Velocity estimation by image-focusing analysis

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. U49-U60 ◽  
Author(s):  
Biondo Biondi
Keyword(s):  
New York ◽  
Seismic Data ◽  
Field Data ◽  
Velocity Estimation ◽  
Data Set ◽  
Velocity Function ◽  
Curvature Correction ◽  
Velocity Information ◽  
Zero Offset ◽  
Inherent Ambiguity

Migration velocity can be estimated from seismic data by analyzing, focusing, and defocusing of residual-migrated images. The accuracy of these velocity estimates is limited by the inherent ambiguity between velocity and reflector curvature. However, velocity resolution improves when reflectors with different curvatures are present. Image focusing is measured by evaluating coherency across structural dips, in addition to coherency across aperture/azimuth angles. The inherent ambiguity between velocity and reflector curvature is directly tackled by introducing a curvature correction into the computation of the semblance functional that estimates image coherency. The resulting velocity estimator provides velocity estimates that are (1) unbiased by reflector curvature and (2) consistent with the velocity information that is routinely obtained by measuring coherency over aperture/azimuth angles. Applications to a 2D synthetic prestack data set and a 2D field prestack data set confirm that the proposed method provides consistent and unbiased velocity information. They also suggest that velocity estimates based on the new image-focusing semblance may be more robust and have higher resolution than estimates based on conventional semblance functionals. Applying the proposed method to zero-offset field data recorded in New York Harbor yields a velocity function that is consistent with available geologic information and clearly improves the focusing of the reflectors.

Focusing analysis of seismic data with peg‐leg multiples

Geophysics ◽  
1997 ◽  
Vol 62 (1) ◽  
pp. 177-182
Author(s):  
Einar Maeland
Keyword(s):  
Seismic Data ◽  
Synthetic Data ◽  
Velocity Estimation ◽  
Time Shift ◽  
Data Set ◽  
Seismic Migration ◽  
Imaging Condition ◽  
Multilayered Medium ◽  
Velocity Information ◽  
Zero Offset

Seismic migration with an erroneous velocity field produces an “image” that must be interpreted to obtain reliable velocity information. Inclusion of multiples in common‐shot data makes velocity estimation more difficult. Zero‐offset migration, based on the exploding reflector model, can be used to identify peg‐leg multiples by application of an extra time shift in the imaging condition. The time and the corresponding position when reflected energy focuses must be detected by inspection of the migrated data set. Formulas are derived and the method is tested on synthetic data from a multilayered medium.

Estimation of reflection coefficients from zero‐offset field data

Geophysics ◽  
1993 ◽  
Vol 58 (11) ◽  
pp. 1634-1645 ◽  
Author(s):  
Børge Arntsen ◽  
Bjørn Ursin
Keyword(s):  
Point Source ◽  
Least Squares ◽  
Seismic Data ◽  
Field Data ◽  
Model Fitting ◽  
Seismic Survey ◽  
Reflection Coefficients ◽  
Data Set ◽  
Seismic Experiment ◽  
Zero Offset

The classical one‐dimensional (1-D) inverse problem consists of estimating reflection coefficients from surface seismic data using the 1-D wave equation. Several authors have found stable solutions to this problem using least‐squares model‐fitting methods. We show that the application of these plane‐wave solutions to seismic data generated with a point source can lead to errors in estimating reflection coefficients. This difficulty is avoided by using a least‐squares model fitting scheme describing vertically traveling waves originating from a point source. It is shown that this method is roughly equivalent to deterministic deconvolution with built‐in multiple removal and compensation for spherical spreading. A true zero‐offset field data set from a specially designed seismic experiment is then used as input to estimate reflection coefficients. Stacking velocities from a conventional seismic survey were used to estimate spherical spreading. The resulting reflection coefficients are shown to correlate well with an available well log.

