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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2022-11-01
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| Series: | Earth and Space Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022EA002626 |