  The interpretation of seismic images faces challenges due to the presence of
several uncertainty sources. Uncertainties exist in data measurements, source
positioning, and subsurface geophysical properties. Understanding
uncertainties' role and how they influence the outcome is an essential part of
the decision-making process in the oil and gas industry. Geophysical imaging is
time-consuming. When we add uncertainty quantification, it becomes both time
and data-intensive. In this work, we propose a workflow for seismic imaging
with quantified uncertainty. We build the workflow upon Bayesian tomography,
reverse time migration, and image interpretation based on statistical
information. The workflow explores an efficient hybrid parallel computational
strategy to decrease the reverse time migration execution time. High levels of
data compression are applied to reduce data transfer among workflow activities
and data storage. We capture and analyze provenance data at runtime to improve
workflow execution, monitoring, and debugging with negligible overhead.
Numerical experiments on the Marmousi2 Velocity Model Benchmark demonstrate the
workflow capabilities. We observe excellent weak and strong scalability, and
results suggest that the use of lossy data compression does not hamper the
seismic imaging uncertainty quantification.
