We have developed an automated method for velocity picking that allows us to estimate appropriate velocity functions for the normal moveout correction of common-depth-point (CDP) gathers, valid for either hyperbolic or nonhyperbolic trajectories. In the hyperbolic velocity analysis case, the process involves the simultaneous search (picking) of a certain number of time-velocity pairs in which the semblance, or any other coherence measure, is high. In the nonhyperbolic velocity analysis case, a third parameter, usually associated with the layering and/or the anisotropy, is added to the searching process. Our technique relies on a simple but effective search of a piecewise linear curve defined by a certain number of nodes in a 2D or 3D space that follows the semblance maxima. The search is carried out efficiently using a constrained very fast simulated annealing algorithm. The constraints consist of static and dynamic bounding restrictions, which are viewed as a means to incorporate prior information about the picking process. This allows us to avoid those maxima that correspond to multiples, spurious events, and other meaningless events. Results using synthetic and field data indicate that our technique permits automatically obtaining accurate and consistent velocity picks that lead to flattened events, in agreement with the manual picks. As an algorithm, the method is very flexible for accommodating additional constraints (e.g., preselected events) and depends on a limited number of parameters. These parameters are easily tuned according to data requirements, available prior information, and the user’s needs. The computational costs are relatively low, ranging from a fraction of a second to, at most, 1–2 s per CDP gather, using a standard PC with a single processor.

中文翻译：

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模拟退火速度分析：自动化拣选过程

我们已经开发了一种自动的速度拾取方法，该方法使我们能够估计适当的速度函数，以进行普通深度点（CDP）道集的正常时差校正，对双曲线或非双曲线轨迹均有效。在双曲线速度分析的情况下，该过程涉及同时搜索（挑选）一定数量的时间-速度对，其中相似性或任何其他相干性度量很高。在非双曲线速度分析的情况下，通常与分层和/或各向异性相关的第三参数被添加到搜索过程中。我们的技术依靠简单而有效的搜索分段线性曲线，该分段线性曲线由遵循相似最大值的2D或3D空间中一定数量的节点定义。使用约束非常快的模拟退火算法可以有效地执行搜索。约束包括静态和动态边界约束，它们被视为合并有关拣配过程的先前信息的一种手段。这使我们能够避免那些对应于倍数，虚假事件和其他无意义事件的最大值。使用合成数据和现场数据得出的结果表明，我们的技术可以自动获取准确且一致的速度拾取，从而导致事件平坦化，与手动拾取一致。作为一种算法，该方法非常灵活，可以适应其他约束（例如，预选事件），并且取决于数量有限的参数。这些参数可以根据数据需求，可用的先验信息以及用户的需求轻松进行调整。