  Contemporary deep learning models have demonstrated promising results across
various applications within seismology and earthquake engineering. These models
rely primarily on utilizing ground motion records for tasks such as earthquake
event classification, localization, earthquake early warning systems, and
structural health monitoring. However, the extent to which these models
effectively learn from these complex time-series signals has not been
thoroughly analyzed. In this study, our objective is to evaluate the degree to
which auxiliary information, such as seismic phase arrival times or seismic
station distribution within a network, dominates the process of deep learning
from ground motion records, potentially hindering its effectiveness. We perform
a hyperparameter search on two deep learning models to assess their
effectiveness in deep learning from ground motion records while also examining
the impact of auxiliary information on model performance. Experimental results
reveal a strong reliance on the highly correlated P and S phase arrival
information. Our observations highlight a potential gap in the field,
indicating an absence of robust methodologies for deep learning of
single-station ground motion recordings independent of any auxiliary
information.
