Forecasting the Rates of Future Aftershocks of All Generations Is Essential to Develop Better Earthquake Forecast Models

• Published in: Journal of geophysical research. Solid earth 2019-08, Vol.124 (8), p.8404-8425
• Main Authors: Nandan, Shyam, Ouillon, Guy, Sornette, Didier, Wiemer, Stefan
• Format: Article
• Language: English
• Subjects: Aftershocks; Earthquake forecasting; Earthquake prediction; Earthquakes; Epidemics; ETAS model; Experiments; Forecasting; Geophysics; Mathematical models; model testing; Parameters; Seismic activity; Seismicity; Spatial variations; Strain rate
• ISSN: 2169-9313; 2169-9356
• DOI: 10.1029/2018JB016668

Currently, one of the best performing earthquake forecasting models relies on the working hypothesis that the “locations of past background earthquakes reveal the probable location of future seismicity.” As an alternative, we present a class of smoothed seismicity models (SSMs) based on the principles of the epidemic‐type aftershock sequence (ETAS) model, which forecast the location, time, and magnitude of all future earthquakes using the estimates of the background seismicity rate and the rates of future aftershocks of all generations. Using the Californian earthquake catalog, we formulate six controlled pseudo‐prospective experiments with different combinations of three target magnitude thresholds: 2.95, 3.95, or 4.95 and two forecasting time horizons: 1 or 5 years. In these experiments, we compare the performance of (1) the ETAS model with spatially homogenous parameters, or GETAS; (2) the ETAS model with spatially variable parameters, or SVETAS; (3) three declustering‐based SSMs; (4) a simple SSM based on undeclustered data, and (5) a model based on strain rate data, in forecasting the location and magnitude of all (undeclustered) target earthquakes during many testing periods. In all conducted experiments, the SVETAS model comes out with consistent superiority compared to all the competing models. Consistently better performance of the SVETAS model with respect to declustering‐based SSMs highlights the importance of forecasting the future aftershocks of all generations for developing better earthquake forecasting models. Among the two ETAS models themselves, accounting for the optimal spatial variation of the parameters leads to stronger improvements in forecasting performance. Key Points Rigorous pseudo‐prospective experiments reveal the forecasting prowess of the ETAS model with spatially variable parameters over the current state‐of‐the‐art smoothed seismicity models Smoothed seismicity models that account for the rates of future aftershocks of all generations have superior forecasting performance relative to only background seismicity‐based models Accounting for spatial variation of parameters of the ETAS model leads to superior forecasting performance compared to ETAS models with spatially invariant parameters