A workflow for seismic imaging with quantified uncertainty

Abstract 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 fundamental in the earth sciences and essential in the oil and gas industry decision-making process. 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 lossy data compression does not hamper the seismic imaging uncertainty quantification. We explore the propagation of the velocity uncertainties to the seismic images by confidence maps and probability density functions in control points. The multi-modal character revealed by such high-order statistics reinforces the need for the machinery we have put together in our workflow.