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Assessing uncertainties in velocity models and images with a fast nonlinear uncertainty quantification method

机译:评估具有快速非线性不确定性定量方法的速度模型和图像的不确定性

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摘要

Seismic imaging is conventionally performed using noisy data and a presumably inexact velocity model. Uncertainties in the input parameters propagate directly into the final image and therefore into any quantity of interest, or qualitative interpretation, obtained from the image. We considered the problem of uncertainty quantification in velocity building and seismic imaging using Bayesian inference. Using a reduced velocity model, a fast field expansion method for simulating recorded wavefields, and the adaptive Metropolis- Hastings algorithm, we efficiently quantify velocity model uncertainty by generating multiple models consistent with low-frequency full-waveform data. A second application of Bayesian inversion to any seismic reflections present in the recorded data reconstructs the corresponding structures' position along with its associated uncertainty. Our analysis complements rather than replaces traditional imaging because it allows us to assess the reliability of visible image features and to take that into account in subsequent interpretations.
机译:通常使用噪声数据和可能的不精确速度模型进行地震成像。输入参数中的不确定性将直接传播到最终图像中,因此进入从图像获得的任何兴趣或定性解释。我们认为使用贝叶斯推理的速度建设和地震成像的不确定性量化问题。使用减小的速度模型,用于模拟记录的波场的快速场扩展方法,以及自适应大学 - 黑阵算法,我们通过产生与低频全波形数据一致的多个模型有效地量化速度模型不确定性。第二次应用贝叶斯反演到记录数据中存在的任何地震反射的反射重建相应的结构的位置以及其相关的不确定性。我们的分析互补,而不是取代传统成像,因为它允许我们评估可见图像功能的可靠性,并在后续解释中考虑到这一点。

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