Question-Driven Ensembles of Flexible ETAS Models

The development of new earthquake forecasting models is often motivated by one of the following complementary goals: to gain new insights into the governing physics and to produce improved forecasts quantified by objective metrics. Often, one comes at the cost of the other. Here, we propose a question-driven ensemble (QDE) modeling approach to address both goals. We first describe flexible epidemic-type aftershock sequence (ETAS) models in which we relax the assumptions of parametrically defined aftershock productivity and background earthquake rates during model calibration. Instead, both productivity and background rates are calibrated with data such that their variability is optimally represented by the model. Then we consider 64 QDE models in pseudoprospective forecasting experiments for southern California and Italy. QDE mod-els are constructed by combining model parameters of different ingredient models, in which the rules for how to combine parameters are defined by questions about the future seismicity. The QDE models can be interpreted as models that address different questions with different ingredient models. We find that certain models best address the same issues in both regions, and that QDE models can substantially outperform the standard ETAS and all ingredient models. The best performing QDE model is obtained through the combination of models allowing flexible background seismicity and flex-ible aftershock productivity, respectively, in which the former parameterizes the spatial distribution of background earthquakes and the partitioning of seismicity into back-ground events and aftershocks, and the latter is used to parameterize the spatiotem-poral occurrence of aftershocks.

Automated summary

This paper proposes a question-driven ensemble (QDE) modeling approach to address the goals of gaining new insights into earthquake physics and producing improved forecasts. Through pseudoprospective forecasting experiments in southern California and Italy, it is found that certain QDE models outperform standard ETAS and ingredient models, effectively addressing the issues in both regions.