2 citations · 4 across the 4 of their papers we have counts for
5 papers
Depth-progressive 3D seismic velocity model building via 2D generative diffusion models
Shijun Cheng, Randy Harsuko, Mohammad H. Taufik +1
Three-dimensional (3D) velocity model building (VMB) remains computationally challenging with conventional approaches and native 3D neural networks, largely due to the prohibitive…
Diffusion priors enhanced velocity model building from time-lag images using a neural operator
Xiao Ma, Mohammad Hasyim Taufik, Tariq Alkhalifah
Velocity model building serves as a crucial component for achieving high precision subsurface imaging. However, conventional velocity model building methods are often computational…
Diffusion Model-Based Posterior Sampling in Full Waveform Inversion
Mohammad H. Taufik, Tariq Alkhalifah
Bayesian full waveform inversion (FWI) offers uncertainty-aware subsurface models; however, posterior sampling directly on observed seismic shot records is rarely practical at the…
Seismic wavefield solutions via physics-guided generative neural operator
Shijun Cheng, Mohammad H. Taufik, Tariq Alkhalifah
Current neural operators often struggle to generalize to complex, out-of-distribution conditions, limiting their ability in seismic wavefield representation. To address this, we pr…
LatentPINNs: Generative physics-informed neural networks via a latent representation learning
Mohammad H. Taufik, Tariq Alkhalifah
Physics-informed neural networks (PINNs) are promising to replace conventional partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. How…