Decomposition of Seasonality and Long-term Trend in Seismological Data: A Bayesian Modelling of Earthquake Detection Capability

Decomposition of Seasonality and Long-term Trend in Seismological Data: A Bayesian Modelling of Earthquake Detection Capability

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Article ID: iaor201525018
Volume: 56
Issue: 3
Start Page Number: 201
End Page Number: 215
Publication Date: Sep 2014
Journal: Australian & New Zealand Journal of Statistics
Authors:
Keywords: time series: forecasting methods
Abstract:

This study demonstrates the decomposition of seasonality and long‐term trend in seismological data observed at irregular time intervals. The decomposition was applied to the estimation of earthquake detection capability using cubic B‐splines and a Bayesian approach, which is similar to the seasonal adjustment model frequently used to analyse economic time‐series data. We employed numerical simulation to verify the method and then applied it to real earthquake datasets obtained in and around the northern Honshu island, Japan. With this approach, we obtained the seasonality of the detection capability related to the annual variation of wind speed and the long‐term trend corresponding to the recent improvement of the seismic network in the studied region.

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