Evolution of statistical earthquake models to account for measurement errors and dependence.

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Numerous strategies have been proposed and developed to lower CO2 emissions within the environment. Carbon Capture and Storage (CCS) systems have been identified as one of the most effective strategies. To enable a safe growth of such geological formations, any dangers over the linked induced seismic activity should first be evaluated. This evaluation requires extending existing approaches for assessing the potential hazards of tectonic earthquakes and human-induced earthquakes. Specifically, our research aims to enhance existing statistical models, such as stochastic point process models (ETAS models) and extreme value methods, commonly used in earthquake analysis, incorporating features overlooked in current approaches. For instance, our research challenges assumptions about earthquake records and covariate information, investigating the effects of assumed independence in aftershock magnitudes.Another novel aspect of our project involves accounting for measurement errors in both point process and extreme value methods, recognising the error properties of earthquake magnitudes, time and locations inferred from sensor network vibrations. Within this research project, we use historical earthquake catalogues. We aim to explore how these strong assumptions affect results and incorporate related studies of uncertainties and dependence in seismicity modelling.Industrial partner: Shell.