1D Laplace-Fourier Acoustic FWI for Near-Surface Characterization and Initial Velocity Model Building

2020 ◽  
Author(s):  
A. Kontakis ◽  
D. Rovetta ◽  
D. Colombo ◽  
E. Sandoval-Curiel ◽  
P.V. Petrov ◽  
...  
Keyword(s):  
Initial Velocity ◽  
Surface Characterization ◽  
Model Building ◽  
Velocity Model ◽  
Near Surface ◽  
Velocity Model Building

CMP-based near-surface velocity model building for mountainous area

2018 ◽  
Author(s):  
Wen Peng ◽  
Jianpeng Yu ◽  
Dan Chen
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Surface Velocity ◽  
Mountainous Area ◽  
Near Surface ◽  
Velocity Model Building

3D near-surface velocity model building by joint seismic-airborne EM inversion

2016 ◽  
Author(s):  
Guy Marquis ◽  
Weizhong Wang ◽  
Jide Ogunbo
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Surface Velocity ◽  
Near Surface ◽  
Velocity Model Building ◽  
Airborne Em

9C 2D Piceance survey: Near‐surface velocity model building and tomostatics solution

2011 ◽  
Author(s):  
Inmaculada Durá‐Gómez ◽  
Brian Zurek
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Surface Velocity ◽  
Near Surface ◽  
Velocity Model Building

Multi-Wave Inversion for Near-Surface Velocity Model Building on a Broadband Land Survey in North Kuwait

2020 ◽  
Author(s):  
O. Bouhdiche ◽  
L. Vivin ◽  
P. Plasterie ◽  
T. Rebert ◽  
M. Retailleau ◽  
...  
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Surface Velocity ◽  
Near Surface ◽  
Wave Inversion ◽  
Velocity Model Building ◽  
Land Survey

Resolving near-seabed velocity anomalies: Deep water offshore eastern India

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE235-VE241 ◽  
Author(s):  
Juergen Fruehn ◽  
Ian F. Jones ◽  
Victoria Valler ◽  
Pranaya Sangvai ◽  
Ajoy Biswal ◽  
...  
Keyword(s):  
Velocity Field ◽  
Deep Water ◽  
Model Building ◽  
Velocity Model ◽  
Narrow Channel ◽  
Small Scale ◽  
Low Velocity ◽  
Near Surface ◽  
Velocity Model Building ◽  
Velocity Anomalies

Imaging in deep-water environments poses a specific set of challenges, both in data preconditioning and velocity model building. These challenges include scattered, complex 3D multiples, aliased noise, and low-velocity shallow anomalies associated with channel fills and gas hydrates. We describe an approach to tackling such problems for data from deep water off the east coast of India, concentrating our attention on iterative velocity model building, and more specifically the resolution of near-surface and other velocity anomalies. In the region under investigation, the velocity field is complicated by narrow buried canyons ([Formula: see text] wide) filled with low-velocity sediments, which give rise to severe pull-down effects; possible free-gas accumulation below an extensive gas-hydrate cap, causing dimming of the image below (perhaps as a result of absorption); and thin-channel bodies with low-velocity fill. Hybrid gridded tomography using a conjugate gradient solver (with [Formula: see text] vertical cell size) was applied to resolve small-scale velocity anomalies (with thicknesses of about [Formula: see text]). Manual picking of narrow-channel features was used to define bodies too small for the tomography to resolve. Prestack depth migration, using a velocity model built with a combination of these techniques, could resolve pull-down and other image distortion effects in the final image. The resulting velocity field shows high-resolution detail useful in identifying anomalous geobodies of potential exploration interest.

Acquisition/Processing

2021 ◽  
Vol 40 (6) ◽  
pp. 460-463
Author(s):  
Lionel J. Woog ◽  
Anthony Vassiliou ◽  
Rodney Stromberg
Keyword(s):  
Model Building ◽  
Seismic Velocity ◽  
Signal To Noise Ratio ◽  
Velocity Model ◽  
High Signal ◽  
Processing Unit ◽  
High Quality ◽  
Near Surface ◽  
Velocity Model Building ◽  
Static Corrections

In seismic data processing, static corrections for near-surface velocities are derived from first-break picking. The quality of the static corrections is paramount to developing an accurate shallow velocity model, a model that in turn greatly impacts the subsequent seismic processing steps. Because even small errors in first-break picking can greatly impact the seismic velocity model building, it is necessary to pick high-quality traveltimes. Whereas various artificial intelligence-based methods have been proposed to automate the process for data with medium to high signal-to-noise ratio (S/N), these methods are not applicable to low-S/N data, which still require intensive labor from skilled operators. We successfully replace 160 hours of skilled human work with 10 hours of processing by a single NVIDIA Quadro P6000 graphical processing unit by reducing the number of human picks from the usual 5%–10% to 0.19% of available gathers. High-quality inferred picks are generated by convolutional neural network-based machine learning trained from the human picks.

Sign in / Sign up

Export Citation Format

Share Document