Machine learning versus manual earthquake location workflow: testing LOC-FLOW on an unusually productive microseismic sequence in northeastern Italy

Sugan, Monica; Peruzza, Laura; Romano, Maria Adelaide; Guidarelli, Mariangela; Moratto, Luca; Sandron, Denis; Plasencia Linares, Milton Percy; Romanelli, Marco

doi:10.1080/19475705.2023.2284120

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2024

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Cited by 2 publications

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Application of AI Algorithms in Earthquake Detection and Location

Qin,

Tong,

Geng

et al. 2024

2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

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Application of AI Algorithms in Earthquake Detection and Location

Qin,

Tong,

Geng

et al. 2024

2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

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A New Catalogue and Insights into the 2022 Adriatic Offshore Seismic Sequence Using a Machine Learning-Based Procedure

Costanzo

2024

Sensors

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This paper presents a new catalogue of the 2022/2023 Adriatic Offshore Seismic Sequence obtained by machine learning-based processing. The procedure performs the automatic picking and association of phases starting from the analysis of the continuous waveforms recorded by 40 seismic stations of the Italian National Seismic Network and 5 stations of the SISMIKO emergency group network. The earthquakes were detected over a 3-month period, between 1 November 2022 and 31 January 2023. This new catalogue consists of 2780 earthquakes with a magnitude equal to or greater than ML 0.4, providing more information about lower-magnitude earthquakes in particular. The results make available, on the one hand, new insights into the offshore sequence, which can contribute to confirming the attribution of the earthquakes to the Adriatic Fault System, and in particular, the mainshocks to the Cornelia fault thrust, as also hypothesised by other works in the literature. Moreover, the work provides a further contribution in showing the great potential of using machine learning-based procedures to build catalogues with a greater degree of completeness, even in very particular cases such as the one represented by the Adriatic offshore sequence, for which the minimum distance from the epicentres is high and the azimuth coverage limited.

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Machine learning versus manual earthquake location workflow: testing LOC-FLOW on an unusually productive microseismic sequence in northeastern Italy

Cited by 2 publications

References 41 publications

Application of AI Algorithms in Earthquake Detection and Location

Application of AI Algorithms in Earthquake Detection and Location

A New Catalogue and Insights into the 2022 Adriatic Offshore Seismic Sequence Using a Machine Learning-Based Procedure

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