Image-space surface-related multiple prediction

Brad Artman; Gabriel Alvarez; Ken Matson

doi:10.1190/1.2436845

Image-space surface-related multiple prediction

Geophysics ◽

10.1190/1.2436845 ◽

2007 ◽

Vol 72 (2) ◽

pp. S113-S122 ◽

Cited By ~ 10

Author(s):

Brad Artman ◽

Gabriel Alvarez ◽

Ken Matson

Keyword(s):

Seismic Data ◽

Velocity Model ◽

Image Space ◽

Depth Level ◽

Image Domain ◽

Data Space ◽

The Earth ◽

Multiple Prediction ◽

The Cost ◽

Multiple Elimination

A very important aspect of removing multiples from seismic data is accurate prediction of their kinematics. We cast the multiple prediction problem as an operation in the image space parallel to the conventional surface-related multiple-prediction methodology. Though developed in the image domain, the technique shares the data-driven strengths of data-domain surface-related multiple elimination (SRME) by being independent of the earth (velocity) model. Also, the data are used to predict the multiples exactly so that a Radon transform need not be designed to separate the two types of events. The cost of the prediction is approximately the same as that of data-space methods, though it can be computed during the course of migration. The additional cost is not significant compared to that incurred by shot-profile migration, though split-spread gathers must be used. Image-space multiple predictions are generated by autoconvolving the traces in each shot-gather at every depth level during the course of a shot-profile migration. The prediction in the image domain is equivalent to that produced by migrating the data-space convolutional prediction. Adaptive subtraction of the prediction from the image is required. Subtraction in the image domain, however, provides the advantages of focused energy in a smaller domain since extrapolation removes some of the imperfections of the input data.

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Multiple attenuation in the image space

Geophysics ◽

10.1190/1.1852789 ◽

2005 ◽

Vol 70 (1) ◽

pp. V10-V20 ◽

Cited By ~ 34

Author(s):

Paul Sava ◽

Antoine Guitton

Keyword(s):

Real Data ◽

Velocity Model ◽

Image Space ◽

Radon Transforms ◽

Data Space ◽

Multiple Attenuation ◽

3D Data ◽

Propagation Effects ◽

Wavefield Extrapolation ◽

Multiple Elimination

Multiples can be suppressed in the angle-domain image space after migration. For a given velocity model, primaries and multiples have different angle-domain moveout and, therefore, can be separated using techniques similar to the ones employed in the data space prior to migration. We use Radon transforms in the image space to discriminate between primaries and multiples and employ accurate functions describing angle-domain moveouts. Since every individual angle-domain common-image gather incorporates complex 3D propagation effects, our method has the advantage of working with 3D data and complicated geology. Therefore, our method offers an alternative to the more expensive surface-related multiple-elimination (SRME) approach operating in the data space. Radon transforms are cheap but not necessarily ideal for separating primaries and multiples, particularly at small angles where the moveout discrepancy between the two kinds of events are not large. Better techniques involving signal/noise separation using prediction-error filters can be employed as well, although such approaches fall outside the scope of this paper. We demonstrate, using synthetic and real data examples, the power of our method in discriminating between primaries and multiples after migration by wavefield extrapolation, followed by transformation to the angle domain.

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Seismic processing in the inverse data space

Geophysics ◽

10.1190/1.2217727 ◽

2006 ◽

Vol 71 (4) ◽

pp. A29-A33 ◽

Cited By ~ 27

Author(s):

A. J. Berkhout

Keyword(s):

Seismic Data ◽

Dominant Role ◽

Temporal Frequency ◽

Data Matrix ◽

Seismic Survey ◽

Seismic Processing ◽

Data Space ◽

Matrix Formula ◽

Multiple Elimination ◽

Zero Time

Until now, seismic processing has been carried out by applying inverse filters in the forward data space. Because the acquired data of a seismic survey is always discrete, seismic measurements in the forward data space can be arranged conveniently in a data matrix [Formula: see text]. Each column in the data matrix represents one shot record. If we represent seismic data in the temporal frequency domain, then each matrix element consists of a complex-valued number. Considering the dominant role of multiple scattering in seismic data, it is proposed to replace data matrix [Formula: see text] by its inverse [Formula: see text] before starting seismic processing. Making use of the feedback model for seismic data, multiple scattered energy is mapped onto the zero time axis of the inverse data space. The practical consequence of this remarkable property may be significant: multiple elimination in the inverse data space simplifies to removing data at zero time only. Moving to the inverse data space may cause a fundamental change in the way we preprocess and image seismic data.

