Comparison between alarm-based and probability-based earthquake forecasting methods

In a recent work, we applied the every earthquake a precursor according to scale (EEPAS) probabilistic model to the pseudo-prospective forecasting of shallow earthquakes with magni- tude M 5.0 in the Italian region. We compared the forecasting performance of EEPAS with that of the epidemic type aftershock sequences (ETAS) forecasting model, using the most recent consistency tests developed within the collaboratory for the study of earthquake predictabil- ity (CSEP). The application of such models for the forecasting of Italian target earthquakes seems to show peculiar characteristics for each of them. In particular, the ETAS model showed higher performance for short-term forecasting, in contrast, the EEPAS model showed higher forecasting performance for the medium/long-term. In this work, we compare the performance of EEPAS and ETAS models with that obtained by a deterministic model based on the occur- rence of strong foreshocks (FORE model) using an alarm-based approach. We apply the two rate-based models (ETAS and EEPAS) estimating the best probability threshold above which we issue an alarm. The model parameters and probability thresholds for issuing the alarms are calibrated on a learning data set from 1990 to 2011 during which 27 target earthquakes have occurred within the analysis region. The pseudo-prospective forecasting performance is as- sessed on a validation data set from 2012 to 2021, which also comprises 27 target earthquakes. Tests to assess the forecasting capability demonstrate that, even if all models outperform a purely random method, which trivially forecast earthquake proportionally to the space–time occupied by alarms, the EEPAS model exhibits lower forecasting performance than ETAS and FORE models. In addition, the relative performance comparison of the three models demonstrates that the forecasting capability of the FORE model appears slightly better than ETAS, but the difference is not statistically significant as it remains within the uncertainty level. However, truly prospective tests are necessary to validate such results, ideally using new testing procedures allowing the analysis of alarm-based models, not yet available within the CSEP.

