Retrospective Evaluation of the Five-Year and Ten-Year CSEP-Italy Earthquake Forecasts

On 1 August 2009, the global Collaboratory for the Study of Earthquake Predictability (CSEP) launched a prospective and comparative earthquake predictability experiment in Italy. The goal of the CSEP-Italy experiment is to test earthquake occurrence hypotheses that have been formalized as probabilistic earthquake forecasts over temporal scales that range from days to years. In the first round of forecast submissions, members of the CSEP-Italy Working Group presented eighteen five-year and ten-year earthquake forecasts to the European CSEP Testing Center at ETH Zurich. We considered the twelve time-independent earthquake forecasts among this set and evaluated them with respect to past seismicity data from two Italian earthquake catalogs. In this article, we present the results of tests that measure the consistency of the forecasts with the past observations. Besides being an evaluation of the submitted time-independent forecasts, this exercise provided insight into a number of important issues in predictability experiments with regard to the specification of the forecasts, the performance of the tests, and the trade-off between the robustness of results and experiment duration. We conclude with suggestions for the future design of earthquake predictability experiments.Comment: 43 pages, 8 figures, 4 table

Simulation of seismic events induced by CO2 injection at In Salah, Algeria

Date of Acceptance: 18/06/2015 Acknowledgments The authors would like to thank the operators of the In Salah JV and JIP, BP, Statoil and Sonatrach, for providing the data shown in this paper, and for giving permission to publish. Midland Valley Exploration are thanked for the use of their Move software for geomechanical restoration. JPV is a Natural Environment Research Council (NERC) Early Career Research Fellow (Grant NE/I021497/1) and ALS is funded by a NERC Partnership Research Grant (Grant NE/I010904).Peer reviewedPublisher PD

A novel approach to assessing nuisance risk from seismicity induced by UK shale gas development, with implications for future policy design

We propose a novel framework for assessing the risk associated with seismicity induced by hydraulic fracturing, which has been a notable source of recent public concern. The framework combines statistical forecast models for injection-induced seismicity, ground motion prediction equations, and exposure models for affected areas, to quantitatively link the volume of fluid injected during operations with the potential for nuisance felt ground motions. Such (relatively small) motions are expected to be more aligned with the public tolerance threshold for induced seismicity than larger ground shaking that could cause structural damage. This proactive type of framework, which facilitates control of the injection volume ahead of time for risk mitigation, has significant advantages over reactive-type magnitude and ground-motion-based systems typically used for induced seismicity management. The framework is applied to the region surrounding the Preston New Road shale gas site in North West England. A notable finding is that the calculations are particularly sensitive to assumptions of the seismicity forecast model used, i.e. whether it limits the cumulative seismic moment released for a given volume or assumes seismicity is consistent with the Gutenberg–Richter distribution for tectonic events. Finally, we discuss how the framework can be used to inform relevant policy

SB-ETAS: using simulation based inference for scalable, likelihood-free inference for the ETAS model of earthquake occurrences

Performing Bayesian inference for the Epidemic-Type Aftershock Sequence (ETAS) model of earthquakes typically requires MCMC sampling using the likelihood function or estimating the latent branching structure. These tasks have computational complexity $O(n^2)$ with the number of earthquakes and therefore do not scale well with new enhanced catalogs, which can now contain an order of $10^6$ events. On the other hand, simulation from the ETAS model can be done more quickly $O(n \log n )$. We present SB-ETAS: simulation-based inference for the ETAS model. This is an approximate Bayesian method which uses Sequential Neural Posterior Estimation (SNPE), a machine learning based algorithm for learning posterior distributions from simulations. SB-ETAS can successfully approximate ETAS posterior distributions on shorter catalogues where it is computationally feasible to compare with MCMC sampling. Furthermore, the scaling of SB-ETAS makes it feasible to fit to very large earthquake catalogs, such as one for Southern California dating back to 1932. SB-ETAS can find Bayesian estimates of ETAS parameters for this catalog in less than 10 hours on a standard laptop, which would have taken over 2 weeks using MCMC. Looking beyond the standard ETAS model, this simulation based framework would allow earthquake modellers to define and infer parameters for much more complex models that have intractable likelihood functions