Exploring Deep Learning Approaches for Seismic Detection and Discrimination in Norway

Earthquake catalogs are essential for monitoring natural hazards and improving our understanding of seismic processes, which depend on accurate detection and classification of both natural earthquakes and anthropogenic signals. This is especially important in intraplate regions like Norway characterized by low to moderate earthquake activity, rare impactful earthquakes and widespread anthropogenic events such as quarry blasts. Nonetheless, traditional detection workflows have struggled to keep pace with the growing number of seismic stations and temporary deployments. Recent advances in machine learning offer promising solutions for efficient detection and discrimination tasks. Here, we present preliminary results toward a fully automated seismic detection and classification system for the Norwegian National Seismic Network (NNSN).

We first evaluate pre-trained deep learning-based phase detection models PhaseNet (Zhu & Beroza 2019) and Earthquake Transformer (EQT, Mousavi et al. 2020) using a catalog of 2567 events from the NNSN bulletin from 2008 to 2025, which includes 1144 earthquakes and 1423 blasts. We find the models can detect earthquake phases with F1-scores of 0.70 and 0.67, for the PhaseNet and EQT respectively, and 0.73 and 0.70 for blasts, revealing slightly higher sensitivity to blasts than earthquakes.

Building on this, and with the goal of developing a robust deep-learning earthquake detection workflow, we set out to quality-control our classification of earthquakes and blasts. We apply a self-supervised approach based on Bootstrap Your Own Latent (BYOL, Grill et al. 2020), which learns representations by aligning augmented views of the same signal, enabling learning without relying on potentially biased labels. The only information provided to the model are the training hyperparameters and the number of classes we aim to identify (two: earthquakes and blasts). Our method achieves an F1-score of 0.93 in distinguishing blasts from earthquakes using self-supervised representations, and up to 0.94 when incorporating an additional supervised layer. Analysis of the results reveals previously misclassified events, demonstrating the effectiveness of self-supervised methods even with limited or biased labeled datasets.

Future work will focus on retraining detection models using NNSN data after BYOL based classification. Additionally, we will analyze the BYOL learned features to gain insights on the physical differences between earthquake and blast signals.