Ranking and selection of earthquake ground-motion models using the stochastic area metric

Sunny, Jaleena and De Angelis, Marco and Edwards, Benjamin (2021) Ranking and selection of earthquake ground-motion models using the stochastic area metric. Seismological Research Letters. 787–797. ISSN 1938-2057 (https://doi.org/10.1785/0220210216)

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Abstract

We introduce the cumulative‐distribution‐based area metric (AM)—also known as stochastic AM—as a scoring metric for earthquake ground‐motion models (GMMs). The AM quantitatively informs the user of the degree to which observed or test data fit with a given model, providing a rankable absolute measure of misfit. The AM considers underlying data distributions and model uncertainties without any assumption of form. We apply this metric, along with existing testing methods, to four GMMs in order to test their performance using earthquake ground‐motion data from the Preston New Road (United Kingdom) induced seismicity sequences in 2018 and 2019. An advantage of the proposed approach is its applicability to sparse datasets. We, therefore, focus on the ranking of models for discrete ranges of magnitude and distance, some of which have few data points. The variable performance of models in different ranges of the data reveals the importance of considering alternative models. We extend the ranking of GMMs through analysis of intermodel variations of the candidate models over different ranges of magnitude and distance using the AM. We find the intermodel AM can be a useful tool for selection of models for the logic‐tree framework in seismic‐hazard analysis. Overall, the AM is shown to be efficient and robust in the process of selection and ranking of GMMs for various applications, particularly for sparse and small‐sized datasets.

ORCID iDs

Sunny, Jaleena, De Angelis, Marco ORCID logoORCID: https://orcid.org/0000-0001-8851-023X and Edwards, Benjamin;
CORE (COnnecting REpositories)