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[HTML][HTML] Review of advanced road materials, structures, equipment, and detection technologies
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 …
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
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 …
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
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 …
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
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 …
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
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 …
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
Full‐waveform inversion (FWI) is a powerful geophysical imaging technique that reproduces
high‐resolution subsurface physical parameters by iteratively minimizing the misfit between …
high‐resolution subsurface physical parameters by iteratively minimizing the misfit between …
Seismic inversion based on acoustic wave equations using physics-informed neural network
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 …
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
Geophysical monitoring of geologic carbon sequestration is critical for risk assessment
during and after carbon dioxide (CO2) injection. Integration of multiple geophysical …
during and after carbon dioxide (CO2) injection. Integration of multiple geophysical …
Seismic wave propagation and inversion with neural operators
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 …
solving the wave equation is a major computational burden that inhibits the progress of …
Implicit seismic full waveform inversion with deep neural representation
Full waveform inversion (FWI) is arguably the current state‐of‐the‐art amongst
methodologies for imaging subsurface structures and physical parameters with seismic data; …
methodologies for imaging subsurface structures and physical parameters with seismic data; …