Modeling of zero-offset reflection profiles with asymptotic ray theory

1981 ◽  
Vol 18 (3) ◽  
pp. 551-560
Author(s):  
George A. McMechan
Keyword(s):  
Seismic Reflection ◽  
Constant Velocity ◽  
Seismic Refraction ◽  
Drill Core ◽  
Ray Theory ◽  
Low Velocity ◽  
Tectonic Structures ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Zero Offset

Synthetic reflection profiles computed by asymptotic ray theory can be used in the interpretation of laterally varying tectonic structures. The algorithm is implemented for normally incident (zero-offset) rays in two-dimensional models that are specified in terms of constant velocity layers separated by piecewise cubic boundaries. Applications include modeling of profiles containing lenses, interbedded high- and low-velocity layers, and oceanic ridges. The method is practical and flexible in the sense that structural, lithologic, drill core, and seismic refraction constraints can be directly combined with the seismic reflection data.

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.

Low velocity layer characterization in the Niger Delta: Implications for seismic reflection data quality

2017 ◽  
Vol 90 (2) ◽  
pp. 187-195
Author(s):  
A. I. Opara ◽  
C. C. Agoha ◽  
C. N. Okereke ◽  
U. P. Adiela ◽  
C. N. Onwubuariri ◽  
...  
Keyword(s):  
Data Quality ◽  
Niger Delta ◽  
Seismic Reflection ◽  
Low Velocity ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Low Velocity Layer ◽  
Velocity Layer

An example of extreme near-surface variability in shallow seismic reflection data

2016 ◽  
Vol 4 (3) ◽  
pp. SH1-SH9
Author(s):  
Steven D. Sloan ◽  
J. Tyler Schwenk ◽  
Robert H. Stevens
Keyword(s):  
Seismic Reflection ◽  
Field Data ◽  
Low Velocity ◽  
Seismic Reflection Data ◽  
Near Surface ◽  
Shallow Subsurface ◽  
Reflection Data ◽  
Shallow Seismic ◽  
Low Velocity Layer ◽  
Velocity Layer

Variability of material properties in the shallow subsurface presents challenges for near-surface geophysical methods and exploration-scale applications. As the depth of investigation decreases, denser sampling is required, especially of the near offsets, to accurately characterize the shallow subsurface. We have developed a field data example using high-resolution shallow seismic reflection data to demonstrate how quickly near-surface properties can change over short distances and the effects on field data and processed sections. The addition of a relatively thin, 20 cm thick, low-velocity layer can lead to masked reflections and an inability to map shallow reflectors. Short receiver intervals, on the order of 10 cm, were necessary to identify the cause of the diminished data quality and would have gone unknown using larger, more conventional station spacing. Combined analysis of first arrivals, surface waves, and reflections aided in determining the effects and extent of a low-velocity layer that inhibited the identification and constructive stacking of the reflection from a shallow water table using normal-moveout-based processing methods. Our results also highlight the benefits of using unprocessed gathers to pragmatically guide processing and interpretation of seismic data.

Application of waveform tomography to marine seismic reflection data from the Queen Charlotte Basin of western Canada

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. B55-B70 ◽  
Author(s):  
E. M. Takam Takougang ◽  
A. J. Calvert
Keyword(s):  
Seismic Reflection ◽  
Field Data ◽  
Velocity Model ◽  
P Wave ◽  
Low Velocity ◽  
Seismic Line ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Sonic Log ◽  
Waveform Tomography

To obtain a higher resolution quantitative P-wave velocity model, 2D waveform tomography was applied to seismic reflection data from the Queen Charlotte sedimentary basin off the west coast of Canada. The forward modeling and inversion were implemented in the frequency domain using the visco-acoustic wave equation. Field data preconditioning consisted of f-k filtering, 2D amplitude scaling, shot-to-shot amplitude balancing, and time windowing. The field data were inverted between 7 and 13.66 Hz, with attenuation introduced for frequencies ≥ 10.5 Hz to improve the final velocity model; two different approaches to sampling the frequencies were evaluated. The limited maximum offset of the marine data (3770 m) and the relatively high starting frequency (7 Hz) were the main challenges encountered during the inversion. An inversion strategy that successively recovered shallow-to-deep structures was designed to mitigate these issues. The inclusion of later arrivals in the waveform tomography resulted in a velocity model that extends to a depth of approximately 1200 m, twice the maximum depth of ray coverage in the ray-based tomography. Overall, there is a good agreement between the velocity model and a sonic log from a well on the seismic line, as well as between modeled shot gathers and field data. Anomalous zones of low velocity in the model correspond to previously identified faults or their upward continuation into the shallow Pliocene section where they are not readily identifiable in the conventional migration.

Modeling and processing of scattered waves in seismic reflection surveys

Geophysics ◽  
1988 ◽  
Vol 53 (4) ◽  
pp. 466-478 ◽  
Author(s):  
Bruce S. Gibson ◽  
Alan R. Levander
Keyword(s):  
Surface Waves ◽  
Seismic Reflection ◽  
Wave Scattering ◽  
Body Wave ◽  
Simple Relation ◽  
Velocity Variation ◽  
Low Velocity ◽  
Spectral Character ◽  
Seismic Reflection Data ◽  
Reflection Data

Various wave‐scattering mechanisms are known to degrade reflection signals by producing noise in seismic reflection data. Synthetic 2-D acoustic‐wave finite‐ difference data sets illustrate the effects of two such mechanisms. Twenty‐five shot gathers were generated for each of two models and the data were processed as standard CMP surveys. In one model, an irregular low‐ velocity surface layer produced multiply scattered surface waves that appear as linear noise trains in common‐shot gathers and stacked sections. The scattering of upcoming reflections at the lower interface of the layer also produced a significant amount of noise. When predictive deconvolution was applied before stack to reduce reverberations, the spectral character of the scattered surface waves seriously inhibited the action of that process. In the second model, a zone of smooth, random velocity variation was imposed between two reflectors deeper in the model. The heterogeneous zone (±5 percent rms velocity variation) substantially degraded the signal reflected from below it; events produced by body‐wave scattering are characterized by higher phase velocities than those seen in the first model. Conventional CMP stacking produced discontinuous subhorizontal events from the disturbed zone. The limited bandwidth of the propagating signal and spatial filtering attributable to CMP stacking cause these events to bear no simple relation to the velocity anomalies of the model, even after migration.

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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