Question-driven ensembles of flexible ETAS models
Leila Mizrahi, Shyam Nandan, William Savran, Stefan Wiemer, Yehuda, Ben-Zion

TL;DR
This paper introduces a question-driven ensemble approach for earthquake forecasting using flexible ETAS models, improving predictive performance by combining models tailored to specific seismic questions.
Contribution
It proposes a novel ensemble modeling framework that relaxes parametric assumptions in ETAS models and combines multiple models based on seismic questions, enhancing forecast accuracy.
Findings
QDE models outperform standard ETAS models in experiments
Flexible background and productivity models improve seismicity prediction
Certain model combinations are consistently best across regions
Abstract
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…
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Taxonomy
Topicsearthquake and tectonic studies · Seismology and Earthquake Studies · Geological and Geophysical Studies Worldwide
