  Earthquakes are commonly estimated using physical seismic stations, however,
due to the installation requirements and costs of these stations, global
coverage quickly becomes impractical. An efficient and lower-cost alternative
is to develop machine learning models to globally monitor earth observation
data to pinpoint regions impacted by these natural disasters. However, due to
the small amount of historically recorded earthquakes, this becomes a low-data
regime problem requiring algorithmic improvements to achieve peak performance
when learning to regress earthquake magnitude. In this paper, we propose to
pose the estimation of earthquake magnitudes as a metric-learning problem,
training models to not only estimate earthquake magnitude from Sentinel-1
satellite imagery but to additionally rank pairwise samples. Our experiments
show at max a 30%+ improvement in MAE over prior regression-only based methods,
particularly transformer-based architectures.
