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.

翻译：本研究探讨将计算机视觉领域的基础模型（FMs）适配至地球科学领域。基础模型是在海量数据集上训练的大型神经网络，凭借卓越的适应性与泛化能力，在多样化任务中表现出色。然而，地球科学领域面临缺乏高质量标注训练数据集、开发专用基础模型计算成本高昂等挑战。本研究考量将计算机视觉基础模型适配至地球科学领域，分析其规模、适应性与泛化能力在地球科学数据分析中的适用性。我们提出一种工作流程，利用现有计算机视觉基础模型，针对地球科学任务进行微调，在提升精度的同时降低开发成本。通过实验，我们证明了该工作流程在处理与解释月球影像、地震数据、DAS阵列等地球科学数据的广泛应用中的有效性。我们的研究将先进机器学习技术引入地球科学，证实了跨领域基础模型适配的可行性与优势，推动了地球科学数据分析的进一步发展，并为基础模型在其他科学领域的应用提供了宝贵见解。