基于离散表示学习与深度自回归网络的地下地球模型生成
Generating Subsurface Earth Models using Discrete Representation Learning and Deep Autoregressive Network
AI总结：
本研究提出结合VQ-VAE-2与PixelSNAIL的深度学习方法替代传统多点统计法生成地下地质模型，支持无条件与有条件生成，引入感知损失优化河道构建，特定压缩比下多属性生成质量更优。
摘要：
地下地球模型（简称地质模型）是表征复杂地下系统的关键工具。多点统计方法是目前生成地质模型的常用手段。本文提出一种基于深度学习的生成方法，作为传统地质模型生成流程的替代方案。该生成方法包含两个深度学习模型，即分层向量量化变分自编码器（VQ-VAE-2）和PixelSNAIL自回归模型。基于神经离散表示学习原理，VQ-VAE-2通过学习对地质模型进行大规模压缩，提取出与每个地质模型对应的低维离散潜在表示。随后，PixelSNAIL利用深度自回归网络学习潜在编码的先验分布。在生成地质模型时，PixelSNAIL从新学习到的潜在编码先验分布中采样，再由VQ-VAE-2的解码器将新采样的潜在编码转换为新构建的地质模型。PixelSNAIL可用于无条件或有条件地质模型生成：无条件生成模式下，生成流程会在无任何约束的情况下生成一组地质模型；而在有条件地质模型生成中，生成流程会生成一组与用户定义的源图像相似的地质模型，最终实现对生成地质模型的控制与调整。为更好地构建地质模型中的河流相河道，本文在VQ-VAE-2模型中采用感知损失替代传统的均方误差损失。在特定压缩比下，多属性地质模型的生成质量优于单属性地质模型的生成质量。
英文摘要：
Subsurface earth models (referred to as geo-models) are crucial for characterizing complex subsurface systems. Multiple-point statistics are commonly used to generate geo-models. In this paper, a deep-learning-based generative method is developed as an alternative to the traditional Geomodel generation procedure. The generative method comprises two deep-learning models, namely the hierarchical vector-quantized variational autoencoder (VQ-VAE-2) and PixelSNAIL autoregressive model. Based on the principle of neural discrete representation learning, the VQ-VAE-2 learns to massively compress the Geomodels to extract the low-dimensional, discrete latent representation corresponding to each Geomodel. Following that, PixelSNAIL uses the deep autoregressive network to learn the prior distribution of the latent codes. For the purpose of Geomodel generation, PixelSNAIL samples from the newly learned prior distribution of latent codes, and then the decoder of the VQ-VAE-2 converts the newly sampled latent code to a newly constructed geo-model. PixelSNAIL can be used for unconditional or conditional geo-model generation. In an unconditional generation, the generative workflow generates an ensemble of geo-models without any constraint. On the other hand, in the conditional geo-model generation, the generative workflow generates an ensemble of geo-models similar to a user-defined source image, which ultimately facilitates the control and manipulation of the generated geo-models. To better construct the fluvial channels in the geo-models, the perceptual loss is implemented in the VQ-VAE-2 model instead of the traditional mean squared error loss. At a specific compression ratio, the quality of multi-attribute geo-model generation is better than that of single-attribute geo-model generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
