  Deep learning is fast emerging as a potential disruptive tool to tackle
longstanding research problems across the sciences. Notwithstanding its success
across disciplines, the recent trend of the overuse of deep learning is
concerning to many machine learning practitioners. Recently, seismologists have
also demonstrated the efficacy of deep learning algorithms in detecting low
magnitude earthquakes. Here, we revisit the problem of seismic event detection
but using a logistic regression model with feature extraction. We select
well-discriminating features from a huge database of time-series operations
collected from interdisciplinary time-series analysis methods. Using a simple
learning model with only five trainable parameters, we detect several
low-magnitude induced earthquakes from the Groningen gas field that are not
present in the catalog. We note that the added advantage of simpler models is
that the selected features add to our understanding of the noise and event
classes present in the dataset. Since simpler models are easy to maintain,
debug, understand, and train, through this study we underscore that it might be
a dangerous pursuit to use deep learning without carefully weighing simpler
alternatives.
