Longo Roberto, Lacanna Giorgio, Innocenti Lorenzo, Ripepe Maurizio
IEEE Trans Pattern Anal Mach Intell. 2024 Dec;46(12):7973-7982. doi: 10.1109/TPAMI.2024.3399689. Epub 2024 Nov 6.
Explosive volcanic blasts can occur suddenly and without any clear precursors. Many volcanoes have erupted in the last years with no evident change in the eruptive parameters and with dramatic consequences for the population living nearby the volcano and the tourists visiting the active areas. In recent years, a big effort has been made to develop Early Warning systems to issue timely alerts to the population. At Stromboli volcano, the development of sensitive instruments to measure the deformation (tilt) of the ground has revealed that the volcano edifice is inflating tens of minutes before the explosion following a recurrent exponential ramp-like pattern. This scale-invariant of ground deformation has allowed the development of a quasi-deterministic Early Warning system which is operative since 2019. In this article we show how Artificial Intelligence and Machine Learning can be successfully applied to improve the efficiency and the sensitivity of Early Warning systems, provided the availability of a comprehensive experimental data set on past explosive events. The approach presented here for the Stromboli case demonstrates promising results also in forecasting the intensity of explosive events, offering valuable insights and new perspectives into the potential risks associated with volcanic activities.
火山爆发可能会突然发生，且没有任何明显的先兆。在过去几年里，许多火山喷发时，喷发参数没有明显变化，却给火山附近的居民和前往活跃区域的游客带来了巨大影响。近年来，人们付出了巨大努力来开发早期预警系统，以便及时向民众发出警报。在斯特龙博利火山，用于测量地面变形（倾斜度）的灵敏仪器的研发表明，火山山体在爆炸前几十分钟会按照一种反复出现的指数级斜坡状模式膨胀。这种地面变形的尺度不变性使得自2019年起开始运行的准确定性早期预警系统得以开发。在本文中，我们展示了只要有关于过去爆炸事件的全面实验数据集，人工智能和机器学习如何能够成功应用于提高早期预警系统的效率和灵敏度。这里针对斯特龙博利火山案例所提出的方法在预测爆炸事件强度方面也显示出了有前景的结果，为与火山活动相关的潜在风险提供了有价值的见解和新的视角。
