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Near Surface Velocity Analysis in the Common Image Cube (CIC) Domain

机译:常见图像立方体(CIC)域附近的表面速度分析

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Low relief structures are increasingly important onshore exploration targets.Accurate resolution of these structures and reservoir characterization can be difficult because seismic reflections of these targets are contaminated with distortions due to complicated near surface geology. Downward continuation wave equation datuming techniques offer a solution to the near surface problem but they require a detailed knowledge of the near surface velocity. Near surface Migration VelocityAnalysis (MVA) can be quite challenging due to the low fold of the data involved. In this paper, we integrate different MVAmethods into the common image cube (CIC) approach. These methods include residual curvature analysis (RCA), depth focusing analysis, and common focus point (CFP). We use the CIC as a domain for near surface velocity analysis to address the low fold problem. For 2D data at a lateral position, instead of invoking the zero-lag imaging condition, we store other lags at each depth level. The result is a cube of data that offers more information than other methods. We illustrate two uses of this cube by applying wave equation datuming to a synthetic dataset, simulated from topography, using correct and incorrect velocity models, respectively.
机译:低浮雕结构越来越重要的血轮勘探目标。这些结构和储层表征的准确性分辨率可能很困难,因为这些靶标的地震反射因近地表地质复杂而导致的扭曲被污染。向下的延续波方程基准技术提供近近表面问题的解决方案,但它们需要详细了解近近表面速度。由于所涉及的数据的低倍数,近表面迁移速度分析(MVA)可能是非常具有挑战性的。在本文中,我们将不同的Mvamethod集成到常见的图像立方体(CIC)方法中。这些方法包括残留曲率分析(RCA),深度聚焦分析和共同焦点(CFP)。我们使用CIC作为域的域,用于近表面速度分析,以解决低折叠问题。对于横向位置的2D数据,而不是调用零滞后成像条件,我们将其他滞后设置在每个深度级别。结果是数据的多维数据集,其提供比其他方法更多的信息。我们通过将波方程数据应用于从地形模拟的合成数据集分别使用正确和不正确的速度模型来说明这两个立方体的两个用途。

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