Common-focus point-based target-oriented imaging approach for continuous seismic reservoir monitoring

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. M41-M48 ◽  
Author(s):  
Hongwei Liu ◽  
Mustafa Naser Al-Ali
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Time Lapse ◽  
Velocity Estimation ◽  
Estimation Methods ◽  
Data Sets ◽  
Data Set ◽  
Velocity Models ◽  
Reservoir Monitoring ◽  
Focus Point

The ideal approach for continuous reservoir monitoring allows generation of fast and accurate images to cope with the massive data sets acquired for such a task. Conventionally, rigorous depth-oriented velocity-estimation methods are performed to produce sufficiently accurate velocity models. Unlike the traditional way, the target-oriented imaging technology based on the common-focus point (CFP) theory can be an alternative for continuous reservoir monitoring. The solution is based on a robust data-driven iterative operator updating strategy without deriving a detailed velocity model. The same focusing operator is applied on successive 3D seismic data sets for the first time to generate efficient and accurate 4D target-oriented seismic stacked images from time-lapse field seismic data sets acquired in a [Formula: see text] injection project in Saudi Arabia. Using the focusing operator, target-oriented prestack angle domain common-image gathers (ADCIGs) could be derived to perform amplitude-versus-angle analysis. To preserve the amplitude information in the ADCIGs, an amplitude-balancing factor is applied by embedding a synthetic data set using the real acquisition geometry to remove the geometry imprint artifact. Applying the CFP-based target-oriented imaging to time-lapse data sets revealed changes at the reservoir level in the poststack and prestack time-lapse signals, which is consistent with the [Formula: see text] injection history and rock physics.

Linear‐transform techniques for processing shear‐wave anisotropy in four‐component seismic data

Geophysics ◽  
1993 ◽  
Vol 58 (2) ◽  
pp. 240-256 ◽  
Author(s):  
Xiang‐Yang Li ◽  
Stuart Crampin
Keyword(s):  
Time Series ◽  
Shear Wave ◽  
Seismic Data ◽  
Shear Waves ◽  
Shear Wave Splitting ◽  
Data Set ◽  
Reflection Data ◽  
Wave Splitting ◽  
Linear Transform ◽  
Zero Offset

Most published techniques for analyzing shear‐wave splitting tend to be computing intensive, and make assumptions, such as the orthogonality of the two split shear waves, which are not necessarily correct. We present a fast linear‐transform technique for analyzing shear‐wave splitting in four‐component (two sources/ two receivers) seismic data, which is flexible and widely applicable. We transform the four‐component data by simple linear transforms so that the complicated shear‐wave motion is linearized in a wide variety of circumstances. This allows various attributes to be measured, including the polarizations of faster split shear waves and the time delays between faster and slower split shear waves, as well as allowing the time series of the faster and slower split shear waves to be separated deterministically. In addition, with minimal assumptions, the geophone orientations can be estimated for zero‐offset verticle seismic profiles (VSPs), and the polarizations of the slower split shear waves can be measured for offset VSPs. The time series of the split shear‐waves can be separated before stack for reflection surveys. The technique has been successfully applied to a number of field VSPs and reflection data sets. Applications to a zero‐offset VSP, an offset VSP, and a reflection data set will be presented to illustrate the technique.

Wave-equation migration velocity analysis by focusing diffractions and reflections

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. U19-U27 ◽  
Author(s):  
Paul C. Sava ◽  
Biondo Biondi ◽  
John Etgen
Keyword(s):  
Wave Equation ◽  
Migration Velocity ◽  
Velocity Estimation ◽  
Velocity Analysis ◽  
Depth Migration ◽  
Interval Velocity ◽  
Migration Velocity Analysis ◽  
Inversion Procedure ◽  
Velocity Information ◽  
Zero Offset

