A Bayesian model for truncated regression for the estimation of empirical ground-motion models

Nicolas Martin Kuehn, Tadahiro Kishida, Mohammad AlHamaydeh, Grigorios Lavrentiadis, Yousef Bozorgnia

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

We present a Bayesian model for the estimation of ground-motion models that allows one to account for truncated data. Truncated data occurs in ground-motion model development because instruments do not record continuously, but only when triggered. The model is formulated as a multi-level model and incorporates event and station terms. The model considers truncation on one variable [e.g., peak ground acceleration (PGA)], and models the joint occurrence of PGA and other ground-motion intensity measures, while conditioning on the truncation for PGA. Initially, we perform numerical experiments on simulated data sets and show that not taking data truncation into account leads to biased models. Regressions using the proposed truncated model can recapture the functions used in the simulation well, and perform comparable to alternative approaches used in the past. Subsequently, we show the impact of the truncated model on observed ground-motion data representing moderate and high trigger levels, 2–4 gal and 10 gal, respectively. Differences to a model that does not take truncation into account occur at larger distances, and are more severe for the high trigger level data. For untruncated regression, the values of the standard deviations are underestimated.

Original languageBritish English
Pages (from-to)6149-6179
Number of pages31
JournalBulletin of Earthquake Engineering
Volume18
Issue number14
DOIs
StatePublished - 1 Nov 2020

Keywords

  • Bayesian regression
  • Ground-motion model
  • Truncated data

Fingerprint

Dive into the research topics of 'A Bayesian model for truncated regression for the estimation of empirical ground-motion models'. Together they form a unique fingerprint.

Cite this