Relaxing ETAS’s Assumptions to Better Capture the Real Behavior of Seismicity

When developing next-generation earthquake forecasting models, the key is to more carefully account for the real world (which has fault systems with different properties, site-specific properties, swarm-like episodes of temporally elevated seismicity, etc.), without constructing overly complicated models that are hard to comprehend and even harder to use. Finding the sweet spot between simplicity and accuracy is what constitutes the art of modelling. Epidemic-Type Aftershock Sequence (ETAS) models, despite being introduced over three decades ago, are still the undisputed reference for earthquake forecasting methods – be it as a benchmark when testing novel forecasting techniques, or as the model of choice for operational earthquake forecasting around the world. ETAS models accurately describe the average behavior of aftershock triggering as a self-exciting point process based on few simple empirical principles, including the Omori-Utsu and Gutenberg-Richter laws.

With this in mind, we are proposing a new model which naturally captures the diversity of conditions under which earthquakes take place. Within the ETAS statistical framework, we relax the assumptions of parametrically defined aftershock productivity and background earthquake rates. Instead, both productivity and background rates are calibrated with data such that their variability is optimally represented by the model. This allows for an impartial view on the behavior of background and triggered seismicity in different regions, different time periods, or different sequences. We perform pseudo-prospective forecasting experiments for Southern California to evaluate models based on their accuracy at forecasting the next event. These experiments reveal when, where, and under which conditions our proposed model yields better forecasts than the standard ETAS null model.