Isotropic and anisotropic velocity model building for subsalt seismic imaging

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. S247-S258 ◽  
Author(s):  
Robert Keys ◽  
Tim Matava ◽  
Douglas Foster ◽  
Don Ashabranner
Keyword(s):  
Material Properties ◽  
Model Building ◽  
Velocity Model ◽  
Physical Models ◽  
Stress Model ◽  
Velocity Models ◽  
Velocity Estimate ◽  
Velocity Model Building ◽  
Geologic Model ◽  
Elastic Coefficients

The effectiveness of basin simulators for deriving subsalt velocity models has been previously shown through the use of a correlation to relate effective stress to velocity. We build on this and others’ work by using physical models to relate porosity to velocity. This process yields a physically realizable isotropic velocity model that is consistent with the geologic model and matches the tomographic velocity model above salt and in regions where the tomographic velocity estimate is accurate. We then use a geomechanical simulator to model the stress distribution in and around allochthonous salt in which material properties between salt and sediment change. Our stress model is the basis for an anisotropic velocity model using Murnaghan’s theory for finite elastic deformation. This formulation, with bounds placed on the elastic coefficients, leads to significant imaging improvements adjacent to salt.

Practical approaches for subsalt velocity model building

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE183-VE194 ◽  
Author(s):  
Junru Jiao ◽  
David R. Lowrey ◽  
John F. Willis ◽  
Ruben D. Martínez
Keyword(s):  
Software Package ◽  
Model Building ◽  
Velocity Model ◽  
Fine Tuning ◽  
Inherent Difficulty ◽  
Velocity Models ◽  
First Approximation ◽  
Velocity Model Building ◽  
Trade Offs ◽  
Efficient Performance

Imaging sediments below salt bodies is challenging because of the inherent difficulty of estimating accurate velocity models. These models can be estimated in a variety of ways with varying degrees of expense and effectiveness. Two methods are commercially viable trade-offs. In the first method, residual-moveout analysis is performed in a layer-stripping mode. The models produced with this method can be used as a first approximation of the subsalt velocity field. A wave-equation migration scanning technique is more suitable for fine-tuning the velocity model below the salt. Both methods can be run as part of a sophisticated interactive velocity interpretation software package that makes velocity interpretation efficient. Performance of these methods has been tested on synthetic and field data examples.

Seismic Velocity Model Building Using Neural Networks: Training Data Design and Learning Generalization

Geophysics ◽  
2021 ◽  
pp. 1-73
Author(s):  
Hani Alzahrani ◽  
Jeffrey Shragge
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Training Data ◽  
Training Process ◽  
Training Models ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Layer Velocity

Data-driven artificial neural networks (ANNs) offer a number of advantages over conventional deterministic methods in a wide range of geophysical problems. For seismic velocity model building, judiciously trained ANNs offer the possibility of estimating high-resolution subsurface velocity models. However, a significant challenge of ANNs is training generalization, which is the ability of an ANN to apply the learning from the training process to test data not previously encountered. In the context of velocity model building, this means learning the relationship between velocity models and the corresponding seismic data from a set of training data, and then using acquired seismic data to accurately estimate unknown velocity models. We ask the following question: what type of velocity model structures need be included in the training process so that the trained ANN can invert seismic data from a different (hypothetical) geological setting? To address this question, we create four sets of training models: geologically inspired and purely geometrical, both with and without background velocity gradients. We find that using geologically inspired training data produce models with well-delineated layer interfaces and fewer intra-layer velocity variations. The absence of a certain geological structure in training models, though, hinders the ANN's ability to recover it in the testing data. We use purely geometric training models consisting of square blocks of varying size to demonstrate the ability of ANNs to recover reasonable approximations of flat, dipping, and curved interfaces. However, the predicted models suffer from intra-layer velocity variations and non-physical artifacts. Overall, the results successfully demonstrate the use of ANNs in recovering accurate velocity model estimates, and highlight the possibility of using such an approach for the generalized seismic velocity inversion problem.

