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Foundation models, as a mainstream technology in artificial intelligence,have demonstrated immense potential across various domains in recent years,particularly in handling complex tasks and multimodal data. In the field ofgeophysics, although the application of foundation models is graduallyexpanding, there is currently a lack of comprehensive reviews discussing thefull workflow of integrating foundation models with geophysical data. Toaddress this gap, this paper presents a complete framework that systematicallyexplores the entire process of developing foundation models in conjunction withgeophysical data. From data collection and preprocessing to model architectureselection, pre-training strategies, and model deployment, we provide a detailedanalysis of the key techniques and methodologies at each stage. In particular,considering the diversity, complexity, and physical consistency constraints ofgeophysical data, we discuss targeted solutions to address these challenges.Furthermore, we discuss how to leverage the transfer learning capabilities offoundation models to reduce reliance on labeled data, enhance computationalefficiency, and incorporate physical constraints into model training, therebyimproving physical consistency and interpretability. Through a comprehensivesummary and analysis of the current technological landscape, this paper notonly fills the gap in the geophysics domain regarding a full-process review offoundation models but also offers valuable practical guidance for theirapplication in geophysical data analysis, driving innovation and advancement inthe field.

On the workflow, opportunities and challenges of developing foundation model in geophysics