Capturing Mesoscale and Submesoscale Ocean Fronts in the Gulf of Mexico With Gaussian Mixture Modeling and Satellite Observations
Abstract This study advances the detection of ocean fronts in the Gulf of Mexico (GoM) by comparing traditional front detection algorithms with a machine learning model. Specifically, we evaluate the performance of three widely used methods, Canny Edge Detection (referred to as Canny), the Cayula‐Co...
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| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
American Geophysical Union (AGU)
2026-04-01
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| coleção: | Earth and Space Science |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1029/2025EA004855 |
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