Projects per year
Supervised deep learning models have become a popular choice for seismic phase arrival detection. However, they do not always perform well on out-of-distribution data and require large training sets to aid generalization and prevent overfitting. This can present issues when using these models in new monitoring settings. In this work, we develop a deep learning model for automating phase arrival detection at Nabro volcano using a limited amount of training data (2,498 event waveforms recorded over 35 days) through a process known as transfer learning. We use the feature extraction layers of an existing, extensively trained seismic phase picking model to form the base of a new all-convolutional model, which we call U-GPD. We demonstrate that transfer learning reduces overfitting and model error relative to training the same model from scratch, particularly for small training sets (e.g., 500 waveforms). The new U-GPD model achieves greater classification accuracy and smaller arrival time residuals than off-the-shelf applications of two existing, extensively-trained baseline models for a test set of 800 event and noise waveforms from Nabro volcano. When applied to 14 months of continuous Nabro data, the new U-GPD model detects 31,387 events with at least four P-wave arrivals and one S-wave arrival, which is more than the original base model (26,808 events) and our existing manual catalog (2,926 events), with smaller location errors. The new model is also more efficient when applied as a sliding window, processing 14 months of data from seven stations in less than 4 h on a single graphics processing unit.
|Number of pages||23|
|Journal||Journal of Geophysical Research: Solid Earth|
|Publication status||Published - 28 Jun 2021|
- volcano seismology
- machine learning
- transfer learning
- phase arrival detection
- earthquake detection
- volcano monitoring
FingerprintDive into the research topics of 'A Little Data goes a Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning'. Together they form a unique fingerprint.
Evaluation, Quantification and Identification of Pathways and Targets for the assessment of Shale Gas RISK (EQUIPT4RISK)
Werner, M., Holmgren, J. M. & Cremen, G. J.
1/09/18 → 31/08/23
PREPARE: Enhancing PREParedness for East African Countries through Seismic Resilience Engineering
Biggs, J. J. & Macdonald, J. H. G.
1/05/17 → 31/03/22
Project: Research, Parent
Detecting and characterising seismicity associated with volcanic and magmatic processes through deep learning and the continuous wavelet transformAuthor: Lapins, S., 2 Dec 2021
Supervisor: Kendall, M. (Supervisor), Cashman, K. V. (Supervisor) & Werner, M. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)File
Nabro volcano event catalogue from Lapins et al., 2021, JGR Solid Earth
Lapins, S. (Creator), Zenodo, 5 Dec 2022
DOI: 10.5281/zenodo.7398824, https://zenodo.org/record/7398824