EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 468 (2023) E3S Web Conf., 468 (2023) 01006 References
Open Access
Issue
E3S Web Conf.
Volume 468, 2023
ICST UGM 2023 - The 4th Geoscience and Environmental Management Symposium
Article Number 01006
Number of page(s) 5
Section Disaster Risk Reduction
DOI https://doi.org/10.1051/e3sconf/202346801006
Published online 21 December 2023
  1. A. Rakhman, Wahyudi, A., B. Santoso, H. Humaida, and W. Suryanto, “Ambient Seismic Noise Analysis Associated with the 2010 Eruption of Merapi Volcano Using Temporal Variations of Randomness and Background Noise,” Int. J. Geophys, 2020, 1–10 (2020), DOI: 10.1155/2020/8853376. [CrossRef] [Google Scholar]
  2. S. Nowé, T. Lecocq, C. Caudron, K. Jónsdóttir, and F. Pattyn, “Permanent, seasonal and episodic seismic sources around Vatnajökull, Iceland, from the analysis of correlograms Sylvain,” Volcánica, 4, 2, 135–147 (2021), DOI: 10.30909/vol.04.02.135147. [CrossRef] [Google Scholar]
  3. S. Yu and J. Ma, “Deep Learning for Geophysics: Current and Future Trends,” Rev. Geophys., 59, 3, 1–36 (2021), DOI: 10.1029/2021RG000742. [Google Scholar]
  4. J. Caldera and S. Wirasinghe, “Analysis and Classification of Volcanic Eruptions,” in International Conference of the International Institute for Infrastructure Resilience and Reconstruction (I3R2), May, 128–133, (2014) DOI: 10.5703/1288284315372. [Google Scholar]
  5. M. Singh, et al., Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia. 2021, 1–17. [Google Scholar]
  6. J. Li, M. He, G. Cui, X. Wang, W. Wang, and J. Wang, “A Novel Method of Seismic Signal Detection Using Waveform,” Appl. Sci., 10, 2919, 1–17 (2020), DOI: 10.3390/app10082919. [Google Scholar]
  7. H. Darmawan, P. Yuliantoro, A. Budi, H. Humaida, and W. Suryanto, “Dynamic velocity and seismic characteristics of gravitational rockfalls at the Merapi lava dome,” J. Volcanol. Geotherm. Res., 404, 107010, 1–10 (2020), DOI: 10.1016/j.jvolgeores.2020.107010. [CrossRef] [Google Scholar]
  8. J. Barrière et al., “Single-Station Seismo-Acoustic Monitoring of Nyiragongo's Lava Lake Activity (D.R. Congo),” Front. Earth Sci., 6, June, 1–17, (2018), DOI: 10.3389/feart.2018.00082. [CrossRef] [Google Scholar]
  9. Y. Zhao, F. Niu, Z. Zhang, X. Li, J. Chen, and J. Yang, “Signal Detection and Enhancement for Seismic Crosscorrelation Using the Wavelet-Domain Kalman Filter,” Surv. Geophys., 42, 1, 43–67 (2021), DOI: 10.1007/s10712-020-09620-6. [CrossRef] [Google Scholar]
  10. J. Han et al., “Seismic Event and Phase Detection Using Deep Learning for the 2016 Gyeongju Earthquake Sequence,” Geosci. J., 27, 3, 285–295 (2023), DOI: 10.1007/s12303-023-0004-y. [CrossRef] [Google Scholar]
  11. D. Bobrov, I. Kitov, and L. Zerbo, Perspective of Cross Correlation in Seismic Monitoring at the International Data Center, 171, 3-5. 439–468, (2014), DOI: 10.1007/s00024-012-0626-x. [Google Scholar]
  12. T. Glade and F. Nadim, “Early warning systems for natural hazards and risks Thomas Glade,” Nat. Hazards, 70, 3, 1669–1671 (2014), DOI: 10.1007/s11069-013-1000-8. [CrossRef] [Google Scholar]
