AVO correction for scalar waves in the case of a thinly layered reflector overburden

Geophysics ◽  
1996 ◽  
Vol 61 (2) ◽  
pp. 520-528 ◽  
Author(s):  
Martin T. Widmaier ◽  
Sergei A. Shapiro ◽  
Peter Hubral
Keyword(s):  
Thin Layer ◽  
Transmission Loss ◽  
Thin Layers ◽  
Statistical Parameters ◽  
Medium Theory ◽  
Seismic Reflection Data ◽  
Amplitude Variation With Offset ◽  
Velocity Anisotropy ◽  
Reflection Data ◽  
Zero Offset

The reflection response of a seismic target is significantly affected by a thinly layered overburden, which creates velocity anisotropy and a transmission loss by scattering attenuation. These effects must be taken into account when imaging a target reflector and when inverting reflection coefficients. Describing scalar wave (i.e., acoustic wave or SH‐wave) propagation through a stack of thin layers by equivalent‐medium theory provides a simple generalized O’Doherty‐Anstey formula. This formulation is defined by a few statistical parameters that depend on the 1-D random fluctuations of the reflector overburden. The formula has been combined with well‐known target‐oriented and amplitude‐preserving migration/inversion algorithms and amplitude variation with offset (AVO) analysis procedures. The application of these combined procedures is demonstrated for SH‐waves in an elastic thinly‐layered medium. These techniques offer a suitable tool to compensate for the thin‐layer influence on traveltimes and amplitudes of seismic reflection data. The thin‐layer sensitive AVO parameters (zero‐offset amplitude and AVO gradient) of a target reflector can thus be better recovered.

Source wavelet and local wave propagation effects on the amplitude-variation-with-offset response of thin-layer models: A physical modeling study

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. N27-N41 ◽  
Author(s):  
Carlos A. M. Assis ◽  
Sérgio A. M. Oliveira ◽  
Roseane M. Misságia ◽  
Marco A. R. de Ceia
Keyword(s):  
Wave Propagation ◽  
Thin Layer ◽  
Thin Layers ◽  
Propagation Mode ◽  
Multiple Reflections ◽  
Amplitude Response ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Avo Analysis ◽  
Reflection Data

In target layers with thicknesses below the vertical seismic resolution as thin layers, the tuning effect/interference between the wave propagation modes may increase the challenge of doing amplitude-variation-with-offset (AVO) analysis because it is difficult to recover the primary PP amplitudes embedded in the data by further seismic data processing. Thus, we have investigated the importance of the primary PP reflections, locally P-SV converted waves, and internal multiple reflections in the amplitude response of two thin-layer seismic physical models. One model consists of a thin water layer embedded between two nylon plates, and another model with a thin acrylic layer surrounded by water. Numerical modeling using the reflectivity method was applied to analyze each wave propagation mode and the source waveform role in the experimental data. Before the experimental reflection data acquisition, we characterized two source and receiver piezoelectric transducer (PET) pairs: one with a circular plane face and the other with a semispherical face. We measured the source wavelet, its dominant frequency, and the PETs’ directivity pattern. Semispherical PETs were chosen to acquire common midpoint reflection data. Thereafter, a processing workflow was applied to remove linear events interfering with the target reflections and to correct amplitudes due to transmission losses, source/receiver directivity, and geometric spreading effects. Finally, we investigated the thin-layer targets near incidence angle amplitude and the AVO response. The results showed that the interference between the primary PP reflections and the locally converted shear waves may considerably affect the observed amplitude response. The source wavelet bandwidth appeared as a second-order effect, and the internal multiple reflections were practically negligible. These results suggested that in real data sets, it is important to investigate the wave propagation modes and source wavelet role in the amplitudes observed, before deciding the AVO analysis/inversion workflow that should be adopted.

Another look at NMO stretch

Geophysics ◽  
1992 ◽  
Vol 57 (5) ◽  
pp. 749-751 ◽  
Author(s):  
Arthur E. Barnes
Keyword(s):  
Seismic Reflection ◽  
Qualitative Assessment ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Amplitude Spectra ◽  
Nmo Correction ◽  
Zero Offset ◽  
Nmo Stretch ◽  
Dominant Frequencies

The normal moveout (NMO) correction is applied to seismic reflection data to transform traces recorded at non‐zero offset into traces that appear to have been recorded at zero offset; this introduces undesirable distortions called NMO stretch (Buchholtz, 1972). NMO stretch must be understood because it lengthens waveforms and thereby reduces resolution. Buchholtz (1972) gives a qualitative assessment of NMO stretch, Dunkin and Levin (1973) derive its effect on the amplitude spectra of narrow waveforms, while Yilmaz (1987, p. 160) considers its effect on dominant frequencies. These works are approximate and do not show how spectral distortions vary through time.

