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.

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使用 U-Net 卷积神经网络进行稀疏地震数据成像的机器学习

研究了使用卷积神经网络 (CNN) 的机器学习以对稀疏采样的地震反射数据进行成像。传统成像方法的局限性在于它们通常需要具有足够空间采样的地震数据。使用CNNs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效果。因此，当数据的空间采样稀疏时，应用于地震成像的 CNN 有可能产生改进的成像结果。然后可以使用成像模型来生成更密集的采样数据，并以此方式用于插入规则或不规则采样数据。尽管地震数据的插值方法有很多，但这里一旦训练了 CNN 模型，就直接使用稀疏地震数据进行地震成像。发现 CNN 模型对来自训练数据集的小变化相对稳健。对于更大的偏差，可能需要更大的训练数据集。如果 CNN 接受了足够数量的数据训练，它就有可能对更复杂的地震剖面进行成像。