Arrival-time picking is an essential step in seismic processing and imaging. The explosion of seismic data volume requires automated arrival-time picking in a faster and more reliable way than existing methods. We have treated arrival-time picking as a binary image segmentation problem and used an improved pixel-wise convolutional network to pick arrival times automatically. Incorporating continuous spatial information in training enables us to preserve the arrival-time correlation between nearby traces, thus helping to reduce the risk of picking outliers that are common in a traditional trace-by-trace picking method. To train the network, we first convert seismic traces into gray-scale images. Image pixels before manually picked arrival times are labeled with zeros, and those after are tagged with ones. After training and validation, the network automatically learns representative features and generates a probability map to predict the arrival time. We apply the network to a field microseismic data set that was not used for training or validation to test the performance of the method. Then, we analyze the effects of training data volume and signal-to-noise ratio on our autopicking method. We also find the difference between 1D and 2D training data with borehole seismic data. Microseismic and borehole seismic data indicate the proposed network can improve efficiency and accuracy over traditional automated picking methods.

中文翻译：

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使用像素级网络自动选择到达时间

到达时间选择是地震处理和成像中必不可少的步骤。地震数据量的爆炸式增长需要比现有方法更快，更可靠的自动到达时间采集方法。我们已经将到达时间选择视为二进制图像分割问题，并使用了改进的像素级卷积网络来自动选择到达时间。在训练中纳入连续的空间信息使我们能够保留附近轨迹之间的到达时间相关性，从而有助于降低传统的逐迹线拾取方法中常见的离群值拾取风险。为了训练网络，我们首先将地震迹线转换为灰度图像。手动选择到达时间之前的图像像素标记为零，之后的图像像素标记为1。经过培训和验证后，网络会自动学习代表特征并生成概率图以预测到达时间。我们将网络应用于现场微震数据集，该数据集未用于训练或验证来测试该方法的性能。然后，我们分析了训练数据量和信噪比对我们的自动选择方法的影响。我们还发现了1D和2D训练数据与井眼地震数据之间的差异。微地震和井眼地震数据表明，与传统的自动采摘方法相比，该网络可以提高效率和准确性。我们分析了训练数据量和信噪比对我们自动选择方法的影响。我们还发现了1D和2D训练数据与井眼地震数据之间的差异。微地震和钻孔地震数据表明，与传统的自动采摘方法相比，该网络可以提高效率和准确性。我们分析了训练数据量和信噪比对我们自动选择方法的影响。我们还发现了1D和2D训练数据与井眼地震数据之间的差异。微地震和井眼地震数据表明，与传统的自动采摘方法相比，该网络可以提高效率和准确性。