  Active volcanoes are globally distributed and pose societal risks at multiple
geographic scales, ranging from local hazards to regional/international
disruptions. Many volcanoes do not have continuous ground monitoring networks;
meaning that satellite observations provide the only record of volcanic
behavior and unrest. Among these remote sensing observations, thermal imagery
is inspected daily by volcanic observatories for examining the early signs,
onset, and evolution of eruptive activity. However, thermal scenes are often
obstructed by clouds, meaning that forecasts must be made off image sequences
whose scenes are only usable intermittently through time. Here, we explore
forecasting this thermal data stream from a deep learning perspective using
existing architectures that model sequences with varying spatiotemporal
considerations. Additionally, we propose and evaluate new architectures that
explicitly model intermittent image sequences. Using ASTER Kinetic Surface
Temperature data for $9$ volcanoes between $1999$ and $2020$, we found that a
proposed architecture (ConvLSTM + Time-LSTM + U-Net) forecasts volcanic
temperature imagery with the lowest RMSE ($4.164^{\circ}$C, other methods:
$4.217-5.291^{\circ}$C). Additionally, we examined performance on multiple time
series derived from the thermal imagery and the effect of training with data
from singular volcanoes. Ultimately, we found that models with the lowest RMSE
on forecasting imagery did not possess the lowest RMSE on recreating time
series derived from that imagery and that training with individual volcanoes
generally worsened performance relative to a multi-volcano data set. This work
highlights the potential of data-driven deep learning models for volcanic
unrest forecasting while revealing the need for carefully constructed
optimization targets.
