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 treat arrival time picking as a binary image segmentation problem and use 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 dataset 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 auto-picking method. We also demonstrate the difference between 1D and 2D training data with borehole seismic data. Both microseismic and borehole seismic data indicate the proposed network can improve efficiency and accuracy over traditional automated picking methods.
The post-1980 regional climate change increased dust AOD by +12.5 ± 15.0% and +43.6 ± 31.2% over the source and downstream areas respectively during a record-breaking dust storm in March 2021 in North China.
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