Global models for short-term earthquake forecasting and predictive skill assessment

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Shyam Nandan , Yavor Kamer, Guy Ouillon, Stefan Hiemer, Didier Sornette


We present rigorous tests of global short-term earthquake forecasts using Epidemic Type Aftershock Sequence models with two different time kernels (one with exponentially tapered Omori kernel (ETOK) and another with linear magnitude dependent Omori kernel (MDOK)). The tests are conducted with three different magnitude cutoffs for the auxiliary catalog (M3, M4 or M5) and two different magnitude cutoffs for the primary catalog (M5 or M6), in 30 day long pseudo prospective experiments designed to forecast worldwide M ≥ 5 and M ≥ 6 earthquakes during the period from January 1981 to October 2019. MDOK ETAS models perform significantly better relative to ETOK ETAS models. The superiority of MDOK ETAS models adds further support to the multifractal stress activation model proposed by Ouillon and Sornette (2005). We find a significant improvement of forecasting skills by lowering the auxiliary catalog magnitude cutoff from 5 to 4. We unearth evidence for a self-similarity of the triggering process as models trained on lower magnitude events have the same forecasting skills as models trained on higher magnitude earthquakes. Expressing our forecasts in terms of the full distribution of earthquake rates at different spatial resolutions, we present tests for the consistency of our model, which is often found satisfactory but also points to a number of potential improvements, such as incorporating anisotropic spatial kernels, and accounting for spatial and depth dependant variations of the ETAS parameters. The model has been implemented as a reference model on the global earthquake prediction platform RichterX, facilitating predictive skill assessment and allowing anyone to review its prospective performance.



Physical Sciences and Mathematics


ETAS models, magnitude dependent Omori law, multifractal stress activation, global earthquake forecasts, RichterX


Published: 2020-12-16 17:12

Last Updated: 2020-12-17 01:12


CC BY Attribution 4.0 International

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