Seismic data come in many forms, from raw waveforms to tomographic models. Throughout acquisition, processing, and inversion, uncertainties propagate and obscure our understanding of Earth's interior and subsurface. Quantifying and interpreting these uncertainties is vital for robust geological and geodynamical inferences.
In seismic tomography and imaging, uncertainties are often described by resolution metrics, such as resolution matrices or resolving kernels, or by summary statistics derived from posterior samples via Bayesian methods. Recently, machine learning techniques—including variational inference, learned distributions, and likelihood-free approaches—have been introduced to quantify uncertainty, offering promising alternatives. However, fully understanding the meaning of these uncertainties, their interactions, and their influence on model interpretation remains a major challenge.
Once quantified, how do these uncertainties affect downstream applications in geodynamics, mineral physics, or earthquake hazard assessment? Are tomographic inferences reliable enough to support these fields, or do uncertainties limit our conclusions?
Beyond observational data, other uncertainty sources—such as model parameterisation, prior assumptions, and the choice of forward models—add complexity. How do these modelling choices influence the recovered Earth structure and its uncertainties? How can we distinguish genuine Earth features from modelling artefacts?
This session invites contributions that:
• Develop or apply novel methods for quantifying uncertainty in seismic tomography,
• Explore how uncertainty impacts Earth structure interpretations,
• Compare different uncertainty quantification approaches,
• Address model validation and benchmarking amid uncertainty,
• Investigate how tomographic uncertainties propagate into fields like geodynamics, mineral physics, or hazard modelling.
We welcome studies covering global and regional scales, body-wave and surface-wave tomography, full-waveform inversion, ambient noise imaging, and any seismic method where uncertainty is crucial. Cross-disciplinary and innovative methodological contributions are particularly encouraged.