Understanding how magma is generated, stored, transferred, mobilised, and ultimately transported toward the surface is central to deciphering volcanic behaviour and thus to improve the assessment of volcanic hazard. The trans-crustal evolution of magmas, and the associated variation in their rheological properties, exert a fundamental influence on magma remobilisation and on the timing, magnitude, and style of volcanic activity. Reconstructing how magma properties evolve through time is therefore essential for understanding the processes that regulate volcanic activity and for interpreting monitoring signals recorded at the surface.

In this framework, the petrological and geochemical record preserved in crystals, melts, and volcanic products provides a unique window into trans-crustal magmatic processes, revealing the physical and chemical evolution of magmatic systems from long-term storage to the final stages preceding eruption. These observations can be combined with field observations, experiments, numerical models, geophysical and monitoring data to constrain where, when, and how magmatic systems become eruptible.

This session aims to bring together studies investigating the pathways from magma generation and storage to mobilisation, ascent, and eruption. We particularly encourage petrological, volcanological, geochemical and geophysical contributions that constrain the architecture of magma plumbing systems, the processes and timescales of magma evolution and migration, their link with eruptive activity, and explore how petrological and geochemical observations can improve our understanding of pre- and syn-eruptive dynamics. Relevant approaches include field observations, mineral and melt chemistry, diffusion chronometry, thermobarometry, melt and fluid inclusions, isotope and trace-element geochemistry, experimental petrology, thermodynamic and kinetic modelling, numerical simulations, and textural and rheological investigations. Contributions integrating petrological information with geophysical, geodetic, or monitoring datasets are particularly encouraged. We also welcome emerging approaches, including automated mineral and textural analysis, machine learning, data-driven modelling, and high-resolution analytical techniques.