GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 1.1 GNGTS 2024 PhaseNet's overall performance is commendable, especially when considering the tme of processing, signifcantly reduced compared to manual picking. The seismicity patern observed vividly depicted the geometry of the actvated fault in both temporal and spatal dimensions (Sugan et al., 2023). Funding This study was carried out in the frame of the Italian PRIN Project ‘Fault segmentaton and seismotectonics of actve thrust systems: the Northern Apennines and Southern Alps laboratories for new Seismic Hazard Assessments in northern Italy (NASA4SHA)’ (PI R. Caputo, UR Responsible L. Peruzza). The research was also supported by OGS and CINECA under HPC-TRES program. References Anikiev D., Birnie C., bin Waheed U., Alkhalifah T., Gu C., Verschuur D.J., Eisner L.; 2023. Machine learning in microseismic monitoring. Earth-Science Reviews. 239:104371. htps:// doi.org/10.1016/j.earscirev.2023.104371. Mousavi S.M., Horton S.P., Langston C.A., Samei B.; 2016. Seismic Features and Automatc Discriminaton of Deep and Shallow Induced-Microearthquakes Using Neural Network and Logistc Regression. Geophys J Int. 207(1):29-46. htps://doi.org/10.1093/gji/ggw258. Peruzza L., Romano M.A., Guidarelli M., Morato L., Garbin M., Priolo E.; 2022. An unusually productve microearthquake sequence brings new insights to the buried actve thrust system of Montello (Southeastern Alps, Northern Italy). Front Earth Sci. 10:1044296. htps://doi.org/10.3389/feart.2022.1044296. Sugan M., Peruzza L., Romano M.A., Guidarelli M., Morato L., Sandron D., Plasencia Linares M.P., Romanelli M.; 2023. Machine learning versus manual earthquake locaton workfow: testng LOC-FLOW on an unusually productve microseismic sequence in northeastern Italy. Geomatcs, Natural Hazards and Risk, 14:1. htps://doi.org/ 10.1080/19475705.2023.2284120. Zhang M., Liu M., Feng T., Wang R., Zhu W.; 2022. LOC-FLOW: An End-to-End Machine- Learning-Based High-Precision Earthquake Locaton Workfow. Seismol Res Let. 93(5):2426-2438. htps://doi.org/10.1785/0220220019 . Zhu W., Beroza G.C.; 2018. PhaseNet: A deep-neural-network based seismic arrival-tme picking method. Geophys J Int. 216(1): 261–273. htps://doi.org/10.1093/gji/ggy423. Corresponding author: msugan@ogs.it