论文标题
地震反转的联合学习：声学阻抗估计案例研究
Joint Learning for Seismic Inversion: An Acoustic Impedance Estimation Case Study
论文作者
论文摘要
地震反演有助于地球物理学家建立准确的储层模型，以探索和生产目的。基于深度学习的地震反演通过训练神经网络来学习从地震数据到岩石属性的映射，使用良好的日志数据作为标签。但是，由于钻井井的高成本，井的数量通常非常有限。如果接受有限的数据培训，机器学习模型可能会遭受过度拟合和概括不佳的概括。在这种情况下，来自其他调查的良好日志数据可以提供急需的有用信息，以更好地概括。我们提出了一个学习方案，我们同时训练两个网络体系结构，每个网络体系结构在不同的数据集上。通过对两个网络之间的重量相似性进行软限制，我们使它们相互学习，对于在各自数据集上更好地概括性能有用。使用少于3 $ \％的可用培训数据，我们能够通过与Marmousi数据集的联合学习来实现接缝数据集的声学阻抗的平均$ r^{2} $系数为0.8399。
Seismic inversion helps geophysicists build accurate reservoir models for exploration and production purposes. Deep learning-based seismic inversion works by training a neural network to learn a mapping from seismic data to rock properties using well log data as the labels. However, well logs are often very limited in number due to the high cost of drilling wells. Machine learning models can suffer overfitting and poor generalization if trained on limited data. In such cases, well log data from other surveys can provide much needed useful information for better generalization. We propose a learning scheme where we simultaneously train two network architectures, each on a different dataset. By placing a soft constraint on the weight similarity between the two networks, we make them learn from each other where useful for better generalization performance on their respective datasets. Using less than 3$\%$ of the available training data, we were able to achieve an average $r^{2}$ coefficient of 0.8399 on the acoustic impedance pseudologs of the SEAM dataset via joint learning with the Marmousi dataset.
