Session

DescriptionThe central goal of computational geophysics is to infer physical properties of the Earth's interior, inaccessible for direct measurements (such as density or wave velocity), from data typically acquired near the surface with appropriate instruments. Inversion techniques are applied to infer unknown parameters of a mathematical model to fit the observed data. The resulting model provides a view of the current physical state and can be used to simulate previous and future states. Such models lead to complicated nonlinear numerical systems, which are usually too large for standard computers. Luckily, HPC facilities' ever-increasing power allows continuous model resolution and complexity growth. Nevertheless, harnessing this power brings new challenges for implementations. Despite the success of deterministic inversion methods, nonuniqueness and nonlinearity of the inverse problems can reduce the meaningfulness of the solutions. Bayesian inference represents an attractive tool to overcome these issues and provide uncertainty quantification. However, it brings another level of complexity that needs to be re-assessed in light of the recent advances in methodology and technologies. This three-sessions minisymposium aims to bring together scientists engaged in theory, numerical methods, algorithms, and scientific software engineering for scalable numerical modelling, inversion and uncertainty quantification in computational geophysics.