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ZENODO
Dataset . 2025
License: CC BY NC SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC SA
Data sources: Datacite
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Global EarthquakE ScEnarios (GEESE): An OpenQuake Engine-Based Rupture Matching Algorithm and Scenarios Database

Authors: Brooks, Christopher; Pagani, Marco; Villani, Manuela; Johnson, Kendra; Styron, Richard; Bayliss, Kirsty;

Global EarthquakE ScEnarios (GEESE): An OpenQuake Engine-Based Rupture Matching Algorithm and Scenarios Database

Abstract

When evaluating the seismic hazard within a region or site of interest (e.g., a densely populated city or a nuclear power plant), it is essential to realistically model the characteristics (size, geometry and surface orientation) of large earthquake ruptures that may occur. The GEESE algorithm retrieves sufficiently representative ruptures for queried earthquakes from seismic hazard models developed within the OpenQuake Engine seismic hazard software. We used the GEESE algorithm to retrieve finite ruptures for the strongest events included in the global-coverage ISC-GEM earthquake catalogue (covering a time window of 1904 to 2019), which we present here as the GEESE database. For each event to which a rupture could be retrieved, the database also included the inputs for an OpenQuake Hazard Scenario calculation and the ground-motion fields (GMFs) computed using some (reconfigurable) default parameters. The GEESE algorithm and database is described in detail in the following open-access journal article: Brooks, C., Pagani, M., Villani, M., Johnson, K., Styron, R., & Bayliss, K. (2025). Global EarthquakE ScEnarios (GEESE): An OpenQuake Engine-Based Rupture Matching Algorithm and Scenarios Database for Seismic Source Model Testing and Rapid Post-Event Response Analysis. Seismica, 4(2). https://doi.org/10.26443/seismica.v4i2.1654

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average