| 基于小样本卷积神经网络的主动源P 波初至拾取方法 |
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| 引用本文： | 于智瀚,王 涛,孙鹏远,王文闯,郭振波.基于小样本卷积神经网络的主动源P 波初至拾取方法[J].高校地质学报,2022,28(3):414-423. |
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| 作者姓名： | 于智瀚 王 涛 孙鹏远 王文闯 郭振波 |
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| 作者单位： | 1. 南京大学 地球科学与工程学院，南京 210023；
2. 中石油东方地球物理公司 物探技术研究中心，涿州 072750 |
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| 摘 要： | 地震走时成像结果的准确性取决于初至到时的拾取精度，人工挑选初至效率低、成本高。前人研究表明深度学习可以应用于初至的自动拾取，然而传统的深度学习方法往往需要大量人工挑选的初至作为神经网络的训练集。文章利用U型卷积神经网络拾取单炮多道P波初至，研究表明P波初至拾取的均方根误差会随着训练集数量的增加而减少。训练集分别采用35炮和597炮数据时，对应的均方根误差分别为11.4和6.5 ms。参考半监督学习中数据增强方法，选取适合主动源数据的增强方法（随机剪裁、随机擦除等）用于拓展训练集。结果显示，以人工拾取总数据量的5%（35炮）作为小样本并进行随机擦除数据增强后，实现了均方根误差在5.5 ms（约3个采样点）以内，比未经增强的误差减少51%。与前人的深度学习方法相比，本文应用的数据增强方法可以在小样本的情况下实现主动源地震初至的高精度拾取。
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| 关 键 词： | 初至拾取 深度学习 U-Net 小样本训练集 数据增强 均方根误差 |
Active Source Seismic First-arrival Pickup Method Based on SmallSample Convolutional Neural Network |
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| YU Zhihan,WANG Tao,SUN Pengyuan,WANG Wenchuang,GUO Zhenbo.Active Source Seismic First-arrival Pickup Method Based on SmallSample Convolutional Neural Network[J].Geological Journal of China Universities,2022,28(3):414-423. |
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| Authors: | YU Zhihan WANG Tao SUN Pengyuan WANG Wenchuang GUO Zhenbo |
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| Institution: | 1. School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China;
2. Research & Development Center, BGP, Zhuozhou 072750, China; |
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| Abstract: | The interpretation of seismic travel-time imaging depends on the accuracy of picking up first arrivals, and it is low efficiency and high cost to pick up arrivals manually. Previous studies showed that deep learning can be applied for automatically picking, but the methods often need a large number of arrivals as the training set. In this study, we trained the U-Net with multichannel images and found that the root mean square error (RMSE) of first P arrivals decreases with the increase in the number of training sets. The errors of 35 and 597 shots are 11.4 and 6.5 ms, respectively. Referring to data augmentation methods in semisupervised
learning, we selected methods (Random Crop, Random Erase, etc.) suitable for seismic data and applied them to the training set. After random erase of the training sets of 35 shots, the RMSE is less than 5.5 ms (about 3 sampling points), which is 51% less than the error with the original training set. Compared with previous deep learning methods, the augmentation methods can be implemented to pick up the first arrivals with higher precision in the case of small samples. |
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| Keywords: | first-arrival pickup deep learning U-Net small sample training set data augmentation root mean square error |
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