Abstract
Summary Geophysical imaging faces challenges in seismic interpretations due to multiple sources of uncertainties related to data measurements, pre-processing and velocity analysis procedures. An essential part of the decision-making process is understanding the uncertainties and how they influence the outcomes. For this, we present a new scientific workflow built upon a Bayesian tomography, Reverse Time Migration, and image interpretation based on machine learning techniques. Our scientific workflow explores an efficient hybrid computational strategy. Besides, high levels of compression are applied to reduce the network data transfer among the workflow activities and to store the final images. The experiments are made with the well known Marmousi Velocity Model Benchmark and run in Lobo Carneiro at the High-Performance Computing Center at the Federal University of Rio de Janeiro, Brazil. The new scientific workflow together with high-performance computing techniques allows obtaining seismic images under uncertainty very fast.
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