A Convolutional Autoencoder Method for Simultaneous Seismic Data Reconstruction and Denoising

Petroleum geophysical exploration is based on seismic data and has been widely affected by deep learning technology in recent years. As a consequence of the high efficiency and nonlinear fitting ability of deep learning models, we propose an improved convolutional autoencoder (CAE) method to achieve...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters Jg. 19; S. 1 - 5
Hauptverfasser:	Jiang, Jinsheng, Ren, Haoran, Zhang, Meng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms CAD Computer aided design Convolution Convolutional autoencoder (CAE) Deep learning denoising Feature extraction Geophysical exploration Image reconstruction Interpolation Machine learning Noise reduction Oil exploration Optimization Petroleum Random noise Reconstruction Seismic activity Seismic data seismic data reconstruction Seismological data Signal processing Signal to noise ratio Training
ISSN:	1545-598X, 1558-0571
Online-Zugang:	Volltext
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