3 citations · 3 across the 3 of their papers we have counts for
6 papers · 1 filter
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…
Diffusion-based subsurface CO multiphysics monitoring and forecasting
Xinquan Huang, Fu Wang, Tariq Alkhalifah
Carbon capture and storage (CCS) plays a crucial role in mitigating greenhouse gas emissions, particularly from industrial outputs. Using seismic monitoring can aid in an accurate…
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…
Learnable Gabor kernels in convolutional neural networks for seismic interpretation tasks
Fu Wang, Tariq Alkhalifah
The use of convolutional neural networks (CNNs) in seismic interpretation tasks, like facies classification, has garnered a lot of attention for its high accuracy. However, its dra…
A prior regularized full waveform inversion using generative diffusion models
Fu Wang, Xinquan Huang, Tariq Alkhalifah
Full waveform inversion (FWI) has the potential to provide high-resolution subsurface model estimations. However, due to limitations in observation, e.g., regional noise, limited s…