scholarly journals Multiple prediction through inversion: A fully data‐driven concept for surface‐related multiple attenuation

Geophysics ◽  
2004 ◽  
Vol 69 (2) ◽  
pp. 547-553 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Data Driven ◽  
Multiple Model ◽  
Data Set ◽  
Multiple Attenuation ◽  
Multiple Prediction ◽  
Surface Operator ◽  
Previous Iteration ◽  
Conventional Scheme

This paper introduces a fully data‐driven concept, multiple prediction through inversion (MPI), for surface‐related multiple attenuation (SMA). It builds the multiple model not by spatial convolution, as in a conventional SMA, but by updating the attenuated multiple wavefield in the previous iteration to generate a multiple prediction for the new iteration, as is usually the case in an iterative inverse problem. Because MPI does not use spatial convolution, it is able to minimize the edge effect that appears in conventional SMA multiple prediction and to eliminate the need to synthesize near‐offset traces, required by a conventional scheme, so that it can deal with a seismic data set with missing near‐offset traces. The MPI concept also eliminates the need for an explicit surface operator, which is required by conventional SMA and is comprised of the inverse source signature and other effects. This method accounts implicitly for the spatial variation of the surface operator in multiple‐model building and attempts to predict multiples which are not only accurate kinematically but are also accurate in phase and amplitude.

scholarly journals Improving adaptive subtraction in seismic multiple attenuation

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. V59-V67 ◽  
Author(s):  
Shoudong Huo ◽  
Yanghua Wang
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Primary Energy ◽  
Multiple Models ◽  
Multiple Model ◽  
Data Set ◽  
Multiple Attenuation ◽  
Matching Filter ◽  
Theoretical Analyses ◽  
Different Order

In seismic multiple attenuation, once the multiple models have been built, the effectiveness of the processing depends on the subtraction step. Usually the primary energy is partially attenuated during the adaptive subtraction if an [Formula: see text]-norm matching filter is used to solve a least-squares problem. The expanded multichannel matching (EMCM) filter generally is effective, but conservative parameters adopted to preserve the primary could lead to some remaining multiples. We have managed to improve the multiple attenuation result through an iterative application of the EMCM filter to accumulate the effect of subtraction. A Butterworth-type masking filter based on the multiple model can be used to preserve most of the primary energy prior to subtraction, and then subtraction can be performed on the remaining part to better suppress the multiples without affecting the primaries. Meanwhile, subtraction can be performed according to the orders of the multiples, as a single subtraction window usually covers different-order multiples with different amplitudes. Theoretical analyses, and synthetic and real seismic data set demonstrations, proved that a combination of these three strategies is effective in improving the adaptive subtraction during seismic multiple attenuation.

Multiple attenuation in complex geology with a pattern-based approach

Geophysics ◽  
2005 ◽  
Vol 70 (4) ◽  
pp. V97-V107 ◽  
Author(s):  
Antoine Guitton
Keyword(s):  
Seismic Data ◽  
Prediction Error ◽  
Input Data ◽  
Multiple Model ◽  
Ideal Case ◽  
Multiple Attenuation ◽  
Time Space ◽  
Multiple Prediction ◽  
Complex Geology ◽  
Space Variant

Primaries (signal) and multiples (noise) often exhibit different kinematics and amplitudes (i.e., patterns) in time and space. Multidimensional prediction-error filters (PEFs) approximate these patterns to separate noise and signal in a least-squares sense. These filters are time-space variant to handle the nonstationarity of multioffset seismic data. PEFs for the primaries and multiples are estimated from pattern models. In an ideal case where accurate pattern models of both noise and signal exist, the pattern-based method recovers the primaries while preserving their amplitudes. In the more general case, the pattern model of the multiples is obtained by using the data as prediction operators. The pattern model of the primaries is obtained by convolving the noise PEFs with the input data. In this situation, 3D PEFs are preferred to separate (in prestack data) the multiples properly and to preserve the primaries. Comparisons of the proposed method with adaptive subtraction with an [Formula: see text] norm demonstrate that for a given multiple model, the pattern-based approach generally attenuates the multiples and recovers the primaries better. In addition, tests on a 2D line from the Gulf of Mexico demonstrate that the proposed technique copes fairly well with modeling inadequacies present in the multiple prediction.

scholarly journals Multiple prediction through inversion: Theoretical advancements and real data application

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. V33-V39 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Explicit Knowledge ◽  
Real Data ◽  
Second Phase ◽  
Multiple Model ◽  
Subsurface Structures ◽  
Multiple Attenuation ◽  
Data Application ◽  
Multiple Prediction ◽  
Marine Seismic ◽  
Surface Operator

Wave-equation-based multiple attenuation seismic methods may be divided into the two distinct phases of multiple modeling and multiple subtraction. These two are interrelated and must be optimized in order to produce an optimal final result. The multiple prediction through inversion (MPI) scheme updates the multiple model iteratively, as we usually do in a linearized inverse problem. The scheme models the multiple wavefield without an explicit knowledge of surface and subsurface structures or of the source signature; both are generally unknown in seismic surveys. However, compared to a conventional surface-related multiple attenuation method, the accuracy of the multiple model is improved both kinematically and dynamically. It is because the MPI scheme implicitly takes account of the spatial variation of the surface reflectivity, the source signature, the detector patterns and receiver ghosts, and other effects included in the so-called surface operator. When the MPI scheme is used in the first phase it also significantly reduces the nonlinearity of the problem in the second phase that involves attenuating multiples without removing or altering primaries. The effectiveness of the MPI scheme is demonstrated by examples involving real marine seismic data.

Deep learning-driven velocity model building workflow

2019 ◽  
Vol 38 (11) ◽  
pp. 872a1-872a9 ◽  
Author(s):  
Mauricio Araya-Polo ◽  
Stuart Farris ◽  
Manuel Florez
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

Multiple attenuation with 3D high-order high-resolution parabolic Radon transform using lower frequency constraints

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V317-V328
Author(s):  
Jitao Ma ◽  
Guoyang Xu ◽  
Xiaohong Chen ◽  
Xiaoliu Wang ◽  
Zhenjiang Hao
Keyword(s):  
High Resolution ◽  
Radon Transform ◽  
Seismic Data ◽  
Computational Cost ◽  
Real Data ◽  
Multiple Model ◽  
Multiple Attenuation ◽  
Common Depth Point ◽  
Frequency Constraint ◽  
Parabolic Radon Transform

The parabolic Radon transform is one of the most commonly used multiple attenuation methods in seismic data processing. The 2D Radon transform cannot consider the azimuth effect on seismic data when processing 3D common-depth point gathers; hence, the result of applying this transform is unreliable. Therefore, the 3D Radon transform should be applied. The theory of the 3D Radon transform is first introduced. To address sparse sampling in the crossline direction, a lower frequency constraint is introduced to reduce spatial aliasing and improve the resolution of the Radon transform. An orthogonal polynomial transform, which can fit the amplitude variations in different parabolic directions, is combined with the dealiased 3D high-resolution Radon transform to account for the amplitude variations with offset of seismic data. A multiple model can be estimated with superior accuracy, and improved results can be achieved. Synthetic and real data examples indicate that even though our method comes at a higher computational cost than existing techniques, the developed approach provides better attenuation of multiples for 3D seismic data with amplitude variations.

Simultaneous inversion of PP and PS seismic data

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. R1-R10 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Model Building ◽  
Joint Inversion ◽  
P Wave ◽  
Inversion Method ◽  
System Of Equations ◽  
S Wave ◽  
Data Set ◽  
Simultaneous Inversion

