PhaseNet Application based on Seismic Event Recording and Waveform Characteristics for Diverse Seismic Regions in Indonesia

References

1. AmaliaR, KimS, SaharaDP (2022) Deep Learning-based Earthquake Detection of the 2019 Ambon Aftershock Sequence. 대한지질학회 학술대회174–174
   [Google Scholar]
2. NugrahaAD, WidiyantoroS, RamdhanM, et al (2013) Analysis of Micro-seismic Events Through Waveform Identification and Hypocenter Determination Around a Geothermal Field in West Java. In: Proceedings of the 11th SEGJ International Symposium, Yokohama, Japan, 18–21 November 2013. Society of Exploration Geophysicists of Japan, pp 418–421
   [Google Scholar]
3. RosaliaS, WidiyantoroS, NugrahaAD, SupendiP (2019) Double-difference tomography of P- and S-wave velocity structure beneath the western part of Java, Indonesia.dzxbywb32:12–25. https://doi.org/10.29382/eqs-2019-0012-2
   [Google Scholar]
4. SaharaDP, RahsetyoPP, NugrahaAD, et al (2021) Use of Local Seismic Network in Analysis of Volcano-Tectonic (VT) Events Preceding the 2017 Agung Volcano Eruption (Bali, Indonesia).Frontiers in Earth Science9. https://doi.org/10.3389/feart.2021.619801
   [Google Scholar]
5. WidiyantoroS, SupendiP, ArdiantoA, et al (2022) Implications for fault locking south of Jakarta from an investigation of seismic activity along the Baribis fault, northwestern Java, Indonesia. Sci Rep12:10143. https://doi.org/10.1038/s41598-022-13896-6
   [Google Scholar]
6. ZhuW, BerozaGC (2019) PhaseNet: a deep-neural-network-based seismic arrival-time picking method.Geophysical Journal International216:261–273. https://doi.org/10.1093/gji/ggy423
   [Google Scholar]