Articles | Volume 22, issue 10
Nat. Hazards Earth Syst. Sci., 22, 3231–3246, 2022
https://doi.org/10.5194/nhess-22-3231-2022
Nat. Hazards Earth Syst. Sci., 22, 3231–3246, 2022
https://doi.org/10.5194/nhess-22-3231-2022
Research article
07 Oct 2022
Research article | 07 Oct 2022

Pseudo-prospective testing of 5-year earthquake forecasts for California using inlabru

Kirsty Bayliss et al.

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Cited articles

Adelfio, G. and Chiodi, M.: Including covariates in a space-time point process with application to seismicity, Stat. Method. Appl., 30, 947–971, https://doi.org/10.1007/s10260-020-00543-5, 2020. a
Bach, C. and Hainzl, S.: Improving empirical aftershock modeling based on additional source information, J. Geophys. Res.-Sol. Ea., 117, B04312, https://doi.org/10.1029/2011JB008901, 2012. a
Bachl, F. E., Lindgren, F., Borchers, D. L., and Illian, J. B.: inlabru: an R package for Bayesian spatial modelling from ecological survey data, Meth. Ecol. Evol., 10, 760–766, https://doi.org/10.1111/2041-210X.13168, 2019. a
Bayliss, K., Naylor, M., Illian, J., and Main, I. G.: Data-Driven Optimization of Seismicity Models Using Diverse Data Sets: Generation, Evaluation, and Ranking Using Inlabru, J. Geophys. Res.-Sol. Ea., 125, e2020JB020226, https://doi.org/10.1029/2020JB020226, 2020. a, b, c, d, e
Bayliss, K., Naylor, M., Kamranzad, F., and Main, I.: Pseudo-prospective testing of 5-year earthquake forecasts for California using inlabru – data and code (v1.0.0), Zenodo [data set] and [code], https://doi.org/10.5281/zenodo.6534724, 2021. a
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We develop probabilistic earthquake forecasts that include different spatial information (e.g. fault locations, strain rate) using a point process method. The performance of these models is tested over three different periods and compared with existing forecasts. We find that our models perform well, with those using simulated catalogues that make use of uncertainty in model parameters performing better, demonstrating potential to improve earthquake forecasting using Bayesian approaches.
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