Machine Learning Using U-Net Convolutional Neural Networks for the Imaging of Sparse Seismic Data
Machine Learning Using U-Net Convolutional Neural Networks for the Imaging of Sparse Seismic Data
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DOI：
10.1007/s00024-019-02412-z
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发表时间：
2020-01
影响因子：
2
通讯作者：
Jiayuan Huang;R. Nowack
中科院分区：
文献类型：
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作者：
Jiayuan Huang;R. Nowack
研究了基于卷积神经网络的机器学习方法在稀疏采样地震反射数据成像中的应用。传统成像方法的一个局限性是，它们往往要求地震数据具有足够的空间采样。使用CNN进行成像，即使数据的空间采样稀疏，仍然可以获得良好的成像结果。因此，当数据的空间采样稀疏时，应用于地震成像的CNN具有改善成像效果的潜力。然后，成像模型可用于生成更密集采样的数据，并以这种方式用于对规则或不规则采样的数据进行内插。虽然有许多方法可以对地震数据进行内插，但在这里，一旦训练好CNN模型，就直接用稀疏的地震数据进行地震成像。CNN模型对于训练数据集的微小变化被发现是相对稳健的。对于更大的偏差，可能需要更大的训练数据集。如果CNN接受了足够多的数据训练，它就有可能对更复杂的地震剖面进行成像。
Machine learning using convolutional neural networks (CNNs) is investigated for the imaging of sparsely sampled seismic reflection data. A limitation of traditional imaging methods is that they often require seismic data with sufficient spatial sampling. Using CNNs for imaging, even if the spatial sampling of the data is sparse, good imaging results can still be obtained. Therefore, CNNs applied to seismic imaging have the potential of producing improved imaging results when spatial sampling of the data is sparse. The imaged model can then be used to generate more densely sampled data and in this way be used to interpolate either regularly or irregularly sampled data. Although there are many approaches for the interpolation of seismic data, here seismic imaging is performed directly with sparse seismic data once the CNN model has been trained. The CNN model is found to be relatively robust to small variations from the training dataset. For greater deviations, a larger training dataset would likely be required. If the CNN is trained with a sufficient amount of data, it has the potential of imaging more complex seismic profiles.