Physics-guided data-driven seismic inversion: Recent progress and future opportunities in full-waveform inversion

Y Lin, J Theiler, B Wohlberg - IEEE Signal Processing …, 2023 - ieeexplore.ieee.org
The goal of seismic inversion is to obtain subsurface properties from surface measurements.
Seismic images have proven valuable, even crucial, for a variety of applications, including …

Applications of deep neural networks in exploration seismology: A technical survey

SM Mousavi, GC Beroza, T Mukerji, M Rasht-Behesht - Geophysics, 2024 - library.seg.org
Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …

Gacnet: Generate adversarial-driven cross-aware network for hyperspectral wheat variety identification

W Zhang, Z Li, G Li, P Zhuang, G Hou… - … on Geoscience and …, 2023 - ieeexplore.ieee.org
Wheat variety identification from hyperspectral images holds significant importance in both
fine breeding and intelligent agriculture. However, the discriminatory accuracy of some …

Unsupervised learning of full-waveform inversion: Connecting CNN and partial differential equation in a loop

P Jin, X Zhang, Y Chen, SX Huang, Z Liu… - arXiv preprint arXiv …, 2021 - arxiv.org
This paper investigates unsupervised learning of Full-Waveform Inversion (FWI), which has
been widely used in geophysics to estimate subsurface velocity maps from seismic data …

Multiscale data-driven seismic full-waveform inversion with field data study

S Feng, Y Lin, B Wohlberg - IEEE transactions on geoscience …, 2021 - ieeexplore.ieee.org
Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-
resolution subsurface models from seismograms, is a powerful imaging technique in …

Efficient progressive transfer learning for full-waveform inversion with extrapolated low-frequency reflection seismic data

Y Jin, W Hu, S Wang, Y Zi, X Wu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The low-frequency seismic data provide crucial information for guiding the full-waveform
inversion (FWI), especially when strong reflectors exist in the velocity model. However …

Deep Bayesian inference for seismic imaging with tasks

A Siahkoohi, G Rizzuti, FJ Herrmann - Geophysics, 2022 - library.seg.org
We use techniques from Bayesian inference and deep neural networks to translate
uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as …

Making invisible visible: Data-driven seismic inversion with spatio-temporally constrained data augmentation

Y Yang, X Zhang, Q Guan, Y Lin - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep learning and data-driven approaches have shown great potential in scientific
domains. The promise of data-driven techniques relies on the availability of a large volume …

Deep-learning-based seismic variable-size velocity model building

M Du, S Cheng, W Mao - IEEE Geoscience and Remote …, 2022 - ieeexplore.ieee.org
Current data-driven inversion methods based on deep learning (DL) use an end-to-end
learning to obtain a mapping relationship from seismic data to the velocity model. These …

Seismic waveform inversion capability on resource-constrained edge devices

D Manu, PM Tshakwanda, Y Lin, W Jiang, L Yang - Journal of Imaging, 2022 - mdpi.com
Seismic full wave inversion (FWI) is a widely used non-linear seismic imaging method used
to reconstruct subsurface velocity images, however it is time consuming, has high …