Labeling long‐period multiple reflections

Geophysics ◽  
1989 ◽  
Vol 54 (1) ◽  
pp. 122-126 ◽  
Author(s):  
R. J. J. Hardy ◽  
M. R. Warner ◽  
R. W. Hobbs
Keyword(s):  
Seismic Reflection ◽  
High Amplitude ◽  
Data Sets ◽  
Multiple Reflections ◽  
Multiple Series ◽  
Seismic Reflection Data ◽  
Long Period ◽  
Reflection Data ◽  
Zero Offset ◽  
Marine Seismic

The many techniques that have been developed to remove multiple reflections from seismic data all leave remnant energy which can cause ambiguity in interpretation. The removal methods are mostly based on periodicity (e.g., Sinton et al., 1978) or the moveout difference between primary and multiple events (e.g., Schneider et al., 1965). They work on synthetic and selected field data sets but are rather unsatisfactory when applied to high‐amplitude, long‐period multiples in marine seismic reflection data acquired in moderately deep (700 m to 3 km) water. Differential moveout is often better than periodicity at discriminating between types of events because, while a multiple series may look periodic to the eye, it is only exactly so on zero‐offset reflections from horizontal layers. The technique of seismic event labeling described below works by returning offset information from CDP gathers to a stacked section by color coding, thereby discriminating between seismic reflection events by differential normal moveout. Events appear as a superposition of colors; the direction of color fringes indicates whether an event has been overcorrected or undercorrected for its hyperbolic normal moveout.

Estimation of Q from surface seismic reflection data

Geophysics ◽  
1998 ◽  
Vol 63 (6) ◽  
pp. 2120-2128 ◽  
Author(s):  
Rahul Dasgupta ◽  
Roger A. Clark
Keyword(s):  
Attenuation Factor ◽  
Spectral Ratio ◽  
Ratio Method ◽  
Amplitude Analysis ◽  
Gas Reservoir ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Anelastic Attenuation ◽  
Zero Offset ◽  
Nmo Stretch

Reliable estimates of the anelastic attenuation factor, Q, are desirable for improved resolution through inverse Q deconvolution and to facilitate amplitude analysis. Q is a useful petrophysical parameter itself, yet Q is rarely measured. Estimates must currently be made from borehole seismology. This paper presents a simple technique for determining Q from conventional surface seismic common midpoint (CMP) gathers. It is essentially the classic spectral ratio method applied on a trace‐by‐trace basis to a designatured and NMO stretch‐corrected CMP gather. The variation of apparent Q versus offset (QVO) is extrapolated to give a zero‐offset Q estimate. Studies on synthetics suggest that, for reasonable data quality (S/N ratios better than 3:1, shallow (<5°) dips, and stacking velocity accuracy <5%), source‐to‐reflector average Q is recoverable to within some 3% and Q for a specific interval (depending on its two‐way time thickness and depth) is recoverable to 15–20%. Three case studies are reported. First, Q versus offset and vertical seismic profiling (VSP) Q estimates for a southern North Sea line were in close agreement, validating the method. For Chalk, Mushelkalk‐Keuper, and Bunter‐Zechstein, Q was estimated as 130 ± 15, 47 ± 8, and 156 ± 18, respectively. Next, two alternative lithological interpretations of a structure seen in a frontier area were discriminated between when Q estimates of 680 to 820 were obtained (compared to some 130–170 in the overlying units), favoring a metamorphic/crystalline lithology rather than (prospective) sediments. This was later confirmed by drilling. Third, a profile of Q estimates along a 200-ms-thick interval, known to include a gas reservoir, showed a clear and systematic reduction in Q to a low of 50 ± 11, coincident with the maximum reservoir thickness, from some 90–105 outside the reservoir. Q for the reservoir interval itself was estimated at 17 ± 7.

