Results 31 to 40 of about 3,297,474 (208)

Waveform Energy Focusing Tomography With Passive Seismic Sources

open access: yesFrontiers in Earth Science, 2022
By taking advantage of the information carried by the entire seismic wavefield, Full Waveform Inversion (FWI) is able to yield higher resolution subsurface velocity models than seismic traveltime tomography.
Yueqiao Hu   +4 more
doaj   +1 more source

CHARACTERIZATION OF A GROUND PENETRATING RADAR SHIELDED ANTENNA USING LABORATORY MEASUREMENTS, FDTD MODELING AND SWARM GLOBAL OPTIMIZATION

open access: yesCT&F Ciencia, Tecnología & Futuro, 2022
Full Waveform Inversion (FWI) is an optimization method that retrieves high-quality images of the ground's internal electromagnetic properties, such as permittivity, permeability, or conductivity.
Andrés-Fernando Plata-Galvis   +3 more
doaj   +1 more source

Source-Independent Waveform Inversion Method for Ground Penetrating Radar Based on Envelope Objective Function

open access: yesRemote Sensing, 2022
For the full waveform inversion, it is necessary to provide an accurate source wavelet for forwarding modeling in the iteration. The source wavelet estimation method based on deconvolution technology can solve this problem to some extent, but we find ...
Xintong Liu   +6 more
doaj   +1 more source

Image-domain full waveform inversion [PDF]

open access: yes, 2013
The main difficulty with the data-domain full waveform inversion (FWI) is that it tends to get stuck in the local minima associated with the waveform misfit function.
Zhang, Sanzong, Schuster, Gerard T.
core   +1 more source

Emancipating Fwi Imaging from Traveltime Tomography in Valhall Via Optimal Transport Joint Full Waveform Inversion

open access: yes83rd EAGE Annual Conference & Exhibition, 2022
In the Valhall field ocean-bottom-cable (OBC) dataset, a wide literature have shown that FWI enriches an traveltime-based initial velocity model with geologically meaningful details, improving the imaging of shallow multi-layered low velocity zones and complex deep structures. The quality of traveltime prediction in the starting model makes it possible
Provenzano, Giuseppe   +3 more
openaire   +2 more sources

An adaptive multiscale algorithm for efficient extended waveform inversion

open access: yes, 2017
Subsurface-offset extended full-waveform inversion (FWI) may converge to kinematically accurate velocity models without the low-frequency data accuracy required for standard data-domain FWI.
Lei Fu   +3 more
core   +1 more source

Full-waveform inversion, Part 3: Optimization [PDF]

open access: yes, 2018
This tutorial is the third part of a full-waveform inversion (FWI) tutorial series with a step-by-step walkthrough of setting up forward and adjoint wave equations and building a basic FWI inversion framework. For discretizing and solving wave equations,
Luporini, F   +15 more
core   +1 more source

Wave Equation‐Based Local Traveltime Inversion

open access: yesEarth and Space Science, 2020
Full waveform inversion (FWI) is a strongly nonlinear optimization problem, which suffers from cycle skipping when the initial velocity model is not good enough or the seismic data lack low frequencies.
Y. Hu, L. G. Han, Y. S. Liu, Z. Y. Jin
doaj   +1 more source

Dual-Parameter Simultaneous Full Waveform Inversion of Ground-Penetrating Radar for Arctic Sea Ice

open access: yesRemote Sensing, 2023
With global warming, Arctic sea ice, as one of the important factors regulating climate, has put forward new requirements for research. At present, the ground penetrating radar (GPR) is a powerful tool to obtain the structure of Arctic sea ice ...
Ying Liu   +3 more
doaj   +1 more source

Physics‐Constrained Variational Autoencoder for Uncertainty Quantification of Full Waveform Inversion

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract We propose a variational autoencoder framework to directly assess uncertainties in subsurface models produced by single‐ and multiparameter full waveform inversion (FWI). The new method does not require pretraining on labeled data, thus it significantly reduces computational cost and storage requirements.
Abdelrahman Elmeliegy   +4 more
wiley   +1 more source

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