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Title

Locating hydrothermal fluid injection of the 2018 phreatic eruption at
Kusatsu-Shirane volcano with volcanic tremor amplitude

Authors

Taishi Yamada, Aika K Kurokawa, Akihiko Terada, Wataru Kanda,
Hideki Ueda, Hiroshi Aoyama, Takahiro Ohkura, Yasuo Ogawa,Toshikazu Tanada

Citation

Earth, Planets and Space, 73, ,Pub. date

2021, 1

DOI

https://doi.org/10.1186/s40623-020-01349-1

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(2021) 73:14
Yamada et al. Earth, Planets and Space
https://doi.org/10.1186/s40623-020-01349-1

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Locating hydrothermal fluid injectionof the 2018 phreatic eruption at Kusatsu‑Shirane
volcano with volcanic tremor amplitude
Taishi Yamada1,2* , Aika K. Kurokawa2, Akihiko Terada3, Wataru Kanda3, Hideki Ueda2, Hiroshi Aoyama4,
Takahiro Ohkura5, Yasuo Ogawa3 and Toshikazu Tanada2

Abstract
Kusatsu-Shirane volcano hosts numerous thermal springs, fumaroles, and the crater lake of Yugama. Hence, it has
been a particular study field for hydrothermal systems and phreatic eruptions. On 23 January 2018, a phreatic eruption occurred at the Motoshirane cone of Kusatsu-Shirane, where no considerable volcanic activity had been reported
in observational and historical records. To understand the eruption process of this unique event, we analyzed seismic,
tilt, and infrasound records. The onset of surface activity accompanied by infrasound signal was preceded by volcanic
tremor and inflation of the volcano for ~ 2 min. Tremor signals with a frequency band of 5–20 Hz remarkably coincidewith the rapid inflation. We apply an amplitude source location method to seismic signals in the 5–20 Hz band to
estimate tremor source locations. Our analysis locates tremor sources at 1 km north of Motoshirane and at a depth of
0.5–1 km from the surface. Inferred source locations correspond to a conductive layer of impermeable cap-rock estimated by magnetotelluric investigations. An upper portion of the seismogenic region suggests hydrothermal activity
hosted beneath the cap-rock. Examined seismic signals in the 5–20 Hz band are typically excited by volcano-tectonic
events with faulting mechanism. Based on the above characteristics and background, we interpret that excitationof examined volcanic tremor reflects small shear fractures induced by sudden hydrothermal fluid injection to the
cap-rock layer. The horizontal distance of 1 km between inferred tremor sources and Motoshirane implies lateralmigration of the hydrothermal fluid, although direct evidence is not available. Kusatsu-Shirane has exhibited unrest at
the Yugama lake since 2014. However, the inferred tremor source locations do not overlap active seismicity beneath
Yugama. Therefore, our result suggests that the 2018 eruption was triggered by hydrothermal fluid injection througha different pathway from that has driven unrest activities at Yugama.
Keywords: Kusatsu-shirane, Phreatic eruptions, Volcanic tremor, Hydrothermal system
Introduction
Kusatsu-Shirane volcano consists of three pyroclastic
cones (Shirane, Ainomine, and Motoshirane), central
Japan (Fig. 1). A distinguishing characteristic of KusatsuShirane is a well-developed hydrothermal system beneaththe volcano, evidenced by a significant amount of thermal*Correspondence: yamada.taishi.8m@kyoto‑u.ac.jp
1
Disaster Prevention Research Institute, Sakurajima Volcano
Research Center, Kyoto University, 1722‑19 Sakurajima‑Yokoyama,
Kagoshima 891‑1419, Japan
Full list of author information is available at the end of the article

springs, fumaroles, and a crater lake (Yugama) at thesummit of the Shirane cone. Therefore, it has been a particular study field of volcano hydrothermal system andphreatic eruptions (e.g., Ossaka et al. 1980; Ohba et al.
1994; Terada and Hashimoto, 2017; Terada 2018). On 23
January 2018, a phreatic eruption occurred at KusatsuShirane and formed several small craters with diametersof 15–20 m at the summit area of the Motoshirane cone
(Earthquake Research Institute, 2018). The 2018 eruption
occurred at Motoshirane, while all documented (after1882) phreatic eruptions have occurred at the Shirane© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material
in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material
is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds thepermitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat​iveco​
mmons​.org/licen​ses/by/4.0/.

