Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands
This work addresses the problem of overusing deep learning in seismology by demonstrating that simpler models can be effective for earthquake detection, though it is incremental as it applies an existing method to a specific domain.
The authors tackled earthquake detection in the Groningen gas field by using a logistic regression model with feature extraction, achieving detection of several low-magnitude induced earthquakes not in existing catalogs with only five trainable parameters.
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.