Data‐Driven Optimization of Seismicity Models Using Diverse Data Sets: Generation, Evaluation, and Ranking Using Inlabru

Kirsty Bayliss,Mark Naylor,Janine Illian,Ian G Main

doi:10.1029/2020jb020226

Abstract

AbstractRecent developments in earthquake forecasting models have demonstrated the need for a robust method for identifying which model components are most beneficial to understanding spatial patterns of seismicity. Borrowing from ecology, we use Log‐Gaussian Cox process models to describe the spatially varying intensity of earthquake locations. These models are constructed using elements which may influence earthquake locations, including the underlying fault map and past seismicity models, and a random field to account for any excess spatial variation that cannot be explained by deterministic model components. Comparing the alternative models allows the assessment of the performance of models of varying complexity composed of different components and therefore identifies which elements are most useful for describing the distribution of earthquake locations. We demonstrate the effectiveness of this approach using synthetic data and by making use of the earthquake and fault information available for California, including an application to the 2019 Ridgecrest sequence. We show the flexibility of this modeling approach and how it might be applied in areas where we do not have the same abundance of detailed information. We find results consistent with existing literature on the performance of past seismicity models that slip rates are beneficial for describing the spatial locations of larger magnitude events and that strain rate maps can constrain the spatial limits of seismicity in California. We also demonstrate that maps of distance to the nearest fault can benefit spatial models of seismicity, even those that also include the primary fault geometry used to construct them.

Highlights

For a variety of reasons, including the lack of clear, reliable precursors, and the inherent non-linearity and complexity of the underlying process, the deterministic prediction of individual earthquakes remains an elusive goal (Jordan et al, 2011)
We demonstrate the ease with which models describing the longer-term spatial distribution of seismicity can be constructed and compared using synthetic data and data used in the UCERF3 model
We have considered so far the UCERF3 fault geometry and slip rate data, but the above comparisons of model component combinations at different magnitudes demonstrate that the slip rates may have different importance for different datasets

Summary

Introduction

For a variety of reasons, including the lack of clear, reliable precursors, and the inherent non-linearity and complexity of the underlying process, the deterministic prediction of individual earthquakes remains an elusive goal (Jordan et al, 2011). Instead the focus has shifted to forecasting the probability of occurrence of a population of events in space and time, in an attempt to determine the degree of predictability of earthquakes (Vere-Jones, 1995; Field, 2007; Jordan & Jones, 2010). Statistical point process models have been used to describe earthquake occurrence for many years (Vere-Jones & Davies, 1966; Vere-Jones, 1970; Ogata, 1998). The aim of these models is to describe the occurrence of earthquakes as a series of points in time, space, or both, with an appropriate ’mark’ such as earthquake magnitude.

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Conclusion