[HTML][HTML] Review of advanced road materials, structures, equipment, and detection technologies

JREE Office, MC Cavalli, D Chen, Q Chen… - Journal of Road …, 2023 - Elsevier
As a vital and integral component of transportation infrastructure, pavement has a direct and
tangible impact on socio-economic sustainability. In recent years, an influx of …

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 …

Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness

M Zhu, S Feng, Y Lin, L Lu - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
Full waveform inversion (FWI) infers the subsurface structure information from seismic
waveform data by solving a non-convex optimization problem. Data-driven FWI has been …

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 …

Sensing prior constraints in deep neural networks for solving exploration geophysical problems

X Wu, J Ma, X Si, Z Bi, J Yang, H Gao, D **e… - Proceedings of the …, 2023 - pnas.org
One of the key objectives in geophysics is to characterize the subsurface through the
process of analyzing and interpreting geophysical field data that are typically acquired at the …

FWIGAN: Full‐waveform inversion via a physics‐informed generative adversarial network

F Yang, J Ma - Journal of Geophysical Research: Solid Earth, 2023 - Wiley Online Library
Full‐waveform inversion (FWI) is a powerful geophysical imaging technique that reproduces
high‐resolution subsurface physical parameters by iteratively minimizing the misfit between …

Seismic inversion based on acoustic wave equations using physics-informed neural network

Y Zhang, X Zhu, J Gao - IEEE transactions on geoscience and …, 2023 - ieeexplore.ieee.org
Seismic inversion is a significant tool for exploring the structure and characteristics of the
underground. However, the conventional inversion strategy strongly depends on the initial …

Joint inversion of geophysical data for geologic carbon sequestration monitoring: A differentiable physics‐informed neural network model

M Liu, D Vashisth, D Grana… - Journal of Geophysical …, 2023 - Wiley Online Library
Geophysical monitoring of geologic carbon sequestration is critical for risk assessment
during and after carbon dioxide (CO2) injection. Integration of multiple geophysical …

Seismic wave propagation and inversion with neural operators

Y Yang, AF Gao, JC Castellanos… - The Seismic …, 2021 - pubs.geoscienceworld.org
Seismic wave propagation forms the basis for most aspects of seismological research, yet
solving the wave equation is a major computational burden that inhibits the progress of …

Implicit seismic full waveform inversion with deep neural representation

J Sun, K Innanen, T Zhang… - Journal of Geophysical …, 2023 - Wiley Online Library
Full waveform inversion (FWI) is arguably the current state‐of‐the‐art amongst
methodologies for imaging subsurface structures and physical parameters with seismic data; …