  Earthquake nowcasting has been proposed as a means of tracking the change in
large earthquake potential in a seismically active area. The method was
developed using observable seismic data, in which probabilities of future large
earthquakes can be computed using Receiver Operating Characteristic (ROC)
methods. Furthermore, analysis of the Shannon information content of the
earthquake catalogs has been used to show that there is information contained
in the catalogs, and that it can vary in time. Here we discuss a new method for
earthquake nowcasting that uses an AI-enhanced deep learning model "QuakeGPT"
that is based on an attention-based science transformer adapted for time series
forecasting. Such dot product attention-based transformers were introduced by
Vaswani et al. (2017), and are the basis for the new large language models such
as ChatGPT. To use these science transformers, they must first be trained on a
large corpus of data. A problem is that the existing history of reliable
earthquake catalog data extends back in time only a few decades, which is
almost certainly too short to train a model for reliable
nowcasting/forecasting. As a result, we turn to earthquake simulations to train
the transformer model. Specifically we discuss a simple stochastic earthquake
simulation model "ERAS" that has recently been introduced. The ERAS model is
similar to the more common "ETAS" models, the difference being that the ERAS
model has only 2 novel, adjustable parameters, rather than the 6-8 adjustable
parameters that characterize most ETAS models. Using this ERAS model, we then
define a transformer model and train it using a long catalog of ERAS
simulations, then apply it to an ERAS validation dataset with the transformer
model. In this paper, we describe this new method and assess the applicability
to observed earthquake catalogs for use in nowcasting/forecasting.