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Attenuation of specular and diffracted 2D multiples in image space

Geophysics ◽

10.1190/1.2759439 ◽

2007 ◽

Vol 72 (5) ◽

pp. V97-V109 ◽

Cited By ~ 4

Author(s):

Gabriel Alvarez ◽

Biondo Biondi ◽

Antoine Guitton

Keyword(s):

Radon Transform ◽

3D Model ◽

Model Space ◽

Image Space ◽

Attractive Alternative ◽

Seismic Line ◽

Data Space ◽

Zero Curvature ◽

Multiple Elimination ◽

Water Bottom

In complex areas, the attenuation of specular and diffracted multiples in image space is an attractive alternative to surface-related multiple elimination (SRME) and to data space Radon filtering. We present the equations that map, via wave-equation migration, 2D diffracted and specular water-bottom multiples from data space to image space. We show the equations for both subsurface-offset-domain common-image-gathers (SODCIGs) and angle-domain common-image-gathers (ADCIGs). We demonstrate that when migrated with sediment velocities, the over-migrated multiples map to predictable regions in both SODCIGs and ADCIGs. Specular multiples focus similarly to primaries, whereas diffracted multiples do not. In particular, the apex of the residual moveout curve of diffracted multiples in ADCIGs is not located at the zero aperture angle. We use our equation of the residual moveout of the multiples in ADCIGs to design an apex-shifted Radon transform that maps the 2D ADCIGs into a 3D model space cube whose dimensions are depth, curvature, and apex-shift distance. Well-corrected primaries map to or near the zero-curvature plane and specularly reflected multiples map to or near the zero apex-shift plane. Diffracted multiples map elsewhere in the cube according to their curvature and apex-shift distance. Thus, specularly reflected as well as diffracted multiples can be attenuated simultaneously. We show the application of our apex-shifted Radon transform to a 2D seismic line from the Gulf of Mexico. Diffracted multiples originate at the edges of the salt body and we show that we can successfully attenuate them, along with the specular multiples, in the image Radon domain.

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Interferometric redatuming by sparse inversion

Geophysical Journal International ◽

10.1093/gji/ggs052 ◽

2012 ◽

Vol 192 (2) ◽

pp. 666-670 ◽

Cited By ~ 20

Author(s):

Joost van der Neut ◽

Felix J. Herrmann

Keyword(s):

Least Squares ◽

Image Space ◽

Seismic Sources ◽

Transform Domain ◽

Frequency Content ◽

Depth Level ◽

Data Space ◽

Sparse Inversion ◽

Least Squares Migration ◽

Incomplete Acquisition

Abstract Assuming that transmission responses are known between the surface and a particular depth level in the subsurface, seismic sources can be effectively mapped to this level by a process called interferometric redatuming. After redatuming, the obtained wavefields can be used for imaging below this particular depth level. Interferometric redatuming consists of two steps, namely (i) the decomposition of the observed wavefields into downgoing and upgoing constituents and (ii) a multidimensional deconvolution of the upgoing constituents with the downgoing constituents. While this method works in theory, sensitivity to noise and artefacts due to incomplete acquisition require a different formulation. In this letter, we demonstrate the benefits of formulating the two steps that undergird interferometric redatuming in terms of a transform-domain sparsity-promoting program. By exploiting compressibility of seismic wavefields in the curvelet domain, the method not only becomes robust with respect to noise but we are also able to remove certain artefacts while preserving the frequency content. Although we observe improvements when we promote sparsity in the redatumed data space, we expect better results when interferometric redatuming would be combined or integrated with least-squares migration with sparsity promotion in the image space.

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Automatic velocity analysis by differential semblance optimization

Geophysics ◽

10.1190/1.1500380 ◽

2002 ◽

Vol 67 (4) ◽

pp. 1184-1191 ◽

Cited By ~ 76

Author(s):

W. A. Mulder ◽

A. P. E. ten Kroode

Keyword(s):

Seismic Data ◽

Order Approximation ◽

Velocity Model ◽

Velocity Analysis ◽

Cost Functional ◽

Adjoint State ◽

First Order Approximation ◽

The Time Domain ◽

The Cost ◽

Adjoint State Method

We present a method for automatic velocity analysis of seismic data based on differential semblance optimization (DSO). The data are mapped for each offset from the time domain to the depth domain by a Born migration scheme using ray tracing with the efficient wavefront construction method. The DSO cost functional is evaluated by taking differences of the migration images for neighboring offsets. The gradient of this functional with respect to the underlying velocity model is obtained by a first‐order approximation of the adjoint‐state method, leading to an optimal complexity: the cost of evaluating the gradient is about the same as that of evaluating the functional. The method has been applied to a marine line. Multiples turned out to be a problem, but were handled effectively by incorporating a multiple filter inside the DSO cost functional.

