鲁棒范数在降低周期跳变情况下FWI不确定性中的应用

J. Ramos-Martínez, A. Valenciano, N. Chemingui, T. Martin
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引用次数: 0

摘要

如果初始模型与真实模型不够接近,或者数据中没有足够的低频,那么由于周期跳变,全波形反演(FWI)可能会产生不准确的模型。此外,在长波长特征的分辨率之前,FWI模型更新可能会受到反射率印记的影响。用不正确的模型进行成像将造成结构上的不确定性,并将妨碍对潜在前景的评估。可以通过使用鲁棒范数来测量数据不匹配(w2范数)而不是传统的l2范数来减轻周期跳过。使用速度梯度去除反射率的印记,我们演示了一个应用程序,以解决初始模型中不存在的高速层的数据。通过井数据的验证,得到的地球模型准确地反映了地下,从而减少了最终构造图像的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Use of a Robust Norm in Reducing FWI Uncertainty in the Presence of Cycle Skipping
Summary Full Waveform Inversion (FWI) can create on an inaccurate model as a result of cycle skipping, if the initial model is not close enough to the true one, or there is insufficient low frequencies in the data. Furthermore, FWI model updates can be affected by a reflectivity imprint prior to the resolution of long-wavelength features. Imaging with the resulting incorrect model will create structural uncertainty, and will hamper an evaluation of potential prospects. Cycle skipping can be mitigated by using a robust norm for measuring the data misfit (W2-norm), instead of a traditional L2-norm. Used with a velocity gradient that removes the imprint of the reflectivity, we demonstrate an application to data resolving a high-velocity layer that was not present in the inital model. Corroborated by well data, the resulting earth model accurately reflects the subsurface, which, in turn, reduces uncertainty in the final structural image.
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