Improving Physics‐Based Aftershock Forecasts During the 2016–2017 Central Italy Earthquake Cascade. Issue 8 (14th August 2019) - Mancini, S.; Segou, M.; Werner, M. J.; Cattania, C.
Abstract: The 2016–2017 Central Apennines earthquake sequence is a recent example of how damages from subsequent aftershocks can exceed those caused by the initial mainshock. Recent studies reveal that physics‐based aftershock forecasts present comparable skills to their statistical counterparts, but their performance remains a controversial subject. Here we employ physics‐based models that combine the elasto‐static stress transfer with rate‐and‐state friction laws, and short‐term statistical Epidemic Type Aftershock Sequence (ETAS) models to describe the spatiotemporal evolution of the earthquake cascade. We then track the absolute and relative model performance using log‐likelihood statistics for a 1‐year horizon after the 24 August 2016 M w = 6.0 Amatrice earthquake. We perform a series of pseudoprospective experiments by producing seven classes of Coulomb rate‐state (CRS) forecasts with gradual increase in data input quality and model complexity. Our goal is to investigate the influence of data quality on the predictive power of physics‐based models and to assess the comparative performance of the forecasts in critical time windows, such as the period following the 26 October Visso earthquakes leading to the 30 October M w = 6.5 Norcia mainshock. We find that (1) the spatiotemporal performance of the basic CRS models is poor and progressively improves as more refined data are used, (2) CRS forecasts are about as informative as ETAS when secondary triggering effects from M 3+ earthquakes are included together with spatially variable slip models, spatially heterogeneous receiver faults, and optimized rate‐and‐state parameters. After the Visso earthquakes, the more elaborate CRS model outperforms ETAS highlighting the importance of the static stress transfer for operational earthquake forecasting. Key Points: Elaborate physics‐based forecasts perform close to short‐term statistical models The predictive power of physics‐based models increases when source and fault heterogeneities are considered In the days preceding the M w = 6.5 Norcia earthquake, the near‐source seismicity patterns are well explained by static stress transfer