61 citations · 65 across the 8 of their papers we have counts for
9 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…
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…
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…
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…
Optimizing a Transformer-based network for a deep learning seismic processing workflow
Randy Harsuko, Tariq Alkhalifah
StorSeismic is a recently introduced model based on the Transformer to adapt to various seismic processing tasks through its pretraining and fine-tuning training strategy. In the o…
Meta-Processing: A robust framework for multi-tasks seismic processing
Shijun Cheng, Randy Harsuko, Tariq Alkhalifah
Machine learning-based seismic processing models are typically trained separately to perform specific seismic processing tasks (SPTs), and as a result, require plenty of training d…
StorSeismic: A new paradigm in deep learning for seismic processing
Randy Harsuko, Tariq Alkhalifah
Machine learned tasks on seismic data are often trained sequentially and separately, even though they utilize the same features (i.e. geometrical) of the data. We present StorSeism…