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

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.

Recent advances in earthquake seismology using machine learning

“…Scotto di Uccio et al ( 2022) reported that a similar waveform search using FAST potentially complements events not detected using DL-based methods, although they achieved the best performance using a templatematching technique based on a template catalog developed using a DL-based method. The FAST algorithm has been used for induced earthquakes due to hydraulic fracturing and wastewater injection (Yoon et al 2017), induced earthquakes in gas fields (Scala et al 2022), swarms (Festa et al 2021), foreshocks (Yoon et al 2019b), and volcano seismicity (Garza-Girón et al 2023).…”

Delay Time (δt) and Polarization Direction (φ) Analysis Based on Shear Wave Splitting (SWS) Method

“…The next step is filtering based on the frequency limit with a bandpass filter [20]. Micro-earthquake data is being carried out from the frequency spectrum and the spectrogram to determine the frequency width for the filtering process [21], [22]. Aside from that, spectrogram analysis is also needed to identify the variation of harmonic signal frequency towards the time [23].…”

Comprehensive study of micro-seismicity by using an automatic monitoring platform

“…The comprehensive study of microseismicity can provide a valuable description of the geological medium properties and earthquake related processes in the investigated crustal volumes, such as for instance the identification and geometrical characterization of active fault structures (Shearer, 2002;Hauksson and Shearer, 2005;Lin et al, 2007;De Landro et al, 2015;Adinolfi et al, 2019;Battimelli et al, 2019;Adinolfi et al, 2022), the study of the regional stress field (De Matteis et al, 2012;Terakawa, 2017;Maeda et al, 2020;De Matteis et al, 2021), the small-scale variability of faulting style, stress and strength (Prieto et al, 2004;Hardebeck, 2006;Syracuse et al, 2010;Adinolfi et al, 2015;Stabile et al, 2012;Festa et al, 2021), and the assessment of seismic hazard (Schorlemmer and Wiemer, 2005;Bernard et al, 2006;Emolo et al, 2011). Such achievements have led to an increasing demand for managing and analyzing large amounts of seismic data, mostly consisting of small-magnitude seismic events with signals comparable to or even below the noise level, for which analysts' manual operations are unfeasible (Yoon et al, 2015;Perol et al, 2018;Mousavi et al, 2019;Scafidi et al, 2019;Scala et al, 2022). Seismic monitoring is moving towards the development of fully automated and robust processing approaches, able to exploit the nowadays available huge amount of continuous data and to speed up seismic analyses, which are important for seismic risk assessment and reduction practices (Spallarossa et al, 2021a).…”