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.
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-17123-6 |