Citation

Calais E, Symithe S, Monfret T, Delouis B, Lomax A, Courboulex F, Ampuero JP, Lara PE, Bletery Q, Chèze J, Peix F, Deschamps A, de Lépinay B, Raimbault B, Jolivet R, Paul S, St Fleur S, Boisson D, Fukushima Y, Duputel Z, Xu L, Meng L. Science 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, American Association for the Advancement of Science)

DOI

10.1126/science.abn1045

PMID

35271301

Abstract

The August 14, Mw7.2, Nippes earthquake in Haiti occurred within the same fault zone as its devastating, Mw7.0, 2010 predecessor but struck the country when field access was limited by insecurity and conventional seismometers from the national network were inoperative. A network of citizen seismometers installed in 2019 provided near-field data critical to rapidly understand the mechanism of the mainshock and monitor its aftershock sequence. Their real-time data define two aftershock clusters that coincide with two areas of coseismic slip derived from inversions of conventional seismological and geodetic data. Machine learning applied to data from the citizen seismometer closest to the mainshock allows us to forecast aftershocks as accurately as with the network-derived catalog. This shows the utility of citizen science contributing to the understanding of a major earthquake.

Language: en