Dip moveout in shot profiles

Geophysics ◽  
1987 ◽  
Vol 52 (11) ◽  
pp. 1473-1482 ◽  
Author(s):  
Biondo Biondi ◽  
Joshua Ronen

All the known dip‐moveout (DMO) algorithms that are not integral methods require the seismic data to be sorted in regularly sampled constant‐offset sections. In contrast, the dip‐moveout method proposed here can be applied directly to recorded shot profiles and thus can handle data that cannot be sorted in regular constant‐offset sections. The definition of the shot‐DMO operator is analogous to that of the dip‐moveout operator for constant‐offset sections. The two operators have impulse responses with the same projection on the zero‐offset plane, i.e., the stacking plane; therefore, the application of dip moveout in constant‐offset sections or in shot profiles gives the same stacked section. Dip moveout transforms shot profiles to zero‐offset data, to which any poststack migration can be applied. The shot‐DMO operator is space‐variant and time‐ variant; thus direct application of the operator would be computationally expensive. Fortunately, after a logarithmic transformation of both the time and the space coordinates, the operator becomes time‐invariant and space‐invariant; then dip moveout can be performed efficiently as a multiplication in the Fourier domain. Shot dip moveout is also a useful tool for improving the accuracy of residual velocity analysis performed after the DMO process. Field‐data examples show that the shot‐profile dip‐moveout method yields stacked sections similar to those from Hale’s (1984) dip moveout for constant‐offset sections.

2021 ◽  
Vol 11 (1) ◽  
pp. 78
Author(s):  
Jianbo He ◽  
Zhenyu Wang ◽  
Mingdong Zhang

When the signal to noise ratio of seismic data is very low, velocity spectrum focusing will be poor., the velocity model obtained by conventional velocity analysis methods is not accurate enough, which results in inaccurate migration. For the low signal noise ratio (SNR) data, this paper proposes to use partial Common Reflection Surface (CRS) stack to build CRS gathers, making full use of all of the reflection information of the first Fresnel zone, and improves the signal to noise ratio of pre-stack gathers by increasing the number of folds. In consideration of the CRS parameters of the zero-offset rays emitted angle and normal wave front curvature radius are searched on zero offset profile, we use ellipse evolving stacking to improve the zero offset section quality, in order to improve the reliability of CRS parameters. After CRS gathers are obtained, we use principal component analysis (PCA) approach to do velocity analysis, which improves the noise immunity of velocity analysis. Models and actual data results demonstrate the effectiveness of this method.


Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1846-1858 ◽  
Author(s):  
Claudio Bagaini ◽  
Umberto Spagnolini

Continuation to zero offset [better known as dip moveout (DMO)] is a standard tool for seismic data processing. In this paper, the concept of DMO is extended by introducing a set of operators: the continuation operators. These operators, which are implemented in integral form with a defined amplitude distribution, perform the mapping between common shot or common offset gathers for a given velocity model. The application of the shot continuation operator for dip‐independent velocity analysis allows a direct implementation in the acquisition domain by exploiting the comparison between real data and data continued in the shot domain. Shot and offset continuation allow the restoration of missing shot or missing offset by using a velocity model provided by common shot velocity analysis or another dip‐independent velocity analysis method.


Geophysics ◽  
1993 ◽  
Vol 58 (8) ◽  
pp. 1099-1111 ◽  
Author(s):  
Guillaume Cambois ◽  
Paul Stoffa

In the log/Fourier domain, decomposing the amplitude spectra of seismic data into surface‐consistent terms is a linear problem that can be solved, very efficiently, one frequency at a time. However, the nonunique definition of the complex logarithm makes it much more difficult to decompose the phase spectra. The instability of phase unwrapping has previously prevented any attempt to decompose phase spectra in the log/Fourier domain. We develop a fast and robust partial unwrapping algorithm, which makes it possible to efficiently decompose the phase spectra of normal moveout‐corrected (NMO‐) data into surface‐consistent terms, in the log/Fourier domain. The dual recovery of amplitude and phase spectra yields a surface‐consistent deconvolution technique where only the average reflectivity is assumed to be white, and only the average wavelet is required to be minimum‐phase. Each individual deconvolution operator may be mixed‐phase, depending on its estimated phase spectra. For example, surface‐consistent time shifts and phase rotations, as well as any other surface‐consistent phase effects, are included in the phase spectra of the surface‐consistent deconvolution operators. Consequently, static shifts are estimated and removed without ever picking horizons or crosscorrelations.


Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. U1-U12
Author(s):  
Michelângelo G. Silva ◽  
Milton J. Porsani ◽  
Bjorn Ursin

Velocity-independent seismic data processing requires information about the local slope in the data. From estimates of local time and space derivatives of the data, a total least-squares algorithm gives an estimate of the local slope at each data point. Total least squares minimizes the orthogonal distance from the data points (the local time and space derivatives) to the fitted straight line defining the local slope. This gives a more consistent estimate of the local slope than standard least squares because it takes into account uncertainty in the temporal and spatial derivatives. The total least-squares slope estimate is the same as the one obtained from using the structure tensor with a rectangular window function. The estimate of the local slope field is used to extrapolate all traces in a seismic gather to the smallest recorded offset without using velocity information. Extrapolation to zero offset is done using a hyperbolic traveltime function in which slope information replaces the knowledge of the normal moveout (NMO) velocity. The new data processing method requires no velocity analysis and there is little stretch effect. All major reflections and diffractions that are present at zero offset will be reproduced in the output zero-offset section. Therefore, if multiple reflections are undesired in the output, they should be removed before data extrapolation to zero offset. The automatic method is sensitive to noise, so for poor signal-to-noise ratios, standard NMO velocities for primary reflections can be used to compute the slope field. Synthetic and field data examples indicate that compared with standard seismic data processing (velocity analysis, mute, NMO correction, and stack), our method provides an improved zero-offset section in complex data areas.


Geophysics ◽  
1989 ◽  
Vol 54 (11) ◽  
pp. 1455-1465 ◽  
Author(s):  
William S. Harlan

Hyperbolic reflections and convolutional wavelets are fundamental models for seismic data processing. Each sample of a “stacked” zero‐offset section can parameterize an impulsive hyperbolic reflection in a midpoint gather. Convolutional wavelets can model source waveforms and near‐surface filtering at the shot and geophone positions. An optimized inversion of the combined modeling equations for hyperbolic traveltimes and convolutional wavelets makes explicit any interdependence and nonuniqueness in these two sets of parameters. I first estimate stacked traces that best model the recorded data and then find nonimpulsive wavelets to improve the fit with the data. These wavelets are used for a new estimate of the stacked traces, and so on. Estimated stacked traces model short average wavelets with a superposition of approximately parallel hyperbolas; estimated wavelets adjust the phases and amplitudes of inconsistent traces, including static shifts. Deconvolution of land data with estimated wavelets makes wavelets consistent over offset; remaining static shifts are midpoint‐consistent. This phase balancing improves the resolution of stacked data and of velocity analyses. If precise velocity functions are not known, then many stacked traces can be inverted simultaneously, each with a different velocity function. However, the increased number of overlain hyperbolas can more easily model the effects of inconsistent wavelets. As a compromise, I limit velocity functions to reasonable regions selected from a stacking velocity analysis—a few functions cover velocities of primary and multiple reflections. Multiple reflections are modeled separately and then subtracted from marine data. The model can be extended to include more complicated amplitude changes in reflectivity. Migrated reflectivity functions would add an extra constraint on the continuity of reflections over midpoint. Including the effect of dip moveout in the model would make stacking and migration velocities equivalent.


1996 ◽  
Author(s):  
Dennis Corrigan ◽  
Robert Withers ◽  
Jim Darnall ◽  
Tracey Skopinski

2021 ◽  
Author(s):  
A.G. Yaroslavtsev ◽  
M.V. Tarantin ◽  
T.V. Baibakova

2021 ◽  
pp. 562-598
Author(s):  
Stevan Berber

Due to the importance of the concept of independent variable modification, the definition of linear-time-invariant system, and their implications for discrete-time signal processing, Chapter 11 presents basic deterministic continuous-time signals and systems. These signals, expressed in the form of functions and functionals such as the Dirac delta function, are used throughout the book for deterministic and stochastic signal analysis, in both the continuous-time and the discrete-time domains. The definition of the autocorrelation function, and an explanation of the convolution procedure in linear-time-invariant systems, are presented in detail, due to their importance in communication systems analysis and synthesis. A linear modification of the independent continuous variable is presented for specific cases, like time shift, time reversal, and time and amplitude scaling.


2010 ◽  
Author(s):  
Didier Lecerf ◽  
Jean-Luc Boelle ◽  
Amhed Belmokhtar ◽  
Abdeljebbar Ladmek

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