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Adaptive Smoothing of Seismicity in Time, Space, and Magnitude for Time-Dependent Earthquake Forecasts in California

Agnes Helmstetter, & Maximilian J. Werner

Published April 2014, SCEC Contribution #1765

We present new methods for short-term earthquake forecasting that employ space, time and magnitude kernels to smooth seismicity. These methods are purely statistical and rely on very few assumptions about seismicity. In particular, we do not use Omori’s law, and only one of our two new models assumes a Gutenberg-Richter law to model the magnitude distribution; the second model estimates the magnitude distribution non-parametrically with kernels. We employ adaptive kernels of variable bandwidths to estimate seismicity in space, time and magnitude bins. To project rates over short time-scales into the future, we simply assume persistence, that is, a constant rate over short time windows. The resulting forecasts from the two new kernel models are compared with those of the ETAS model generated by Werner et al. (2011). While our new methods are simpler and require fewer parameters than ETAS, the obtained probability gains are surprisingly close. Nonetheless, ETAS performs significantly better in most comparisons, and the kernel model with a Gutenberg-Richter law attains larger gains than the kernel model that non-parametrically estimates the magnitude distribution. Finally, we show that combining ETAS and kernel model forecasts, by simply averaging the expected rate in each bin, can provide greater predictive skill than ETAS or the kernel models can achieve individually.

Helmstetter, A., & Werner, M. J. (2014). Adaptive Smoothing of Seismicity in Time, Space, and Magnitude for Time-Dependent Earthquake Forecasts in California. Bulletin of the Seismological Society of America, 104(2), 809-822. doi: 10.1785/0120130105.

Related Projects & Working Groups
Earthquake Forecasting and Predictability