Research & Teaching

Research

Machine Learning in Petrology, Geochemistry, and Volcanology

I aim to systematically investigate the use of machine learning (ML) techniques for the solution of problems that are relevant to geological applications, with emphasis on igneous petrology, geochemistry, and volcanology. This line of research was sparked in 2014 thanks to the funding grants “Challenge: Coupling macHine leArning, Leading anaLytical mEthods aNd larGe gEochemical datasets in tephra studies” (PI: Maurizio Petrelli, 2015/2017), “ENGAGE: machinE learNinG Applications for Geological problEms” (PI: Maurizio Petrelli, 2019/2020), the Microsoft Machine Learning Award (PI: Maurizio Petrelli, 2015) provided by the University of Perugia and Microsoft Inc., respectively. It is now supported by many active grants that I lead or act as co-PI.

Basic concepts to introduce NN algorithms: (a) biological neuron, (b) artificial neuron, and (c) Multilayer Perceptron (MPL). An NN is a computational algorithm designed to mimic the human brain’s structure and operational mechanisms, from: https://doi.org/10.1038/s41467-018-02987-6

My work on machine learning in petrology and volcanology has developed through a series of interconnected studies. It began with the application of classification methods to geochemical data for tectonic setting recognition, was later extended to volcanology and tephrochronology, and then progressed to regression-based approaches for estimating magmatic pressure and temperature as a complement to established thermodynamic methods. These developments were later synthesized in a broader review of the field, and I am currently contributing a book chapter on recent methodological advances and the growing role of artificial intelligence in volcanology.

Selected Publications:

Petrelli, M., Anantrasirichai, N., Bean, C. J., Biggs, J., Malfante, M., Wilding, J. D., Zhu, W. (2025). Methodological Advances in Volcanology: The Role of Artificial Intelligence in Volcano Monitoring, Modelling, and Hazard Assessment. The Encyclopedia of Volcanoes 3rd Edition. Bonadonna C. (Ed). Elsevier. Accepted for Publication
Petrelli M., 2024. Machine Learning in Petrology: State-of-the-Art and Future Perspectives. Journal of Petrology. doi: 10.1093/petrology/egae036
Petrelli M., 2023. Machine Learning for Earth Sciences. Springer. ISBN:978-3-031-35113-6
Jorgenson C., Higgins O., Petrelli M., Bégué F., Caricchi L., 2022. A Machine Learning-Based Approach to Clinopyroxene Thermobarometry: Model Optimization and Distribution for Use in Earth Sciences. JGR: Solid Earth. doi:10.1029/2021JB022904
Petrelli M., Caricchi L., Perugini, D. (2020). Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas. JGR: Solid Earth. DOI: 10.1029/2020JB020130
Caricchi L., Petrelli M., Bali E., Sheldrake T., Pioli L., Simpson G. (2020). A Data Driven Approach to Investigate the Chemical Variability of Clinopyroxenes From the 2014–2015 Holuhraun–Bárdarbunga Eruption (Iceland). Frontiers in Earth Science. DOI: 10.3389/feart.2020.00018
Petrelli M., Bizzarri R., Morgavi D., Baldanza A., Perugini, D. (2017). Combining machine learning techniques, microanalyses and large geochemical datasets for tephrochronological studies in complex volcanic areas: New age constraints for the Pleistocene magmatism of central Italy. Quaternary Geochronology. DOI: 10.1016/j.quageo.2016.12.003
Petrelli M., Perugini D. (2016). Solving petrological problems through machine learning: the study case of tectonic discrimination using geochemical and isotopic data. Contributions to Mineralogy and Petrology. DOI: 10.1007/s00410-016-1292-2

Dynamics and Timescales of Volcanic Plumbing Systems

This line of research focuses on the geochemical, petrological, and volcanological characterization of magmatic systems, with particular emphasis on time-scale estimates of magmatic processes occurring in volcanic plumbing systems before eruptions. To do that, I combine numerical methods, experimental petrology, and the study of natural samples. The main aim is to provide methods and tools to support public authorities and decision-makers in volcanic hazard assessment and risk mitigation.

