Improving the seismic catalogue completeness of Tenerife (Canary Islands, Spain) through deep learning

The volcanic island of Tenerife (Canary Islands, Spain) is characterized by low-magnitude background seismicity associated with local hydrothermal and volcano-tectonic processes. The island has been experiencing, since 2016, a slight increase in seismic activity, with earthquakes generally having magnitudes below 2. For this reason, we are revising the seismic catalogue using deep learning tools to improve its completeness.

Over the last decade, machine learning methods—particularly deep learning approaches—have gained traction across multiple disciplines due to their increased computational efficiency, high accuracy, and reduced need for manual supervision. One such method, PhaseNet [1], is a deep convolutional neural network based on the U-Net architecture [2] that has shown strong performance in waveform-based seismic phase detection. Its ability to process large volumes of seismic data and automatically identify relevant signal features represents a significant opportunity to enhance the quality and completeness of seismic catalogs. Nevertheless, applying a neural network to data with a different nature from that used for its training phase can lead to a substantial decrease in performance. In particular, PhaseNet was primarily trained on tectonic seismicity, whereas seismic events in Tenerife are predominantly volcanic-hydrothermal. Consequently, retraining the network on waveforms representative of the target seismicity is essential to ensure a reliable inference.

Using PhaseNet as a baseline, we conducted an extensive comparative analysis of several training configurations to adapt the original network to the seismic data from the Canary Islands (Tenerife). Our study focused on four key aspects: model initialization, learning rate selection, data clustering strategies, and model partitioning. The model initialization strategies include fine-tuning from pre-trained weights and training from randomly initialized weights. Regarding model partitioning, we evaluated a global model (a single model trained on all data), local models (one model per station), and cluster-based models (trained on groups of stations with similar characteristics). The performance of each configuration was evaluated on an independent dataset using multiple metrics to provide a comprehensive assessment. Specifically, we analyzed precision, recall, and ROC curves to identify suitable trade-offs between detection sensitivity and specificity.

These preliminary results will be beneficial for subsequent analysis aimed at a better characterization of the island's microseismicity and its relationship with the activity of its volcanic-hydrothermal system.

References:

• [1] Zhu and G. C. Beroza, “PhaseNet: a Deep-Neural-Network-Based seismic arrival time picking method,” Geophysical Journal International, Oct. 2018, doi: 10.1093/gji/ggy423.
• [2] O. Ronneberger, P. Fischer, and T. Brox, “U-NET: Convolutional Networks for Biomedical Image Segmentation,” in Lecture notes in computer science, 2015, pp. 234–241. doi: 10.1007/978-3-319-24574-4_28.