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of 99
pro vyhledávání: '"Sheng, Hanlin"'
Seismic geobody interpretation is crucial for structural geology studies and various engineering applications. Existing deep learning methods show promise but lack support for multi-modal inputs and struggle to generalize to different geobody types o
Externí odkaz:
http://arxiv.org/abs/2409.04962
We explore adapting foundation models (FMs) from the computer vision domain to geoscience. FMs, large neural networks trained on massive datasets, excel in diverse tasks with remarkable adaptability and generality. However, geoscience faces challenge
Externí odkaz:
http://arxiv.org/abs/2408.12396
While computer science has seen remarkable advancements in foundation models, which remain underexplored in geoscience. Addressing this gap, we introduce a workflow to develop geophysical foundation models, including data preparation, model pre-train
Externí odkaz:
http://arxiv.org/abs/2309.02791
Training specific deep learning models for particular tasks is common across various domains within seismology. However, this approach encounters two limitations: inadequate labeled data for certain tasks and limited generalization across regions. To
Externí odkaz:
http://arxiv.org/abs/2309.02320
Publikováno v:
In Expert Systems With Applications 1 December 2024 255 Part B
Publikováno v:
In Aerospace Science and Technology August 2024 151
Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight
Publikováno v:
In Chinese Journal of Aeronautics July 2024 37(7):352-374
Publikováno v:
In Aerospace Science and Technology February 2024 145
Publikováno v:
In Chinese Journal of Aeronautics May 2024
Akademický článek
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