A high-resolution weighted AB semblance for dealing with amplitude-variation-with-offset phenomenon

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. V85-V93 ◽  
Author(s):  
Saleh Ebrahimi ◽  
Amin Roshandel Kahoo ◽  
Yangkang Chen ◽  
Milton Porsani
Keyword(s):  
Time Window ◽  
Weighting Function ◽  
Singular Values ◽  
Superior Performance ◽  
Amplitude Variation ◽  
Lower Velocity ◽  
Seismic Reflection Data ◽  
Amplitude Variation With Offset ◽  
Reflection Data ◽  
Velocity Spectra

Velocity analysis is an essential step in seismic reflection data processing. The conventional and fastest method to estimate how velocity changes with increasing depth is to calculate semblance coefficients. Traditional semblance has two problems: low time and velocity resolution and an inability to handle amplitude variation-with-offset (AVO) phenomenon. Although a method known as the AB semblance can arrive at peak velocities in the areas with an AVO anomaly, it has a lower velocity resolution than conventional semblance. We have developed a weighted AB semblance method that can handle both problems simultaneously. We have developed two new weighting functions to weight the AB semblance to enhance the resolution of velocity spectra in the time and velocity directions. In this way, we increase the time and velocity resolution while eliminating the AVO problem. The first weighting function is defined based on the ratio between the first and the second singular values of the time window to improve the resolution of velocity spectra in velocity direction. The second weighting function is based on the position of the seismic wavelet in the time window, thus enhancing the resolution of velocity spectra in time direction. We use synthetic and field data examples to show the superior performance of our approach over the traditional one.

Effects of transverse isotropy on P‐wave AVO for gas sands

Geophysics ◽  
1993 ◽  
Vol 58 (6) ◽  
pp. 883-888 ◽  
Author(s):  
Ki Young Kim ◽  
Keith H. Wrolstad ◽  
Fred Aminzadeh
Keyword(s):  
Transverse Isotropy ◽  
Transversely Isotropic ◽  
P Wave ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Velocity Anisotropy ◽  
Special Cases ◽  
Class 1 ◽  
Zero Offset ◽  
Ti Media

Velocity anisotropy should be taken into account when analyzing the amplitude variation with offset (AVO) response of gas sands encased in shales. The anisotropic effects on the AVO of gas sands in transversely isotropic (TI) media are reviewed. Reflection coefficients in TI media are computed using a planewave formula based on ray theory. We present results of modeling special cases of exploration interest having positive reflectivity, near‐zero reflectivity, and negative reflectivity. The AVO reflectivity in anisotropic media can be decomposed into two parts; one for isotropy and the other for anisotropy. Zero‐offset reflectivity and Poisson’s ratio contrast are the most significant parameters for the isotropic component while the δ difference (Δδ) between shale and gas sand is the most important factor for the anisotropic component. For typical values of Tl anisotropy in shale (positive δ and ε), both δ difference (Δδ) and ε difference (Δε) amplify AVO effects. For small angles of incidence, Δδ plays an important role in AVO while Δε dominates for large angles of incidence. For typical values of δ and ε, the effects of anisotropy in shale are: (1) a more rapid increase in AVO for Class 3 and Class 2 gas sands, (2) a more rapid decrease in AVO for Class 1 gas sands, and (3) a shift in the offset of polarity reversal for some Class 1 and Class 2 gas sands.

Inversion of reflection times in three dimensions

Geophysics ◽  
1981 ◽  
Vol 46 (7) ◽  
pp. 972-983 ◽  
Author(s):  
Håvar Gjøystdal ◽  
Bjørn Ursin
Keyword(s):  
Real Data ◽  
Normal Incidence ◽  
Thin Layers ◽  
Three Dimensions ◽  
Good Resolution ◽  
Inversion Algorithm ◽  
Linear Inversion ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Interval Velocities

When reflection data are available from a grid of crossing seismic lines, it is possible to construct normal incidence time maps from interpreted stacked sections and then apply three‐dimensional (3-D) ray‐tracing techniques following the normal‐incidence raypaths down to the various reflectors. The main disadvantage of this well‐known “time map migration” procedure is that interval velocities must be known a priori, and they must be estimated in advance by some approximate method. A technique is presented here which combines the above procedure with an inversion algorithm, providing direct calculations of interval velocities from the additional use of nonzero offset traveltime observations. A generalized linear inversion scheme is used, making possible a complete calculation of interval velocities and reflection interfaces, the latter represented by bicubic spline functions. To test the method in practice, we have applied it to (1) synthetic data generated from a constructed model, and (2) real data obtained from marine seismic sections. In the latter case, velocities and reflector depths obtained were compared to those obtained directly from a well log in the area. These results show a reasonably good resolution for layers that are not too deep relative to the shot/receiver offsets used. For deep and/or thin layers, the results are not satisfactory. This indicates the general limitation of seismic reflection data to resolve interval velocity, even in the presence of horizontally layered structure.

