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Seismic Stratum Segmentation Using an Encoder–Decoder Convolutional Neural Network
Mathematical Geosciences ( IF 2.6 ) Pub Date : 2021-02-12 , DOI: 10.1007/s11004-020-09916-8
Detao Wang , Guoxiong Chen

As an essential step in reservoir characterization, seismic stratigraphic interpretation is often dependent on the development of powerful computer-based interpretation tools that can simulate the intelligence of experienced interpreters. With the success of machine/deep learning applications in many aspects of geoscience in recent decades, geophysicists have become more dedicated to exploring seismic big data in a smarter and more sophisticated way to better image subsurface reservoirs/structures. In this paper, a specific U-shaped fully convolutional network (U-Net) is established for automatic seismic stratigraphic interpretation. Specifically, this task is formulated as a semantic segmentation problem by identifying strata at the pixel level and classifying each pixel in the image into a specific stratum category. An experiment using the Netherlands F3 seismic dataset suggests that, compared with previously established deep learning models requiring a large number of training sets, the proposed U-Net method can achieve high evaluation indicators and better stratum segmentations in the case of a limited training set. During the test, the proposed U-Net model outperforms the Bayesian neural network (BNN) model for seismic stratum segmentation with regard to the training time, prediction speed, and segmentation accuracy. These results indicate the great potential of using U-Net-based deep learning for intelligent seismic stratigraphic interpretation.



中文翻译:

使用编码器-解码器卷积神经网络的地震地层分割

作为储层表征的重要步骤,地震地层解释通常取决于强大的基于计算机的解释工具的开发,该工具可以模拟经验丰富的解释者的情报。近几十年来,随着机器/深度学习应用在地球科学的许多方面取得成功,地球物理学家变得更加致力于以更智能,更复杂的方式探索地震大数据,以更好地成像地下储层/结构。在本文中,建立了用于自动地震地层解释的特定U形全卷积网络(U-Net)。具体而言,通过在像素级别识别阶层并将图像中的每个像素分类为特定的阶层类别,可以将该任务表述为语义分割问题。使用荷兰F3地震数据集进行的实验表明,与以前建立的需要大量训练集的深度学习模型相比,在训练集有限的情况下,提出的U-Net方法可以实现较高的评估指标和更好的地层分割。在测试过程中,就训练时间,预测速度和分割精度而言,提出的U-Net模型在地震地层分割方面优于贝叶斯神经网络(BNN)模型。这些结果表明使用基于U-Net的深度学习进行智能地震地层解释的巨大潜力。在训练集有限的情况下,提出的U-Net方法可以实现较高的评估指标和更好的地层细分。在测试过程中,就训练时间,预测速度和分割精度而言,提出的U-Net模型在地震地层分割方面优于贝叶斯神经网络(BNN)模型。这些结果表明使用基于U-Net的深度学习进行智能地震地层解释的巨大潜力。在训练集有限的情况下,提出的U-Net方法可以实现较高的评估指标和更好的地层细分。在测试过程中,就训练时间,预测速度和分割精度而言,提出的U-Net模型在地震地层分割方面优于贝叶斯神经网络(BNN)模型。这些结果表明使用基于U-Net的深度学习进行智能地震地层解释的巨大潜力。

更新日期:2021-02-12
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