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Seismic volumetric flattening and segmentation.

机译：地震体积展平和分段。

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Two novel algorithms provide seismic interpretation solutions that use the full dimensionality of the data. The first is volumetric flattening and the second is image segmentation for tracking salt boundaries. Volumetric flattening is an efficient full-volume automatic dense-picking method applied to seismic data. First local dips (step-outs) are calculated over the entire seismic volume. The dips are then resolved into time shifts (or depth shifts) in a least-squares sense. To handle faults (discontinuous reflections), I apply a weighted inversion scheme. Additional information is incorporated in this flattening algorithm as geological constraints. The method is tested successfully on both synthetic and field data sets of varying degrees of complexity including salt piercements, angular unconformities, and laterally limited faults. The second full-volume interpretation method uses normalized cuts image segmentation to track salt interfaces. I apply a modified version of the normalized cuts image segmentation (NCIS) method to partition seismic images along salt interfaces. The method is capable of tracking interfaces that are not continuous, where conventional horizon tracking algorithms may fail. This method partitions the seismic image into two groups. One group is inside the salt body and the other is outside. Where the two groups meet is the salt boundary. By imposing bounds and by distributing the algorithm on a parallel cluster, I significantly increase efficiency and robustness. This method is demonstrated to be effective on both 2D and 3D seismic data sets.
机译：两种新颖的算法提供了使用数据完整维度的地震解释解决方案。第一个是体积展平，第二个是用于跟踪盐分边界的图像分割。体积展平是一种适用于地震数据的有效的全体积自动密集拾取方法。首先在整个地震体积上计算局部倾角（失步）。然后将这些下沉分解为最小二乘的时移（或深度平移）。为了处理故障（不连续反射），我应用了加权反演方案。附加信息作为地质约束纳入此展平算法中。该方法已在各种复杂程度的合成和现场数据集上成功测试，包括盐穿透，角度不整合面和侧向受限断层。第二种全体积解释方法使用归一化的切割图像分割来跟踪盐界面。我应用了规范化切割图像分割（NCIS）方法的修改版本，以沿盐界面分割地震图像。该方法能够跟踪不连续的接口，而传统的地平线跟踪算法可能会失败。该方法将地震图像分为两组。一组在盐体内，另一组在盐体内。两组相遇的地方是盐分边界。通过施加界限并在并行集群上分布算法，我显着提高了效率和鲁棒性。事实证明，该方法对2D和3D地震数据集均有效。

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