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 …
Solving elastodynamics via physics-informed neural network frequency domain method
Despite the fact that physics-informed neural networks (PINN) have been developed rapidly
in recent years, their inherent spectral bias makes it difficult to approximate multi-frequency …
in recent years, their inherent spectral bias makes it difficult to approximate multi-frequency …
Machine learning for subsurface geological feature identification from seismic data: Methods, datasets, challenges, and opportunities
Identification of geological features from seismic data such as faults, salt bodies, and
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …
Helmholtz-equation solution in nonsmooth media by a physics-informed neural network incorporating quadratic terms and a perfectly matching layer condition
Frequency-domain simulation of seismic waves plays an important role in seismic inversion,
but it remains challenging in large models. The recently proposed physics-informed neural …
but it remains challenging in large models. The recently proposed physics-informed neural …
Fourier warm start for physics-informed neural networks
Physics-informed neural networks (PINNs) have shown applicability in a wide range of
engineering domains. However, there remain some challenges in their use, namely, PINNs …
engineering domains. However, there remain some challenges in their use, namely, PINNs …
Physics-informed neural wavefields with Gabor basis functions
T Alkhalifah, X Huang - Neural Networks, 2024 - Elsevier
Abstract Recently, Physics-Informed Neural Networks (PINNs) have gained significant
attention for their versatile interpolation capabilities in solving partial differential equations …
attention for their versatile interpolation capabilities in solving partial differential equations …
PINNslope: seismic data interpolation and local slope estimation with physics informed neural networks
Interpolation of aliased seismic data constitutes a key step in a seismic processing workflow
to obtain high-quality velocity models and seismic images. Building on the idea of describing …
to obtain high-quality velocity models and seismic images. Building on the idea of describing …
Evolutionary probability density reconstruction of stochastic dynamic responses based on physics-aided deep learning
Probability density evolution is the vital probabilistic information for stochastic dynamic
system. However, it may face big challenges when using numerical methods to solve the …
system. However, it may face big challenges when using numerical methods to solve the …
Simulating multicomponent elastic seismic wavefield using deep learning
Simulating seismic wave propagation by solving the wave equation is one of the most
fundamental topics in applied geophysics. Considering the elastic nature of the Earth, it is …
fundamental topics in applied geophysics. Considering the elastic nature of the Earth, it is …
[HTML][HTML] Multi-frequency wavefield modeling of acoustic VTI wave equation using physics informed neural networks
Incorporating anisotropy is crucial for accurately modeling seismic wave propagation.
However, numerical solutions are susceptible to dispersion artifacts, and they often require …
However, numerical solutions are susceptible to dispersion artifacts, and they often require …