Co-organized by EMRP1/SM7/TS10
Convener: Ludovic RässECSECS | Co-conveners: Marie BocherECSECS, Thibault Duretz, Boris Kaus, Dave May, Georg ReuberECSECS, Sabrina SanchezECSECS, Ylona van DintherECSECS
| Attendance Mon, 04 May, 16:15–18:00 (CEST)

Geological and geophysical data sets are in essence the output of physical processes governing the Earth’s evolution. Such data sets are widely varied and range from the internal structure of the Earth (e.g. seismic tomography), plate kinematics (e.g. GPS), composition of geomaterials (e.g. petrography), estimation of physical conditions and dating of key geological events (e.g. thermobarometry), thermal state of the Earth (e.g heat-flow measurements) to more shallow processes such as natural and “engineered” reservoir dynamics and waste sequestration in the subsurface (e.g. seismic imaging).

Combining the abundant data to process-based numerical models fosters our understanding of the dynamical Earth. Process-based models are powerful tools to predict the evolution of complex natural systems resolving the feedbacks among various physical processes. Integrating high-quality data into direct numerical simulations leads to a constructive workflow to further constrain the key parameters within the models. Innovative inversion strategies, linking forward dynamic models with observables, are topics triggering a growing interest within the community.

The complexity of geological systems arises from their multi-physics nature, as they combine hydrological, thermal, chemical and mechanical. Multi-physics couplings are prone to nonlinear interactions ultimately leading to spontaneous localisation of flow and deformation. Understanding the couplings among those processes requires the development of appropriate tools to capture spontaneous localisation and represents a challenging though essential research direction.

We invite contributions from the following two complementary themes:

#1 Computational advances associated with
- alternative spatial and/or temporal discretisation for existing forward/inverse models
- scalable HPC implementations of new and existing methodologies (GPUs / multi-core)
- solver and preconditioner developments
- AI / Machine learning-based approaches
- code and methodology comparisons (“benchmarks”)
- open source implementations for the community

#2 Physics advances associated with
- development of partial differential equations to describe geological processes
- inversion strategies and adjoint-based modelling
- numerical model validation through comparison with observables (data)
- scientific discovery enabled by 2D and 3D modelling
- utilisation of coupled models to explore nonlinear interactions

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