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

TL;DR

This paper addresses the lack of a full-process review for geophysical foundation models by proposing a comprehensive workflow that spans data acquisition, preprocessing, model design, pretraining, and deployment. It emphasizes domain-specific challenges—diverse multimodal geophysical data, physical consistency, and data scarcity—and discusses strategies such as self-supervised and multimodal pretraining, physics-informed fine-tuning, model interaction, and retrieval-grounded deployment to improve robustness and interpretability. Key contributions include a systematic framework for building geophysical foundation models, guidance on data preparation and standardization, and deployment strategies with efficiency considerations (distillation, pruning, quantization, RAG, and agent-based approaches). The framework aims to accelerate practical adoption in subsurface imaging, resource exploration, disaster warning, and digital earth initiatives, enabling more scalable, interpretable, and socially responsible AI for Earth sciences.

Abstract

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 of geophysics, although the application of foundation models is gradually expanding, there is currently a lack of comprehensive reviews discussing the full workflow of integrating foundation models with geophysical data. To address this gap, this paper presents a complete framework that systematically explores the entire process of developing foundation models in conjunction with geophysical data. From data collection and preprocessing to model architecture selection, pre-training strategies, and model deployment, we provide a detailed analysis of the key techniques and methodologies at each stage. In particular, considering the diversity, complexity, and physical consistency constraints of geophysical data, we discuss targeted solutions to address these challenges. Furthermore, we discuss how to leverage the transfer learning capabilities of foundation models to reduce reliance on labeled data, enhance computational efficiency, and incorporate physical constraints into model training, thereby improving physical consistency and interpretability. Through a comprehensive summary and analysis of the current technological landscape, this paper not only fills the gap in the geophysics domain regarding a full-process review of foundation models but also offers valuable practical guidance for their application in geophysical data analysis, driving innovation and advancement in the field.

Figures (15)

• Figure 1: Geophysics encompasses a variety of data acquisition methods, including well logging, seismology, magnetics, gravimetry, electrical methods, distributed acoustic sensing (DAS), and remote sensing. The extensive application of these techniques in geophysical exploration, along with their respective advantages, has generated massive volumes of data in diverse formats, spanning various types such as time-series and spatially distributed datasets. With the rapid increase in data volume and the growing diversity of formats, efficiently processing, analyzing, and integrating these datasets has become a critical challenge in contemporary geophysics. (Some images are modified from online sources.)
• Figure 2: The application of deep learning in geophysics faces several major challenges, including the scarcity of labeled data, limited generalization ability, the absence of benchmark datasets, poor physical consistency, low interpretability, and high demands for large-scale memory and computational power. These challenges have constrained the widespread adoption of deep learning in geophysical exploration. Overcoming these limitations requires technological innovations and the development of comprehensive datasets to drive further advancements in this field.
• Figure 3: Foundation models have witnessed rapid advancements across language, audio, vision, and multi-modal systems, which in turn have catalyzed progress in various scientific domains such as physics, chemistry, biology, medicine, and atmosphere. This trend underscores the growing influence of foundation models across a wide range of research disciplines.
• Figure 4: In the field of geophysics, several foundation models have recently emerged, including SFM, GSFM, and SeisCLIP.
• Figure 5: The integration of foundation models into geophysical research involves several critical components, including data collection, preprocessing, model training, and model deployment. (Some images are modified from online sources.)