10 papers
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…