@article{https://doi.org/10.60692/80c91-hse77,
doi = {10.60692/80C91-HSE77},
url = {https://gresis.osc.int//doi/10.60692/80c91-hse77},
author = {Raphaël De Plaen,
and Víctor Hugo Márquez-Ramírez,
and Xyoli Pérez‐Campos,
and F. Ramón Zúñiga,
and Quetzalcoatl Rodríguez‐Pérez,
and Juan Martín Gómez González,
and Lucía Capra,
},
keywords = {Machine Learning for Earthquake Early Warning Systems,
Artificial Intelligence,
Computer Science,
Physical Sciences,
Study of Earthquake Precursor Phenomena,
Geophysics,
FOS: Earth and related environmental sciences,
Earth and Planetary Sciences,
Anomaly Detection in High-Dimensional Data,
Seismic Activity Monitoring,
Seismometer,
Context (archaeology),
Geography,
Pandemic,
The Internet,
Population,
Coronavirus disease 2019 (COVID-19),
Scale (ratio),
Cartography,
Seismology,
Computer science,
Demography,
FOS: Sociology,
Sociology,
Geology,
World Wide Web,
Infectious disease (medical specialty),
Medicine,
Disease,
Archaeology,
Pathology},
language = {en},
title = {Seismic signature of the COVID-19 lockdown at the city-scale: A case study with low-cost seismometers in the city of Querétaro,
Mexico},
publisher = {OpenAlex},
year = {2020},
copyright = {cc-by} }