Application of image segmentation to tracking 3D salt boundaries

Geophysics ◽  
2007 ◽  
Vol 72 (4) ◽  
pp. P47-P56 ◽  
Author(s):  
Jesse Lomask ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
Image Segmentation ◽  
Seismic Data ◽  
Model Building ◽  
Velocity Model ◽  
Data Cube ◽  
Velocity Model Building ◽  
Normalized Cuts ◽  
Building Process ◽  
Seismic Images ◽  
3D Seismic Data

Delineating salt boundaries is a necessary step in the velocity-model building process. The salt-delineation problem can be thought of as an image-segmentation problem. Normalized cuts image segmentation (NCIS) finds the cut (or cuts) that result in an image being broken into portions which have dissimilar, by some measure, characteristics. We apply a modified version of the NCIS method to partition seismic images along salt boundaries. NCIS can track boundaries that are not continuous, where conventional horizon-tracking algorithms may fail, by calculating a weight connecting each pixel in the image to every other pixel within a local neighborhood. The weights are determined using problem-dependent combinations of attributes, the most important being instantanteous amplitude and dip. The weights for the entire image are used to segment the image via an eigenvector calculation. The weight matrices for 3D seismic data cubes can be quite large and computationally expensive. By imposing bounds and by distributing the algorithm on a parallel cluster, we significantly increase efficiency. This method is demonstrated to be effective on a 3D field seismic data cube.

Combined pre-stack and post-stack interpretation for velocity model building and hydrocarbon prospectivity: a learning case study from 3D seismic data offshore Gabon

First Break ◽  
2018 ◽  
Vol 36 (12) ◽  
pp. 99-103
Author(s):  
Paolo Esestime ◽  
Milos Cvetkovic ◽  
Jonathan Rogers ◽  
Howard Nicholls ◽  
Karyna Rodriguez
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Velocity Model ◽  
3D Seismic ◽  
Velocity Model Building ◽  
3D Seismic Data

Data-domain reflection tomography for initial velocity model building using challenging 3d seismic data

2019 ◽  
Author(s):  
Andrey Bakulin ◽  
Ilya Silvestrov ◽  
Maxim Dmitriev ◽  
Dmitry Neklyudov ◽  
Maxim Protasov ◽  
...  
Keyword(s):  
Seismic Data ◽  
Initial Velocity ◽  
Model Building ◽  
Velocity Model ◽  
3D Seismic ◽  
Velocity Model Building ◽  
Reflection Tomography ◽  
3D Seismic Data

Geophysical basin modeling: Methodology and application in deepwater Gulf of Mexico

2015 ◽  
Vol 3 (3) ◽  
pp. SZ49-SZ58 ◽  
Author(s):  
Teresa Szydlik ◽  
Hans Kristian Helgesen ◽  
Ivar Brevik ◽  
Giuseppe De Prisco ◽  
Stephen Anthony Clark ◽  
...  
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Velocity Model ◽  
Rock Properties ◽  
Primary Control ◽  
Basin Modeling ◽  
Modeling Methodology ◽  
Velocity Model Building ◽  
Data Limitations ◽  
Seismic Images

A truly integrated velocity model building method has been developed and applied for seismic imaging. Geophysical basin modeling is designed to mitigate seismic data limitations and constrains the velocity model building by taking advantage of information provided by geologic and geophysical input. The information from geologic concepts and understanding is quantified using basin model simulations to model primary control fields for rock properties, temperature, and effective stress. Transformation of the basin model fields to velocity is made by universally calibrated rock models. Applications show that high-quality seismic images are produced in areas of geologic complexity, where it is challenging to define these properties from seismic data alone. This multidisciplinary operation is of high value in exploration because it offers a significant reduction in the time and effort required to build a velocity model, while also improving the resulting image quality.

Full Wavefield Redatuming: Accurate Velocity Modelling for Imaging Beneath Complex Overburden

2021 ◽  
Author(s):  
Farah Syazana Dzulkefli ◽  
Kefeng Xin ◽  
Ahmad Riza Ghazali ◽  
Guo Qiang ◽  
Tariq Alkhalifah
Keyword(s):  
Wave Propagation ◽  
Seismic Data ◽  
Model Building ◽  
Velocity Model ◽  
Surrounding Rock ◽  
Target Area ◽  
Low Density ◽  
Velocity Model Building ◽  
Seismic Image ◽  
Seismic Wavefield

Abstract Salt is known for having a generally low density and higher velocity compared with the surrounding rock layers which causes the energy to scatter once the seismic wavefield hits the salt body and relatively less energy is transmitted through the salt to the deeper subsurface. As a result, most of imaging approaches are unable to image the base of the salt and the reservoir below the salt. Even the velocity model building such as FWI often fails to illuminate the deeper parts of salt area. In this paper, we show that Full Wavefield Redatuming (FWR) is used to retrieved and enhance the seismic data below the salt area, leading to a better seismic image quality and allowing us to focus on updating the velocity in target area below the salt. However, this redatuming approach requires a good overburden velocity model to retrieved good redatumed data. Thus, by using synthetic SEAM model, our objective is to study on the accuracy of the overburden velocity model required for imaging beneath complex overburden. The results show that the kinematic components of wave propagation are preserved through redatuming even with heavily smoothed overburden velocity model.

