Seismic foundation model: A next generation deep-learning model in geophysics

ABSTRACT Although computer science has seen remarkable advancements in foundation models, they remain underexplored in geoscience. Addressing this gap, we introduce a workflow to develop geophysical foundation models, such as data preparation, model pretraining, and adaption to downstream tasks. From 192 globally collected 3D seismic volumes, we create a carefully curated data set of 2,286,422 2D seismic images. To fully use these unlabeled images, we use self-supervised learning to pretrain a transformer-based seismic foundation model 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 pretrained model demonstrates versatility, generalization, scalability, and superior performance compared with baseline models. In conclusion, we provide a foundation model and vast data set to advance artificial intelligence (AI) in geophysics, addressing the challenges (poor generalization, a lack of labels, and repetitive training for task-specific models) of applying AI to geophysics and paving the way for future innovations in geoscience.