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Shrey, S. K., & Baker, J. W. (2011). Regression Models for Predicting the Probability of Near-fault Earthquake Ground Motion Pulses, and Their Period. In Proceedings of 11th International Conference on Applications of Statistics and Probability in Civil Engineering (8 p.). Zurich, Switzerland.

has been cited by the following article:

• TITLE: Estimating the Probability of Earthquake Occurrence and Return Period Using Generalized Linear Models

  AUTHORS: Noora Shrestha

  KEYWORDS: Earthquake, Generalized Linear Model, Gutenberg-Richter Relation, Poisson Regression, Seismic Hazard

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.7 No.9, September 16, 2019

  ABSTRACT: In this paper, the frequency of an earthquake occurrence and magnitude relationship has been modeled with generalized linear models for the set of earthquake data of Nepal. A goodness of fit of a statistical model is applied for generalized linear models and considering the model selection information criterion, Akaike information criterion and Bayesian information criterion, generalized Poisson regression model has been selected as a suitable model for the study. The objective of this study is to determine the parameters (a and b values), estimate the probability of an earthquake occurrence and its return period using a Poisson regression model and compared with the Gutenberg-Richter model. The study suggests that the probabilities of earthquake occurrences and return periods estimated by both the models are relatively close to each other. The return periods from the generalized Poisson regression model are comparatively smaller than the Gutenberg-Richter model.