Congratulations to Moloud on a successful PhD defence today! Moloud’s thesis is entitled “Machine learning and plate tectonic analysis for mantle heterogeneity, paleoclimate, and critical minerals”. Her committee is formed by Jonny Wu (Arizona), Susan Beck (Arizona), Patricia Persaud (Arizona), and Lorenzo Colli (Modelway).
Moloud’s thesis chapters are listed below:
Ch. 1 – Assessing large-scale mantle compositional heterogeneity from machine learning analysis of 28 global P- and S-wave tomography models.
Rahimzadeh Bajgiran, M., Colli, L., & Wu, J. (2023). Assessing large-scale mantle compositional heterogeneity from machine learning analysis of 28 global P-and S-wave tomography models. Geophysical Journal International, 235(3), 2778-2793.
Ch. 2 – Deep mantle to atmosphere: Understanding the role of vanished intra-oceanic subduction zones on paleoclimate since 200 Ma (under review)
Rahimzadeh Bajgiran, M., Wu, J., Zahirovic, S., Wu, J.T.J., Colli, L., Lin, Y.A., “Deep Mantle to Atmosphere: Understanding the Role of Vanished Intra-Oceanic Subduction Zones on Paleoclimate Since 200 Ma. Submitted to Geochemistry, Geophysics, Geosystems.
Ch. 3 – Integrating plate tectonics and Machine Learning to predict undiscovered porphyry copper systems