| _version_ | 1866902238464573440 |
|---|---|
| author | Fernández-Carabantes, Javier Titos, Manuel D'Auria, Luca García, Jesús García, Luz Benítez Ortúzar, M. Carmen |
| author_facet | Fernández-Carabantes, Javier Titos, Manuel D'Auria, Luca García, Jesús García, Luz Benítez Ortúzar, M. Carmen |
| contents | <p><em>RNN-DAS: A New Deep Learning Approach for Detection and Real-Time Monitoring of Volcano-Tectonic Events Using Distributed Acoustic Sensing (v1.1.1)</em></p> <p>This is a permanent repository that hosts the latest version of the GitHub implementation of the RNN-DAS model (Fernández-Carabantes et al., 2025).</p> <p>https://github.com/Javier-FernandezCarabantes/RNN-DAS </p> <p>Its use is permitted provided that the corresponding article and the associated software repository are properly cited.</p> <p><strong>Article:</strong></p> <p>Fernández-Carabantes, J., Titos, M., D'Auria, L., García, J., García, L., & Benítez, C. (2025). RNN-DAS: A new deep learning approach for detection and real-time monitoring of volcano-tectonic events using distributed acoustic sensing. Journal of Geophysical Research: Solid Earth, 130, e2025JB031756. https://doi.org/10.1029/2025JB031756</p> <p><strong>Software Repository:</strong></p> <p>Fernández-Carabantes, J., Titos, M., D'Auria, L., García, J., García, L., & Benítez, C. (2025). Javier-FernandezCarabantes/RNN-DAS: RNN-DAS v1.1.0 (v1.1.1). Zenodo. https://doi.org/10.5281/zenodo.15858492</p> <p>You can reach the corresponding author (Javier Fernández-Carabantes) at: <em>javierfyc@ugr.es </em><br>Departamento de Física Teórica y del Cosmos, Universidad de Granada, Granada, Spain</p> <p>Funding:</p> <p>This software was developed as part of the DigiVolCan project - A digital infrastructure for forecasting volcanic eruptions in the Canary Islands.<br>The results from the RNN-DAS model are the outcome of collaboration between the University of Granada, the Canary Islands Volcanological Institute (INVOLCAN), the Institute of Technological and Renewable Energies (ITER), the University of La Laguna, and Aragón Photonics. The project was funded by the Ministry of Science, Innovation, and Universities / State Research Agency (MICIU/AEI) of Spain, and the European Union through the Recovery, Transformation, and Resilience Plan, Next Generation EU Funds. Project: PLEC2022-009271 funded by MICIU/AEI /10.13039/501100011033 and by the European Union Next GenerationEU/ PRTR.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17153568 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Javier-FernandezCarabantes/RNN-DAS: v1.1.1 Fernández-Carabantes, Javier Titos, Manuel D'Auria, Luca García, Jesús García, Luz Benítez Ortúzar, M. Carmen <p><em>RNN-DAS: A New Deep Learning Approach for Detection and Real-Time Monitoring of Volcano-Tectonic Events Using Distributed Acoustic Sensing (v1.1.1)</em></p> <p>This is a permanent repository that hosts the latest version of the GitHub implementation of the RNN-DAS model (Fernández-Carabantes et al., 2025).</p> <p>https://github.com/Javier-FernandezCarabantes/RNN-DAS </p> <p>Its use is permitted provided that the corresponding article and the associated software repository are properly cited.</p> <p><strong>Article:</strong></p> <p>Fernández-Carabantes, J., Titos, M., D'Auria, L., García, J., García, L., & Benítez, C. (2025). RNN-DAS: A new deep learning approach for detection and real-time monitoring of volcano-tectonic events using distributed acoustic sensing. Journal of Geophysical Research: Solid Earth, 130, e2025JB031756. https://doi.org/10.1029/2025JB031756</p> <p><strong>Software Repository:</strong></p> <p>Fernández-Carabantes, J., Titos, M., D'Auria, L., García, J., García, L., & Benítez, C. (2025). Javier-FernandezCarabantes/RNN-DAS: RNN-DAS v1.1.0 (v1.1.1). Zenodo. https://doi.org/10.5281/zenodo.15858492</p> <p>You can reach the corresponding author (Javier Fernández-Carabantes) at: <em>javierfyc@ugr.es </em><br>Departamento de Física Teórica y del Cosmos, Universidad de Granada, Granada, Spain</p> <p>Funding:</p> <p>This software was developed as part of the DigiVolCan project - A digital infrastructure for forecasting volcanic eruptions in the Canary Islands.<br>The results from the RNN-DAS model are the outcome of collaboration between the University of Granada, the Canary Islands Volcanological Institute (INVOLCAN), the Institute of Technological and Renewable Energies (ITER), the University of La Laguna, and Aragón Photonics. The project was funded by the Ministry of Science, Innovation, and Universities / State Research Agency (MICIU/AEI) of Spain, and the European Union through the Recovery, Transformation, and Resilience Plan, Next Generation EU Funds. Project: PLEC2022-009271 funded by MICIU/AEI /10.13039/501100011033 and by the European Union Next GenerationEU/ PRTR.</p> |
| title | Javier-FernandezCarabantes/RNN-DAS: v1.1.1 |
| url | https://doi.org/10.5281/zenodo.17153568 |