Seismic parameters for media with elliptical velocity dependencies

Geophysics ◽  
1982 ◽  
Vol 47 (12) ◽  
pp. 1621-1626 ◽  
Author(s):  
Bok S. Byun
Keyword(s):  
Three Dimensional ◽  
Isotropic Case ◽  
Model Parameters ◽  
Seismic Reflection Data ◽  
Seismic Properties ◽  
Velocity Anisotropy ◽  
Reflection Data ◽  
Wave Surfaces ◽  
Ray Parameter ◽  
Anisotropic Case

The concept of wavefront curvature has been discussed extensively in the literature to relate surface seismic reflection data to subsurface geologic parameters. Developed initially for the case of homogeneous, isotropic, but arbitrarily dipping layered media, this technique has been extended to the inhomogeneous case. Now with the advent of new seismic techniques, such as vertical seismic profiling, three‐dimensional seismic methods, and shear‐wave techniques, the problem of velocity anisotropy is of growing concern to exploration seismologists. The essence of this paper is to extend the method of wavefront curvature to the “elliptically anisotropic” case in which the ray velocity varies elliptically with the direction of propagation. A fundamental feature of wave propagation in the anisotropic medium is that the direction of propagation of the disturbance (or the ray velocity direction) generally differs from that of the wavefront (or the phase velocity direction). Based on the assumption of two‐dimensional dipping layers in which velocity is “elliptically dependent” on the angle of propagation, relationships are developed between important seismic properties and model parameters. First, a relationship between the incident and refracted rays across the interface is expressed through the ray parameter. A geometrical divergence law is then developed relating the radii of the elliptical wave surfaces of the incident and refracted rays. A Dix‐type formula is finally derived which relates the normal moveout (NMO) velocity to the subsurface parameters. An example is shown to compare the radius of the wave surface and the NMO velocity for the elliptically anisotropic case with those for the equivalent isotropic case.

Automatic NMO correction in anisotropic media and non-hyperbolic NMO velocity field estimation

2016 ◽  
Vol 56 (2) ◽  
pp. 592
Author(s):  
Mohamed Sedek ◽  
Lutz Gross
Keyword(s):  
Velocity Field ◽  
Seismic Reflection ◽  
Fast Method ◽  
Gas Field ◽  
Seismic Reflection Data ◽  
Anisotropic Effect ◽  
Reflection Data ◽  
Nmo Correction ◽  
Zero Offset ◽  
The One

The authors propose a new method to automatically normal move-out correct pre-stack seismic reflection data that is sorted by CDP gathers, and to estimate the normal move-out (NMO) velocity (Vnmo) as a full common depth point (CDP) velocity field that instantaneously varies with offsets/azimuths. The method is based on doing a pre-defined number of NMO velocity iterations using linear vertical interpolation of different NMO velocities at each seismic trace individually. At each iteration the seismic trace is shifted and multiplied by the zero offset trace followed by the summation of the product. Then, after all the iterations are done, the one with the maximum summation value is chosen, which is assumed to be the most suitable NMO velocity trace that accurately flattens seismic reflection events. The other traces follow the same process, and a final velocity field is then extracted. Another new, simple and fast method is also introduced to estimate the anisotropic effect from the extracted NMO velocity field. The method runs by calculating the spatial variation of the estimated NMO velocities at each arrival time and offset/azimuth, therefore instantaneously estimating the anisotropic effect. Isotropic and anisotropic synthetic geological models were built based on a ray-tracing algorithm to test the method. A range of synthetic background noise was applied, starting from 10–30%. The method has also been tested on Hess’s model and coal seam gas field data CDP examples. An Alaskan pre-stack seismic CDP field example has also been used.

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