基于标度律的神经点过程用于地震序列预测
Scaling-law-informed neural point processes for earthquake sequence forecasting
浏览论文内容
中文总结 AI 辅助
该研究开发了基于标度律的神经点过程Fusion，结合ETAS模型与Gutenberg–Richter定律，在地震序列预测中优于ETAS等基线模型，证实低震级目录历史与标度律信息可提升神经序列学习的时间预测性能。
中文摘要 AI 辅助
地震序列预测需要能够学习非线性历史依赖关系同时保留稳健统计结构的模型。我们开发了一种基于标度律的神经标记点过程，命名为Fusion，它将目录历史的神经表示与来自 Epidemic-Type Aftershock Sequence（ETAS）模型的时间特征、以及来自Gutenberg–Richter定律的震级信息相结合。该模型将应用于输入目录的震级截止值与固定的目标事件阈值分离，使得低震级地震能够为预测提供信息，而无需改变目标事件集合。对于2016-2017年的Amatrice–Visso–Norcia序列，当保留低震级事件时，Fusion在目标事件时间似然上取得了最高值，其性能优于ETAS和纯神经点过程基线。逐事件和累积分析显示，在Visso和Norcia序列的大部分时段中，Fusion持续获得时间精度提升。在五个基准目录中，采用固定ETAS先验的目录特定神经训练，在评估的最小震级截止值下取得了最高的时间似然。震级似然在基于Gutenberg–Richter的ETAS参考之外未显示出一致的预测增益，表明Fusion捕获的额外信息主要是时间维度的。这些结果表明，低震级目录历史和经验标度律信息可补充神经序列学习，以实现目标事件的时间预测。
英文摘要
Earthquake sequence forecasting requires models that can learn nonlinear history dependence while retaining robust statistical structure. We develop a scaling-law-informed neural marked point process, termed Fusion, that combines neural representations of catalog history with temporal features derived from the Epidemic-Type Aftershock Sequence model and magnitude information derived from the Gutenberg--Richter law. The model separates the magnitude cutoff applied to the input catalog from the fixed target-event threshold, allowing lower-magnitude earthquakes to inform forecasts without changing the target-event set. For the 2016--2017 Amatrice--Visso--Norcia sequence, Fusion achieves the highest target-event temporal likelihood when lower-magnitude events are retained, outperforming both ETAS and a purely neural point-process baseline. Event-wise and cumulative analyses show sustained timing gains through substantial portions of the Visso and Norcia sequences. Across five benchmark catalogs, catalog-specific neural training with a fixed ETAS prior yields the highest temporal likelihood at the minimum evaluated magnitude cutoff. Magnitude likelihood shows no consistent predictive gain beyond the Gutenberg--Richter-based ETAS reference, indicating that the additional information captured by Fusion is primarily temporal. These results show that lower-magnitude catalog histories and empirical scaling-law information complement neural sequence learning for target-event timing.
