Adil Mohamed Ali Ebrahim, Maha (2025) Advanced Methodologies for Monitoring and Managing Fluid-Driven Processes in Geo-resources Exploitation Sites and Volcanic Regions. [Tesi di dottorato]

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Abstract

Understanding dynamic processes within Earth’s subsurface is crucial for resource management, seismic hazard assessment, and geophysical research. This thesis presents advanced methodologies in the monitoring and management of fluid-driven processes in geo-resources exploitation sites and volcanic regions, focusing on seismic velocity changes as indicators of changes in the subsurface due to natural and anthropogenic processes. The research work focuses on two primary objectives: identifying repeating earthquakes through unsupervised machine learning techniques and detecting temporal and spatial medium velocity changes using Coda Wave Interferometry (CWI). CWI is a technique that exploits the sensitivity of scattered seismic waves (coda waves) to estimate changes in wave propagation medium by comparing the coda waves before and after the perturbation. This method enables precise tracking of regions where stress redistribution, fluid migration, and thermal fluctuations may have occurred. At The Geysers geothermal field in California, medium velocity changes were analyzed as a response to injection operations at the wells Prati-9 and Prati-29. The results reveal localized decreases in seismic velocity following the injection at Prati-29, which indicate underlying changes due to fluid movement and stress redistribution. At the Val d’Agri oil field in Italy, changes in velocity were related to the seasonal water level oscillations in the Pertusillo artificial lake. A clear temporal shift was observed between water level peaks and medium velocity maxima, suggesting the role of pore pressure diffusion in modulating the subsurface properties. At Shishaldin volcano in Alaska, a significant increase in medium velocity was detected during a volcanic crisis, reflecting magma dynamics and pressure changes in the volcanic plumbing system. The findings highlight the effectiveness of combining machine learning and CWI for high-resolution seismic monitoring. This integrated framework enhances geophysical methodologies, offering practical tools for geohazard mitigation, optimizing resource management, and deepening our understanding of dynamic subsurface processes.

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