Seismic Random Noise Attenuation via Self-Supervised Transfer Learning

Random noise attenuation is an important step in seismic data processing. Unfortunately, most conventional denoising methods heavily rely on specific prior knowledge and fine-tuning of the parameters. Therefore, they often fail to suppress random noise. Recent works based on supervised learning tech...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Hauptverfasser:	Sun, Huimin, Yang, Fangshu, Ma, Jianwei
Format:	Artikel
Sprache:	eng
Schlagworte:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">J -invariant function Attenuation Convolutional neural network (CNN) Data processing Datasets Noise Noise measurement Noise reduction Random noise random noise attenuation Seismic activity Seismic data processing Seismological data self-supervised learning Signal to noise ratio Supervised learning Training Transfer learning Unsupervised learning
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