Elastic parameters derived from seismic data are valuable input for reservoir characterization because they can be related to lithology and fluid content of the reservoir through empirical relationships. The relationship between physical properties of rocks and fluids and P-wave seismic data is nonunique. This leads to large uncertainties in reservoir models derived from P-wave seismic data. Because S- waves do not propagate through fluids, the combined use of P-and S-wave seismic data might increase our ability to derive fluid and lithology effects from seismic data, reducing the uncertainty in reservoir characterization and thereby improving 3D reservoir model-building. We present a joint inversion method for PP and PS seismic data by solving approximated linear expressions of PP and PS reflection coefficients simultaneously using a least-squares estimation algorithm. The resulting system of equations is solved by singular-value decomposition (SVD). By combining the two independent measurements (PP and PS seismic data), we stabilize the system of equations for PP and PS seismic data separately, leading to more robust parameter estimation. The method does not require any knowledge of PP and PS wavelets. We tested the stability of this joint inversion method on a 1D synthetic data set. We also applied the methodology to North Sea multicomponent field data to identify sand layers in a shallow formation. The identified sand layers from our inverted sections are consistent with observations from nearby well logs.

Building tilted transversely isotropic depth models using localized anisotropic tomography with well information

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. D27-D36 ◽  
Author(s):  
Andrey Bakulin ◽  
Marta Woodward ◽  
Dave Nichols ◽  
Konstantin Osypov ◽  
Olga Zdraveva
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Symmetry Axis ◽  
Transverse Isotropy ◽  
Synthetic Data ◽  
Rock Physics ◽  
Transversely Isotropic ◽  
Data Set ◽  
Anisotropic Models ◽  
Well Data

Tilted transverse isotropy (TTI) is increasingly recognized as a more geologically plausible description of anisotropy in sedimentary formations than vertical transverse isotropy (VTI). Although model-building approaches for VTI media are well understood, similar approaches for TTI media are in their infancy, even when the symmetry-axis direction is assumed known. We describe a tomographic approach that builds localized anisotropic models by jointly inverting surface-seismic and well data. We present a synthetic data example of anisotropic tomography applied to a layered TTI model with a symmetry-axis tilt of 45 degrees. We demonstrate three scenarios for constraining the solution. In the first scenario, velocity along the symmetry axis is known and tomography inverts for Thomsen’s [Formula: see text] and [Formula: see text] parame-ters. In the second scenario, tomography inverts for [Formula: see text], [Formula: see text], and velocity, using surface-seismic data and vertical check-shot traveltimes. In contrast to the VTI case, both these inversions are nonunique. To combat nonuniqueness, in the third scenario, we supplement check-shot and seismic data with the [Formula: see text] profile from an offset well. This allows recovery of the correct profiles for velocity along the symmetry axis and [Formula: see text]. We conclude that TTI is more ambiguous than VTI for model building. Additional well data or rock-physics assumptions may be required to constrain the tomography and arrive at geologically plausible TTI models. Furthermore, we demonstrate that VTI models with atypical Thomsen parameters can also fit the same joint seismic and check-shot data set. In this case, although imaging with VTI models can focus the TTI data and match vertical event depths, it leads to substantial lateral mispositioning of the reflections.

Postmigration multiple prediction and removal in the depth domain

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB217-WB223 ◽  
Author(s):  
Bin Wang ◽  
Jun Cai ◽  
Manhong Guo ◽  
Chuck Mason ◽  
Sampath Gajawada ◽  
...  
Keyword(s):  
Field Data ◽  
Model Building ◽  
Migration Velocity ◽  
Multiple Model ◽  
Multiple Events ◽  
Multiple Prediction ◽  
Wavefield Extrapolation

We have developed a new methodology for predicting and removing multiples in the postmigration depth domain based on wavefield extrapolation and attribute-based subtraction. The inputs for the multiple prediction are a 3D prestack depth-migrated stack volume and the corresponding migration velocity volume. The output is the predicted multiple model in the migration depth domain. In some cases, the strong residual top of salt multiple may be erroneously picked as the base of salt reflection. With the predicted multiple model available for comparison during the salt model building stage, there is a better chance of building an accurate salt model and avoid picking multiple events. In an effort to further improve the final migrated images, the predicted multiple model is used to remove residual multiples in the migration depth domain. A poststack wavefield extrapolation-based multiple prediction is used to identify and confirm the multiple events in the migration depth domain. Once multiple events are identified, an effective and efficient demultiple technique is applied to remove the residual multiples from the final migration. The key ingredient of this new demultiple methodology is the attribute-based subtraction. We describe the main steps of this methodology and demonstrate its effectiveness by showing some field data applications.