We propose a method for estimating interval velocity using the kinematic information in defocused diffractions and reflections. We extract velocity information from defocused migrated events by analyzing their residual focusing in physical space (depth and midpoint) using prestack residual migration. The results of this residual-focusing analysis are fed to a linearized inversion procedure that produces interval velocity updates. Our inversion procedure uses a wavefield-continuation operator linking perturbations of interval velocities to perturbations of migrated images, based on the principles of wave-equation migration velocity analysis introduced in recent years. We measure the accuracy of the migration velocity using a diffraction-focusing criterion instead of the criterion of flatness of migrated common-image gathers that is commonly used in migration velocity analysis. This new criterion enables us to extract velocity information from events that would be challenging to use with conventional velocity analysis methods; thus, our method is a powerful complement to those conventional techniques. We demonstrate the effectiveness of the proposed methodology using two examples. In the first example, we estimate interval velocity above a rugose salt top interface by using only the information contained in defocused diffracted and reflected events present in zero-offset data. By comparing the results of full prestack depth migration before and after the velocity updating, we confirm that our analysis of the diffracted events improves the velocity model. In the second example, we estimate the migration velocity function for a 2D, zero-offset, ground-penetrating radar data set. Depth migration after the velocity estimation improves the continuity of reflectors while focusing the diffracted energy.

Structurally constrained impedance inversion

2016 ◽  
Vol 4 (4) ◽  
pp. T577-T589 ◽  
Author(s):  
Haitham Hamid ◽  
Adam Pidlisecky
Keyword(s):  
Objective Function ◽  
Seismic Data ◽  
Field Data ◽  
A Priori ◽  
Structural Constraints ◽  
Inversion Algorithm ◽  
Data Set ◽  
Impedance Inversion ◽  
Single Trace ◽  
Complex Geology

In complex geology, the presence of highly dipping structures can complicate impedance inversion. We have developed a structurally constrained inversion in which a computationally well-behaved objective function is minimized subject to structural constraints. This approach allows the objective function to incorporate structural orientation in the form of dips into our inversion algorithm. Our method involves a multitrace impedance inversion and a rotation of an orthogonal system of derivative operators. Local dips used to constrain the derivative operators were estimated from migrated seismic data. In addition to imposing structural constraints on the inversion model, this algorithm allows for the inclusion of a priori knowledge from boreholes. We investigated this algorithm on a complex synthetic 2D model as well as a seismic field data set. We compared the result obtained with this approach with the results from single trace-based inversion and laterally constrained inversion. The inversion carried out using dip information produces a model that has higher resolution that is more geologically realistic compared with other methods.

Poststack migration of P-SV seismic data

Geophysics ◽  
1993 ◽  
Vol 58 (8) ◽  
pp. 1127-1135 ◽  
Author(s):  
Mark P. Harrison ◽  
Robert R. Stewart
Keyword(s):  
Phase Shift ◽  
Inhomogeneous Medium ◽  
Seismic Data ◽  
Migration Velocity ◽  
Velocity Function ◽  
Zero Offset ◽  
Migration Velocities

The exploding‐reflector model is only satisfied for zero‐offset P-SV data when [Formula: see text] is depth‐invariant. P-SV diffractions in a vertically‐inhomogeneous medium are approximately hyperbolic, and an expression for their migration velocity is derived. The resulting migration velocities are 6–11 percent less than the corresponding P-SV rms velocities. Migration of DMO‐corrected synthetic P-SV stacked data, using a conventional phase‐shift algorithm and the derived migration velocities, is found to adequately collapse diffractions, whereas migration using the rms velocity function gives significant overmigration.

Nonstretch moveout correction of long-offset multichannel seismic data for subbasalt imaging: Example from the North Atlantic

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. R83-R91 ◽  
Author(s):  
Hassan Masoomzadeh ◽  
Penny J. Barton ◽  
Satish C. Singh
Keyword(s):  
North Atlantic ◽  
Seismic Data ◽  
Cost Effective ◽  
Data Set ◽  
Velocity Function ◽  
The North ◽  
Long Offset ◽  
The North Atlantic ◽  
New Processing ◽  
Multichannel Seismic Data

We have developed a pragmatic new processing strategy to enhance seismic information obtained from long-offset multichannel seismic data. The conventional processing approach, which treats data on a sample-by-sample basis, is applied at a coarser scale on groups of samples. Using this approach, a reflected event and its vicinity remain unstretched during the normal moveout correction. Isomoveout curves (lines of equal moveout) in the time-velocity panel are employed to apply a constant moveout correction to selected individual events, leading to a nonstretch stack. A zigzag stacking-velocity function is introduced as a combination of segments of appropriate isomoveout curves. By employing a zigzag velocity function, stretching of key events is avoided and thus information at far offset is preserved in the stack. The method is also computationally cost-effective. However, the zigzag stacking-velocity field must be consistent with target horizons. This method of horizon-consistent nonstretch moveout has been applied to a wide-angle data set from the North Atlantic margin, providing improved images of the basement interface, which was previously poorly imaged.