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Elastic Full-Waveform Inversion Using Migration-Based Depth Reflector Representation in the Data Domain

Geosciences ◽

10.3390/geosciences11020076 ◽

2021 ◽

Vol 11 (2) ◽

pp. 76

Author(s):

Vladimir Tcheverda ◽

Kirill Gadylshin

Keyword(s):

Data Processing ◽

Seismic Data ◽

Waveform Inversion ◽

Velocity Model ◽

Critical Element ◽

Full Waveform Inversion ◽

Seismic Data Processing ◽

Velocity Models ◽

Data Space ◽

Full Waveform

The depth velocity model is a critical element for providing seismic data processing success, as it is responsible for the times of waves’ propagation and, therefore, prescribes the location of geological objects in the resulting seismic images. Constructing a deep velocity model is the most time-consuming part of the entire seismic data processing, which usually requires interactive human intervention. This article introduces the consistently numerical method for reconstructing a depth velocity model based on the modified version of the elastic Full Waveform Inversion (FWI). The specific feature of this approach to FWI is the decomposition of the space of admissible velocity models into subspaces of propagator (macro velocity) and reflector components. In turn, the latter transforms to the data space reflectivity on the base of migration transformation. Finally, we perform minimisation in two different spaces: (1) Macro velocity as a smooth spatial function; (2) Migration transforms data space reflectivity to the spatial reflectivity. We present numerical experiments confirming less sensitiveness of the modified version of FWI to the lack of the low time frequencies in the data acquired. In our computations, we use synthetic data with valuable time frequencies from 5 Hz.

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Accurate imaging of surface seismic data without a velocity model

10.2523/11523-ms ◽

2007 ◽

Author(s):

Sverre Brandsberg-Dahl ◽

Brian E. Hornby ◽

Xiang Xiao

Keyword(s):

Seismic Data ◽

Velocity Model ◽

Accurate Imaging

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Effects of incomplete parameterization on full-wavefield viscoelastic seismic data for petrophysical reservoir properties

Geophysics ◽

10.1190/1.2716631 ◽

2007 ◽

Vol 72 (3) ◽

pp. O9-O17 ◽

Cited By ~ 10

Author(s):

Upendra K. Tiwari ◽

George A. McMechan

Keyword(s):

Seismic Data ◽

Input Data ◽

Elastic Model ◽

Quality Factors ◽

Noise Levels ◽

Reservoir Properties ◽

Estimated Parameters ◽

Model Parameterization ◽

The Earth ◽

Incomplete Model

In inversion of viscoelastic full-wavefield seismic data, the choice of model parameterization influences the uncertainties and biases in estimating seismic and petrophysical parameters. Using an incomplete model parameterization results in solutions in which the effects of missing parameters are attributed erroneously to the parameters that are included. Incompleteness in this context means assuming the earth is elastic rather than viscoelastic. The inclusion of compressional and shear-wave quality factors [Formula: see text] and [Formula: see text] in inversion gives better estimates of reservoir properties than the less complete (elastic) model parameterization. [Formula: see text] and [Formula: see text] are sensitive primarily to fluid types and saturations. The parameter correlations are sensitive also to the model parameterization. As noise increases in the viscoelastic input data, the resolution of the estimated parameters decreases, but the parameter correlations are relatively unaffected by modest noise levels.

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Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope

Pure and Applied Geophysics ◽

10.1007/s00024-021-02727-w ◽

2021 ◽

Author(s):

Ehsan Jamali Hondori ◽

Chen Guo ◽

Hitoshi Mikada ◽

Jin-Oh Park

Keyword(s):

Seismic Data ◽

Initial Velocity ◽

Waveform Inversion ◽

Velocity Model ◽

Control Measures ◽

Japan Trench ◽

Full Waveform Inversion ◽

Full Waveform ◽

Time Migration ◽

Shallow Sediments

AbstractFull-waveform inversion (FWI) of limited-offset marine seismic data is a challenging task due to the lack of refracted energy and diving waves from the shallow sediments, which are fundamentally required to update the long-wavelength background velocity model in a tomographic fashion. When these events are absent, a reliable initial velocity model is necessary to ensure that the observed and simulated waveforms kinematically fit within an error of less than half a wavelength to protect the FWI iterative local optimization scheme from cycle skipping. We use a migration-based velocity analysis (MVA) method, including a combination of the layer-stripping approach and iterations of Kirchhoff prestack depth migration (KPSDM), to build an accurate initial velocity model for the FWI application on 2D seismic data with a maximum offset of 5.8 km. The data are acquired in the Japan Trench subduction zone, and we focus on the area where the shallow sediments overlying a highly reflective basement on top of the Cretaceous erosional unconformity are severely faulted and deformed. Despite the limited offsets available in the seismic data, our carefully designed workflow for data preconditioning, initial model building, and waveform inversion provides a velocity model that could improve the depth images down to almost 3.5 km. We present several quality control measures to assess the reliability of the resulting FWI model, including ray path illuminations, sensitivity kernels, reverse time migration (RTM) images, and KPSDM common image gathers. A direct comparison between the FWI and MVA velocity profiles reveals a sharp boundary at the Cretaceous basement interface, a feature that could not be observed in the MVA velocity model. The normal faults caused by the basal erosion of the upper plate in the study area reach the seafloor with evident subsidence of the shallow strata, implying that the faults are active.

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