09:00 〜 09:15
[SVC28-01] Development of a Real-Time Forecasting Tool for Application of Ambient Noise Interferometry at Kīlauea Volcano, Hawaii
★Invited Papers
キーワード：ambient noise interferometry, volcanoes, eruptions
Ambient noise interferometry (ANI) capitalizes on ubiquitous ambient noise signals to probe the subsurface of the Earth for temporal changes in seismic velocity. Recent studies have applied ANI to seismic data recorded across active volcanic systems to demonstrate its utility in identifying volcanic activity. These studies have shown changes in seismic velocity associated with volcanic activity such as pre-eruption inflation, eruption onset, and co-eruptive deformation. For this reason, ANI has become a methodology of great interest to volcano observatories worldwide. However, retrospective ANI studies have the advantage of knowing when a particular volcanic event was observed (e.g. onset of an eruption), allowing the investigators to mine that time window of seismic data for changes in seismic velocity that occurred contemporaneously.
In lieu of volcanic activity, previous studies have shown that seasonal variations in the hydrologic cycle cause observable changes in seismic velocity. The amplitude and timing of these seasonal velocity changes can vary from year-to-year based on the amount of annual rainfall, snowpack, and/or snow melt, as well as the exact timing of these seasonal events. Furthermore, major meteorological events, e.g., extreme rainfall due to tropical storms, can cause major, shorter-term changes in seismic velocity. A grand challenge to using ANI as a forecasting tool lies in determining whether observed changes in a real-time environment are driven by volcanic activity or due solely to other sources (i.e., hydrologic or meteorologic events). Volcanic activity will always occur in tandem with seasonal events (e.g. annual rainfall, snowpack, or snow melt), and can also occur during major meteorological events. Thus, changes in seismic velocity will reflect the confluence of variations in the hydrologic cycle, meteorologic events (if they are occurring), and volcanic activity (if it is occurring). In this study, we develop a new forecasting tool that is tested in real-time on the 2020 eruption and ongoing eruption at Kīlauea volcano. We are able to determine when changes in seismic velocity at Kīlauea have moved outside of a seasonal trend and/or beyond the impact of a major meteorological event, and are indicative of volcanic activity.
In lieu of volcanic activity, previous studies have shown that seasonal variations in the hydrologic cycle cause observable changes in seismic velocity. The amplitude and timing of these seasonal velocity changes can vary from year-to-year based on the amount of annual rainfall, snowpack, and/or snow melt, as well as the exact timing of these seasonal events. Furthermore, major meteorological events, e.g., extreme rainfall due to tropical storms, can cause major, shorter-term changes in seismic velocity. A grand challenge to using ANI as a forecasting tool lies in determining whether observed changes in a real-time environment are driven by volcanic activity or due solely to other sources (i.e., hydrologic or meteorologic events). Volcanic activity will always occur in tandem with seasonal events (e.g. annual rainfall, snowpack, or snow melt), and can also occur during major meteorological events. Thus, changes in seismic velocity will reflect the confluence of variations in the hydrologic cycle, meteorologic events (if they are occurring), and volcanic activity (if it is occurring). In this study, we develop a new forecasting tool that is tested in real-time on the 2020 eruption and ongoing eruption at Kīlauea volcano. We are able to determine when changes in seismic velocity at Kīlauea have moved outside of a seasonal trend and/or beyond the impact of a major meteorological event, and are indicative of volcanic activity.