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Spatio‐Temporal Modelling of Extreme Induced Seismicity in the Presence of An Evolving Measurement Network

Author

Listed:
  • Conor Murphy
  • Jonathan A. Tawn
  • Zak Varty
  • Ross Towe
  • Peter M. Atkinson

Abstract

Earthquakes induced by injecting or extracting gas from underground reservoirs can pose a significant hazard to surrounding infrastructure and populations. Safeguarding against future seismic hazards requires accurate models for the upper tail of the earthquake magnitude distribution that are able to represent various intervention strategies. For these models, we need efficient inference methods and reliable estimates of uncertainty. We propose a novel extreme value modelling procedure, which uses known changes in the earthquake measurement network, to automatically select a parametric spatio‐temporal extreme value threshold which accounts for undetected earthquake values. We introduce methods to propagate the uncertainties in the extreme value model parameters, the threshold parameters and the threshold functional formulation through to future hazard estimates. We apply our methodology to the earthquake catalogue from the Groningen gas field in the Netherlands, delivering clear improvements over existing analyses and providing the first quantification of the different sources of uncertainty in such estimates. The procedure has the potential to be useful for a broad range of extreme value contexts to account for threshold uncertainty when parametric threshold models are used, or where data are missing due to limitations in measurement equipment.

Suggested Citation

  • Conor Murphy & Jonathan A. Tawn & Zak Varty & Ross Towe & Peter M. Atkinson, 2026. "Spatio‐Temporal Modelling of Extreme Induced Seismicity in the Presence of An Evolving Measurement Network," Environmetrics, John Wiley & Sons, Ltd., vol. 37(3), April.
  • Handle: RePEc:wly:envmet:v:37:y:2026:i:3:n:e70090
    DOI: 10.1002/env.70090
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    References listed on IDEAS

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    2. Stuart G. Coles & Jonathan A. Tawn, 1996. "A Bayesian Analysis of Extreme Rainfall Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(4), pages 463-478, December.
    3. Benjamin D. Youngman, 2019. "Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1865-1879, October.
    4. Jan Beirlant & Andrzej Kijko & Tom Reynkens & John H. J. Einmahl, 2019. "Estimating the maximum possible earthquake magnitude using extreme value methodology: the Groningen case," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(3), pages 1091-1113, September.
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