气候变化领域集成服务门户(CCPortal): Data-Driven Optimization of Seismicity Models Using Diverse Data Sets: Generation, Evaluation, and Ranking Using Inlabru

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DOI	10.1029/2020JB020226
	Data-Driven Optimization of Seismicity Models Using Diverse Data Sets: Generation, Evaluation, and Ranking Using Inlabru
	Bayliss K.; Naylor M.; Illian J.; Main I.G.
发表日期	2020
ISSN	21699313
卷号	125 期号:11
英文摘要	Recent 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. ©2020. The Authors.
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187498
作者单位	School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom; School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
推荐引用方式 GB/T 7714	Bayliss K.,Naylor M.,Illian J.,et al. Data-Driven Optimization of Seismicity Models Using Diverse Data Sets: Generation, Evaluation, and Ranking Using Inlabru[J],2020,125(11).
APA	Bayliss K.,Naylor M.,Illian J.,&Main I.G..(2020).Data-Driven Optimization of Seismicity Models Using Diverse Data Sets: Generation, Evaluation, and Ranking Using Inlabru.Journal of Geophysical Research: Solid Earth,125(11).
MLA	Bayliss K.,et al."Data-Driven Optimization of Seismicity Models Using Diverse Data Sets: Generation, Evaluation, and Ranking Using Inlabru".Journal of Geophysical Research: Solid Earth 125.11(2020).