全自动盐驱全波形反演

M. Kalita, V. Kazei, Yunseok Choi, T. Alkhalifah
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引用次数: 2

摘要

为了从地震数据中获取未知的地下模型,全波形反演(FWI)常常试图解决不适定非线性优化问题。通过模型正则化,我们将最小化问题解耦为两部分,减轻了与盐体影响数据集相关的FWI的不适定性。我们最小化数据不拟合和模型的总变化,寻求一个具有尖锐界面的倒模型。在第二次优化中,我们对模型中的急剧速度下降进行惩罚,这相当于计算速度场的泛滥。除了最少的人为干预外,我们的技术不需要任何关于盐层顶部的信息,而传统的工业盐驱需要这些信息。这些特征在与BP 2004模型相对应的数据集上进行了演示,为了使数据更加实用,对频率小于3 Hz的数据进行了静音处理。如果使用相同的等密度声学代码来准备观测数据,则可以很好地检索模型,这仍然是最常见的FWI测试之一。然而,我们的方法仍然允许我们通过可变密度模型独立合成的数据重建合理的盐结构描述。
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Full-Waveform Inversion for Automated Salt Flooding
Summary Full-waveform inversion (FWI) often attempts to resolve an ill-posed non-linear optimization problem in order to retrieve the unknown subsurface model from the seismic data. With model regularization, we alleviate the ill-posedness of FWI associated with salt bodies affected datasets by decoupling the minimization problem into two parts. We minimize the data misfit along with the total variation in the model, seeking an inverted model with sharp interfaces. In the second optimization, we penalize sharp velocity drops in the model, which is equivalent to computationally flooding of velocity field. Besides the minimal human intervention, our technique requires no information whatsoever of the top of the salt, which is required for conventional industrial salt flooding. Those features are demonstrated on a dataset corresponding to the BP 2004 model with frequencies less than 3 Hz muted to make the data more practical. The model is well retrieved if the same constant density acoustic code is used for preparing the observed data, which is still one of the most common FWI tests. However, our approach still allows us to reconstruct a reasonable depiction of the salt structure from data synthesized independently with a variable density model.
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