Illumination compensation for image-domain wavefield tomography

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. U65-U76 ◽  
Author(s):  
Tongning Yang ◽  
Jeffrey Shragge ◽  
Paul Sava
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Model Errors ◽  
Image Domain ◽  
Illumination Compensation ◽  
Illumination Problem ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Penalty Operator ◽  
Seismic Images

Image-domain wavefield tomography is a velocity model building technique using seismic images as the input and seismic wavefields as the information carrier. However, the method suffers from the uneven illumination problem when it applies a penalty operator to highlighting image inaccuracies due to the velocity model error. The uneven illumination caused by complex geology such as salt or by incomplete data creates defocusing in common-image gathers even when the migration velocity model is correct. This additional defocusing violates the wavefield tomography assumption stating that the migrated images are perfectly focused in the case of the correct model. Therefore, defocusing rising from illumination mixes with defocusing rising from the model errors and degrades the model reconstruction. We addressed this problem by incorporating the illumination effects into the penalty operator such that only the defocusing by model errors was used for model construction. This was done by first characterizing the illumination defocusing in gathers by illumination analysis. Then an illumination-based penalty was constructed that does not penalize the illumination defocusing. This method improved the robustness and effectiveness of image-domain wavefield tomography applied in areas characterized by poor illumination. Our tests on synthetic examples demonstrated that velocity models were more accurately reconstructed by our method using the illumination compensation, leading to a more accurate model and better subsurface images than those in the conventional approach without illumination compensation.

scholarly journals Deep-learning inversion: A next-generation seismic velocity model building method

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. R583-R599 ◽  
Author(s):  
Fangshu Yang ◽  
Jianwei Ma
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Computational Time ◽  
Learning Methods ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Training Stage

Seismic velocity is one of the most important parameters used in seismic exploration. Accurate velocity models are the key prerequisites for reverse time migration and other high-resolution seismic imaging techniques. Such velocity information has traditionally been derived by tomography or full-waveform inversion (FWI), which are time consuming and computationally expensive, and they rely heavily on human interaction and quality control. We have investigated a novel method based on the supervised deep fully convolutional neural network for velocity-model building directly from raw seismograms. Unlike the conventional inversion method based on physical models, supervised deep-learning methods are based on big-data training rather than prior-knowledge assumptions. During the training stage, the network establishes a nonlinear projection from the multishot seismic data to the corresponding velocity models. During the prediction stage, the trained network can be used to estimate the velocity models from the new input seismic data. One key characteristic of the deep-learning method is that it can automatically extract multilayer useful features without the need for human-curated activities and an initial velocity setup. The data-driven method usually requires more time during the training stage, and actual predictions take less time, with only seconds needed. Therefore, the computational time of geophysical inversions, including real-time inversions, can be dramatically reduced once a good generalized network is built. By using numerical experiments on synthetic models, the promising performance of our proposed method is shown in comparison with conventional FWI even when the input data are in more realistic scenarios. We have also evaluated deep-learning methods, the training data set, the lack of low frequencies, and the advantages and disadvantages of our method.

Velocity model building by semblance maximization of modulated-shot gathers

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. U67-U73 ◽  
Author(s):  
Robert Soubaras ◽  
Bruno Gratacos
Keyword(s):  
Wave Equation ◽  
Model Building ◽  
Velocity Model ◽  
Multiscale Approach ◽  
Nonlinear Inversion ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Frequency Maximum ◽  
The Cost ◽  
Starting Model

In recent years, wave-equation migration has greatly enhanced imaging in complex velocity models. However, velocity model building is still dependent on ray-theory approximations. We propose a full wave-equation methodology for velocity model building based on the nonlinear inversion of a semblance criterion with respect to the velocity field. A newly described type of migration, called the modulated-shot migration, is used to obtain the necessary gathers, which are indexed in surface angle. The semblance of these gathers, after spatial averaging, is used as the cost function. This methodology is shown to successfully image the Marmousi model and the subsalt part of the Sigsbee model, especially in terms of focusing, which is as good as with the true model, but also in terms of depthing which is enhanced compared with the initial model. Realistic constraints are used in terms of minimum frequency, maximum offset, and crudeness of the starting model. A key point in the success of this methodology is the multiscale approach wherein the iterations are started on a coarse scale, and ended at a finer scale.

