ANADEF：用于双参数地震预报的嵌套置换警报
ANADEF: A Nested-Permutation Alarm for Dual-Parameter Earthquake Forecasting
- University of Beira Interior（比拉伊内里乌大学）
- Atmosphere and Ocean Research Institute (AORI), the University of Tokyo（东京大学大气海洋研究所）
- Farhangian University（法尔坎吉安大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出ANADEF流程，结合应力敏感b值场与背景速率进行双参数地震预报，在扎格罗斯带测试显示减少警报面积并保持高命中率，但增量信息显著性依赖于零模型选择。
AI中文摘要：
空间分辨的应力代理和基于速率的地震活动模型正越来越多地结合用于区域地震预报，但对其非冗余性的正式检验在很大程度上仍未得到解决。我们提出了用于双参数地震预报的嵌套置换警报（ANADEF）流程，该流程将对应力敏感的Gutenberg-Richter $b$值场（通过惩罚二维B样条反演估计）与来自时空ETAS随机去丛的平稳背景速率（$\mu$）相结合。将该方法应用于扎格罗斯褶皱冲断带，使用18年目录（$n=40{,}731$，$M_{\mathrm{N}}\geq1.5$，2006-2024年），并采用两阶段协议，训练窗口（2006-2014年）和目标窗口（2015-2024年）不重叠。对于$M_w\geq5.0$（$N=55$），该模型实现了回顾性面积技能评分$S=0.69$（95%置信区间：0.64-0.73），将警报面积从$\tau\approx0.38$（仅$\mu$）减少到$\tau\approx0.28$，同时保持命中率$\nu=92.7\\%$；由于阈值是在评估目录上校准的，这些是样本内而非样本外估计。嵌套置换程序在每个零假设实现中重新优化阈值，以检验$b$值是否在$\mu$之外增加了信息，并吸收了优化偏差。显著性依赖于零模型：逐单元随机化产生$p=0.012$，而保守的保持空间结构的零模型给出较弱且不显著的证据（$p=0.057$）——增量应力信息是提示性的，但尚未明确确立。校准的阈值被冻结并应用于更新的2015-2024年场，生成了未经验证的、面向未来的2025-2029年空间警报模板。这些结果建立了一种可重复、统计透明的方法论，用于检验而非假设应力敏感预测因子与基于速率预测因子之间的互补性，并提供了一个待前瞻性验证的候选操作模板。
英文摘要：
Spatially resolved stress proxies and rate-based seismicity models are increasingly combined for regional earthquake forecasting, yet formally testing their non-redundancy remains largely unaddressed. We present the Nested-Permutation Alarm for Dual-Parameter Earthquake Forecasting (ANADEF) pipeline, integrating a stress-sensitive Gutenberg--Richter $b$-value field, estimated via a penalized 2D B-spline inversion, with a stationary background rate ($μ$) from space--time ETAS stochastic declustering. Applied to the Zagros Fold--Thrust Belt using an 18-year catalog ($n=40{,}731$, $M_{\mathrm{N}}\geq1.5$, 2006--2024) under a two-stage protocol with non-overlapping training (2006--2014) and target (2015--2024) windows, the model achieves, for $M_w\geq5.0$ ($N=55$), a retrospective Area Skill Score $S=0.69$ (95\% CI: 0.64--0.73), reducing alarmed area from $τ\approx0.38$ ($μ$-only) to $τ\approx0.28$ while retaining hit rate $ν=92.7\%$; since thresholds are calibrated on the evaluation catalog, these are in-sample, not out-of-sample, estimates. A nested permutation procedure re-optimizing thresholds within each null realization tests whether $b$-value adds information beyond $μ$, absorbing the optimization bias. Significance is null-model dependent: cell-wise randomization yields $p=0.012$, while a conservative spatial-structure-preserving null gives weaker, non-significant evidence ($p=0.057$)---incremental stress information is suggestive but not unambiguously established. Calibrated thresholds were frozen and applied to updated 2015--2024 fields, generating an unvalidated, forward-looking spatial alarm template for 2025--2029. These results establish a reproducible, statistically transparent methodology for testing, rather than assuming, complementarity between stress-sensitive and rate-based predictors, offering a candidate operational template pending prospective validation.
