用于地理建模的生成式人工智能

S. Misra, Jungang Chen, Polina Churilova, Y. Falola
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引用次数: 0

摘要

地下地球模型,又称地质模型,对于描述和开发复杂的地下系统至关重要。传统的地质模型生成方法(如多点统计)耗时长,计算成本高。生成式人工智能(AI)提供了一种前景广阔的替代方法,有可能更快、更高效地生成高质量的地质模型。本文提出了一种基于深度学习的地理建模生成式人工智能,它由两个深度学习模型组成:分层向量量化变分自动编码器(VQ-VAE-2)和 PixelSNAIL 自回归模型。VQ-VAE-2 通过学习将地理模型大规模压缩成低维、离散的潜在表示。然后,PixelSNAIL 学习潜在代码的先验分布。为了生成地理模型,PixelSNAIL 从潜在代码的先验分布中采样,VQ-VAE-2 的解码器将采样的潜在代码转换为新构建的地理模型。PixelSNAIL 可用于无条件或有条件地理模型生成。在无条件生成过程中,生成工作流程会生成一个不带任何约束条件的地质模型集合。在有条件的地质模型生成中,生成工作流生成的地质模型集合与用户定义的源地质模型相似。这有助于对生成的地质模型进行控制和操作。为了改进地质模型中河道的生成,我们在 VQ-VAE-2 模型中使用了感知损失而不是传统的平均绝对误差损失。在特定的压缩比下,建议的生成式人工智能方法生成的多属性地质模型比单属性地质模型质量更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generative Artificial Intelligence for Geomodeling
Subsurface earth models, also known as geomodels, are essential for characterizing and developing complex subsurface systems. Traditional geomodel generation methods, such as multiple-point statistics, can be time-consuming and computationally expensive. Generative Artificial Intelligence (AI) offers a promising alternative, with the potential to generate high-quality geomodels more quickly and efficiently. This paper proposes a deep-learning-based generative AI for geomodeling that comprises two deep learning models: a hierarchical vector-quantized variational autoencoder (VQ-VAE-2) and a PixelSNAIL autoregressive model. The VQ-VAE-2 learns to massively compress geomodels into a low-dimensional, discrete latent representation. The PixelSNAIL then learns the prior distribution of the latent codes. To generate a geomodel, the PixelSNAIL samples from the prior distribution of latent codes and the decoder of the VQ-VAE-2 converts the sampled latent code to a newly constructed geomodel. The PixelSNAIL can be used for unconditional or conditional geomodel generation. In unconditional generation, the generative workflow generates an ensemble of geomodels without any constraint. In conditional geomodel generation, the generative workflow generates an ensemble of geomodels similar to a user-defined source geomodel. This facilitates the control and manipulation of the generated geomodels. To improve the generation of fluvial channels in the geomodels, we use perceptual loss instead of the traditional mean absolute error loss in the VQ-VAE-2 model. At a specific compression ratio, the proposed Generative AI method generates multi-attribute geomodels of higher quality than single-attribute geomodels.
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