Cross‐Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis
Abstract We explore adapting foundation models (FMs) from the computer vision domain to geoscience. FMs, which are large neural networks trained on massive data sets, excel in diverse tasks with remarkable adaptability and generality. However, geoscience faces challenges like lacking curated trainin...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2025JH000601 |
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| Summary: | Abstract We explore adapting foundation models (FMs) from the computer vision domain to geoscience. FMs, which are large neural networks trained on massive data sets, excel in diverse tasks with remarkable adaptability and generality. However, geoscience faces challenges like lacking curated training data sets and high computational cost for developing specialized FMs. This study considers adapting FMs from computer vision to geoscience, analyzing their scale, adaptability, and generality for geoscientific data analysis. We introduce a workflow that leverages existing computer vision FMs, fine‐tuning them for geoscientific tasks, reducing development costs while enhancing accuracy. Through experiments, we demonstrate this workflow's effectiveness in broad applications to process and interpret geoscientific data of lunar images, seismic data, DAS arrays and so on. Our findings introduce advanced ML techniques to geoscience, proving the feasibility and advantages of cross‐domain FMs adaptation, driving further advancements in geoscientific data analysis and offering valuable insights for FMs applications in other scientific domains. |
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| ISSN: | 2993-5210 |