Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection

TL;DR

This study compares deep learning and simpler logistic regression models for earthquake detection, finding that logistic regression with engineered features outperforms CNNs in noisy data scenarios, highlighting the importance of model transparency.

Contribution

The paper demonstrates that a transparent logistic regression model with engineered features can outperform deep CNNs in earthquake detection under noisy conditions.

Findings

Logistic regression detected all earthquakes across noise levels.

Deep CNN missed nearly 20% of seismic events at higher noise.

Engineered features improved model robustness to noise.

Abstract

While deep learning models have seen recent high uptake in the geosciences, and are appealing in their ability to learn from minimally processed input data, as black box models they do not provide an easy means to understand how a decision is reached, which in safety-critical tasks especially can be problematical. An alternative route is to use simpler, more transparent white box models, in which task-specific feature construction replaces the more opaque feature discovery process performed automatically within deep learning models. Using data from the Groningen Gas Field in the Netherlands, we build on an existing logistic regression model by the addition of four further features discovered using elastic net driven data mining within the catch22 time series analysis package. We then evaluate the performance of the augmented logistic regression model relative to a deep (CNN) model, pre-trained on the Groningen data, on progressively increasing noise-to-signal ratios. We discover that, for each ratio, our logistic regression model correctly detects every earthquake, while the deep model fails to detect nearly 20 % of seismic events, thus justifying at least a degree of caution in the application of deep models, especially to data with higher noise-to-signal ratios.