Fig. 1: Architecture of the transformer-based MEQ forecasting model.
From: Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes
Given input history from time steps t0 through tn, the model predicts MEQ features at future time steps tn+1 through \({t}_{n}+{l}_{{{{\rm{future}}}}}\), where \({l}_{{{{\rm{future}}}}}\) is the forecast range (see section “Method: Transformer neural network architecture”).
