4 citations · 9 across the 17 of their papers we have counts for
18 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…
Shallow-to-deep velocity model building via diffusion models-Part II: Realistic scenarios
Shijun Cheng, Randy Harsuko, Tariq Alkhalifah
Full-waveform inversion (FWI) requires accurate initial velocity models to avoid cycle-skipping, but constructing such models remains challenging in practice. Building on the depth…
Shallow-to-deep velocity model building via diffusion models-Part I: Method and Proof of concept
Shijun Cheng, Randy Harsuko, Tariq Alkhalifah
Seismic velocity model building (VMB) is fundamental for understanding subsurface structures. Traditional methods demand high-quality starting models and, also, remain limited in r…
Generative wave propagator
Shijun Cheng, Tariq Alkhalifah
Seismic wavefield simulation is fundamental to seismology, but conventional finite-difference (FD) methods remain limited by numerical dispersion and stability constraints, which o…
Meta-learning-enhanced implicit full waveform inversion
Zefeng Wang, Shijun Cheng, Weijian Mao +2
Implicit full waveform inversion (IFWI) introduces implicit neural representations to parameterize the subsurface velocity model as a continuous function of spatial coordinates, wh…
Propagating the prior from far to near offset: A self-supervised diffusion framework for progressively recovering near-offsets of towed-streamer data
Shijun Cheng, Tariq Alkhalifah
In marine towed-streamer seismic acquisition, the nearest hydrophone is often two hundred meter away from the source resulting in missing near-offset traces, which degrades critica…
Physics-informed conditional diffusion model for generalizable elastic wave-mode separation
Shijun Cheng, Xinru Mu, Tariq Alkhalifah
Traditional elastic wavefield separation methods, while accurate, often demand substantial computational resources, especially for large geological models or 3D scenarios. Purely d…
DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation
Shijun Cheng, Tariq Alkhalifah
Physics-informed neural networks (PINNs) offer a powerful framework for seismic wavefield modeling, yet they typically require time-consuming retraining when applied to different v…
Self-supervised surface-related multiple suppression with multidimensional convolution
Shijun Cheng, Ning Wang, Tariq Alkhalifah
Surface-related multiples pose significant challenges in seismic data processing, often obscuring primary reflections and reducing imaging quality. Traditional methods rely on comp…
SeparationPINN: Physics-Informed Neural Networks for Seismic P- and S-Wave Mode Separation
Xinru Mu, Shijun Cheng, Tariq Alkhalifah
Accurate separation of P- and S-waves is essential for multi-component seismic data processing, as it helps eliminate interference between wave modes during imaging or inversion, w…
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…
A generative foundation model for an all-in-one seismic processing framework
Shijun Cheng, Randy Harsuko, Tariq Alkhalifah
Seismic data often face challenges in their utilization due to noise contamination, incomplete acquisition, and limited low-frequency information, which hinder accurate subsurface…
Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network
Shijun Cheng, Tariq Alkhalifah
Physics-informed neural networks (PINNs) face significant challenges in modeling multi-frequency wavefields in complex velocity models due to their slow convergence, difficulty in…
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…
Discovery of physically interpretable wave equations
Shijun Cheng, Tariq Alkhalifah
Using symbolic regression to discover physical laws from observed data is an emerging field. In previous work, we combined genetic algorithm (GA) and machine learning to present a…
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…
A self-supervised learning framework for seismic low-frequency extrapolation
Shijun Cheng, Yi Wang, Qingchen Zhang +2
Full waveform inversion (FWI) is capable of generating high-resolution subsurface parameter models, but it is susceptible to cycle-skipping when the data lack low-frequency. Unfort…
Robust data driven discovery of a seismic wave equation
Shijun Cheng, Tariq Alkhalifah
Despite the fact that our physical observations can often be described by derived physical laws, such as the wave equation, in many cases, we observe data that do not match the law…