Azarbakht, A., Rudman, A. & Douglas, J., 2021. A decision-making approach for operational earthquake forecasting, Int. J. Disaster Risk Reduct., 66, doi:10.1016/j.ijdrr.2021.102591.
Bayona, J.A., Savran, W.H., Rhoades, D.A. & Werner, M.J., 2022. Prospec- tive evaluation of multiplicative hybrid earthquake forecasting models in California, Geophys. J. Int., 229, 1736–1753.
Biondini, E., Rhoades, D.A. & Gasperini, P., 2023. Application of the EEPAS earthquake forecasting model to Italy, Geophys. J. Int., 234(3), 1681– 1700.
Console, R. & Murru, M. 2001. A simple and testable model for earthquake clustering, J. geophys. Res., 106(), 8699–8711.
Console, R., Murru, M. & Catalli, F., 2006. Physical and stochastic models of earthquake clustering, Tectonophysics, 417, 141–153.
Console, R., Murru, M. & Falcone, G., 2010. Retrospective forecasting of M ≥ 4.0 earthquakes in New Zealand, Pure appl. Geophys., 167, 693–707. Falcone, G., Console, R. & Murru, M., 2010. Short-term and long-term
earthquake occurrence models for Italy: ETES, ERS and LTST, Ann.
Geophys., 53(3), 41–50.
Gasperini, P., Biondini, E., Lolli, B., Petruccelli, A. & Vannucci, G.,
2021. Retrospective short-term forecasting experiment in Italy based on the occurrence of strong (fore) shocks, Geophys. J. Int., 225, 1192–1206.
Jones, L.M., 1984. Foreshocks (1966-1980) in the San Andreas system, California, Bull. seism. Soc. Am., 74, 1361–1380.
Jones, L.M., 1985. Foreshocks and time-dependent earthquake hazard as- sessment in Southern California, Bull. seism. Soc. Am., 75, 1669–1679.
Jones, L.M., 1994. Foreshocks, aftershocks, and earthquake probabilities: accounting for the Landers earthquake, Bull. seism. Soc. Am., 84, 892– 899.
Jordan, T.H. et al., 2011. Operational earthquake forecasting: state of knowl- edge and guidelines for utilization, Ann. Geophys., 54(4), doi:10.4401/ag- 5350.
Jordan, T.H., 2006. Earthquake predictability, brick by brick, Seismol. Res. Lett., 77(1), 3–6.
Jordan, T.H., 2009. Earthquake system science: potential for seismic risk reduction, Sci. Iran., 16, 351–366.
Kagan, Y.Y., 2009. Testing long-term earthquake forecasts: likelihood methods and error diagrams, Geophys. J. Int., 177, 532–542. doi: 10.1111/j.1365-246X.2008.04064.x.
Lolli, B., Randazzo, D., Vannucci, G. & Gasperini, P., 2020. The Homog- enized instrumental Seismic Catalog (HORUS) of Italy from 1960 to present, Seismol. Res. Lett., 91, 3208–3222.
Lombardi, A.M. & Marzocchi, W., 2010. The ETAS model for daily fore- casting of Italian seismicity in the CSEP experiment, Ann. Geophys., 53(3), doi:10.4401/ag-4848.
MacPherson-Krutsky, C., Lindell, M.K. & D. Brand, B., 2023. Residents’ information seeking behavior and protective action for earthquake hazards in the Portland Oregon Metropolitan Area, Risk Anal., 43, 372–390.
Marzocchi, W. & Lombardi, A.M., 2009. Real-time forecasting following a damaging earthquake, Geophys. Res. Lett., 36(21), doi:10.1029/2009GL040233.
Michael, A.J. & Werner, M.J., 2018. Preface to the focus section on the Collaboratory for the Study of Earthquake Predictability (CSEP): new results and future directions, Seismol. Res. Lett., 89(4), 1226–1228.
Mizrahi, L., Nandan, S., Savran, W., Wiemer, S. & Ben-Zion, Y., 2023. Question-driven ensembles of flexible ETAS models, Seismol. Res. Lett., 94, 829–843.
Molchan, G.M., 1990. Strategies in strong earthquake prediction, Phys. Earth planet. Inter., 61, 84–98.
Molchan, G.M., 1991. Structure of optimal strategies in earthquake predic- tion, Tectonophysics, 193, 267–276.
Molchan, G.M. & Kagan, Y.Y., 1992. Earthquake prediction and its opti- mization, J. geophys. Res., 97, 4823.
Murru, M., Console, R. & Falcone, G., 2009. Real time earthquake fore- casting in Italy, Tectonophysics, 470, 214–223.
Ogata, Y., 1988. Statistical models for earthquake occurrences and residual analysis for point processes, J. Am. Stat. Assoc., 83, 9–27.
Ogata, Y. & Zhuang, J., 2006. Space–time ETAS models and an improved extension, Tectonophysics, 413, 13–23.
Rhoades, D.A., 2007. Application of the EEPAS model to forecasting earth- quakes of moderate magnitude in Southern California, Seismol. Res. Lett., 78, 110–115.
Rhoades, D.A., 2011. Application of a long-range forecasting model to earthquakes in the Japan mainland testing region, Earth Planets Space, 63, 197–206.
Rhoades, D.A. & Evison, F.F., 2004. Long-range earthquake forecasting with every earthquake a precursor according to scale, Pure appl. Geophys., 161, 47–72.
Rovida, A., Locati, M., Camassi, R., Lolli, B. & Gasperini, P., 2020. The Italian earthquake catalogue CPTI15, Bull. Earthq. Eng., 18, 2953–2984. Rovida, A., Locati, M., Camassi, R., Lolli, B., Gasperini, P., Antonucci, A., et al. 2022. Italian Parametric Catalogue of Italian Earthquakes (CPTI15), version 4.0, Istituto Nazionale di Geofisica e Vulcanologia
(INGV), doi:10.13127/cpti/cpti15.4.
Rovida, A., Locati, M., Camassi, R., Lolli, B & Gasperini, P.(eds),
2016. CPTI15, the 2015 version of the Parametric Catalogue of Ital- ian Earthquakes, Istituto Nazionale di Geofisica e Vulcanologia, doi: 10.6092/INGV.IT-CPTI15.
Savran, W., Werner, M., Schorlemmer, D. & Maechling, P., 2022a. pyCSEP: a python package for earthquake forecast developers, J. Open Source Software, 7, doi:10.21105/joss.03658.
Savran, W.H. et al., 2022b. pycsep: a python toolkit for earthquake forecast developers, Seismol. Res. Lett., 93(5), 2858–2870.
Schorlemmer, D. et al., 2018. The collaboratory for the study of earthquake predictability: achievements and priorities, Seismol. Res. Lett., 89(4), 1305–1313.
Schorlemmer, D., Gerstenberger, M.C., Wiemer, S., Jackson, D.D. & Rhoades, D.A., 2007. Earthquake likelihood model testing, Seismol. Res. Lett., 78, 17–29.
Shebalin, P., Narteau, C., Holschneider, M. & Schorlemmer, D., 2011. Short- term earthquake forecasting using early aftershock statistics, Bull. seism. Soc. Am., 101, 297–312.
Werner, M.J., Zechar, J.D., Marzocchi, W. & Wiemer, S., 2010. Retrospective evaluation of the five-year and ten-year CSEP-Italy earthquake forecasts, Ann. Geophys., 53(3), doi:10.4401/ag-4840.
Zechar, J.D. & Jordan, T.H., 2008. Testing alarm-based earthquake predic- tions, Geophys. J. Int., 172, 715–724.
Zechar, J.D. & Jordan, T.H., 2010. The area skill score statistic for evaluating earthquake predictability experiments, Pure appl. Geophys., 167, 893– 906.
Zechar, J.D., Schorlemmer, D., Liukis, M., Yu, J., Euchner, F., Maechling, P.J. & Jordan, T.H., 2010b. The collaboratory for the study of earthquake predictability perspective on computational earthquake science, Concur. Comp. Pract. Exp., 22(12), 1836–1847.

This article has been accepted for publication in Geophysical Journal International ©:The Author(s) 2023. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Uploaded in accordance with the publisher's self-archiving policy.
All rights reserved.