Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main,
Germany
Institute of Geosciences, Goethe University Frankfurt, 60438 Frankfurt
am Main, Germany
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Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 1,141 (including HTML, PDF, and XML)
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1,006
106
29
1,141
37
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PDF: 106
XML: 29
Total: 1,141
BibTeX: 37
EndNote: 30
Views and downloads (calculated since 30 May 2022)
Cumulative views and downloads
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Total article views: 926 (including HTML, PDF, and XML)
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791
106
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926
35
29
HTML: 791
PDF: 106
XML: 29
Total: 926
BibTeX: 35
EndNote: 29
Views and downloads (calculated since 10 Nov 2022)
Cumulative views and downloads
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Total article views: 215 (including HTML, PDF, and XML)
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215
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215
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Views and downloads (calculated since 30 May 2022)
Cumulative views and downloads
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Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 1,141 (including HTML, PDF, and XML)
Thereof 1,093 with geography defined
and 48 with unknown origin.
Total article views: 926 (including HTML, PDF, and XML)
Thereof 897 with geography defined
and 29 with unknown origin.
Total article views: 215 (including HTML, PDF, and XML)
Thereof 196 with geography defined
and 19 with unknown origin.
Earthquake magnitude is a crucial parameter in defining its damage potential, and hence its speedy determination is essential to issue an early warning in regions close to the epicentre. This study summarises our findings in an attempt to apply deep-learning-based classifiers to earthquake waveforms, particularly with respect to finding an optimum length of input data. We conclude that the input length has no significant effect on the model accuracy, which varies between 90 %–94 %.
Earthquake magnitude is a crucial parameter in defining its damage potential, and hence its...