Articles | Volume 22, issue 10
https://doi.org/10.5194/nhess-22-3231-2022
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, Mark Naylor, Farnaz Kamranzad, and Ian Main

Related authors

Pysammos 1.0.0: a discrete-to-continuum transformation Python tool to analyse the rheology of granular materials
Claudia Elijas-Parra, Eric C. P. Breard, John P. Morrissey, P. J. Zrelak, and Mark Naylor
EGUsphere, https://doi.org/10.5194/egusphere-2026-2591,https://doi.org/10.5194/egusphere-2026-2591, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Downstream rounding rate of pebbles in the Himalaya
Prakash Pokhrel, Mikael Attal, Hugh D. Sinclair, Simon M. Mudd, and Mark Naylor
Earth Surf. Dynam., 12, 515–536, https://doi.org/10.5194/esurf-12-515-2024,https://doi.org/10.5194/esurf-12-515-2024, 2024
Short summary

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
Download
Short summary
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.
Share
Altmetrics
Final-revised paper
Preprint