Regularizing 3-D data sets with DMO

Geophysics ◽  
1996 ◽  
Vol 61 (4) ◽  
pp. 1103-1114 ◽  
Author(s):  
Anat Canning ◽  
Gerald H. F. Gardner
Keyword(s):  
Computer Program ◽  
Seismic Data ◽  
Physical Modeling ◽  
Data Sets ◽  
Angle Of Incidence ◽  
Data Set ◽  
Velocity Function ◽  
Inherent Problem ◽  
Original Acquisition ◽  
Good Agreement

The combination of DMO and [Formula: see text] is used here to change the original acquisition geometry of a 3-D seismic data set into a more convenient form. For example, irregular 3-D surveys can be projected onto a regular midpoint‐offset grid with zero source‐receiver azimuth and equal increments in offset. The algorithm presented here is based on a new, nonaliased 3-D DMO algorithm in (f, x) domain. It does not require any knowledge of the velocity function for constant or rms velocity variations. The computer program was designed to process and to output very large multifold 3-D data sets. A synthetic example of a point diffractor in 3-D space and a 3-D experiment in a physical modeling tank are used to demonstrate the procedure. In both cases, the results obtained after the data set is regularized are compared with a data set that was acquired initially with the desired configuration. These comparisons show very good agreement. Analysis of the procedure indicates that it may not reconstruct AVO correctly. This is an inherent problem that occurs because the reorganization procedure changes the angle of incidence.

A method for adequate selection of training data sets to reconstruct seismic field data using a convolutional U-Net

Geophysics ◽  
2021 ◽  
pp. 1-103
Author(s):  
Jiho Park ◽  
Jihun Choi ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Young Kim
Keyword(s):  
Data Analysis ◽  
Seismic Data ◽  
Field Data ◽  
Training Data ◽  
Data Reconstruction ◽  
Data Sets ◽  
Training Set ◽  
Data Set ◽  
Seismic Data Reconstruction ◽  
Target Data

Deep learning (DL) methods are recently introduced for seismic signal processing. Using DL methods, many researchers have adopted these novel techniques in an attempt to construct a DL model for seismic data reconstruction. The performance of DL-based methods depends heavily on what is learned from the training data. We focus on constructing the DL model that well reflect the features of target data sets. The main goal is to integrate DL with an intuitive data analysis approach that compares similar patterns prior to the DL training stage. We have developed a two-sequential method consisting of two stage: (i) analyzing training and target data sets simultaneously for determining target-informed training set and (ii) training the DL model with this training data set to effectively interpolate the seismic data. Here, we introduce the convolutional autoencoder t-distributed stochastic neighbor embedding (CAE t-SNE) analysis that can provide the insight into the results of interpolation through the analysis of both the training and target data sets prior to DL model training. The proposed method were tested with synthetic and field data. Dense seismic gathers (e.g. common-shot gathers; CSGs) were used as a labeled training data set, and relatively sparse seismic gather (e.g. common-receiver gathers; CRGs) were reconstructed in both cases. The reconstructed results and SNRs demonstrated that the training data can be efficiently selected using CAE t-SNE analysis and the spatial aliasing of CRGs was successfully alleviated by the trained DL model with this training data, which contain target features. These results imply that the data analysis for selecting target-informed training set is very important for successful DL interpolation. Additionally, the proposed analysis method can also be applied to investigate the similarities between training and target data sets for another DL-based seismic data reconstruction tasks.

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