Automated 3D Tomographic Velocity Analysis of Residual Moveout In Prestack Depth Migrated Common Image Point Gathers.
Automated 3D Tomographic Velocity Analysis of Residual Moveout In Prestack Depth Migrated Common Image Point Gathers.
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DOI：
10.1190/1.1820113
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
M. Woodward;P. Farmer;D. Nichols;S. Charles
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文献类型：
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作者：
M. Woodward;P. Farmer;D. Nichols;S. Charles
利用叠前深度偏移共成像点道集的剩余时差分析，在复杂地质条件下建立速度模型。在速度变化平稳的情况下，通常通过对转换回时间后的道集上的剩余曲率进行叠加速度分析来建立模型。在速度变化更快的情况下，必须通过在深度上的完全层析反演中将残余深度误差投影回单独追踪的射线路径来建立模型。在本文中，我们将后者的方法应用到墨西哥湾沿岸的数据集的结果。3D字段示例以高度自动化的方式运行。深度误差（剩余时差）首先在批处理模式下在稀疏采样的公共图像点集的体积中拾取，然后通过同时在所有深度处的所有拾取的全局反演来最小化。反问题是约束与预处理，以解决最光滑的速度场的第一部分。这种自动化方法通过最大限度地减少人为干预来提高周转率。
Residual moveout analysis of prestack depth migrated common image point gathers is used for velocity model building in areas of complex geology. Where the velocity varies smoothly, the model is commonly built from stacking velocity analysis of the residual curvature on the gathers after conversion back to time. Where the velocity varies more rapidly, the model must be built instead by projecting the residual depth errors back over individually traced raypaths in a full tomographic inversion in depth. In this paper we show results of the latter method applied to a Gulf Coast data set. The 3D field example was run in a highly automated fashion. The depth errors (residual moveout) were first picked in batch mode throughout a sparsely sampled volume of common image point gathers and then minimized through a global inversion of all picks at all depths simultaneously. The inverse problem was constrained with preconditioning to solve for the smoothest part of the velocity field first. This automated approach improves turnaround by minimizing human intervention.