Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis

Zhixiang Guo, Xinming Wu, Luming Liang, Hanlin Sheng, Nuo Chen, Zhengfa Bi
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Abstract

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 challenges like lacking curated training datasets and high computational costs 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.
跨域基础模型适应:用于地球物理数据分析的计算机视觉模型先驱
我们探讨了如何将计算机视觉领域的基础模型(FMs)应用到地球科学领域。基础模型是在海量数据集上训练的大型神经网络,在各种任务中表现出色,具有显著的适应性和通用性。然而,地球科学面临着各种挑战,如缺乏经过精心策划的训练数据集,以及开发专用基础模型的计算成本高昂。本研究考虑将计算机视觉中的调频技术应用到地球科学中,分析它们在地球科学数据分析中的规模、适应性和通用性。我们介绍了一种工作流程,该流程利用现有的计算机视觉调频技术,针对地球科学任务对其进行微调,在提高准确性的同时降低开发成本。通过实验,我们证明了这一工作流程在处理和解释月球图像、地震数据、DAS 阵列等地球科学数据的广泛应用中的有效性。我们的研究成果将先进的 ML 技术引入了地球科学,证明了跨领域调频适应的可行性和优势,推动了地球科学数据分析的进一步发展,并为调频在其他科学领域的应用提供了宝贵的启示。
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