30 citations · 47 across the 5 of their papers we have counts for
5 papers
Physics-informed waveform inversion using pretrained wavefield neural operators
Xinquan Huang, Fu Wang, Tariq Alkhalifah
Full waveform inversion (FWI) is crucial for reconstructing high-resolution subsurface models, but it is often hindered, considering the limited data, by its null space resulting i…
Geological and Well prior assisted full waveform inversion using conditional diffusion models
Fu Wang, Xinquan Huang, Tariq Alkhalifah
Full waveform inversion (FWI) often faces challenges due to inadequate seismic observations, resulting in band-limited and geologically inaccurate inversion results. Incorporating…
Learned frequency-domain scattered wavefield solutions using neural operators
Xinquan Huang, Tariq Alkhalifah
Solving the wave equation is essential to seismic imaging and inversion. The numerical solution of the Helmholtz equation, fundamental to this process, often encounters significant…
Controllable seismic velocity synthesis using generative diffusion models
Fu Wang, Xinquan Huang, Tariq Alkhalifah
Accurate seismic velocity estimations are vital to understanding Earth's subsurface structures, assessing natural resources, and evaluating seismic hazards. Machine learning-based…
Physics-informed neural wavefields with Gabor basis functions
Tariq Alkhalifah, Xinquan Huang
Recently, Physics-Informed Neural Networks (PINNs) have gained significant attention for their versatile interpolation capabilities in solving partial differential equations (PDEs)…