Identi(cid:12)cation of infrasonic and seismic components of tremors in 1 single-station records: application to the 2013 and 2018 events 2 at an isolated volcanic island, Ioto, Japan

Infrasonic stations are sparse at many volcanoes, especially those on remote islands and those with less 7 frequent eruptions. When only a single infrasound station is available, the seismic-infrasonic 8 cross-correlation method has been used to extract infrasound from wind noise. However, it does not 9 work with intense seismicity and sometimes mistakes ground-to-atmosphere signals as infrasound. This 10 paper proposes a complementary method to identify the seismic component and the infrasonic 11 component using a single microphone and a seismometer. We applied the method to estimate the 12 surface activity on the isolated volcanic island, Ioto. We focused on volcanic tremors during the 13 phreatic eruption on April 11, 2013, and during an uncon(cid:12)rmed event on September 12, 2018. We used 14 the spectral amplitude ratios of the vertical ground motion to the pressure oscillation and compared 15 those for the tremors with those for known signals generated by volcano-tectonic earthquakes and 16 airplanes (cid:13)ying over the station. We were able to identify the infrasound component in the part of the 17 seismic tremor with the 2013 eruption. On the other hand, the tremor with the uncon(cid:12)rmed 2018 event 18 was accompanied by no apparent infrasound. We interpreted the results that the infrasound with the 19 2013 event was excited by the vent opening or the ejection of ballistic rocks, and the 2018 event was not 20 an explosive eruption either on the ground or in the shallow water. If there was any gas (and ash) 21 emission, it might have occurred gently undersea. As the method uses the relative values of on-site 22 records instead of the absolute values, it is available even if the instrument sensitivity and the station 23 site eﬀects are poorly calibrated. 24


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Eruption in isolated volcanic islands are becoming the focus of attention for their significant growth, as case in isolated islands, the occurrences, the times, and the sequences of eruptions are not identified due 33 to the lack of observations. The detection is particularly hard for small but frequent eruptions because 34 signals are not strong enough to reach the global monitoring network. 35 Infrasound is generated by activity such as opening vent and emission of volcanic gas and rocks so 36 that it is useful to distinguish the volcano's surface activity from underground processes (e.g. Ripepe et  Ioto is an isolated volcanic island of which seismicity is regularly intense (Ueda et al. 2018). At 48 Ioto, phreatic eruptions frequently occur due to the high geothermal activity (Notsu et al. 2005), and the 49 volcanic activity is pronounced not only on the ground but also undersea detected by remote hydrophones 50 (Matsumoto et al. 2019). In this situation where the volcanic activity is very high throughout the island, 51 there is a need to monitor eruptions and its temporal changes.

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This study aims to identify volcanic infrasound using a single pair of seismometer and microphone at  have not been confirmed in terms of their occurrences, times, and source vents.

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An eruption occurred at about 16:00 on April 11, 2013, has been observed from the ground and the    103 We performed a cross-correlation analysis between the vertical ground velocity and the microphone data

Cross-correlation Analysis
is the wind velocity, and C a is the sound velocity), which guarantees that d is smaller 108 than the infrasound wave lengths and larger than correlation lengths of wind noise (Shields 2005). We 109 assumed that C a is 340 m/s. We also considered that v is smaller than the maximum wind speed of 11.8  Then, the relation is rewritten as 0.5 < f ≤ 44.2 Hz. Therefore, we used the frequency band of 1-10 Hz.

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The cross-correlation coefficient (CC) was calculated for the delay time of the vertical ground velocity to 113 the microphone data from -0.5 to 0.5 s by use of a 5-s time window sliding every 1 s.  the atmosphere, the relation is formulated as where ρ is the density of air (Cook 1971; Donn and Posmentier 1964). There exist records of ground- whether the wave is seismic or infrasonic, we use it to distinguish the waves. For convenience, the observed 133 spectral amplitude ratio will be referred to as (w/p) obs . 134 We calculated (w/p) obs for each of TR1 and TR2 in the following steps. Garces 2007). Therefore, we focused on the frequency range above 1 Hz in searching for volcanic signals.

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For each time window, we calculated the powers of the infrasound data in high-and low-frequency 140 bands, E h = ∫ 10 1 P SD(f )df and E l = The background noise levels were adjusted by c 1 and c 2 so that the mean PSDs for the signal and the 153 noise were equal at 0.5 Hz. Namely, c 1 = P W (0.5)/P b W (0.5), and c 2 = P P (0.5)/P b P (0.5). The shifting 154 was applied to remove the effect of temporal change in wind noise. The spectral characteristics of the 155 tremors and the background noises obtained by the method are compared in Figure 3.

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For reference, we evaluated (w/p) obs for known infrasonic-and seismic-signals, which are airplane 157 sound propagating in the atmosphere (PN) and seismic waves generated by tectonic earthquakes (EQ).

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The method was similar to the above. For PN, we analyzed the data from 16:00-17:00 of April 11, 2013 159 and from 10:00-11:00 of September 12, 2018, in which we found clear airplane signals in the spectrograms.

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As PN signals had powers in high frequency, we changed the frequency range in step 1 ⃝ to 10-40 Hz, and signals that had good cross-correlation between the seismometer and the microphone (the CC larger than 166 0.6) and peak seismic amplitudes larger than 50 µm/s at IOCD were selected. Twenty-second records 167 from 10 s before the peaks were used for calculating P W (f ) and P P (f ). Then, we performed step 5 ⃝ to 168 select the meaningful frequency band.

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The seismic-and infrasonic-records and the seismic-to-infrasonic CCs in the analyzed periods for TR1 171 and TR2 are shown in Figure 4. Figure 5 shows results of the same analysis for the reference signals 5d. It suggests a seismic origin for TR2 signal in both seismic-and infrasonic-data.
181 Figure 6 compares the mean power spectra and (w/p) obs of TR1 and TR2 against PN and EQ. Figure   182 6c shows that (w/p) obs of TR1 is closer to that of PN than EQ. The infrasonic amplitude is too large The values of (w/p) obs 190 The spectral ratios of seismic data to infrasonic data, (w/p) obs , were calculated to discuss the volcanic 191 activities with TR1 and TR2, as presented in Figure 6c and 6d. Here, we consider if the values of (w/p) obs 192 are reasonable, focusing on the reference signals of EQ and PN. EQ that is seismic wave has (w/p) obs in including the topmost soft and thin layer. We avoid further interpretation of the (w/p) obs for infrasound 211 because of many unknown factors. Nevertheless, it is certain that (w/p) obs for atmosphere-to-ground 212 waves is much smaller than that for ground-to-atmosphere waves.

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The proposed method has an advantage that it does not use the absolute values of the record. The data set analyzed in this study is not officially available at the request of JMA and JSDF.

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Competing interests 264 The authors declare that they have no competing interest.