竞争性地理解释的早期排序的生成性反演

中文摘要 AI 辅助

高后果的地下决策往往在严重数据稀缺的情况下做出。专家可能对同一地下系统得出竞争性的解释，然而在项目早期，很少有实用的方法来确定哪一种解释最符合实际。这种不确定性可能会持续到钻探数口井之后，通常耗资数百万美元。现有的评估地质解释的方法要么依赖主观判断，要么依赖在早期调查中很少可用的密集数据。我们提出了一种工作流程，通过将竞争性的地质解释转化为替代的空间先验，并根据它们与地下水头观测的一致性进行排序，来解决这一挑战。对于每种解释，一个文本到图像的基础模型生成1600幅地质图像的集成，一个单独训练的自编码器提供特定于解释的潜在表示。一个有监督的反演网络将水头观测映射到这个潜在空间，冻结的解码器生成一个图像，该图像被映射到对数电导率场。稳态流动模拟随后提供预测的水头，所得的不匹配被转换为高斯形式的兼容性得分。我们使用基于约翰森组和三种与参考表示一致性递减的解释的合成基准来评估该框架。在925个测试案例中，平均水头均方根误差从精确且准确解释的0.197增加到准确解释的0.227和错配解释的0.280。随后，我们将该工作流程应用于废物隔离试验工厂的库莱布拉白云岩段的两个已发表的概念模型。修订后的模型获得0.991的兼容性权重，而原始模型为0.009，这与独立证据一致。

英文摘要

High-consequence subsurface decisions often rely on sparse data that permit competing geological interpretations. Determining consistency of these interpretations with the available observations remains challenging. We present a workflow that addresses this challenge by translating competing geologic interpretations into alternative priors and ranking them according to their consistency with hydraulic-head observations. A key step in this workflow is exploiting the broad knowledge of image-generation foundation models to transform nuanced geologic interpretations into data ready for computer modeling. For each interpretation, a text-to-image foundation model generates an ensemble of geologic images, and a separately trained variational autoencoder learns an interpretation-specific latent representation. A supervised inverse network maps head observations into this latent space, and the frozen decoder reconstructs an image that is mapped to a log-conductivity field. Steady-state flow simulations predict heads, and the aggregate normalized head error determines the ranking. We evaluate the framework using a synthetic benchmark based on the Johansen Formation with three interpretations of decreasing consistency with the reference geology. Across 595 test cases, the Precise \& Accurate interpretation produces lower normalized errors than Accurate in 58.5\% of cases and Mismatched in 82.5\% of cases. Accurate outperforms Mismatched in 65.5\% of cases. We then compare spatial representations of two published conceptual models of the Culebra Dolomite Member at the Waste Isolation Pilot Plant. The revised representation yields an aggregate normalized error of 7.598, compared with 8.595 for the original, consistent with the documented conceptual-model revision. The framework enables quantitative comparison of competing geological interpretations using available hydraulic observations.

发表机构

• Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）

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