Low-Frequency Desert Noise Intelligent Suppression in Seismic Data Based on Multiscale Geometric Analysis Convolutional Neural Network

Existing denoising algorithms often need to meet some premise assumptions and applicable conditions, such as the signal-to-noise ratio (SNR) cannot be too low, and the noise needs to obey a specific distribution (such as Gaussian distribution) and to satisfy some properties (such as stationarity). F...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2020-01, Vol.58 (1), p.650-665
Hauptverfasser:	Zhao, Yuxing, Li, Yue, Yang, Baojun
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Convolutional neural network (CNN) Deserts Energy dissipation Energy loss Feature extraction Frequencies Gaussian distribution Information processing LF noise low-frequency noise suppression Multiscale analysis multiscale geometric analysis (MGA) Neural networks Noise Noise reduction Normal distribution Random noise Records Seismic data seismic exploration Seismograms Signal resolution Signal to noise ratio Surface water waves Surface waves Training training set Transforms
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