Robust high frequency seismic bandwidth extension with a deep neural network trained using synthetic data
Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features. Various deterministic methods based on Wiener filtering exist to increase the temporal frequency bandwidth and compress...
Main Authors: | Paul Zwartjes, Jewoo Yoo |
---|---|
Format: | Article |
Language: | English |
Published: |
KeAi Communications Co. Ltd.
2024-12-01
|
Series: | Artificial Intelligence in Geosciences |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666544124000121 |
Similar Items
-
Attenuation of seismic migration smile artifacts with deep learning
by: Jewoo Yoo, et al.
Published: (2022-12-01) -
A Synthetic Bandwidth Method for High-Resolution SAR Based on PGA in the Range Dimension
by: Jincheng Li, et al.
Published: (2015-06-01) -
A U-Net Based Multi-Scale Deformable Convolution Network for Seismic Random Noise Suppression
by: Haixia Zhao, et al.
Published: (2023-09-01) -
Multi-task learning for end-to-end noise-robust bandwidth extension
by: Hou, Nana, et al.
Published: (2020) -
Pole-converging intrastage bandwidth extension technique for wideband amplifiers
by: Feng, Guangyin, et al.
Published: (2020)