Monitoring the Microseismicity through a Dense Seismic Array and a Similarity Search Detection Technique: Application to the Seismic Monitoring of Collalto Gas-Storage, North Italy

Antonio Scala, Guido Maria Adinolfi, Matteo Picozzi, Francesco Scotto di Uccio, Gaetano Festa, Grazia De Landro, Enrico Priolo, Stefano Parolai, Rosario Riccio and Marco Romanelli
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Antonio Scala: Department of Physics “Ettore Pancini”, University of Napoli Federico II, 80126 Napoli, Italy
Guido Maria Adinolfi: Dipartimento di Scienze e Tecnologie, Università degli Studi del Sannio, 82100 Benevento, Italy
Matteo Picozzi: Department of Physics “Ettore Pancini”, University of Napoli Federico II, 80126 Napoli, Italy
Francesco Scotto di Uccio: Department of Physics “Ettore Pancini”, University of Napoli Federico II, 80126 Napoli, Italy
Gaetano Festa: Department of Physics “Ettore Pancini”, University of Napoli Federico II, 80126 Napoli, Italy
Grazia De Landro: Department of Physics “Ettore Pancini”, University of Napoli Federico II, 80126 Napoli, Italy
Enrico Priolo: Istituto Nazionale di Oceanografia e di Geofisica Sperimentale-OGS, 34010 Sgonico, Italy
Stefano Parolai: Istituto Nazionale di Oceanografia e di Geofisica Sperimentale-OGS, 34010 Sgonico, Italy
Rosario Riccio: Istituto Nazionale di Geofisica e Vulcanologia, Sezione Napoli-Osservatorio Vesuviano, 80125 Napoli, Italy
Marco Romanelli: Istituto Nazionale di Oceanografia e di Geofisica Sperimentale-OGS, 34010 Sgonico, Italy

Energies, 2022, vol. 15, issue 10, 1-17

Abstract: Seismic monitoring in areas where induced earthquakes could occur is a challenging topic for seismologists due to the generally very low signal to noise ratio. Therefore, the seismological community is devoting several efforts to the development of high-quality networks around the areas where fluid injection and storage and geothermal activities take place, also following the national induced seismicity monitoring guidelines. The use of advanced data mining strategies, such as template matching filters, auto-similarity search, and deep-learning approaches, has recently further fostered such monitoring, enhancing the seismic catalogs and lowering the magnitude of completeness of these areas. In this framework, we carried out an experiment where a small-aperture seismic array was installed within the dense seismic network used for monitoring the gas reservoir of Collalto, in North Italy. The continuous velocimetric data, acquired for 25 days, were analysed through the application of the optimized auto-similarity search technique FAST. The array was conceived as a cost-effective network, aimed at integrating, right above the gas storage site, the permanent high-resolution Collalto Seismic Network. The analysis allowed to detect micro-events down to magnitude Ml = −0.4 within a distance of ~15 km from the array. Our results confirmed that the system based on the array installation and the FAST data analysis might contribute to lowering the magnitude of completeness around the site of about 0.7 units.

Keywords: induced seismicity monitoring; seismic arrays; sensor network technology; microearthquake detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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