SeisGAN: Improving Seismic Image Resolution and Reducing Random Noise Using a Generative Adversarial Network-Reference-Cited by-同舟云学术

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SeisGAN: Improving Seismic Image Resolution and Reducing Random Noise Using a Generative Adversarial Network

Published:2023-10-08 Issue: Volume: Page:
ISSN:1874-8961
Container-title:Mathematical Geosciences
language:en
Short-container-title:Math Geosci

Author:

Lin Lei,Zhong Zhi,Cai Chuyang,Li Chenglong,Zhang Heng

Funder

Chinese Academy of Sciences

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,Mathematics (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s11004-023-10103-8.pdf

Reference79 articles.

1. Abma R, Claerbout J (1995) Lateral prediction for noise attenuation by tx and fx techniques. Geophysics 60(6):1887–1896

2. Alaei N, Roshandel Kahoo A, Kamkar Rouhani A, Soleimani M (2018) Seismic resolution enhancement using scale transform in the time-frequency domain. Geophysics 83(6):V305–V314

3. AlBinHassan NM, Luo Y, Al-Faraj MN (2006) 3d edge-preserving smoothing and applications. Geophysics 71(4):P5–P11

4. Anderson DL, Kanamori H, Hart RS, Liu HP (1977) The earth as a seismic absorption band. Science 196(4294):1104–1106

5. Anvari R, Mohammadi M, Kahoo AR, Khan NA, Abdullah AI (2020) Random noise attenuation of 2d seismic data based on sparse low-rank estimation of the seismic signal. Comput Geosci 135:104376

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks;Applied Sciences;2024-05-24

2. Deep Generative Model Conditioned by Phase Picks for Synthesizing Labeled Seismic Waveforms With Limited Data;IEEE Transactions on Geoscience and Remote Sensing;2024

3. Efficient Seismic Data Denoising via Deep Learning With Improved MCA-SCUNet;IEEE Transactions on Geoscience and Remote Sensing;2024

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