Classification of IR Images of Small Eruptions at the Erebus Volcano, Antarctica, With Zernike Moments and Support Vector Machine

Abstract

AbstractTo investigate the explosive nature of the Erebus volcano, Antarctica, millions of infrared images of the Ray lava lake were analyzed to identify time and position of non‐explosive small eruptions (NESEs). We developed a new technique based on two steps: (a) feature recognition using Zernike moments of the IR images and (b) classification using Support Vector Machine (SVM) applied to Zernike moments. Measures of the performance score were at least higher than 97% via different metrics for different categorization tasks. We observed distinctly different Zernike spectrums between NESEs and no‐eruption images. In the three months with the best quality images (December 2013, December 2014, and January 2015), out of about one million images per month, 654, 405, and 3,650 NESEs were detected, respectively. Using k‐means clustering three activity regimes emerged: low (≤4 NESEs per hour), intermediate (4 <NESEs per hour <14), and intense (≥14 NESEs per hour). December 2013 and December 2014 were associated with low and medium activity regimes only, while January 2015 had mainly medium and intense activity. The frequency of large eruptions and of NESEs do not show a correlation, supporting the hypothesis of the existence of multiple deep processes at the origin of the gas release. Space analysis of NESEs shows that they emerge over the surface primarily above the conduit, but not isotropically, pointing to complex interaction with the magma lake convection.