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20212026
most citedPINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting

83 citations · 290 across the 16 of their papers we have counts for

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Showing physics.geo-phShow all

9 papers · 1 filter

physics.geo-ph2025★ 3 cited

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…

physics.geo-ph2024

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…

physics.geo-ph2024

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…

physics.geo-ph2024★ 1 cited

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…

physics.geo-ph2024★ 13 cited

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.geo-ph2023★ 30 cited

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)…

physics.geo-ph2023★ 39 cited

GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks

Xinquan Huang, Tariq Alkhalifah

The computation of the seismic wavefield by solving the Helmholtz equation is crucial to many practical applications, e.g., full waveform inversion. Physics-informed neural network…

physics.geo-ph2023★ 41 cited

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…

physics.geo-ph2023★ 12 cited

Microseismic source imaging using physics-informed neural networks with hard constraints

Xinquan Huang, Tariq Alkhalifah

Microseismic source imaging plays a significant role in passive seismic monitoring. However, such a process is prone to failure due to aliasing when dealing with sparsely measured…