Integration of Seismic and Quasi-Static Signals for Improved Volcanic Monitoring

<p class="p1">The time scale of ground displacement at volcanoes varies between short, sub second seismic events, to days, months or even years. This study is focused on data from seismic and GNSS stations located around Mount Etna. The GNSS and seismic stations operate at different time scales. Data from these different time scales is extracted and combined in order to better understand the subsurface dynamics. The overall aim of this research is to improve volcanic forecasting and monitoring. It does this in a novel way by applying signal processing and machine learning techniques to the rich dataset.</p> <p class="p1">Mount Etna offers an interesting case study as it is a widely monitored volcano with a variety of sensors and with a rich pool of data to analyse. Additionally the volcanic dynamics at Mount Etna are complex. This is a volcano where there is a variety of different sub-surface dynamics due to the movement of both deep and shallow magma. This allows for rich insights to be drawn through the combination of different signal types.</p> <p class="p1">This study looks at combining the information obtained from the seismic array at Mount Etna, with the information obtained from various GNSS stations on the volcano. The seismic array has been able to capture ground velocity data in the frequency range 0.025 Hz to 50 Hz from a range of stations at different locations across the volcano. The GNSS stations measure ground displacement with a sampling frequency of 1 Hz, and they allow for longer term ground dynamic analysis.</p> <p class="p1">We analyse different seismic events, and relate the type and number of the seismic events to the long term ground deformation that we see in the recorded GNSS data. Where links between the two signal types have been identified, research is ongoing to establish a direct connection with known volcanic activity on Mount Etna. This will help establish what the relationship that we are seeing signifies. This integration of data from different types of sensors is a significant step into bridging the gap between seismic and quasi-static ground displacement at active volcanoes and should open the path toward more in depth volcanic monitoring and forecasting.</p>