@article{fdi:010092551,
  title = {{M}onitoring of local earthquakes in {H}aiti using low-cost, citizen-hosted seismometers and regional broadband stations},
  author = {{P}aul, {S}. and {M}onfret, {T}ony and {C}ouboulex, {F}. and {C}h{\`e}ze, {J}. and {C}alais, {E}ric and {S}ymithe, {S}. {J}. and {D}eschamps, {A}. and {P}eix, {F}. and {A}mbrois, {D}. and {M}artin, {X}. and {S}t {F}leur, {S}. and {B}oisson, {D}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}eismic monitoring in {H}aiti is currently provided by a mixed network of low-cost {R}aspberry {S}hake ({RS}) seismic stations hosted by citizens, and short-period and broadband stations located mainly in neighboring countries. {T}he level of earthquake detection is constantly improving for a better spatio-temporal distribution of seismicity as the number of {RS} increases. {I}n this article, we analyze the impact of the quality of the signals recorded by the {RS}-low-cost seismometers with the smallest magnitude that the network can detect by studying the ambient noise level at these stations. {B}ecause the {RS} stations are installed as part of a citizen-science project, their ambient noise estimated by the power spectral density ({PSD}) method often shows a high-noise level at frequencies above 1 {H}z. {I}n the near field (< 50 km), we show that the network detects seismic events of local magnitude on the order of 2.2 with signal-to-noise ratios ({SNR}s) greater than 4. {I}mproving the network detection threshold requires densifying the network with more {RS} stations in locations that are less noisy, if possible. {I}n spite of these limitations, this mixed network has provided near-field data essential to rapidly understand the mechanism of the mainshock of the 14 {A}ugust 2021 {M}-w 7.2 earthquake, to monitor its sequence of aftershocks in near-real time, and to monitor background seismicity in {H}aiti on a routine basis.},
  keywords = {{HAITI}},
  booktitle = {},
  journal = {{S}eismological {R}esearch {L}etters},
  volume = {94},
  numero = {6},
  pages = {2725--2739},
  ISSN = {0895-0695},
  year = {2023},
  DOI = {10.1785/0220230059},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092551},
}