基于物理的人工智能能否用于地震预测？
Is Seismic Forecasting Possible with Physics-based AI?
- CSIRO（联邦科学与工业研究组织）
- Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究结合物理机制与AI辅助流形检测，采用基于物理的吸引子，成功提前一周预测新西兰希库兰吉海沟地震，可预测性极限达5-6周，证明基于物理的地震预测可行。
AI中文摘要：
俯冲带内的慢滑事件为地震预测提供了独特的观测窗口。俯冲板块驱动断层内的脱水反应，引发周期性滑动和可观测的地表位移，随后可通过将控制俯冲过程的多物理场与区域地震活动耦合，在这些位移序列中识别地震的特征信号。然而，数据噪声和传统滤波方法会掩盖潜在的作用机制。本研究采用基于物理的吸引子来缓解这一限制，通过结合俯冲过程的物理机制与人工智能辅助的流形检测，成功提前一周预测新西兰希库兰吉海沟的一次地震，且可预测性极限可延伸至5-6周并具备十年尺度的可重复性，这表明所提出的机制具有基本确定性，证明基于物理的地震预测是可行的。
英文摘要：
Slow slip events within subduction zones offer a unique window into earthquake prediction. The subducting plate drives dehydration reactions in the fault, causing cyclical slip and observable surface displacements. Earthquake footprints can then be identified in these displacement series through coupling the multi-physics governing the subduction process with regional seismic activity. However, data noise and traditional filtering methods obscure the underlying mechanisms. Here, we alleviate this constraint with our physics-based attractor. By accounting for the physics of subduction paired with AI-assisted manifold detection, we are able to predict an earthquake in New Zealand's Hikurangi trench one week early. Additionally, predictability limits extend to 5-6 weeks with decadal repeatability, pointing to the fundamental determinism of the suggested mechanism through which physics-based seismic forecasting is possible.
