Monitoring volcanic hazards through the combination of field observations, satellite data and numerical models presents extremely complex challenges, from the identification and quantification of hazardous phenomena during pre-/syn-eruptive phases to the assessment of impact and risk to people and property.
This session welcomes contributions addressing open questions in the study and modelling of volcanic processes and associated hazards, including but not limited to field and satellite data analysis, physico-mathematical formulations of natural processes, probabilistic forecasting, data assimilation and data fusion, and the development and application of numerical methods. We particularly encourage interdisciplinary contributions that bridge traditional volcano monitoring with emerging innovations in computational science, statistical analysis, Machine Learning (ML), and Artificial Intelligence (AI).
The objectives of the session include: (i) expanding knowledge of complex volcanic processes and their spatio-temporal dynamics; (ii) advancing methods for monitoring, modelling, and forecasting of volcanic phenomena; (iii) assessing the robustness of models through validation against real case studies, analytical solutions, and laboratory experiments; (iv) quantifying uncertainty propagation through both forward (sensitivity analysis) and inverse (optimisation/calibration) modelling; and (v) exploring the potential of AI- and ML-driven techniques to integrate and process multidisciplinary datasets for improved volcanic hazard assessment, risk reduction, mitigation strategies, and decision-support applications.
Andrea Di Muro