82 citations · 172 across the 5 of their papers we have counts for
5 papers
Using Convolutional Neural Networks for Denoising and Deblending of Marine Seismic Data
Sigmund Slang, Jing Sun, Thomas Elboth +2
Processing marine seismic data is computationally demanding and consists of multiple time-consuming steps. Neural network based processing can, in theory, significantly reduce proc…
Deep learning-based shot-domain seismic deblending
Jing Sun, Song Hou, Vetle Vinje +2
To streamline fast-track processing of large data volumes, we have developed a deep learning approach to deblend seismic data in the shot domain based on a practical strategy for g…
A convolutional neural network approach to deblending seismic data
Jing Sun, Sigmund Slang, Thomas Elboth +3
For economic and efficiency reasons, blended acquisition of seismic data is becoming more and more commonplace. Seismic deblending methods are always computationally demanding and…
DNN-based workflow for attenuating seismic interference noise and its application to marine towed streamer data from the Northern Viking Graben
Jing Sun, Song Hou, Alaa Triki
To separate seismic interference (SI) noise while ensuring high signal fidelity, we propose a deep neural network (DNN)-based workflow applied to common shot gathers (CSGs). In our…
Attenuation of marine seismic interference noise employing a customized U-Net
Jing Sun, Sigmund Slang, Thomas Elboth +3
Marine seismic interference noise occurs when energy from nearby marine seismic source vessels is recorded during a seismic survey. Such noise tends to be well preserved over large…