Scotto di Uccio, Francesco (2024) Detection and characterization of microseismicity using advanced techniques. [Tesi di dottorato]

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Abstract

Earthquakes are catastrophic phenomena which cause damage and raise concerns among population and institutions, with an increasing necessity of identifying actions for mitigating their impact and effects. Moreover, since the occurrence rate of high magnitude, damaging events is low, direct access to their observation is limited. Nevertheless, microseismicity continuously occurs within the same active seismogenic faults where a major earthquake might be generated, and thus, the analysis of small magnitude events can provide crucial insights into the mechanical and stress state of the faults and the preparatory phase of large earthquakes. However, the capability of identifying micro-earthquakes is strongly affected by the low amplitude level on the seismic records, which is typically comparable to the ambient noise. The growth of advanced monitoring systems and the development of improved strategies for earthquake identification and characterization are acting as a powerful lens to reveal the small-scale rupture processes. In this thesis, I apply advanced techniques for characterizing the microseismicity in terms of earthquake detection, accurate location and seismic source properties estimation. I generate enhanced seismic catalogs using machine learning and waveform similarity detection techniques, identifying one order of magnitude more earthquakes as compared to the existing catalogs, which I investigate for identifying seismogenic structures in different tectonic and volcanic environments. The achieved resolution on the hypocentral locations and on the properties characterizing the seismic sources led to the development of models for understanding the generation and the evolution of earthquakes.

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