SaltNet: A production-scale deep learning pipeline for automated salt model building
SaltNet: A production-scale deep learning pipeline for automated salt model building
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DOI：
10.1190/tle39030195.1
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发表时间：
2020-03
期刊：
影响因子：
3.3
通讯作者：
S. Sen;Sribharath Kainkaryam;C. Ong;Arvind K. Sharma
中科院分区：
文献类型：
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作者：
S. Sen;Sribharath Kainkaryam;C. Ong;Arvind K. Sharma
在墨西哥湾等含盐盆地建立地震成像速度模型时，最重要的步骤之一是对盐层几何形状进行迭代细化。传统上，这一步很难实现自动化，而且生产工作流程需要大量领域专家的干预，才能准确解释用不正确的中间速度模型迁移的图像上的盐体。为了缓解这个问题，我们提出了一个端到端半监督深度学习管道，SaltNet，能够在初始模型构建迭代期间全自动解释盐。研究表明，该方法可以在没有领域专家干预的情况下构建初始盐模型（salt-1的顶部和盐-1的底部或盐体-1迭代），同时达到接近人类专家的精度。与现有的基于卷积神经网络（CNN）的含盐量解释应用不同，该方法旨在处理在初始模型构建阶段通常会遇到的噪声低分辨率实际数据地震图像。它也可以推广到从以前未见过的调查中迁移的图像。这是通过使用多视图半监督学习方案训练一套深度高容量CNN模型来实现的，该方案利用数据和模型蒸馏概念，使这些模型对来自新目标调查的图像可能显示的潜在大域差异具有鲁棒性。因此，CNN模型在这些新调查上达到了人类水平的解释精度，而无需手动解释目标调查的任何部分。墨西哥湾调查的现场测试结果表明，传统的解译器选择和saltnet选择的初始盐模型生成的迁移图像之间具有良好的一致性。
One of the most important steps in velocity model building for seismic imaging in salt basins such as the Gulf of Mexico is the iterative refinement of the salt geometry. Traditionally, this step is difficult to automate, and production workflows require extensive domain expert intervention to accurately interpret the salt bodies on images migrated with an incorrect intermediate velocity model. To alleviate this problem, we propose an end-to-end semisupervised deep learning pipeline, SaltNet, capable of fully automated salt interpretation during initial model building iterations. We show that the method can be used to build the initial salt model (top of salt-1 and base of salt-1 or salt body-1 iterations) without domain expert intervention while achieving accuracy close to that of a human expert. Unlike existing convolutional neural network (CNN)-based salt interpretation applications, this method is designed to work on noisy low-resolution real-data seismic images that are typically encountered during the initial model building stage. It is also generalizable to migrated images from previously unseen surveys. This is achieved by training a suite of deep high-capacity CNN models with a multiview semisupervised learning scheme that leverages data and model distillation concepts to make these models robust to potentially large domain differences that images from a new target survey may exhibit. Consequently, CNN models achieve human-level interpretation accuracy on such new surveys without the need to manually interpret any portion of the target survey. Results from a field test on a Gulf of Mexico survey show excellent agreement between migrated images generated by the conventional interpreter-picked and SaltNet-picked initial salt model.