Cargando…

The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data

‘Every Earthquake a Precursor According to Scale’ (EEPAS) is a catalogue-based model to forecast earthquakes within the coming months, years and decades, depending on magnitude. EEPAS has been shown to perform well in seismically active regions like New Zealand (NZ). It is based on the observation t...

Descripción completa

Detalles Bibliográficos
Autores principales: Rhoades, David A., Rastin, Sepideh J., Christophersen, Annemarie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712439/
https://www.ncbi.nlm.nih.gov/pubmed/33287032
http://dx.doi.org/10.3390/e22111264
_version_ 1783618375647232000
author Rhoades, David A.
Rastin, Sepideh J.
Christophersen, Annemarie
author_facet Rhoades, David A.
Rastin, Sepideh J.
Christophersen, Annemarie
author_sort Rhoades, David A.
collection PubMed
description ‘Every Earthquake a Precursor According to Scale’ (EEPAS) is a catalogue-based model to forecast earthquakes within the coming months, years and decades, depending on magnitude. EEPAS has been shown to perform well in seismically active regions like New Zealand (NZ). It is based on the observation that seismicity increases prior to major earthquakes. This increase follows predictive scaling relations. For larger target earthquakes, the precursor time is longer and precursory seismicity may have occurred prior to the start of the catalogue. Here, we derive a formula for the completeness of precursory earthquake contributions to a target earthquake as a function of its magnitude and lead time, where the lead time is the length of time from the start of the catalogue to its time of occurrence. We develop two new versions of EEPAS and apply them to NZ data. The Fixed Lead time EEPAS (FLEEPAS) model is used to examine the effect of the lead time on forecasting, and the Fixed Lead time Compensated EEPAS (FLCEEPAS) model compensates for incompleteness of precursory earthquake contributions. FLEEPAS reveals a space-time trade-off of precursory seismicity that requires further investigation. Both models improve forecasting performance at short lead times, although the improvement is achieved in different ways.
format Online
Article
Text
id pubmed-7712439
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-77124392021-02-24 The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data Rhoades, David A. Rastin, Sepideh J. Christophersen, Annemarie Entropy (Basel) Article ‘Every Earthquake a Precursor According to Scale’ (EEPAS) is a catalogue-based model to forecast earthquakes within the coming months, years and decades, depending on magnitude. EEPAS has been shown to perform well in seismically active regions like New Zealand (NZ). It is based on the observation that seismicity increases prior to major earthquakes. This increase follows predictive scaling relations. For larger target earthquakes, the precursor time is longer and precursory seismicity may have occurred prior to the start of the catalogue. Here, we derive a formula for the completeness of precursory earthquake contributions to a target earthquake as a function of its magnitude and lead time, where the lead time is the length of time from the start of the catalogue to its time of occurrence. We develop two new versions of EEPAS and apply them to NZ data. The Fixed Lead time EEPAS (FLEEPAS) model is used to examine the effect of the lead time on forecasting, and the Fixed Lead time Compensated EEPAS (FLCEEPAS) model compensates for incompleteness of precursory earthquake contributions. FLEEPAS reveals a space-time trade-off of precursory seismicity that requires further investigation. Both models improve forecasting performance at short lead times, although the improvement is achieved in different ways. MDPI 2020-11-06 /pmc/articles/PMC7712439/ /pubmed/33287032 http://dx.doi.org/10.3390/e22111264 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rhoades, David A.
Rastin, Sepideh J.
Christophersen, Annemarie
The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data
title The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data
title_full The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data
title_fullStr The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data
title_full_unstemmed The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data
title_short The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data
title_sort effect of catalogue lead time on medium-term earthquake forecasting with application to new zealand data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712439/
https://www.ncbi.nlm.nih.gov/pubmed/33287032
http://dx.doi.org/10.3390/e22111264
work_keys_str_mv AT rhoadesdavida theeffectofcatalogueleadtimeonmediumtermearthquakeforecastingwithapplicationtonewzealanddata
AT rastinsepidehj theeffectofcatalogueleadtimeonmediumtermearthquakeforecastingwithapplicationtonewzealanddata
AT christophersenannemarie theeffectofcatalogueleadtimeonmediumtermearthquakeforecastingwithapplicationtonewzealanddata
AT rhoadesdavida effectofcatalogueleadtimeonmediumtermearthquakeforecastingwithapplicationtonewzealanddata
AT rastinsepidehj effectofcatalogueleadtimeonmediumtermearthquakeforecastingwithapplicationtonewzealanddata
AT christophersenannemarie effectofcatalogueleadtimeonmediumtermearthquakeforecastingwithapplicationtonewzealanddata