Monitoring sources of volcanic activity at Mount Etna using pattern recognition techniques on infrasound signals
نویسندگان
چکیده
SUMMARY Infrasound monitoring plays an important role in the framework of surveillance Mt. Etna, Europe’s largest active volcano. Compared to seismic monitoring, which is particularly effective for buried sources, infrasound signals mirror activity shallow sources like Strombolian explosions or degassing. The interpretation difficult untrained eye, as we have account volcanic and non-volcanic sources. problem handling large complex data sets can be tackled with machine learning, namely pattern recognition techniques. Here, focus on so-called ‘Unsupervised Learning’, where identify groups patterns being similar each other. degree similarity based a metric measuring distance among features patterns. This work aims at identification typical regimes radiation their relation state Etna. For this goal, defined describing any pattern. These were obtained using wavelet transform. We applied ‘Self-Organizing Maps’ (SOM) projecting them 2-D representation space—the ‘map’. An intriguing aspect SOM resides fact that position map expressed by colour code, manner are assigned code. simplified multivariate allows follow development characteristics time efficiently. During training phase considered reference set, encompassed variety scenarios. identified correspond specific regime activity, representative volcano noise conditions. form areas maps. In second step, test was not used during phase. Applying same pre-processing data, blindly found before, identifying one whose code most calculated thus able assess validity prediction. classification scheme presented provides reliable assessment adds useful supplementary details results real-time automatic system operation Istituto Nazionale di Geofisica e Vulcanologia—Osservarorio Etneo. particular importance when no visible information available either unfavourable meteorological conditions night time.
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ژورنال
عنوان ژورنال: Geophysical Journal International
سال: 2022
ISSN: ['1365-246X', '0956-540X']
DOI: https://doi.org/10.1093/gji/ggac278