Presentation

· Contributors · Organizations · Search Program

Advancing Foundation Models in Earthquake Nowcasting

SessionAccelerating the Development and Use of Generative AI for Science and Engineering: The Trillion Parameter Consortium (TPC)

DescriptionWe present a comparison of twenty different Time Series deep learning models for the important but challenging problem of Earthquake nowcasting in Southern California. We find that pattern models where general architectures are trained on this problem outperform foundation models that do not exploit some key features of earthquake time series. A graph neural network expressing spatial locality has the best performance. We introduce a new general approach termed MultiFoundationPattern that combines a bespoke model with other pattern and foundation model results handled as auxiliary streams. In the earthquake case, the resultant MultiFoundationQuake model achieves the best overall performance. This work in progress is being extended in different directions, including the study of different earthquake regions and the use of simulations for better training. Further, we are examining the importance of patterns and the MultiFoundationPattern integration model in other geospatial applications, including the CAMELS and CARAVAN datasets in hydrology.

Author/Presenters