PISAS v1.0: A Physics-Informed Neural Surrogate for Earthquake Productivity and Aftershock-Sequence Modeling from Open Seismic Catalogs

Abstract. Rapid estimates of aftershock productivity and temporal decay are valuable immediately after a large earthquake, when sequence-specific observations are still sparse and catalogue completeness may be degraded. Here I present PISAS v1.0, a physics-informed neural surrogate that predicts the quality-controlled number of aftershocks expected during the first 30 d and the Omori–Utsu decay exponent using only information available at mainshock origin time. The model uses mainshock magnitude, hypocentral depth, and three multiscale measures of pre-mainshock seismicity derived from the open U.S. Geological Survey Comprehensive Earthquake Catalog. An analytical decoder converts the neural outputs into a complete mean aftershock-rate history while enforcing positive productivity, bounded decay exponents, monotonic temporal decay, and exact agreement between the predicted event count and the integral of the reconstructed rate. The primary dataset contains 175 independent global sequences from 2000–2025, with model development based on rolling-origin validation and final evaluation on an untouched 2020–2025 temporal test. PISAS reproduced a moderate and transferable component of 30 d productivity variability, with coefficients of determination of 0.454 in out-of-fold validation and 0.534 on the temporal test. Relative to a magnitude-only linear model, productivity root-mean-square error improved by 3.9 % out of fold, 5.0 % on the temporal test, and 4.3 % under leave-one-region-out validation. Prediction of the decay exponent was substantially weaker, with near-zero or negative explained variance in out-of-fold and regional evaluation. Mainshock magnitude was the dominant productivity predictor, whereas hypocentral depth provided the most stable, but limited, information about temporal decay. Compared with an otherwise equivalent unconstrained neural network, PISAS matched or improved predictive accuracy while eliminating all violations of rate normalization and output admissibility. PISAS v1.0 therefore provides a reproducible, physically consistent initial estimate of aftershock productivity and mean temporal evolution, while clearly delimiting the weaker predictability of sequence-specific decay.