29 citations · 40 across the 40 of their papers we have counts for
6 papers · 1 filter
Conditional Image Prior for Uncertainty Quantification in Full Waveform Inversion
Lingyun Yang, Omar M. Saad, Guochen Wu +1
Full Waveform Inversion (FWI) is a technique employed to attain a high resolution subsurface velocity model. However, FWI results are effected by the limited illumination of the mo…
Propagating the prior from shallow to deep with a pre-trained velocity-model Generative Transformer network
Randy Harsuko, Shijun Cheng, Tariq Alkhalifah
Building subsurface velocity models is essential to our goals in utilizing seismic data for Earth discovery and exploration, as well as monitoring. With the dawn of machine learnin…
Signal Enhancement in Distributed Acoustic Sensing Data Using a Guided Unsupervised Deep Learning Network
Omar M. Saad, Matteo Ravasi, Tariq Alkhalifah
Distributed Acoustic Sensing (DAS) is a promising technology introducing a new paradigm in the acquisition of high-resolution seismic data. However, DAS data often show weak signal…
Ensemble Deep Learning for enhanced seismic data reconstruction
Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah
Seismic data often contain gaps due to various obstacles in the investigated area and recording instrument failures. Deep learning techniques offer promising solutions for reconstr…
Robust Full Waveform Inversion with deep Hessian deblurring
Mustafa Alfarhan, Matteo Ravasi, Fuqiang Chen +1
Full Waveform Inversion (FWI) is a technique widely used in geophysics to obtain high-resolution subsurface velocity models from waveform seismic data. Due to its large computation…
Meta-PINN: Meta learning for improved neural network wavefield solutions
Shijun Cheng, Tariq Alkhalifah
Physics-informed neural networks (PINNs) provide a flexible and effective alternative for estimating seismic wavefield solutions due to their typical mesh-free and unsupervised fea…