深地质处置背景下基于高斯过程的模型校准
Gaussian Processes enabled model calibration in the context of deep geological disposal
AI总结：
针对岩盐深地质处置库安置巷道力学模拟计算昂贵的问题，提出基于Gaussian Processes的代理模型用于敏感性分析与校准，可降低计算成本并识别关键参数。
摘要：
深地质处置库对于危险物质的长期储存至关重要，而理解安置巷道的力学行为是安全保障的关键。本研究针对岩盐地层中安置巷道的力学响应提出了一种代理建模方法，采用高斯过程（GPs）。该代理模型在多查询场景（包括时变敏感性分析和校准任务）中作为高保真力学模拟的高效替代。通过显著降低计算需求，该方法促进了更快的设计迭代，并增强了对监测数据的解释。研究结果表明，仅需少数关键参数即可在复杂岩盐模型中准确反映原位条件。识别这些参数对于确保深地质处置系统的可靠性和安全性至关重要。
英文摘要：
Deep geological repositories are critical for the long-term storage of hazardous materials, where understanding the mechanical behavior of emplacement drifts is essential for safety assurance. This study presents a surrogate modeling approach for the mechanical response of emplacement drifts in rock salt formations, utilizing Gaussian Processes (GPs). The surrogate model serves as an efficient substitute for high-fidelity mechanical simulations in many-query scenarios, including time-dependent sensitivity analyses and calibration tasks. By significantly reducing computational demands, this approach facilitates faster design iterations and enhances the interpretation of monitoring data. The findings indicate that only a few key parameters are sufficient to accurately reflect in-situ conditions in complex rock salt models. Identifying these parameters is crucial for ensuring the reliability and safety of deep geological disposal systems.
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