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Constrained Full Waveform Inversion for Borehole Multicomponent Seismic Data

机译:钻孔多分量地震数据的约束全波形反演

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Full-waveform inversion for borehole seismic data is an ill-posed problem and constraining the problem is crucial. Constraints can be imposed on the data and model space through covariance matrices. Usually, they are set to a diagonal matrix. For the data space, signal polarization information can be used to evaluate the data uncertainties. The inversion forces the synthetic data to fit the polarization of observed data. A synthetic inversion for a 2D-2C data estimating a 1D elastic model shows a clear improvement, especially at the level of the receivers. For the model space, horizontal and vertical spatial correlations using a Laplace distribution can be used to fill the model space covariance matrix. This approach reduces the degree of freedom of the inverse problem, which can be quantitatively evaluated. Strong horizontal spatial correlation distances favor a tabular geological model whenever it does not contradict the data. The relaxation of the spatial correlation distances from large to small during the iterative inversion process allows the recovery of geological objects of the same size, which regularizes the inverse problem. Synthetic constrained and unconstrained inversions for 2D-2C crosswell data show the clear improvement of the inversion results when constraints are used.
机译:井筒地震数据的全波形反演是一个不适当地的问题,限制这一问题至关重要。可以通过协方差矩阵对数据和模型空间施加约束。通常,它们被设置为对角矩阵。对于数据空间,可以使用信号极化信息来评估数据不确定性。反演迫使合成数据适合观测数据的极化。估算1D弹性模型的2D-2C数据的综合反演显示出明显的改善,尤其是在接收器层面。对于模型空间,可以使用使用拉普拉斯分布的水平和垂直空间相关性来填充模型空间协方差矩阵。这种方法降低了反问题的自由度,可以对它进行定量评估。只要不与数据矛盾,强大的水平空间相关距离就有利于建立表格地质模型。在迭代反演过程中,空间相关距离从大到小的弛豫可以恢复相同大小的地质对象,从而解决了反问题。 2D-2C井间数据的综合约束反演和非约束反演显示了使用约束时反演结果的明显改善。

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