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.

中文翻译：

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具有量化不确定性的地震成像工作流程

摘要 由于存在多个不确定源，地震图像的解释面临挑战。数据测量、源定位和地下地球物理特性存在不确定性。了解不确定性的作用以及它们如何影响结果是地球科学的基础，也是石油和天然气行业决策过程的关键。地球物理成像非常耗时。当我们添加不确定性量化时，它既是时间密集型又是数据密集型。在这项工作中，我们提出了一种具有量化不确定性的地震成像工作流程。我们在贝叶斯断层扫描、逆时偏移和基于统计信息的图像解释上构建工作流程。该工作流探索了一种有效的混合并行计算策略，以减少逆时迁移执行时间。应用高级数据压缩以减少工作流活动和数据存储之间的数据传输。我们在运行时捕获和分析来源数据，以可忽略的开销改进工作流执行、监控和调试。Marmousi2 Velocity Model Benchmark 上的数值实验展示了工作流程功能。我们观察到出色的弱可扩展性和强可扩展性，结果表明有损数据压缩不会妨碍地震成像不确定性量化。我们通过控制点中的置信图和概率密度函数探索速度不确定性向地震图像的传播。这种高阶统计数据揭示的多模态特征加强了对我们在工作流程中组合在一起的机器的需求。