Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands

TL;DR

This paper demonstrates that simple logistic regression models with carefully selected features can effectively detect low-magnitude earthquakes in Groningen, challenging the notion that complex deep learning models are always necessary for seismic event detection.

Contribution

The study introduces a simple, interpretable logistic regression approach with feature extraction for earthquake detection, showing competitive results with less complexity.

Findings

Simple models can detect earthquakes not listed in catalogs.

Feature selection enhances understanding of noise and event classes.

Simple models are easier to maintain and interpret.

Abstract

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.