论文标题
使用深卷积自动编码器无监督的地震相分类
Unsupervised seismic facies classification using deep convolutional autoencoder
论文作者
论文摘要
随着地震调查的规模和复杂性的增加，地震相的手动标记已成为一个重大挑战。自动方法在地震相解释中的应用可以显着降低常规方法中存在的特定口译员的手工劳动和主观性。最近出现的一组方法基于深度神经网络。这些方法是数据驱动的，需要大型标记的数据集进行网络培训。我们将深度卷积自动编码器应用于无监督的地震相分类，这不需要手动标记的示例。这些相图是通过聚集从输入数据获得的深功能向量生成的。我们的方法在实际数据上得出准确的结果，并立即提供它们。提出的方法为实时分析地质模式的可能性开辟了可能性，而无需人工干预。
With the increased size and complexity of seismic surveys, manual labeling of seismic facies has become a significant challenge. Application of automatic methods for seismic facies interpretation could significantly reduce the manual labor and subjectivity of a particular interpreter present in conventional methods. A recently emerged group of methods is based on deep neural networks. These approaches are data-driven and require large labeled datasets for network training. We apply a deep convolutional autoencoder for unsupervised seismic facies classification, which does not require manually labeled examples. The facies maps are generated by clustering the deep-feature vectors obtained from the input data. Our method yields accurate results on real data and provides them instantaneously. The proposed approach opens up possibilities to analyze geological patterns in real time without human intervention.
