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Machine-Learning-Based High-Resolution Earthquake Catalog For the 2016-2017 Central Italy Sequence

Authors: Tan, Yen Joe; Waldhauser, Felix; Ellsworth, William;

Machine-Learning-Based High-Resolution Earthquake Catalog For the 2016-2017 Central Italy Sequence

Abstract

This is the earthquake catalog published as part of the manuscript Tan et al. (2021), Machine-Learning-Based High-Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated During the 2016-2017 Central Italy Sequence, The Seismic Record. The meaning of column headers: ml_n: number of amplitude measurements that went into the local magnitude estimate ml_mean: local magnitude taken as mean of all stations ml_std: local magnitude sample standard deviation ml_median: local magnitude taken as median of all stations mw: moment magnitude converted from ml_median split: 1 are potential split events, identified for having similar origin time, location, and magnitude as another event but with significantly fewer associated arrival time picks

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selected citations
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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).
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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!
views
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