  Training specific deep learning models for particular tasks is common across
various domains within seismology. However, this approach encounters two
limitations: inadequate labeled data for certain tasks and limited
generalization across regions. To address these challenges, we develop
SeisCLIP, a seismology foundation model trained through contrastive learning
from multi-modal data. It consists of a transformer encoder for extracting
crucial features from time-frequency seismic spectrum and an MLP encoder for
integrating the phase and source information of the same event. These encoders
are jointly pre-trained on a vast dataset and the spectrum encoder is
subsequently fine-tuned on smaller datasets for various downstream tasks.
Notably, SeisCLIP's performance surpasses that of baseline methods in event
classification, localization, and focal mechanism analysis tasks, employing
distinct datasets from different regions. In conclusion, SeisCLIP holds
significant potential as a foundational model in the field of seismology,
paving the way for innovative directions in foundation-model-based seismology
research.