  13. S. M. Mousavi and G. C. Beroza, “Machine Learning in Earthquake Seismology,” Annu. Rev. Earth Planet. Sci., 51, November, 105–129, (2023), DOI: 10.1146/annurev-earth-071822-100323. [CrossRef] [Google Scholar]
  14. B. Chouet, “Volcano Seismology,” Pure Appl. Geophys., 160, 3-4, 739–788, (2003), DOI: 10.1007/PL00012556. [CrossRef] [Google Scholar]
  15. E. Onajite, Un derstanding Seismic Wave Propagation. 2014. [Google Scholar]
  16. I. A. Zavedevkin, A. A. Shakirova, and P. P. Firstov, “DrumCorr Program for Selecting Volcanic Earthquake Multiplets Based on Cross-Correlation Analysis,” in Journal of Physics: Conference Series, 2094, 3, 1–8 (2021), DOI: 10.1088/1742-6596/2094/3/032048. [CrossRef] [Google Scholar]
  17. M. Malfante, M. Dalla Mura, J. P. Metaxian, J. I. Mars, O. Macedo, and A. Inza, “Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives,” IEEE Signal Process. Mag., 35, 2, 20–30 (2018), DOI: 10.1109/MSP.2017.2779166. [Google Scholar]
  18. S. G. Mosher and P. Audet, “Automatic Detection and Location ofSeismic Events From Time-Delay Projection Mapping and Neural Network Classification,” J. Geophys. Res. Solid Earth, 125, 10, 1–18 (2020), DOI: 10.1029/2020JB019426. [CrossRef] [Google Scholar]
  19. A. Nieto-Torres, L. F. Guimarães, C. Bonadonna, and C. Frischknecht, “A New Inclusive Volcanic Risk Ranking, Part 1 : Methodology,” Front. Earth Sci., 9, August, 1–22, (2021), DOI: 10.3389/feart.2021.697451. [CrossRef] [Google Scholar]
  20. L. Z. Dmitry Bobrov, Ivan Kitov, “Perspectives of Cross Correlation in Seismic Monitoring at the International Data Centre.” 1—18, (2014), DOI: 10.1007/s00024-012-0626-x [Google Scholar]
  21. J. Hu, Y. Ding, S. Lin, H. Zhang, and C. Jin, “A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion,” Appl. Sci., 13, 14, 1–18 (2023), DOI: 10.3390/app13148232. [Google Scholar]
  22. M. Ripepe et al., “Infrasonic Early Warning System for Explosive Eruptions M.,” J. Geophys. Res. Solid Earth, 123, 11, 9570–9585 (2018), DOI: 10.1029/2018JB015561. [CrossRef] [Google Scholar]
  23. R. Scarpa and R. I. Tilling, “Sesimic Monitoring and Eruption Forecasting of Volcanoes: A Review of the State-Of-the-Art and Case Histories,” Monit. Mitig. Volcano Hazards, 15, January, 99–146, (1996), DOI: 10.1007/978-3-642-80087-0. [Google Scholar]
  24. P. Mathieu and C. Aubrecht, Earth Observation Open Science and Innovation. (2018), Springer Nature, Washington, USA. [CrossRef] [Google Scholar]
  25. R. S. Jakka and S. Garg, “Understanding and Parameter Setting of STA/LTA Trigger Algorithm,” Earthq. Eng. Eng. Vib., 14, 1, 27–35 (2015), DOI: 10.1007/s11803-015-0004-7. [CrossRef] [Google Scholar]
  26. M. Malfante, M. Dalla M. J. I. Mars, J. P. Métaxian, O. Macedo, and A. Inza, “Automatic Classification of Volcano Seismic Signatures Marielle,” J. Geophys. Res. Solid Earth, 123, 12, 1–14 (2018), DOI: 10.1029/2018JB015470. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.