Articles | Volume 18, issue 1
https://doi.org/10.5194/essd-18-1-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-1-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Seismicity catalogue of the entire Chilean margin (18 to 56° S) from an automated approach
Departamento de Ciencias de la Tierra, Universidad de Concepción, Concepción, Chile
Christian Sippl
Institute of Geophysics, Czech Academy of Sciences, Prague, Czech Republic
Andrés Tassara
Departamento de Ciencias de la Tierra, Universidad de Concepción, Concepción, Chile
Sergio Ruiz
Departamento de Geofísica, Universidad de Chile, Santiago, Chile
Bertrand Potin
Departamento de Geofísica, Universidad de Chile, Santiago, Chile
Jorge Puente
Institute of Geophysics, Czech Academy of Sciences, Prague, Czech Republic
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Solid Earth, 14, 69–87, https://doi.org/10.5194/se-14-69-2023, https://doi.org/10.5194/se-14-69-2023, 2023
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Infrequent eruptions of acidic magma are one of the most dangerous natural phenomena, but almost none of them have been witnessed by modern science. We present the first systematic characterization of seismicity recorded near an erupting acidic volcano (Cordón Caulle 2011). We define different phases of unrest and eruption, which combined with previous findings allows us to discuss the main processes associated with this type of violent eruption, with implications for their volcanic hazard.
Javier Ojeda and Sergio Ruiz
Solid Earth, 12, 1075–1085, https://doi.org/10.5194/se-12-1075-2021, https://doi.org/10.5194/se-12-1075-2021, 2021
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In Santiago, Chile, the lockdown imposed due to COVID-19 was recorded by seismological instruments. This analysis shows temporal changes in the surface vibrations controlled by lockdown phases, mobility, and epidemiological factors. Our findings suggest that
dynamic lockdownand the early deconfinement in April 2020 caused an increase in mobility and therefore virus transmission. We propose that seismic networks could be used to monitor urban mobility as a new proxy in public policies.
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Short summary
Seismic networks produce large volumes of data, that are hard to process through traditional methods. Here we create an automatic workflow using machine and deep learning techniques to process five years of data in the western coast of South America. This allows us to detect smaller earthquakes than before, creating a catalogue of seismicity that contains far more events than other regional catalogues.
Seismic networks produce large volumes of data, that are hard to process through traditional...
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