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Database Generation for Deep Learning Inversion of 2.5D Borehole Electromagnetic Measurements using Refined Isogeometric Analysis
arXiv - CS - Numerical Analysis Pub Date : 2020-09-17 , DOI: arxiv-2009.08132
Ali Hashemian, Daniel Garcia, Jon Ander Rivera, David Pardo

Borehole resistivity measurements are routinely inverted in real-time during geosteering operations. The inversion process can be efficiently performed with the help of advanced artificial intelligence algorithms such as deep learning. These methods require a large dataset that relates multiple earth models with the corresponding borehole resistivity measurements. In here, we propose to use an advanced numerical method --refined isogeometric analysis (rIGA)-- to perform rapid and accurate 2.5D simulations and generate databases when considering arbitrary 2D earth models. Numerical results show that we can generate a meaningful synthetic database composed of 100,000 earth models with the corresponding measurements in 56 hours using a workstation equipped with two CPUs.

中文翻译:

使用精细等几何分析对 2.5D 钻孔电磁测量进行深度学习反演的数据库生成

在地质导向操作期间,钻孔电阻率测量值通常会实时反演。借助深度学习等先进的人工智能算法,可以有效地执行反演过程。这些方法需要一个大型数据集,将多个地球模型与相应的钻孔电阻率测量值联系起来。在这里,我们建议在考虑任意 2D 地球模型时,使用先进的数值方法 - 精细等几何分析 (rIGA) - 执行快速准确的 2.5D 模拟并生成数据库。数值结果表明,使用配备两个 CPU 的工作站,我们可以在 56 小时内生成一个由 100,000 个地球模型和相应测量值组成的有意义的合成数据库。
更新日期:2020-09-18
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