Question-driven ensembles of flexible ETAS models

Leila Mizrahi, Shayam Nandan, William H. Savran, Stefan Wiemer, & Yehuda Ben-Zion

Submitted July 20, 2022, SCEC Contribution #11882

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 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 pseudo-prospective forecasting experiments for Southern California and Italy. QDE models are constructed by combining model parameters of different ingredient models, where the rules for how to combine parameters are defined by questions about the future seismicity. A QDE model can then be interpreted as a model which addresses 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.

Mizrahi, L., Nandan, S., Savran, W. H., Wiemer, S., & Ben-Zion, Y. (2022). Question-driven ensembles of flexible ETAS models. Seismological Research Letters, (submitted).

Related Projects & Working Groups
Collaboratory for the Study of Earthquake Predictability, Earthquake Forecasting & Predictability