Estimating Earthquake Magnitude in Sentinel-1 Imagery via Ranking
This addresses the challenge of global earthquake monitoring in low-data regimes, offering a more efficient alternative to physical seismic stations.
The paper tackles the problem of estimating earthquake magnitude from Sentinel-1 satellite imagery by framing it as a metric-learning task to rank pairwise samples, achieving up to a 30% improvement in MAE over prior regression-only methods.
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.