Comparative Study on Three Autoencoder‐Based Deep Learning Algorithms for Geochemical Anomaly Identification
Abstract Deep autoencoder (AE) networks show a powerful ability for geochemical anomaly identification. Because of little contribution to the AE network, small probability samples (again, please check this) having comparatively high reconstructed errors can be recognized by the trained model as anom...
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| Asıl Yazarlar: | , , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
American Geophysical Union (AGU)
2022-11-01
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| Seri Bilgileri: | Earth and Space Science |
| Konular: | |
| Online Erişim: | https://doi.org/10.1029/2022EA002626 |
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