Velocity model building in a crosswell acquisition geometry with image-trained artificial neural networks

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. U31-U46
Author(s):  
Wenlong Wang ◽  
Jianwei Ma
Keyword(s):  
Wave Equations ◽  
Model Building ◽  
Velocity Model ◽  
P Wave ◽  
Common Source ◽  
Data Set ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Artificial Neural ◽  
The Cost

We have developed an artificial neural network to estimate P-wave velocity models directly from prestack common-source gathers. Our network is composed of a fully connected layer set and a modified fully convolutional layer set. The parameters in the network are tuned through supervised learning to map multishot common-source gathers to velocity models. To boost the generalization ability, the network is trained on a massive data set in which the velocity models are modified from natural images that are collected from an online repository. Multishot seismic traces are simulated from those models with acoustic wave equations in a crosswell acquisition geometry. Shot gathers from different source positions are transformed as channels in the network to increase data redundancy. The training process is expensive, but it only occurs once up front. The cost for predicting velocity models is negligible once the training is complete. Different variations of the network are trained and analyzed. The trained networks indicate encouraging results for predicting velocity models from prestack seismic data that are acquired with the same geometry as in the training set.

3-D joint inversion of seismic waveform and airborne gravity gradiometry data

2020 ◽  
Vol 223 (2) ◽  
pp. 746-764
Author(s):  
Wenbin Jiang
Keyword(s):  
Density Distribution ◽  
Model Building ◽  
Joint Inversion ◽  
Velocity Model ◽  
Airborne Gravity ◽  
Inversion Method ◽  
Gravity Gradiometry ◽  
Velocity Models ◽  
Seismic Waveform ◽  
Velocity Model Building

SUMMARY Seismic full waveform inversion (FWI) is a robust velocity model building technique for hydrocarbon exploration. However, the density reconstruction within the framework of multiparameter FWI leads to more degrees of freedom in the parametrization, and the sensitivity of the inversion change significantly from velocity to density, thereby increasing the ill-posedness of the inverse problem. Gravity gradiometry data inversion is an effective method for resolving density distribution. Combining gravity gradiometry data in FWI could alleviate the non-linearity of the inversion by contributing additional density information for the velocity model building. I develop a 3-D joint seismic waveform and gravity gradiometry inversion method for estimating the velocity and density distribution in the subsurface. The method alternatingly minimizes the waveform and gravity gradiometry misfit. The cross-gradient constraint is applied to enhance the structural similarity between the density and velocity models. The effectiveness of the joint inversion algorithm is demonstrated by a 3-D checkerboard model and 3-D SEAM model. Synthetic examples demonstrate that the joint inversion can improve the image quality in geologically complex areas. A case study from the South China Sea shows that the joint inversion improves the velocity and density solutions compared to a standalone seismic FWI. The joint inversion results are consistent with the pre-stack depth migration section and the shape of the salt body is well resolved.

Geologically constrained seismic imaging — Workflow

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE313-VE319 ◽  
Author(s):  
Stig-Kyrre Foss ◽  
Mark Rhodes ◽  
Bjørn Dalstrøm ◽  
Christian Gram ◽  
Alastair Welbon
Keyword(s):  
Image Quality ◽  
Model Building ◽  
Velocity Model ◽  
Seismic Interpretation ◽  
Gravity Modeling ◽  
Final Model ◽  
Depth Migration ◽  
Velocity Models ◽  
Velocity Model Building

