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.

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.

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.

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.

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.

Target-oriented wavefield tomography using synthesized Born data

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB191-WB207 ◽  
Author(s):  
Yaxun Tang ◽  
Biondo Biondi
Keyword(s):  
Model Building ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Velocity Model ◽  
Velocity Analysis ◽  
Data Set ◽  
Velocity Model Building ◽  
Migration Velocity Analysis ◽  
New Strategy ◽  
Modeling Strategy

We present a new strategy for efficient wave-equation migration-velocity analysis in complex geological settings. The proposed strategy has two main steps: simulating a new data set using an initial unfocused image and performing wavefield-based tomography using this data set. We demonstrated that the new data set can be synthesized by using generalized Born wavefield modeling for a specific target region where velocities are inaccurate. We also showed that the new data set can be much smaller than the original one because of the target-oriented modeling strategy, but it contains necessary velocity information for successful velocity analysis. These interesting features make this new data set suitable for target-oriented, fast and interactive velocity model-building. We demonstrate the performance of our method on both a synthetic data set and a field data set acquired from the Gulf of Mexico, where we update the subsalt velocity in a target-oriented fashion and obtain a subsalt image with improved continuities, signal-to-noise ratio and flattened angle-domain common-image gathers.

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.

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.

Tomographic modeling of a cross‐borehole data set

Geophysics ◽  
1989 ◽  
Vol 54 (10) ◽  
pp. 1249-1257 ◽  
Author(s):  
Larry R. Lines ◽  
Edward D. LaFehr
Keyword(s):  
Wave Equation ◽  
Ray Tracing ◽  
Velocity Model ◽  
Velocity Estimation ◽  
P Wave ◽  
Seismic Survey ◽  
Equation Modeling ◽  
Data Set ◽  
True Solution ◽  
Velocity Models

In this paper we describe a methodology for estimating P‐wave velocities from a cross‐borehole seismic survey that uses straight‐ray tomography, ray tracing, and finite‐difference wave‐equation modeling to produce velocity models that fit the first‐break traveltimes. After a starting model is established by straight‐ray tomography, the velocity model is checked by ray tracing and wave‐equation modeling. Since the models for each procedure show consistent results and the modeled traveltimes closely match those traveltimes from the actual data, we felt our interpretation was confirmed. However, the fitting of cross‐well first break traveltimes is only a necessary validity check and is not sufficient to guarantee that the true solution has been found. Two wells were drilled through the areas that were anomalous on the derived tomogram and check‐shot velocity surveys were run. Due primarily to a lateral ambiguity in velocity estimation caused by too few near‐vertical raypaths, the check‐shot surveys did not agree with the tomogram velocities. However, subsequently the check‐shot traveltimes were used to place bounds on velocity in a constrained least‐squares procedure; the combined modeling of uphole and cross‐well rays produced an optimum velocity model which satisfies all available data.

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.

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