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3-D non-linear travel-time tomography: Imaging high contrast velocity anomalies

机译:3-D非线性行进时间层析成像:成像高对比度速度异常

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In this paper we present an approach for 3-D travel-time tomography, which works well in reconstructing high contrast velocity anomalies in both location and strength. It uses a revised 'irregular' approach to the shortest-path method as the ray tracer and a damped minimum norm, and constrained least-squares CG approach as the inversion solver. In ray tracing, the advantages of the revised 'irregular' over the 'irregular' approach are that the secondary nodes introduced on the cell surfaces significantly improve accuracy of computed travel times, without dramatically increasing the total number of cells and nodes; the tri-linear velocity function defined across the cell guarantees accurate ray tracing in a high velocity contrast medium; and the capacity to calculate a relatively large 3-D model, due to the fast run speed (at least one order of magnitude over the 'regular' approach) and less number of total nodes. The introduction of 'soft' and 'hard' bounds into the inversion process changes the conditioning and makes the solution meaningful in a physical sense. Thus the artifacts caused by noise and high velocity contrasts are substantially suppressed and the image quality is considerably improved, making the solution realistic with noisy or inconsistent travel-time data. Several numerical tests indicate that we can obtain good quality images even for high velocity contrast anomalies (say more than 20%) in the target region. This means the inversion algorithm is an efficient and effective procedure. Meanwhile, the inversion procedure is not very sensitive to the quality of the travel-time data, which is promising for practical usage.
机译:在本文中,我们提出了一种用于3-D行程时间层析成像的方法,该方法在重建位置和强度方面的高对比度速度异常中效果很好。它使用修正的“不规则”方法将最短路径方法用作射线跟踪器,并使用阻尼最小范数,并将约束最小二乘CG方法用作反演求解器。在光线跟踪中,与“不规则”方法相比,修改后的“不规则”方法的优势在于,引入到单元表面的辅助节点显着提高了计算的传播时间的准确性,而不会显着增加单元和节点的总数。跨细胞定义的三线速度函数可确保在高速造影剂中进行精确的光线追踪。由于运行速度快(“常规”方法至少一个数量级)和较少的总节点数,因此具有计算相对较大的3D模型的能力。在反转过程中引入“软”和“硬”界限会改变条件,并使解决方案在物理意义上有意义。因此,基本上抑制了由噪声和高速对比度引起的伪影,并且显着改善了图像质量,从而使解决方案在嘈杂或不一致的行进时间数据下变得现实。几个数值测试表明,即使在目标区域出现高速对比异常(例如超过20%),我们也可以获得高质量的图像。这意味着反演算法是一种有效的程序。同时,反演过程对旅行时间数据的质量不是很敏感,这对实际应用是有希望的。

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