Automating Seismic Event Discrimination: A Comparative Study of Convolutional Neural Networks and Vision Transformers

To date, the discrimination of seismic events recorded in seismic networks is often performed manually by experts, classifying events into categories such as earthquakes, quarry blasts, or mining events. While recent studies have shown that deep learning algorithms, particularly Convolutional Neural Networks (CNNs), can efficiently and accurately distinguish between different types of seismic events, their application for automated seismic event discrimination remains limited. This limitation arises from several factors, including the absence of globally applicable models that maintain high precision for local seismic networks, the scarcity of data required for fine-tuning Deep Learning (DL) models, and the lack of interpretability in the decision-making processes of these black-box models.

In this contribution, we explore the use of Vision Transformers (ViTs) as a novel approach for automating seismic event discrimination. To assess their potential for accuracy and explainability, we applied CNNs and ViTs to classify seismic events such as earthquakes, quarry blasts, and mining events. For this purpose, we pretrained the models on openly available seismic event data from Utah and Northern California and then fine-tuned and tested them on data from the Seismic Network of the Ruhr-University Bochum (RuhrNet) and the Thuringian Seismic Network (TSN).

Our findings reveal that ViTs can analyze the entire spectrogram of a seismic event in a coherent manner, offering superior generalizability in pattern recognition compared to CNNs. In addition to achieving high discrimination accuracy, the attention weights of ViTs provide insights into the models’ decision-making process, offering a transparent and interpretable explanation of the underlying mechanisms driving its classifications.