Automated classification of seismic signals recorded on the Åknes rock slope, Western Norway, using a convolutional neural network

Langet, Nadège; Silverberg, Fred Marcus John

doi:https://doi.org/10.5194/esurf-11-89-2023

Articles | Volume 11, issue 1

https://doi.org/10.5194/esurf-11-89-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esurf-11-89-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 1

Research article

|

28 Feb 2023

Research article |

| 28 Feb 2023

Automated classification of seismic signals recorded on the Åknes rock slope, Western Norway, using a convolutional neural network

Nadège Langet and Fred Marcus John Silverberg

Data sets

Data repository - Åknes surface geophone data for the time period 2007-2021 NORSAR https://doi.org/10.21348/d.no.0003

NORSAR Station Network NORSAR https://doi.org/10.21348/d.no.0001

Short summary

Microseismic events recorded on the Åknes rock slope in Norway during the past 15 years are automatically divided into eight classes. The results are analysed and compared to meteorological data, showing a strong increase in the microseismic activity in spring mainly due to freezing and thawing processes.