A deep-learning-based workflow is proposed in this paper to solve the first-arrival picking problem for near-surface velocity model building. Traditional methods, such as the short-term average/long-term average method, perform poorly when the signal-to-noise ratio is low or near-surface geologic structures are complex. This challenging task is formulated as a segmentation problem accompanied by a novel postprocessing approach to identify pickings along the segmentation boundary. The workflow includes three parts: a deep U-net for segmentation, a recurrent neural network (RNN) for picking, and a weight adaptation approach to be generalized for new data sets. In particular, we have evaluated the importance of selecting a proper loss function for training the network. Instead of taking an end-to-end approach to solve the picking problem, we emphasize the performance gain obtained by using an RNN to optimize the picking. Finally, we adopt a simple transfer learning scheme and test its robustness via a weight adaptation approach to maintain the picking performance on new data sets. Our tests on synthetic data sets reveal the advantage of our workflow compared with existing deep-learning methods that focus only on segmentation performance. Our tests on field data sets illustrate that a good postprocessing picking step is essential for correcting the segmentation errors and that the overall workflow is efficient in minimizing human interventions for the first-arrival picking task.

中文翻译：

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使用卷积和递归神经网络的强大的先到先得工作流程

本文提出了一种基于深度学习的工作流，以解决近地表速度模型构建中的初到拾取问题。当信噪比低或近地表地质结构复杂时，传统的方法（例如短期平均/长期平均方法）效果较差。这个具有挑战性的任务被表述为一个分割问题，并伴随着一种新颖的后处理方法来识别沿分割边界的采摘。工作流程包括三个部分：用于分割的深层U-net，用于拣选的递归神经网络（RNN），以及将权重自适应方法推广到新数据集的方法。特别是，我们评估了选择适当的损失函数来训练网络的重要性。与其采用端到端的方法来解决拣货问题，我们强调通过使用RNN优化选择来获得的性能提升。最后，我们采用一种简单的转移学习方案，并通过权重自适应方法测试其鲁棒性，以维持新数据集的拣选性能。与仅专注于细分效果的现有深度学习方法相比，我们对综合数据集的测试揭示了我们工作流程的优势。我们对现场数据集的测试表明，良好的后处理拣选步骤对于纠正分段错误至关重要，并且整个工作流程可以有效地最大程度地减少对首次到达的拣选任务的人工干预。我们采用简单的转移学习方案，并通过权重自适应方法测试其健壮性，以维持新数据集的拣选性能。与仅专注于细分效果的现有深度学习方法相比，我们对综合数据集的测试揭示了我们工作流程的优势。我们对现场数据集的测试表明，良好的后处理拣选步骤对于纠正分段错误至关重要，并且整个工作流程可以有效地最大程度地减少对首次到达的拣选任务的人工干预。我们采用简单的转移学习方案，并通过权重自适应方法测试其健壮性，以维持新数据集的拣选性能。与仅专注于细分效果的现有深度学习方法相比，我们对综合数据集的测试揭示了我们工作流程的优势。我们对现场数据集的测试表明，良好的后处理拣选步骤对于纠正分段错误至关重要，并且整个工作流程可以有效地最大程度地减少对首次到达的拣选任务的人工干预。