AI 中文总结
本研究开发美国全国液化灾害图（NLHMs），结合地理空间机器学习模型与2023年美国全国地震灾害模型，分析震级选择、灾害形式差异及液化与社会经济脆弱性的叠加，为工程实践与政策提供支撑。
AI 中文摘要
本研究介绍了采用力学驱动的地理空间机器学习模型开发的美国全国液化灾害图（NLHMs），该模型替代了当前工程实践中的液化模型，利用大量地理空间预测因子推断地下条件，并通过原位测试数据锚定实测条件。将该地理空间液化模型与2023年美国全国地震灾害模型结合，借助高性能计算完成高分辨率震级分解，在约90米分辨率下绘制了美国本土的液化灾害图，涵盖条件性（2475年设计事件）和无条件性（地面破坏重现期）两种形式。生成的NLHMs为土地利用政策、初步场地评估、区域地震模拟与响应规划以及监管执行筛选工具等应用提供了参考。除量化和可视化液化灾害外，本研究还利用NLHMs在连续空间域中探讨了工程实践与政策的三个问题：（i）条件性分析中选择模态震级与平均震级的影响；（ii）条件性与无条件性灾害形式的差异；（iii）液化灾害与社会经济脆弱性的叠加程度。结果阐明了震级选择如何改变灾害计算值；无条件性图揭示了当前建筑规范中便捷且广泛使用的单场景图所掩盖的重要空间偏差，这类图虽便捷但不完全合理；且液化暴露存在适度但具统计显著性的社会经济梯度。
英文摘要
This study introduces U.S. national liquefaction hazard maps (NLHMs) developed using a mechanics-informed, geospatial machine learning model which surrogates state-of-practice liquefaction models, exploits a large library of geospatial predictors to infer subsurface conditions, and is anchored to measured conditions with in-situ test data. By convolving this geospatial liquefaction model with the 2023 U.S. national seismic hazard model, liquefaction hazard is mapped across the contiguous U.S. at ~90 m resolution within both conditional (2,475-year design event) and unconditional (return period of ground failure) formulations using high-performance computing for the high-resolution magnitude-disaggregation. The resulting NLHMs provide insights for land-use policy, preliminary site assessment, regional-scale earthquake simulation and response planning, and screening tools for regulatory enforcement, among other applications. Beyond quantifying and visualizing liquefaction hazard, the NLHMs are used herein to examine three questions of engineering practice and policy across a continuous spatial domain: (i) the effect of selecting modal versus mean magnitude in conditional analyses; (ii) the differences between conditional and unconditional hazard formulations; and (iii) the extent to which liquefaction hazard compounds with socioeconomic vulnerability. Results elucidate where and how the choice of magnitude alters computed hazards; that unconditional maps reveal important spatial deviations suppressed by single-scenario maps, which are convenient and widely used in current building codes, but less than completely rational; and that modest but statistically significant socioeconomic gradients in liquefaction exposure exist.
