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Moveout-based wave-equation migration velocity analysis

机译:基于运动的波动方程偏移速度分析

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Current wave-equation migration velocity analysis schemes suffer from problems such as severe nonlinearity (which causes the issue of cycle skipping) and imprecise objective functions (which can accrue velocity errors by honoring residuals caused by model complexity and incomplete acquisition). To provide an improvement, we developed an alternative method to perform wave-equation migration velocity analysis by maximizing the flatness of the angle-domain common image gathers. We replaced the ray-based tomographic operator with the wave-equation-based one, although keeping the conventional moveout-based tomography work flow. Instead of maximizing the image-stack-power objective function directly with respect to the slowness, we linked the objective function to the slowness indirectly through an intermediate moveout parameter. By focusing on the common image gather kinematics, this approach greatly reduced the risk of cycle skipping in the absence of low-frequency data, and it produced high-quality gradients. In addition, the proposed method did not require explicit picking of the moveout parameters. Our numerical examples demonstrated the great potential of this method: in the first example, in which there is a Gaussian-shaped slowness anomaly, our method produced a well-behaved gradient; in the second example, in which there is a horizontally gradual increase of slowness, the result verified that our method is robust against cycle skipping; in the third example, the result showed that our method works well with reflectors of variable dips. Finally, our test on the Marmousi models concluded that the proposed method converges to a high-quality model that uniformly flattens the angle-domain common-image gathers.
机译:当前的波方程迁移速度分析方案存在诸如严重的非线性(这会引起循环跳跃的问题)和不精确的目标函数(其可能会因尊重模型复杂性和不完全采集引起的残差而产生速度误差)等问题。为了提供改进,我们开发了一种替代方法,通过最大化角域公共图像集的平坦度来执行波方程偏移速度分析。尽管保留了传统的基于动量的层析成像工作流程,但我们用基于波方程的射线替代了基于射线的层析成像算子。我们没有直接针对慢度最大化图像堆栈功率目标函数,而是通过中间偏移参数将目标函数间接链接至慢度。通过专注于常见的图像采集运动学,该方法大大降低了在缺乏低频数据的情况下循环跳过的风险,并且产生了高质量的渐变。另外,所提出的方法不需要显式选择运动参数。我们的数值例子证明了这种方法的巨大潜力:在第一个例子中,在一个高斯形状的慢度异常中,我们的方法产生了一个行为良好的梯度。在第二个示例中,缓慢度在水平方向上逐渐增加,结果验证了我们的方法对周期跳跃的鲁棒性。在第三个示例中,结果表明我们的方法适用于可变倾角的反射器。最后,我们对Marmousi模型的测试得出的结论是,所提出的方法收敛于一个高质量的模型,该模型均匀地展平了角域共同图像集。

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