Magnetotelluric forward modeling on fine grid via deep learning with physical information constraints
Abstract In traditional magnetotelluric (MT) forward modeling, fine grids ensure accuracy but trap conventional methods in efficiency bottlenecks due to exponentially growing matrix computation time. Meanwhile, deep learning (DL) for MT forward modeling often loses physical information during traini...
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| Autori principali: | , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Nature Portfolio
2026-01-01
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| Serie: | Scientific Reports |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1038/s41598-026-37645-1 |
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