Diffraction imaging and depth-velocity inversion with 3D P-Cable seismic data

2021 ◽  
Author(s):  
Alexander Bauer ◽  
Benjamin Schwarz ◽  
Dirk Gajewski
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Computational Cost ◽  
Velocity Model ◽  
Data Cube ◽  
Velocity Inversion ◽  
Velocity Models ◽  
North East ◽  
3D Velocity Model ◽  
Zero Offset

<p>Most established methods for the estimation of subsurface velocity models rely on the measurements of reflected or diving waves and therefore require data with sufficiently large source-receiver offsets. For seismic data that lacks these offsets, such as vintage data, low-fold academic data or near zero-offset P-Cable data, these methods fail. Building on recent studies, we apply a workflow that exploits the diffracted wavefield for depth-velocity-model building. This workflow consists of three principal steps: (1) revealing the diffracted wavefield by modeling and adaptively subtracting reflections from the raw data, (2) characterizing the diffractions with physically meaningful wavefront attributes, (3) estimating depth-velocity models with wavefront tomography. We propose a hybrid 2D/3D approach, in which we apply the well-established and automated 2D workflow to numerous inlines of a high-resolution 3D P-Cable dataset acquired near Ritter Island, a small volcanic island located north-east of New Guinea known for a catastrophic flank collapse in 1888. We use the obtained set of parallel 2D velocity models to interpolate a 3D velocity model for the whole data cube, thus overcoming possible issues such as varying data quality in inline and crossline direction and the high computational cost of 3D data analysis. Even though the 2D workflow may suffer from out-of-plane effects, we obtain a smooth 3D velocity model that is consistent with the data.</p>

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.

The Wolfspar experience with low-frequency seismic source field data: Motivation, processing, and implications

2022 ◽  
Vol 41 (1) ◽  
pp. 9-18
Author(s):  
Andrew Brenders ◽  
Joe Dellinger ◽  
Imtiaz Ahmed ◽  
Esteban Díaz ◽  
Mariana Gherasim ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Seismic Data ◽  
Field Data ◽  
Model Building ◽  
Low Frequency ◽  
Seismic Source ◽  
Velocity Model ◽  
Seismic Sources ◽  
Velocity Model Building ◽  
Air Gun

The promise of fully automatic full-waveform inversion (FWI) — a (seismic) data-driven velocity model building process — has proven elusive in complex geologic settings, with impactful examples using field data unavailable until recently. In 2015, success with FWI at the Atlantis Field in the U.S. Gulf of Mexico demonstrated that semiautomatic velocity model building is possible, but it also raised the question of what more might be possible if seismic data tailor-made for FWI were available (e.g., with increased source-receiver offsets and bespoke low-frequency seismic sources). Motivated by the initial value case for FWI in settings such as the Gulf of Mexico, beginning in 2007 and continuing into 2021 BP designed, built, and field tested Wolfspar, an ultralow-frequency seismic source designed to produce seismic data tailor-made for FWI. A 3D field trial of Wolfspar was conducted over the Mad Dog Field in the Gulf of Mexico in 2017–2018. Low-frequency source (LFS) data were shot on a sparse grid (280 m inline, 2 to 4 km crossline) and recorded into ocean-bottom nodes simultaneously with air gun sources shooting on a conventional dense grid (50 m inline, 50 m crossline). Using the LFS data with FWI to improve the velocity model for imaging produced only incremental uplift in the subsalt image of the reservoir, albeit with image improvements at depths greater than 25,000 ft (approximately 7620 m). To better understand this, reprocessing and further analyses were conducted. We found that (1) the LFS achieved its design signal-to-noise ratio (S/N) goals over its frequency range; (2) the wave-extrapolation and imaging operators built into FWI and migration are very effective at suppressing low-frequency noise, so that densely sampled air gun data with a low S/N can still produce useable model updates with low frequencies; and (3) data density becomes less important at wider offsets. These results may have significant implications for future acquisition designs with low-frequency seismic sources going forward.

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.

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