Data for: Machine Learning as a Detection Method of Strombolian Eruptions in Infrared Images from Mount Erebus, Antarctica

Jupyter notebooks written in python to identify strombolian eruptions in infrared images in the Ray lava lake atop Mount Erebus in Antarctica and cross-correlate volcano seismic data from the strombolian eruptions. These notebooks are related to the research by Brian Dye and Gabriele Morra titled, &...

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Bibliographic Details
Main Author: Dye, Brian
Format: Dataset
Language:unknown
Published: Mendeley 2020
Subjects:
Geology
FOS Earth and related environmental sciences
Artificial Intelligence
Volcanology
Volcanic Seismology
Machine Learning
Volcano
Volcanism
Volcanic Eruption
Lava Lake
Mount Erebus
Antarc*
Antarctica
Online Access:https://dx.doi.org/10.17632/ycnjmkttj4
https://data.mendeley.com/datasets/ycnjmkttj4
Description
Summary:Jupyter notebooks written in python to identify strombolian eruptions in infrared images in the Ray lava lake atop Mount Erebus in Antarctica and cross-correlate volcano seismic data from the strombolian eruptions. These notebooks are related to the research by Brian Dye and Gabriele Morra titled, "Machine Learning as a Detection Method of Strombolian Eruptions in Infrared Images from Mount Erebus, Antarctica." The notebooks were written to be ran in Google CoLab with the current file names under the "Colab Notebooks" folder located in the "My Drive" folder of Google Drive. Some notebooks require ObsPy miniseed data, those should be run outside of Google CoLab. All outputs are contained within the folder so any notebook may be ran in or out of numerical order.

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