Bridging time scales for comprehensive ETAS modelling to accommodate short-term to long-term incompleteness of seismicity catalogues

Earthquake catalogues, vital for understanding earthquake dynamics, often grapple with incompleteness across varying time scales. Our research pioneers an innovative strategy to seamlessly integrate time-varying incompleteness into the Epidemic-Type Aftershock Sequence (ETAS) model. Leveraging the Bayesian prowess of inlabru package in R programming language, which is based on the Integrated Nested Laplace Approximation (INLA) method, we not only capture uncertainties but also forge a robust bridge between short-term to long-term gaps in records of earthquakes.

Our methodology, a fusion of the ETAS model and inlabru, provides a comprehensive framework that adapts to diverse scales of incompleteness. We address the complex nature of seismic patterns by considering both short-term gaps in early aftershocks (minutes to a few days) and long-term irregularities (years to centuries) in historical earthquake data records. Technically, the short-term incompleteness period arises from seismic network saturation during periods of high activity, resulting in the underrecording of small events, while the long-term incompleteness originates from sparse network coverage and inability to detect events over extended time. Bayesian foundation of inlabru enriches the model with posterior distributions, empowering us to navigate uncertainties and refine seismic hazard assessments. By utilising a combination of simulated synthetic data and real earthquake catalogues, our results showcase the impact of this approach on the ETAS model, markedly improving its predictive accuracy across various temporal scales of incompleteness.

In this study, we present an initiative in seismicity modelling that bridges temporal gaps, allowing the ETAS model to evolve with the ever-changing landscape of earthquake data incompleteness. This research not only enriches our understanding of spatiotemporal seismicity patterns but also lays the groundwork for more resilient and adaptive aftershock forecasting, ultimately equipping decision-makers with more reliable information about seismic hazards, and enhancing community resilience in the face of earthquakes.