Storage timescales and melt rising velocities. Idealized continental crust section reporting the potential storage times at mid- to deep-crustal levels and the estimated timescales for the rising of a hydrous residual melt, from: https://doi.org/10.1093/petrology/egae036

Selected Publications:

Petrelli M., Ágreda López M., Pisello A., Perugini, D. (2023). Pre-eruptive dynamics at the Campi Flegrei Caldera: from evidence of magma mixing to timescales estimates. Earth, Planets and Space, doi: 10.1186/s40623-023-01765-z
Rooyakkers S.M., Stix J., Berlo K., Petrelli M., Sigmundsson F., 2021. Eruption risks from covert silicic magma bodies. Geology, doi:10.1130/G48697.1
Petrelli M., Zellmer G.F., 2022. Rates and Timescales of Magma Transfer, Storage, Emplacement, and Eruption. In Dynamic Magma Evolution (Francesco Vetere Editor), Geophysical Monograph Series, 254, American Geophysical Union and Wiley.
Petrelli M., El Omari K., Spina L., Le Guer, Y., La Spina G., Perugini D., 2018. Timescales of water accumulation in magmas and implications for short warning times of explosive eruptions. Nature Communications, doi: 10.1038/s41467-018-02987-6
Petrelli M., El Omari K., Le Guer Y., Perugini D., 2016. Effects of chaotic advection on the timescales of cooling and crystallization of magma bodies at mid crustal levels. Geochemistry, Geophysics, Geosystems, doi: 10.1002/2015GC006109
Petrelli, M., Perugini, D., Poli, G., 2011. Transition to chaos and implications for time-scales of magma hybridization during mixing processes in magma chambers. Lithos 125, 211-22

Development of the LA-ICP-MS Laboratory @UNIPG for Geochemical Investigations

In 2001, I started the development of the LA-ICP-MS laboratory at the Department of Geology, University of Perugia. Since 2002 (with a pause from 2010 to 2014), I have successfully run the LA-ICP-MS with activities that include the maintenance of the instrumentation, the development of analytical protocols, the analysis of natural and experimental samples, and the support of external users. Currently, the LA-ICP-MS lab in Perugia hosts two LA-ICP-MS systems and is actively involved in many scientific projects, with the active and invaluable support of Mónica Ágreda López.

Selected Publications:

Petrelli M., Laeger K., Perugini D. (2016). High spatial resolution trace element determination of geological samples by laser ablation quadrupole plasma mass spectrometry: implications for glass analysis in volcanic products. Geosciences Journal. DOI: 10.1007/s12303-016-0007-z
Alagna K.E., Petrelli M., Perugini D., Poli G. (2008). Micro‐analytical zircon and monazite U‐Pb isotope dating by laser ablation‐inductively coupled plasma‐quadrupole mass spectrometry. Geostandards and Geoanalytical research. DOI: 10.1111/j.1751-908X.2008.00866.x
Petrelli M., Caricchi L., Ulmer P. (2007). Application of high spatial resolution laser ablation ICP‐MS to crystal‐melt trace element partition coefficient determination. Geostandards and Geoanalytical Research. DOI: 10.1111/j.1751-908X.2007.00825.x

Ph.D. Students

Mónica Ágreda López

Unravelling plumbing system dynamics by Machine Learning

Giulia Fisauli

Tephra investigation coupling conventional and Machine Learning techniques

Teaching Activities

2019 – now: University of Perugia, Master degree in Geological Sciences and Technologies. Teaching course in “Introduction to experimental petro-volcanology ”.

2019 – now: University of Perugia, Master degree in Geological Sciences and Technologies. Teaching course in “Mathematical methods in Earth Sciences ”.

Mar 2023: Department of Petrology & Geochemistry, Eötvös University Budapest (ELTE), Short course in “Introduction to Machine Learning for the Earth Sciences”.

Feb 2023: Leibniz Universität Hannover Institut für Mineralogie, Short course in “Introduction to Machine Learning for the Earth Sciences”.

August 2022: Zhejiang University, Short course (Remote Teaching) in “Introduction to Machine Learning in Python for the Earth Sciences”.

May 2022: Universidade dos Açores, Short course in “Introduction to Python Programming in Earth Sciences”.

August 2021: Zhejiang University, Short course (Remote Teaching) in “Introduction to Python Programming in Earth Sciences”.

Feb 2020: Leibniz Universität Hannover Institut für Mineralogie, Short course in “Introduction to Python Programming in Earth Sciences”.

Dec 2018: Department of Petrology & Geochemistry, Eötvös University Budapest (ELTE), Short course in “Introduction to Python Programming in Earth Sciences”.

2017 – 2019: University of Perugia, Master degree in Geological Sciences and Technologies. Teaching course in “Data analysis and data interpretation in geological sciences”.

2015 – 2016: University of Perugia, Master degree in Geological Sciences and Technologies. Teaching course in “Igneous Petrology”.

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