Alberto Manuel Garcia Navarro

Ph.D. candidate in Materiali, Processi Sostenibili E Sistemi Per La Transizione , 38th cycle (2022-2025)
Department of Environment, Land and Infrastructure Engineering (DIATI)




Research interests

Cluster analysis
Insar data
Machine learning
Petroleum engineering
Project management
R programming
Underground gas storage


PhD Student on Materials, Sustainable Processes and System for the Energy Transition - DIATI Polito (November 2022). We will study how machine learning and big data analytics tools, using InSAR data, can expand the horizon of alternatives for the comprehension of subsidence effects, related to the production/injection of fluids in the underground (H2, water and hydrocarbons). Outside Research Collaborator - DIATI Polito (2021-2022). Focus on the development of innovative tools and methodologies using unsupervised machine learning techniques, applied to the study of anthropic-caused subsidence using InSAR data (effects characterization due to underground gas storage, Emilia-Romagna and Lombardy regions). Petroleum and Mining Engineering, POLITO 2021, with the thesis: Application of the data clusterization approach to the satellite measures of altimetric variation in the Emilia-Romagna Region (Italy). Tut. Vera Rocca (DIATI-POLITO), Alfonso Capozzoli (DENERG-POLITO), Luisa Perini (CNR-BOLOGNA). Petroleum Engineering. Universidad Central de Venezuela, 2017 - Pending diploma, double-degree agreement POLITO-UCV. Solid background in reservoir engineering, project management, GIS systems and programming (R, Python, SQL e VBA). Doctoral Research interview "Machine Learning and InSAR" - "Biennale di Tecnologia 2022" - Polytechnic of Turin -


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Research topics

  • Machine learning and Big Data Analytics tools applications for anthropic-related subsidence, both characterization and study. Focus on the injection/production of fluids in the underground (H2, water and hydrocarbons) over the Po Plain, Italy.


Other activities and projects related to research

We study how machine learning and big data analytics, using InSAR data, can help us to better understand the subsidence-related effects.


PoliTO co-authors

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