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20232026
most citedA convolutional neural network approach to deblending seismic data

82 citations

Showing physics.geo-phShow all

5 papers · 1 filter

physics.geo-ph2024★ 16 cited

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…

physics.geo-ph2024★ 82 cited

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…

physics.geo-ph2024★ 1 cited

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…

physics.geo-ph2024★ 58 cited

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…

physics.geo-ph2024★ 1 cited

Towards a multi-physics multi-scale approach of deep geothermal exploration

M. Darnet, S. Vedrine, F. Bretaudeau +10

A wide range of geophysical methods is used for the exploration of deep geothermal resources. It aimsat characterizing the deep fractured network and its capacity for fluid/heat ex…