AI 中文总结
研究地震余震预测问题，提出将其重塑为条件生成时空场，开发QuakeGen扩散模型，该模型在全球序列和每日预测上表现优于现有方法，有望更准确地预测地震序列时空演变。
AI 中文摘要
预测大地震后余震在时空上的演变是统计地震学的核心问题，也是地震业务预报的基础。现有方法基于统计点过程模型，虽能匹配平均统计数据，但无法捕捉真实序列的断层控制空间模式和序列间变化的生产率。神经点过程模型有所改进但未超越ETAS。本文将余震预测重塑为时空场的条件生成，开发了扩散模型QuakeGen，在全球序列和每日预测上表现出色，条件生成建模有望更准确预测地震序列时空演变。
英文摘要
Forecasting how aftershocks evolve in space and time after a large earthquake is a central problem in statistical seismology and underpins operational earthquake forecasting. Existing forecasting methods rest on statistical point-process models such as the epidemic-type aftershock sequence (ETAS) and Reasenberg-Jones models, which prescribe a fixed decay in time and an isotropic kernel in space. They match the average Omori-Utsu and Gutenberg-Richter statistics well but do not capture the fault-controlled spatial patterns of real sequences or the productivity that varies among sequences. Neural point-process models relax these fixed forms but keep the event-by-event view and have not consistently surpassed ETAS on common benchmarks. Rather than modeling individual events as a point process, we recast aftershock forecasting as conditional generation of a spatiotemporal field. We develop QuakeGen, a diffusion model that generates the evolving fields of aftershock rate and maximum magnitude conditioned on recent seismicity and the forecasting horizon. The same conditional generative framework can be trained on rich seismic catalogs to forecast global aftershock sequences and regional daily seismicity, and could further condition on physical fields such as fault geometry or geodetic deformation. On global sequences, the data-driven approach outperforms the operational USGS Reasenberg-Jones forecast, recovering the fault-controlled, anisotropic spatial structure that fixed kernels cannot express. On daily forecasting, QuakeGen also matches the well-tuned ETAS baselines, which neural point-process models have yet to surpass on the regional benchmark. Conditional generative modeling, which has transformed prediction in fields as diverse as weather forecasting and protein structure prediction, holds the potential to forecast more accurately how earthquake sequences unfold in space and time.
