17:15 〜 18:45
[SVC26-P08] Exploring Waveform Characteristics and Inter-station Spectral Ratios in Very-Long-Period Events at Asama Volcano
キーワード：Very-Long-Period Event, Volcano Seismology, Volcano Monitoring, Volcano Monitoring Network
Low-frequency seismic events (LP) and tremors are vital indicators of volcanic activity, often serving as precursors to eruptions. Very-long-period (VLP) earthquakes are linked to significant fluid movements during volcanic processes. Studying VLPs offers insights into fluid dynamics and volcano plumbing systems but usually poses challenges for monitoring due to their complex source interpretations. While an ideal monitoring involves well-distributed stations near the source, it is not always feasible. A modern monitoring network established around Asama volcano in 2003 provides valuable data on VLPs.
If near-source stations are lost, the VLP monitoring of Asama would be challenging in the current method but might be possible through advanced analyses. Our research aim is to understand the relationship between inter-station relative features of recorded data and the actual source, separating the station-specific effects, including the source-to-receiver propagation. This understanding can aid in identifying VLPs with remaining stations even when they are far from the source. Analyzing the stability and variation of the inter-station parameters over time can provide further insights into Asama volcano dynamics and improve volcanic hazard assessment.
For the preliminary stage of this research, we start with a VLP catalog at Asama volcano in June 2009, which lists 3592 VLP candidates. We chose this period because gas measurements were conducted at the summit on June 3, 2009, in correlation with VLPs (Kazahaya et al., 2015). We selected from the catalog on June 1–5 those that showed to the eyes good signal-to-noise ratios or that were energetic enough to stand out clearly in the drum-plot seismograms. After further depuration of the previous selection, we had a total amount of 340 events. We proceeded to study the spectra and inter-station spectral ratios of the selected events. For this task, we used waveforms sampled at 100 Hz starting 30 seconds before and ending 120 seconds after triggering, applied a low-pass filter with a corner frequency of 1 Hz, and decimated at 10 Hz to obtain 1500 data points in 150 s. Adding 548 zeros and applying a Hamming window, we made a fast Fourier transformation of each event. Then, we computed the spectral ratio among stations. For a better visualization of the stability and variation of the events, we made a pseudocolor plot stacking all analyzed event spectra and inter-station spectral ratios.
The ratios have shown more stable features than the individual event spectra at each station and also have exhibited a group of events presenting higher energy in frequencies just below 0.1 Hz at further stations. We plan to make more quantitative classifications.
If near-source stations are lost, the VLP monitoring of Asama would be challenging in the current method but might be possible through advanced analyses. Our research aim is to understand the relationship between inter-station relative features of recorded data and the actual source, separating the station-specific effects, including the source-to-receiver propagation. This understanding can aid in identifying VLPs with remaining stations even when they are far from the source. Analyzing the stability and variation of the inter-station parameters over time can provide further insights into Asama volcano dynamics and improve volcanic hazard assessment.
For the preliminary stage of this research, we start with a VLP catalog at Asama volcano in June 2009, which lists 3592 VLP candidates. We chose this period because gas measurements were conducted at the summit on June 3, 2009, in correlation with VLPs (Kazahaya et al., 2015). We selected from the catalog on June 1–5 those that showed to the eyes good signal-to-noise ratios or that were energetic enough to stand out clearly in the drum-plot seismograms. After further depuration of the previous selection, we had a total amount of 340 events. We proceeded to study the spectra and inter-station spectral ratios of the selected events. For this task, we used waveforms sampled at 100 Hz starting 30 seconds before and ending 120 seconds after triggering, applied a low-pass filter with a corner frequency of 1 Hz, and decimated at 10 Hz to obtain 1500 data points in 150 s. Adding 548 zeros and applying a Hamming window, we made a fast Fourier transformation of each event. Then, we computed the spectral ratio among stations. For a better visualization of the stability and variation of the events, we made a pseudocolor plot stacking all analyzed event spectra and inter-station spectral ratios.
The ratios have shown more stable features than the individual event spectra at each station and also have exhibited a group of events presenting higher energy in frequencies just below 0.1 Hz at further stations. We plan to make more quantitative classifications.