Seismicity at active volcanoes provides crucial constraints on the dynamics of magma systems and complex fault activation processes preceding and during an eruption. We characterize time-dependent spectral features of volcanic earthquakes at Axial Seamount with unsupervised machine learning (ML) methods, revealing mixed frequency signals that rapidly increase in number about 15 hr before eruption onset. The events migrate along pre-existing fissures, suggesting that they represent brittle crack opening driven by influx of magma or volatiles. These results demonstrate the power of unsupervised ML algorithms to characterize subtle changes in magmatic processes associated with eruption preparation, offering new possibilities for forecasting Axial's anticipated next eruption. This analysis is generalizable and can be employed to identify similar precursory signals at other active volcanoes.

中文翻译：

---

机器学习揭示的火山前体提供了新的喷发预测能力


活火山的地震活动对岩浆系统的动力学和喷发前后的复杂断层激活过程提供了关键约束。我们使用无监督机器学习 （ML） 方法表征了轴向海山火山地震的时间依赖性光谱特征，揭示了在喷发前约 15 小时数量迅速增加的混合频率信号。这些事件沿着预先存在的裂缝迁移，表明它们代表了由岩浆或挥发物涌入驱动的脆性裂缝开口。这些结果表明，无监督 ML 算法能够表征与喷发准备相关的岩浆过程的细微变化，为预测 Axial 预期的下一次喷发提供了新的可能性。这种分析是可推广的，可用于识别其他活火山的类似前兆信号。