@article{fdi:010084692,
  title = {{C}itizen seismology helps decipher the 2021 {H}aiti earthquake},
  author = {{C}alais, {E}ric and {S}ymithe, {S}. and {M}onfret, {T}ony and {D}elouis, {B}. and {L}omax, {A}. and {C}ourboulex, {F}. and {A}mpuero, {J}ean-{P}aul and {L}ara, {P}. {E}. and {B}letery, {Q}uentin and {C}heze, {J}. and {P}eix, {F}. and {D}eschamps, {A}. and de {L}epinay, {B}. and {R}aimbault, {B}. and {J}olivet, {R}. and {P}aul, {S}. and {S}t {F}leur, {S}. and {B}oisson, {D}. and {F}ukushima, {Y}. and {D}uputel, {Z}. and {X}u, {L}. and {M}eng, {L}.},
  editor = {},
  language = {{ENG}},
  abstract = {{O}n 14 {A}ugust 2021, the moment magnitude ({M}-w) 7.2 {N}ippes earthquake in {H}aiti occurred within the same fault zone as its devastating 2010 {M}-w 7.0 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. {T}heir real-time data defined two aftershock clusters that coincide with two areas of coseismic slip derived from inversions of conventional seismological and geodetic data. {M}achine 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. {T}his shows the utility of citizen science contributing to our understanding of a major earthquake.},
  keywords = {{HAITI}},
  booktitle = {},
  journal = {{S}cience},
  volume = {376},
  numero = {6590},
  pages = {283--287},
  ISSN = {0036-8075},
  year = {2022},
  DOI = {10.1126/science.abn1045},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084692},
}