High-resolution characterization of near-surface structures by surface-wave inversions: from dispersion curve to full waveform
Surface waves are widely used in near-surface geophysics and provide a noninvasive way
to determine near-surface structures. By extracting and inverting dispersion curves to obtain …
to determine near-surface structures. By extracting and inverting dispersion curves to obtain …
Deep learning for low-frequency extrapolation from multioffset seismic data
Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to
reliable subsurface properties. However, it is challenging to acquire field data with an …
reliable subsurface properties. However, it is challenging to acquire field data with an …
Wavefield reconstruction inversion via physics-informed neural networks
C Song, TA Alkhalifah - IEEE Transactions on Geoscience and …, 2021 - ieeexplore.ieee.org
Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization
problem to reduce cycle skipping in full-waveform inversion (FWI). WRI is often implemented …
problem to reduce cycle skipping in full-waveform inversion (FWI). WRI is often implemented …
Extrapolated full-waveform inversion with deep learning
H Sun, L Demanet - Geophysics, 2020 - pubs.geoscienceworld.org
The lack of low-frequency information and a good initial model can seriously affect the
success of full-waveform inversion (FWI), due to the inherent cycle skipping problem …
success of full-waveform inversion (FWI), due to the inherent cycle skipping problem …
Salt structure elastic full waveform inversion based on the multiscale signed envelope
G Chen, W Yang, Y Liu, H Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Building high-fidelity velocity models for salt structures is a valuable and difficult problem in
seismic exploration. Acoustic-based full-waveform inversion (FWI) methods usually produce …
seismic exploration. Acoustic-based full-waveform inversion (FWI) methods usually produce …
Multi-task learning for low-frequency extrapolation and elastic model building from seismic data
Low-frequency (LF) signal content in seismic data as well as a realistic initial model are key
ingredients for robust and efficient full-waveform inversions (FWIs). However, acquiring LF …
ingredients for robust and efficient full-waveform inversions (FWIs). However, acquiring LF …
[图书][B] Seismic inversion
GT Schuster - 2017 - library.seg.org
This book describes the theory and practice of inverting seismic data for the subsurface rock
properties of the earth. The primary application is for inverting reflection and/or transmission …
properties of the earth. The primary application is for inverting reflection and/or transmission …
Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification
Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity
structure by minimizing the misfit between recorded and predicted seismic waveforms …
structure by minimizing the misfit between recorded and predicted seismic waveforms …
Deep-learning full-waveform inversion using seismic migration images
Data-driven deep-learning full-waveform inversion (DD-DLFWI) can efficiently reconstruct a
velocity image of the subsurface from prestack seismic recordings, once the deep-learning …
velocity image of the subsurface from prestack seismic recordings, once the deep-learning …