Picking stack velocity from seismic velocity spectra is a fundamental method in seismic stack velocity analysis. With the increase in the scale of seismic data acquisition, manual picking cannot achieve the required efficiency. Therefore, an automatic picking algorithm is urgently needed now. Despite some supervised deep learning–based picking approaches that have been proposed, they heavily rely on sufficient training samples and lack interpretability. In contrast, utilizing physical knowledge to develop semi‐data‐driven methods has the potential to efficiently solve this problem. Thus, we propose a semi‐supervised ensemble learning method to reduce the reliance on manually labelled data and improve interpretability by incorporating the interval velocity constraint. Semi‐supervised ensemble learning fuses the information of the estimated spectrum, nearby velocity spectra and few‐shot manual picking to recognize the velocity picking. Test results of both the synthetic and field datasets indicate that semi‐supervised ensemble learning achieves more reliable and precise picking than traditional clustering‐based techniques and the currently popular convolutional neural network method.

中文翻译：

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使用半监督集成学习方法的自动堆栈速度拾取

从地震速度谱中提取叠加速度是地震叠加速度分析的基本方法。随着地震数据采集规模的增大，人工拾取已经无法达到所需的效率。因此，现在迫切需要一种自动拣货算法。尽管已经提出了一些基于监督深度学习的挑选方法，但它们严重依赖于足够的训练样本并且缺乏可解释性。相比之下，利用物理知识开发半数据驱动的方法有可能有效解决这个问题。因此，我们提出了一种半监督集成学习方法，以减少对手动标记数据的依赖，并通过结合层速度约束来提高可解释性。半监督集成学习融合了估计谱、附近速度谱和小样本手动拾取的信息来识别速度拾取。合成数据集和现场数据集的测试结果表明，半监督集成学习比传统的基于聚类的技术和当前流行的卷积神经网络方法实现了更可靠和更精确的挑选。