The definition of reliable velocity functions is paramount for obtaining high-quality poststack seismic data. Velocity functions are commonly created with the interpreter interactively selecting high-energy peaks in velocity spectra and verifying if the derived velocity functions match the traveltime trajectories in the corresponding common midpoint (CMP) gathers. Modern software further allows the interpreter to apply resulting moveout corrections and verify if the desired overall flatness of reflection events is achieved. This very detailed process takes a significant amount of time, not necessarily delivering the globally optimal velocity function, and ultimately impacting the cost and duration of a typical seismic interpretation procedure. In this work, we present a hybrid regression approach based on convolutional neural networks (CNN) to speed up the velocity analysis workflow. The proposed methodology consists of an automatic initial velocity function estimation followed by a supervised refinement process that requires only a handful of gathers to be manually picked. Experiments performed on five field data sets show that the proposed methodology can not only produce human-grade velocity pickings but also outperform the interpreter in some cases, in a fraction of the time taken by the human counterpart.

中文翻译：

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使用卷积神经网络的混合回归方法进行自动速度分析

可靠的速度函数的定义对于获得高质量的叠后地震数据至关重要。通常由解释器交互选择速度谱中的高能峰并验证导出的速度函数是否与相应公共中点（CMP）集合中的行进时间轨迹相匹配来创建速度函数。现代软件还允许解释器应用结果偏差校正并验证是否实现了所需的反射事件总体平坦度。这个非常详细的过程要花费大量时间，不一定提供全局最佳速度函数，并最终影响典型地震解释程序的成本和持续时间。在这项工作中，我们提出一种基于卷积神经网络（CNN）的混合回归方法，以加快速度分析工作流程。所提出的方法包括一个自动的初始速度函数估计，然后是一个监督的精炼过程，该过程仅需要手动采集少数几个道集。在五个现场数据集上进行的实验表明，所提出的方法不仅可以产生人类级别的速度选择，而且在某些情况下，其性能要优于解释器，而所需的时间却是人类的一小部分。