: it passes all temporal frequencies of the input traces in the innermost portion of the aperture (referring to the shallow dips), and gradually cuts out the higher frequencies as it approaches the outer portion of the aperture. Thus, while all frequencies of the input data contribute to the shallow-dip portion of the migrated image, only the permissible low frequencies of the input data contribute to imaging the steepest dips. Using a simple realization of a frequency-selective Kirchhoff migration operator, this technique is illustrated on a synthetic data set involving greater than vertical dips.     

Median filter behaviour with seismic data1

Median filters may be used with seismic data to attenuate coherent wavefields. An example is the attenuation of the downgoing wavefield in VSP data processing. The filter is applied across the traces in the ‘direction’ of the wavefield. The final result is given by subtracting the filtered version of the record from the original record. This method of median filtering may be called ‘median filtering operated in subtraction’. The method may be extended by automatically estimating the slowness of coherent wavefields on a record. The filter is then applied in a time- and-space varying manner across the record on the basis of the slowness values at each point on the record. Median filters are non-linear and hence their behaviour is more difficult to determine than linear filters. However, there are a number of methods that may be used to analyse median filter behaviour: (1) pseudo-transfer functions to specific time series; (2) the response of median filters to simple seismic models; and (3) the response of median filters to steps that simulate terminating wavefields, such as faults on stacked data. These simple methods provide an intuitive insight into the behaviour of these filters, as well as providing a semiquantitative measurement of performance. The performance degradation of median filters in the presence of trace-to-trace variations in amplitude is shown to be similar to that of linear filters. The performance of median filters (in terms of signal distortion) applied obliquely across a record may be improved by low-pass filtering (in the t-dimension). The response of median filters to steps is shown to be affected by background noise levels. The distortion of steps introduced by median filters approaches the distortion of steps introduced by the corresponding linear filter for high levels of noise.     

Residual dip moveout in VTI media

Dip‐moveout (DMO) correction is often applied to common‐offset sections of seismic data using a homogeneous isotropic medium assumption, which results in a fast execution. Velocity‐residual DMO is developed to correct for the medium‐treatment limitation of the fast DMO. For reasonable‐sized velocity perturbations, the residual DMO operator is small, and thus is an efficient means of applying a conventional Kirchhoff approach. However, the shape of the residual DMO operator is complicated and may form caustics. We use the Fourier domain for the operator development part of the residual DMO, while performing the convolution with common‐offset data in the space–time domain. Since the application is based on an integral (Kirchhoff) method, this residual DMO preserves all the flexibility features of an integral DMO. An application to synthetic and real data demonstrates effectiveness of the velocity‐residual DMO in data processing and velocity analysis.     

ω-k FILTER DESIGN*

Two-dimensional band-pass filters can be constructed by a simple extension of the theory of one-dimensional band-pass filters. Similarly to the one-dimensional analogue the shape of the two-dimensional filter is important in determining its effectiveness. The band-pass filter formulation can be further refined so that the filter will concentrate its rejection energies in certain areas of the ω, k plane. Such band-pass, band-reject filters are found by solving a set of simultaneous equations.     

ω-k FILTER DESIGN*

Two-dimensional band-pass filters can be constructed by a simple extension of the theory of one-dimensional band-pass filters. Similarly to the one-dimensional analogue the shape of the two-dimensional filter is important in determining its effectiveness. The band-pass filter formulation can be further refined so that the filter will concentrate its rejection energies in certain areas of the ω, k plane. Such band-pass, band-reject filters are found by solving a set of simultaneous equations.     

A FREQUENCY DOMAIN APPROACH TO TWO-DIMENSIONAL MIGRATION *

The problem of the propagation of acoustic waves in a two-dimensional layered medium can be easily solved in the frequency domain if the Dix approximation is used, i.e. when only the primary reflections are considered. The migrated data at a depth z are obtained by convolving the time section with a proper two-dimensional operator dependent on z. The same result can be obtained by multiplying their two-dimensional spectra and summing for all the values of the temporal frequency. The aspect of the operator in the time-space domain has the classic hyperbolic structure together with the prescribed temporal and spatial decay. The main advantages of the frequency domain approach consist in the noticeable computer time savings and in the better approximation. On the other hand lateral velocity variations are very difficult to be taken into account. This can be done if a space variant filter is used in the time-space domain. To reduce computer time, this filter has to be recursive; the problem has been solved by Claerbout by transforming the hyperbolic partial differential equation into a parabolic one, and using the latter to generate the recursion operator. In the presentation a method is given for the generation of recursive filters with a better phase characteristics that have a pulse response with the requested hyperbolic shape instead of the parabocli one. This allows a better migration of steeper dips.     

Seismic data interpolation using frequency‐domain complex nonstationary autoregression

We have developed a novel method for missing seismic data interpolation using f‐x‐domain regularised nonstationary autoregression. f‐x regularised nonstationary autoregression interpolation can deal with the events that have space‐varying dips. We assume that the coefficients of f‐x regularised nonstationary autoregression are smoothly varying along the space axis. This method includes two steps: the estimation of the coefficients and the interpolation of missing traces using estimated coefficients. We estimate the f‐x regularised nonstationary autoregression coefficients for the completed data using weighted nonstationary autoregression equations with smoothing constraints. For regularly missing data, similar to Spitz f‐x interpolation, we use autoregression coefficients estimated from low‐frequency components without aliasing to obtain autoregression coefficients of high‐frequency components with aliasing. For irregularly missing or gapped data, we use known traces to establish nonstationary autoregression equations with regularisation to estimate the f‐x autoregression coefficients of the complete data. We implement the algorithm by iterated scheme using a frequency‐domain conjugate gradient method with shaping regularisation. The proposed method improves the calculation efficiency by applying shaping regularisation and implementation in the frequency domain. The applicability and effectiveness of the proposed method are examined by synthetic and field data examples.