  While computer science has seen remarkable advancements in foundation models,
which remain underexplored in geoscience. Addressing this gap, we introduce a
workflow to develop geophysical foundation models, including data preparation,
model pre-training, and adaption to downstream tasks. From 192 globally
collected 3-D seismic volumes, we create a carefully curated dataset of
2,286,422 2-D seismic images. Fully using these unlabeled images, we employ the
self-supervised learning to pre-train a Transformer-based Seismic Foundation
Model (SFM) for producing all-purpose seismic features that work across various
tasks and surveys. Through experiments on seismic facies classification,
geobody identification, interpolation, denoising, and inversion, our
pre-trained model demonstrates versatility, generalization, scalability, and
superior performance over baseline models. Conclusively, we provide a
foundation model and vast dataset to advance AI in geophysics, addressing
challenges (poor generalization, lacking labels, and repetitive training for
task-specified models) of applying AI in geophysics and paving the way for
future innovations in geoscience.
