Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification

The authors present an automated design approach to propose a new neural network architecture for seismic data analysis. The new architecture classifies multiple seismic reflection datasets at extremely low computational cost compared with conventional architectures for image classification.

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Detalles Bibliográficos
Autores principales: Zhi Geng, Yanfei Wang
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
Materias:
Q
Acceso en línea:https://doaj.org/article/47df02517dab422f98ad99c5a7c03762
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