A Novel Generative Adversarial Network for the Removal of Noise and Baseline Drift in Seismic Signals-Reference-Cited by-同舟云学术

A Novel Generative Adversarial Network for the Removal of Noise and Baseline Drift in Seismic Signals

Published:2024 Issue: Volume:62 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Chen Youming¹,Ji Duofa¹^ORCID,Ma Qiang²,Zhai Changhai¹^ORCID,Ma Yuhong³

Affiliation:

1. Key Laboratory of Structures Dynamic Behavior and Control of Ministry of Education, Harbin Institute of Technology, Harbin, China

2. Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, China

3. Earthquake Engineering Research and Test Center, Guangzhou University, Guangzhou, China

Funder

National Natural Science Foundation of China

Open Research Fund Program of Guangdong Key Laboratory of Earthquake Engineering and Application Technology

Funds for Creative Research Groups of National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10415058.pdf?arnumber=10415058

Reference75 articles.

1. OptShrink: An Algorithm for Improved Low-Rank Signal Matrix Denoising by Optimal, Data-Driven Singular Value Shrinkage

2. Multiple-Reflection Noise Attenuation Using Adaptive Randomized-Order Empirical Mode Decomposition

3. Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation

4. Spike-Like Blending Noise Attenuation Using Structural Low-Rank Decomposition

5. Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter