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A new hybrid Coulomb/statistical model for forecasting aftershock rates

Sandy Steacy, Matthew C. Gerstenberger, Charles A. Williams, David A. Rhoades, & Annemarie Christophersen

Published January 8, 2014, SCEC Contribution #6059

Forecasting the spatial and temporal distribution of aftershocks is of great importance to earthquake scientists, civil protection authorities and the general public as these events cause disproportionate damage and consternation relative to their size. At present, there are two main approaches to such forecasts—purely statistical methods based on observations of the initial portions of aftershock sequences and a physics-based approach based on Coulomb stress changes caused by the main shock. Here we develop a new method which combines the spatial constraints from the Coulomb model with the statistical power of the STEP (short-term earthquake probability) approach. We test this pseudo prospectively and retrospectively on the Canterbury sequence against the STEP model and a Coulomb rate–state method, using data from the first 10 d following each main event to forecast the rate of M ≥ 4 events in the following 100 d. We find that in retrospective tests the new model outperforms STEP for two events in the sequence but this is not the case for pseudo-prospective tests. Further, the Coulomb rate–state approach never performs better than STEP. Our results suggest that incorporating the physical constraints from Coulomb stress changes can increase the forecasting power of statistical models and clearly show the importance of good data quality if prospective forecasts are to be implemented in practice.

Key Words
probabilistic forecasting, earthquake interaction, forecasting, prediction, statistical seismology

Steacy, S., Gerstenberger, M. C., Williams, C. A., Rhoades, D. A., & Christophersen, A. (2014). A new hybrid Coulomb/statistical model for forecasting aftershock rates. Geophysical Journal International, 196(2), 918-923. doi: 10.1093/gji/ggt404.

Related Projects & Working Groups
Earthquake Forecasting and Predictability