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- W4322001968 abstract "Usually, the earthquake catalog for a given region represents a collection of all detected and localized earthquakes and, thus, contains not only the main shocks, but also fore- and aftershocks. In order to perform an independent seismic event and seismic hazard analysis we require a catalog that, ideally, contains only mainshocks. Thus, the removal of dependent fore- and aftershocks from an earthquake catalogby declustering is a crucial step in seismic hazard analysis. Machine learning methods can potentially offer improvements in speed and accuracy in comparison to classical declustering approaches.Here, we propose a hybrid approach to identify the temporal clusters of earthquakes from the catalogs of California (USGS) and Japan (ISC). We combine unsupervised 1-D clustering algorithms with seismologically informed methods and machine learning techniques. We use epidemic type aftershock sequence (ETAS) generated catalogs as well as classically declustered catalogs to benchmark the method." @default.
- W4322001968 created "2023-02-26" @default.
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- W4322001968 date "2023-05-15" @default.
- W4322001968 modified "2023-10-16" @default.
- W4322001968 title "A hybrid approach for declustering of earthquake catalogs" @default.
- W4322001968 doi "https://doi.org/10.5194/egusphere-egu23-11462" @default.
- W4322001968 hasPublicationYear "2023" @default.
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