论文标题
使用潜在空间分解对地震图像的自我监督注释
Self-Supervised Annotation of Seismic Images using Latent Space Factorization
论文作者
论文摘要
注释地震数据是由于地震解释者获得熟练程度所需的年数，因此昂贵，费力和主观。在本文中，我们开发了一个框架来自动化地震图像的注释像素，以描绘给分配给每个图像的图像级标签的地质结构元素。我们的框架通过将潜在空间投影到学习的子空间来分配深编码器网络的潜在空间。使用像素空间中的约束，将地震图像进一步分解以揭示与感兴趣的地质元素相关的像素上的置信值。提供了带注释的图像的详细信息，以进行分析，并与类似的框架进行定性比较。
Annotating seismic data is expensive, laborious and subjective due to the number of years required for seismic interpreters to attain proficiency in interpretation. In this paper, we develop a framework to automate annotating pixels of a seismic image to delineate geological structural elements given image-level labels assigned to each image. Our framework factorizes the latent space of a deep encoder-decoder network by projecting the latent space to learned sub-spaces. Using constraints in the pixel space, the seismic image is further factorized to reveal confidence values on pixels associated with the geological element of interest. Details of the annotated image are provided for analysis and qualitative comparison is made with similar frameworks.
