Results 151 to 160 of about 819 (209)

A realistic 2D multi-offset, multi-frequency synthetic GPR data set as a benchmark for testing new algorithms. [PDF]

Sci Data
Roncoroni G, Koyan P, Forte E, Tronicke J, Pipan M.   +4 more
europepmc   +1 more source

THz-induced phonon mode mixing and collective dynamics in a polar nanolattice

Skoropata E, Derlet P, Ueda H, Mankowsky R, Sander M, Lemke H, Winkler R, Deng Y, Radovic M, Savoini M, Liu B, Basini M, Ovuka V, Johnson S, Rossell M, Staub U.   +15 more
europepmc   +1 more source

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Elastic full-waveform inversion for surface topography

Geophysics, 2017
Elastic full-waveform inversion (EFWI) attempts to find high-resolution model parameters that are able to match observed data exactly by minimizing residuals between the observed and predicted data. However, irregular surface topography can generate problematic EFWI results.
Zhe Guan, Zhenchun Li, Yingming Qu
exaly   +2 more sources

Well-Guided Multisource Elastic Full-Waveform Inversion

IEEE Transactions on Geoscience and Remote Sensing, 2022
Full waveform inversion (FWI) has been considered one of the most promising approaches to estimating the high-resolution subsurface parameters, which takes advantage of the kinematics and dynamics information of seismic data. However, FWI is greatly dependent on the accuracy of the initial model and vulnerable to the issue of local minimum.
Qingqing Li, Qingchen Zhang 0002, Qizhen Du, Shijun Cheng   +3 more
openaire   +2 more sources

Elastic 3D full-waveform inversion

SEG Technical Program Expanded Abstracts 2012, 2012
s, U038. © 2012 SEG DOI http://dx.doi.org/10.1190/segam2012-1239.1 SEG Las Vegas 2012 Annual Meeting Page 5 This article has been cited by: 1. Joanna Morgan, Michael Warner, Gillean Arnoux, Emilie Hooft, Douglas Toomey, Brandon VanderBeek, William Wilcock. 2016.
Lluís Guasch, Mike Warner, Tenice Nangoo, Jo Morgan, Adrian Umpleby, Ivan Stekl, Nikhil Shah   +6 more
openaire   +1 more source

Strategies for elastic full waveform inversion

SEG Technical Program Expanded Abstracts 2014, 2014
Ocean-bottom cables (OBC) have become common in reservoir monitoring of oil and gas production, and also for CO2 capture and storage experiments. Compared to conventional streamer data, OBC data contain more information, particularly about shear waves.
Espen Birger Raknes*, Børge Arntsen
openaire   +1 more source

Elastic Reflection Full Waveform Inversion

Proceedings, 2018
Reflection full waveform inversion is a formulation of full waveform inversion designed to extract low wavenumber velocity information from the reflected wavefield. In this works we extend reflection full waveform inversion to an elastic framework. This allows the estimation of both P-wave and S-wave velocities using reflected waveforms.
W. Weibull, F. Ebrahim
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Elastic Full Waveform Inversion Benefits (and Pitfalls)

82nd EAGE Annual Conference & Exhibition, 2020
Summary Elastic FWI can improve depth imaging by honoring both the phase and the waveform. An efficient numerical scheme and smart parameterization have been implemented to make a satisfactory compromise between numerical accuracy and computational efficiency. We discuss the added value of elastic FWI to recover P-wave velocity in the vicinity of sharp
C. Rivera, P. Trinh, E. Bergounioux
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Elastic Full Waveform Inversion With Angle Decomposition and Wavefield Decoupling

IEEE Transactions on Geoscience and Remote Sensing, 2021
Full waveform inversion (FWI) is a powerful tool to understand the real complicated earth model. As FWI is a highly nonlinear problem and depends strongly on the initial model, how to effectively retrieve the large-scale background model is critical for the success of FWI. For elastic FWI (EFWI), the inversion challenge increases because the P-wave and
Jingrui Luo, Benfeng Wang, Ru-Shan Wu, Jinghuai Gao   +3 more
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The influence of anisotropy on elastic full-waveform inversion

SEG Technical Program Expanded Abstracts 2015, 2015
Elastic full-waveform inversion (FWI) is most commonly used for isotropic media, whereas anisotropic FWI is usually done in the acoustic approximation. We have compared different assumptions commonly used in FWI: elastic versus acoustic and isotropic versus anisotropic (VTI) using a dataset that is anisotropic and elastic.
Tore S. Bergslid*, Espen Birger Raknes, Børge Arntsen   +2 more
openaire   +1 more source