Abstract

We are developing a new approach to earthquake nowcasting based on science transformers (GC Fox et al., Geohazards, 2022). As explained in the seminal paper by Vaswani et al. (NIPS, 2017), a transformer is a type of deep learning model that learns the context of a set of time series values by means of tracking the relationships in a sequence of data, such as the words in a sentence. Transformers extend deep learning in the adoption of a context-sensitive protocol "attention", which is used to tag important sequences of data, and to identify relationships between those tagged data. Pretrained transformers are the foundational technology that underpins the new AI models ChatGPT (Generative Pretrained Transformers) from openAI.com, and Bard, from Google.com. In our case, we hypothesize that a transformer might be able to learn the sequence of events leading up to a major earthquake. Typically, the data used to train the model is in the billions or larger, so these models, when applied to earthquake problems, need the size of data sets that only long numerical earthquake simulations can provide. In this research, we are developing the Earthquake Generative Pretrained Transformer model, "QuakeGPT", in a similar vein. For simulations, we are using simulation catalogs from the physics-based model Virtual Quake, the statistical model ETAS, and a statistical physics model based on invasion percolation. Observed data, which is the data to anticipate with nowcasting, is taken from the USGS online catalog for California. In this talk, we discuss the architecture of QuakeGPT and report first results. We also report results using other types of simulated seismicity such as slider block models, to quantify how well a Wednesday, 13 December 2023 14:45-14:55 2016-West (Level 2, West, Moscone Center) Nowcasting Earthquakes with QuakeGPT: An AI-Enhanced Earthquak.