Multiple attenuation in the parabolic τ-p domain using wavefront characteristics of multiple generating primaries

Geophysics ◽  
1999 ◽  
Vol 64 (6) ◽  
pp. 1806-1815 ◽  
Author(s):  
Evgeny Landa ◽  
Igor Belfer ◽  
Shemer Keydar
Keyword(s):  
Radon Transform ◽  
Time Windows ◽  
Real Data ◽  
Multiple Model ◽  
Transform Method ◽  
Multiple Attenuation ◽  
The Common ◽  
P Domain ◽  
Multiple Prediction ◽  
Parabolic Radon Transform

The problem of multiple attenuation has been solved only partially. One of the most common methods of attenuating multiples is an approach based on the Radon transform. It is commonly accepted that the parabolic Radon transform method is only able to attenuate multiples with significant moveouts. We propose a new 2-D method for attenuation of both surface‐related and interbed multiples in the parabolic τ-p domain. The method is based on the prediction of a multiple model from the wavefront characteristics of the primary events. Multiple prediction comprises the following steps: 1) For a given multiple code, the angles of emergence and the radii of wavefront curvatures are estimated for primary reflections for each receiver in the common‐shotpoint gather. 2) The intermediate points which compose a specified multiple event are determined for each shot‐receiver pair. 3) Traveltimes of the multiples are calculated. Wavefields within time windows around the predicted traveltime curves may be considered as multiple model traces which we use for multiple attenuation process. Using the predicted multiple traveltimes, we can define the area in the τ-p domain which contains the main energy of the multiple event. Resolution improvement of the parabolic Radon operator can be achieved through a simple multiplication of each sample in the τ-p space by a nonlinear semblance function. In this work, we follow the idea of defining the multiple reject areas automatically by comparing the energy of the multiple model and the original input data in the τ-p space. We illustrate the usefulness of this algorithm for the attenuation of multiples on both synthetic and real data.

Localized anisotropic tomography with well information in VTI media

Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. D37-D45 ◽  
Author(s):  
Andrey Bakulin ◽  
Marta Woodward ◽  
Dave Nichols ◽  
Konstantin Osypov ◽  
Olga Zdraveva
Keyword(s):  
Vertical Velocity ◽  
Seismic Data ◽  
Model Building ◽  
Synthetic Data ◽  
Resolution Limit ◽  
Data Set ◽  
Anisotropic Models ◽  
Velocity Models ◽  
First Case ◽  
Well Data

We develop a concept of localized seismic grid tomography constrained by well information and apply it to building vertically transversely isotropic (VTI) velocity models in depth. The goal is to use a highly automated migration velocity analysis to build anisotropic models that combine optimal image focusing with accurate depth positioning in one step. We localize tomography to a limited volume around the well and jointly invert the surface seismic and well data. Well information is propagated into the local volume by using the method of preconditioning, whereby model updates are shaped to follow geologic layers with spatial smoothing constraints. We analyze our concept with a synthetic data example of anisotropic tomography applied to a 1D VTI model. We demonstrate four cases of introducing additionalinformation. In the first case, vertical velocity is assumed to be known, and the tomography inverts only for Thomsen’s [Formula: see text] and [Formula: see text] profiles using surface seismic data alone. In the second case, tomography simultaneously inverts for all three VTI parameters, including vertical velocity, using a joint data set that consists of surface seismic data and vertical check-shot traveltimes. In the third and fourth cases, sparse depth markers and walkaway vertical seismic profiling (VSP) are used, respectively, to supplement the seismic data. For all four examples, tomography reliably recovers the anisotropic velocity field up to a vertical resolution comparable to that of the well data. Even though walkaway VSP has the additional dimension of angle or offset, it offers no further increase in this resolution limit. Anisotropic tomography with well constraints has multiple advantages over other approaches and deserves a place in the portfolio of model-building tools.

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