We present the geologically constrained workflow for velocity-model building as a case study from offshore Brazil. The workflow involves basin reconstruction, gravity modeling, and seismic interpretation in addition to standard prestack depth migration (PSDM) model-building tools. Building a salt model based on seismic evidence can be highly nonunique. In a geologically constrained seismic-processing workflow, the main aim is to use geologic understanding with geophysical models and datasets to improve an input velocity realization for the PSDM loop, thereby improving image quality. All of these methods are inherently uncertain, and a final model is based on a range of subjective choices. Thus a final result that agrees with all sciences still can be completely wrong. However, an understanding of these choices enables a unique way of testing and constraining the number of antimodels: velocity models that fit the observations but are different from the final result. This can reduce time spent and uncertainty in geologic evaluation.

Comparison of prestack stereotomography and NIP wave tomography for velocity model building: Instances from the Messinian evaporites

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE291-VE302 ◽  
Author(s):  
Stefan Dümmong ◽  
Kristina Meier ◽  
Dirk Gajewski ◽  
Christian Hübscher
Keyword(s):  
Velocity Distribution ◽  
Input Data ◽  
Model Building ◽  
Velocity Model ◽  
Depth Imaging ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Wave Tomography ◽  
Messinian Evaporites ◽  
The Impact

Velocity-model determination during seismic data processing is crucial for any kind of depth imaging. We compared two approaches of grid tomography: prestack stereotomography and normal-incidence-point (NIP) wave tomography. Whereas NIP wave tomography is based on wavefield attributes obtained during the common reflection surface stack and thus on the underlying hyperbolic second-order traveltime approximation, prestack stereotomography describes traveltimes by local slopes (i.e., linearly) in the prestack data domain. To analyze the impact of the different traveltime approximations and the different input-data domains on velocity model building, we applied two implementations of these techniques to two profiles of a field marine data set from the Levante Basin, eastern Mediterranean. Because ofthe presence of a thick, tabular mobile unit of the Messinian evaporites, strong vertical and lateral velocity contrasts had been expected. The velocity models revealed the reconstruction of high-velocity contrasts by grid tomographic methods is limited because of the smooth description of the velocity distribution. The lateral resolution of velocities obtained from prestack stereotomography appears to be better than those from NIP wave tomography, which is related to the difference in the approximation of traveltimes, the determination of input data, and the description of the velocity distribution. Other differences are caused mainly by different implementations of the inversion schemes. Nevertheless, both algorithms provide suitable models for high-quality depth imaging, whereas most of the reflections are fairly flat in CIGs.

Tomographic velocity model building of the near surface with velocity-inversion interfaces: A test using the Yilmaz model

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. U39-U47 ◽  
Author(s):  
Hui Liu ◽  
Hua-wei Zhou ◽  
Wenge Liu ◽  
Peiming Li ◽  
Zhihui Zou
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Surface Velocity ◽  
Low Velocity ◽  
Near Surface ◽  
Velocity Inversion ◽  
Velocity Models ◽  
Velocity Model Building ◽  
First Arrival ◽  
The Impact

First-arrival traveltime tomography is a popular approach to building the near-surface velocity models for oil and gas exploration, mining, geoengineering, and environmental studies. However, the presence of velocity-inversion interfaces (VIIs), across which the overlying velocity is higher than the underlying velocity, might corrupt the tomographic solutions. This is because most first-arrival raypaths will not traverse along any VII, such as the top of a low-velocity zone. We have examined the impact of VIIs on first-arrival tomographic velocity model building of the near surface using a synthetic near-surface velocity model. This examination confirms the severe impact of VIIs on first-arrival tomography. When the source-to-receiver offset is greater than the lateral extent of the VIIs, good near-surface velocity models can still be established using a multiscale deformable-layer tomography (DLT), which uses a layer-based model parameterization and a multiscale scheme as regularization. Compared with the results from a commercial grid-based tomography, the DLT delivers much better near-surface statics solutions and less error in the images of deep reflectors.

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