Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
GMPV8.2 | Building the next generation of realistic models of magma propagation and volcano deformation
EDI
Building the next generation of realistic models of magma propagation and volcano deformation
Convener: Sam Poppe | Co-conveners: Claire HarnettECSECS, Tim DavisECSECS
In this session we will review and discuss the latest magma propagation and ground deformation models. The data collected at active volcanoes is rapidly increasing in quality; there has been an explosion in high-resolution geodetic and seismic data that captures magma movement and storage conditions in the subsurface. It is becoming routine to fit ground deformation and seismic signals of such events using static models, typically with constant opening or piece-wise static deformation sources in homogeneous elastic half-spaces. Simple fitting of such models lacks predictive power about what will happen to the system next and provides little insight into the physics of the system. Mechanical modelling can answer how such intrusions develop through time, can help investigate the processes controlling where and when magma erupts and can quantify the influence of mechanical complexities and when these should be considered. Such models are typically theoretical, but due to rapid increases in the data quality of magmatic events we can begin to test the predictive power of these models.

We welcome contributions across numerical and laboratory modelling, physical volcanology, planetary geology, geodesy and geophysics that focus on building and informing cutting art mechanical models of magma-induced deformation by:

- simulating more realistic rheologies and mechanical heterogeneities from rock testing, geophysical measurements or geological field observation
- exploring limitations of typical model assumptions by comparing and integrating field/experimental/numerical methods
- developing new modelling applications that simulate previously unconstrained mechanics and dynamic propagation
- developing dedicated 3D modelling approaches
- using AI or machine learning to analyse model sensitivities in large data sets