| Autor: |
Giovanni Messuti, Silvia Scarpetta, Ortensia Amoroso, Ferdinando Napolitano, Mariarosaria Falanga, Paolo Capuano |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Frontiers in Earth Science, Vol 11 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2296-6463 |
| DOI: |
10.3389/feart.2023.1223686 |
| Popis: |
First-motion polarity determination is essential for deriving volcanic and tectonic earthquakes’ focal mechanisms, which provide crucial information about fault structures and stress fields. Manual procedures for polarity determination are time-consuming and prone to human error, leading to inaccurate results. Automated algorithms can overcome these limitations, but accurately identifying first-motion polarity is challenging. In this study, we present the Convolutional First Motion (CFM) neural network, a label-noise robust strategy based on a Convolutional Neural Network, to automatically identify first-motion polarities of seismic records. CFM is trained on a large dataset of more than 140,000 waveforms and achieves a high accuracy of 97.4% and 96.3% on two independent test sets. We also demonstrate CFM’s ability to correct mislabeled waveforms in 92% of cases, even when they belong to the training set. Our findings highlight the effectiveness of deep learning approaches for first-motion polarity determination and suggest the potential for combining CFM with other deep learning techniques in volcano seismology. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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