scholarly journals When There Is No Offset: A Demonstration of Seismic Diffraction Imaging and Depth‐Velocity Model Building in the Southern Aegean Sea

2020 ◽  
Vol 125 (9) ◽  
Author(s):  
J. Preine ◽  
B. Schwarz ◽  
A. Bauer ◽  
C. Hübscher
Keyword(s):  
Model Building ◽  
Aegean Sea ◽  
Velocity Model ◽  
Velocity Model Building ◽  
Diffraction Imaging

Extraction of diffractions from seismic data using convolutional U-net and transfer learning

Geophysics ◽  
2021 ◽  
pp. 1-87
Author(s):  
Sooyoon Kim ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Seokmin Oh
Keyword(s):  
Transfer Learning ◽  
Seismic Data ◽  
Model Building ◽  
Velocity Model ◽  
Training Dataset ◽  
Diffraction Reflection ◽  
Velocity Model Building ◽  
Wavelength Scale ◽  
Diffraction Imaging ◽  
Synthetic Modeling

Diffraction images can be used for modeling reservoir heterogeneities at or below the seismic wavelength scale. However, the extraction of diffractions is challenging because their amplitude is weaker than that of overlapping reflections. Recently, deep learning (DL) approaches have been used as a powerful tool for diffraction extraction. Most DL approaches use a classification algorithm that classifies pixels in the seismic data as diffraction, reflection, noise, or diffraction with reflection, and takes whole values for the classified diffraction pixels. Thus, these DL methods cannot extract diffraction energy from pixels for which diffractions are masked by reflections. We proposed a DL-based diffraction extraction method that preserves the amplitude and phase characteristics of diffractions. Through the systematic generation of a training dataset using synthetic modeling based on t-distributed stochastic neighbor embedding (t-SNE) analysis, this technique extracts not only faint diffractions, but also diffraction tails overlapped by strong reflection events. We also demonstrated that the DL model pre-trained with basic synthetic dataset can be applied to seismic field data through transfer learning. Because the diffractions extracted by our method preserve the amplitude and phase, they can be used for velocity model building and high-resolution diffraction imaging.

scholarly journals Common-reflection-surface-based prestack diffraction separation and imaging

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. S47-S55 ◽  
Author(s):  
Parsa Bakhtiari Rad ◽  
Benjamin Schwarz ◽  
Dirk Gajewski ◽  
Claudia Vanelle
Keyword(s):  
Model Building ◽  
Synthetic Data ◽  
Velocity Model ◽  
Small Scale ◽  
Time Migration ◽  
Velocity Model Building ◽  
Common Reflection Surface ◽  
Diffraction Imaging ◽  
Zero Offset ◽  
Velocity Spectra

Diffraction imaging can lead to high-resolution characterization of small-scale subsurface structures. A key step of diffraction imaging and tomography is diffraction separation and enhancement, especially in the full prestack data volume. We have considered point diffractors and developed a robust and fully data-driven workflow for prestack diffraction separation based on wavefront attributes, which are determined using the common-reflection-surface (CRS) method. In the first of two steps, we apply a zero-offset-based extrapolation operator for prestack diffraction separation, which combines the robustness and stability of the zero-offset CRS processing with enhanced resolution and improved illumination of the finite-offset CRS processing. In the second step, when the finite-offset diffracted events are separated, we apply a diffraction-based time migration velocity model building that provides high-quality diffraction velocity spectra. Applications of the new workflow to 2D/3D complex synthetic data confirm the superiority of prestack diffraction separation over the poststack method as well as the high potential of diffractions for improved time imaging.

Tilted orthorhombic velocity model building and imaging of Zamzama gas field with full-azimuth land data

2014 ◽  
Vol 33 (9) ◽  
pp. 1024-1028 ◽  
Author(s):  
Matthew Karazincir ◽  
Retti Orumwense
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Gas Field ◽  
Velocity Model Building

New velocity model building techniques to reduce sub‐salt exploration risk

2008 ◽  
Author(s):  
R. Stephan Petmecky ◽  
Martin L. Albertin ◽  
Nick Burke
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Velocity Model Building ◽  
Exploration Risk

Velocity model building using transfer learning

2021 ◽  
Author(s):  
Jérome Simon ◽  
Gabriel Fabien-Ouellet ◽  
Erwan Gloaguen ◽  
Ishan Khurjekar ◽  
Mauricio Araya-Polo
Keyword(s):  
Transfer Learning ◽  
Model Building ◽  
Velocity Model ◽  
Velocity Model Building

Pre-Stack Full Waveform Inversion for Velocity Model Building

2011 ◽  
Author(s):  
S. Jerry Kapoor ◽  
Denes Vigh ◽  
Timothy John Bunting
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Velocity Model Building

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.

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