EGU24-10191, updated on 08 Mar 2024
https://doi.org/10.5194/egusphere-egu24-10191
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

A detailed earthquake catalog using Machine Learning-based methods for Tuscany, Italy

Juan Porras1, Konstantinos Michailos1, Genevieve Savard1, Domenico Montanari2, Gilberto Saccorotti3, Marco Bonini2, Davide Piccinini3, Nicola Piana-Agostinetti5, Chiara Del Ventisette4, and Matteo Lupi1
Juan Porras et al.
  • 1University of Geneva, Department of Earth Sciences, Renens, Switzerland (juan.porrasloria@unige.ch)
  • 2Institute of Geosciences and Earth Resources- CNR, Florence, Italy
  • 3Istituto Nazionale di Geofisica e Vulcanologia, Italy
  • 4Department of Earth Sciences, University of Florence, Italy
  • 5Department of Earth and Environmental Sciences, Universita' di Milano Bicocca, Italy

Seismic activity in Tuscany, Italy, is driven by the interplay between complex tectonics and local geological processes. Fluid-driven seismic sequences may occur in high-enthalpy geothermal regions, such as the Larderello-Travale Geothermal Field (LTGF), the oldest and among the most productive geothermal systems of the world. To better understand the regional tectonic setting, we build a detailed seismic catalog of earthquake hypocenters and magnitudes from a composite seismic network consisting of 30 temporary stations deployed in Tuscany in the framework of a temporary experiment (TEMPEST), during a period of one year (from September 2020 to September 2021) and 30 permanent seismic stations from the Istituto Nazionale di Geofisica e Vulcanologia (INGV).

We applied an automated processing routine including a machine learning (ML) phase picker, PhaseNet, and the Gaussian Mixture Model Association (GAMMA) algorithm, a sequential earthquake association and location workflow. We initially obtain nearly 1 million P-phases and 2 million S-phases, yielding in around 5 thousand detected events. We then located the events with NonLinLoc and applied a quality factor metrics to filter out potential false detections (22%) and recognize the high quality solutions which represents 30% of the initial 5 thousand locations with moment magnitudes (Mw) ranging between 0.5 to 2.9, and depths generally shallower than 15 km. Further steps involve the location analysis of the remaining events from the initial catalog. Moreover, we will apply relative earthquake location methods to better constrain already evident seismicity clusters. We also plan to calculate focal mechanisms from first-motion polarities and Moment Tensor (MT) inversion to investigate the earthquake sources in the highlighted tectonic features.

This work represents the starting point of the project “Multidisciplinary and InteGRated Approach for geoThermal Exploration” (MIGRATE). The goal of MIGRATE is to streamline passive seismic exploration methods for the investigation of geothermal resources, while addressing relevant scientific questions. This will result in the development of an automatized end-to-end tool to prospect the upper crust and identify potential geothermal targets.

How to cite: Porras, J., Michailos, K., Savard, G., Montanari, D., Saccorotti, G., Bonini, M., Piccinini, D., Piana-Agostinetti, N., Del Ventisette, C., and Lupi, M.: A detailed earthquake catalog using Machine Learning-based methods for Tuscany, Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10191, https://doi.org/10.5194/egusphere-egu24-10191, 2024.

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