用神经网络加速地质断层渗透率模拟，揭示岩性控制的非线性效应。
Lithological Controls on the Permeability of Geologic Faults: Surrogate Modeling and Sensitivity Analysis
- 构建神经网络替代耗时的流体上采样步骤，大幅降低计算成本。
- 发现岩性参数间存在传统方法无法捕捉的显著非线性交互作用。
- 适用于地质建模、地下水流动分析及含水层评估的研究者。
断层带具有复杂的异质渗透结构，受地层、成分和构造因素影响，是地下水流模拟中的关键但不确定环节。本研究采用PREDICT框架——一种概率性工作流程，结合随机断层几何生成、物理约束的材料布置及基于流体的上采样。其中，基于流体的上采样步骤计算成本极高，成为全局敏感性分析（GSA）的瓶颈，因需数百万次模型运行。为此，我们开发了一个神经网络代理模型来模拟该步骤，显著降低计算开销并保持高精度，使GSA成为可能。基于代理模型的GSA揭示了岩性控制对断层渗透率的新认识：不仅识别出主导与次要参数，还发现参数间存在重要非线性交互作用，这是传统局部敏感性方法无法捕捉的。
原文摘要 · Abstract (English)
Fault zones exhibit complex and heterogeneous permeability structures influenced by stratigraphic, compositional, and structural factors, making them critical yet uncertain components in subsurface flow modeling. In this study, we investigate how lithological controls influence fault permeability using the PREDICT framework: a probabilistic workflow that couples stochastic fault geometry generation, physically constrained material placement, and flow-based upscaling. The flow-based upscaling step, however, is a very computationally expensive component of the workflow and presents a major bottleneck that makes global sensitivity analysis (GSA) intractable, as it requires millions of model evaluations. To overcome this challenge, we develop a neural network surrogate to emulate the flow-based upscaling step. This surrogate model dramatically reduces the computational cost while maintaining high accuracy, thereby making GSA feasible. The surrogate-model-enabled GSA reveals new insights into the effects of lithological controls on fault permeability. In addition to identifying dominant parameters and negligible ones, the analysis uncovers significant nonlinear interactions between parameters that cannot be captured by traditional local sensitivity methods.
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