Yamada et al. Earth, Planets and Space(2021) 73:14

a

b

c

Fig. 1 a Location of Kusatsu-Shirane volcano. Black triangles denote
active volcanoes defined by JMA. b Locations of V-net stations at
Kusatsu-Shirane. Pink diamonds correspond to major thermal springsites. A black square is the map area of c. c A detailed map including
Yugama, Ainomine, and Motoshirane. Blue and white squares show
observation stations operated by KSVO and JMA. Vent locations of
the 2018 eruption and unidentified past events are denoted by red
and violet colors (GSI, 2018). Seismic data at YNE and KSJ are adopted
in hypocenter determinations by KSVO, although we do not adopt
these into the ASL analysis in this papercone, including recent unrest events in 1989–1991 and2014 (Terada 2018). No considerable precursory changes
related to Motoshirane have been reported before theevent. Phreatic eruptions are not always accompanied
by clear precursors (Barberi et al. 1992). Assessing thepotential of upcoming phreatic eruptions is an important

Page 2 of 15

challenge for volcano research and monitoring. Hence,case studies for each event are still valuable to improve
our understanding of possible phreatic eruption mechanisms (e.g., Mannen et al. 2018; Ueda et al. 2018; Stix andde Moor, 2018; Battaglia et al. 2019).
Phreatic eruptions may be regarded as relatively small
events from a view of the total ejected material amount(VEI: Volcano Explosive Index, Newhall and Self, 1992),
with the VEI of 0–2 (e.g., Brown and Lawless, 2001;
Mayer et al. 2015; Maeno et al. 2016; Geshi and Itoh 2018;
Mannen et al. 2018, The Joint Research Team for ash fallin Kusatsu-Shirane 2018 eruption, 2018). However, phreatic eruptions and related unrest activities often excite
seismic signals and transient ground deformation withconsiderable amplitudes (e.g., Aoyama and Oshima 2008,2015; Maeda et al. 2015). Previous studies have demonstrated that seismic analysis is vital for understanding
the phreatic eruption mechanism and related hydrothermal activities with locations, force system, and its temporal changes of seismic wave sources (e.g., Jolly et al.
2010, 2018; Kato et al. 2015; Maeda et al. 2015; Yukutake
et al. 2017). Amplitude Source Location (ASL) method
has been applied at numerous volcanoes to constrain
the source location of volcanic tremor (e.g., Yamasato,
1997; Battaglia and Aki 2003, Kumagai et al. 2010; Kurokawa et al. 2016; Ichihara and Matsumoto 2017; Ichimura
et al. 2018; Walsh et al. 2019). Since no investigation has
been reported at Motoshirane in terms of geophysicalground observations, our primary objective is to locate
the hydrothermal system that drove the 2018 phreatic
eruption. We apply the ASL technique to seismic records
accompanying the 2018 eruption to estimate volcanic
tremor source locations. The relation between our result
and the hydrothermal system structures of Kusatsu-Shirane is discussed to illustrate the source process of the2018 eruption.

Observation networks
Kusatsu-Shirane Volcano Observatory (KSVO), Volcano
Fluid Research Center, Tokyo Institute of Technology, hasconducted seismic observation at Kusatsu-Shirane since1990 (Mori et al. 2006). The seismic network of KSVO
focuses on the Shirane cone, the center for active seismicity in Kusatsu-Shirane (Fig. 1c). We adopt seismic data
recorded at KSE, KSS, KSW, KSYG, JIE, and infrasounddata at KSRH. National Research Institute for Earth Science and Disaster Resilience (NIED) also has a multiparametric observation network (V-net) at Kusatsu-Shirane
since 2014 (Tanada et al. 2017). Seismic and tilt dataobtained by borehole instruments at KSHV, KSYV, and
KSNV (Fig. 1b) are adopted in this paper. We also analyze
ground velocity and infrasound data recorded at KSAO
and KSHA (Fig. 1c), operated by Japan MeteorologicalYamada et al. Earth, Planets and Space(2021) 73:14

Page 3 of 15

Agency (JMA). All adopted seismometers have a natural
frequency of 1 Hz. Table 1 summarizes our dataset.

Seismicity and hydrothermal system structuresat Kusatsu‑Shirane
Figure 2a shows hypocenter distributions of volcanic
earthquakes at Kusatsu-Shirane routinely determined by
KSVO. Two earthquake clusters characterize the seismicity at Kusatsu-Shirane: the major one is beneath the
Yugama lake (Yugama cluster) at a depth of 500–1700 m.
Most events in Fig. 2 are Volcanic Tectonic (VT) type,although some events in the Yugama cluster are LongPeriod (LP) events which may be ascribed to a fluiddriven mechanism (Nakano et al. 1998, 2003; Kumagai
et al. 2002; Fujita and Ida 2003). Another cluster can befound beneath the Ainomine cone (Ainomine cluster).
Since no seismic station covers the south of Ainomine(Fig. 1 and 2) up to the 2018 eruption (Yamamoto et al.
2018), the precision of hypocenter determination for the
Ainomine cluster may be less than that of the Yugama
cluster. The number of volcanic earthquakes significantly
decreases below sea level (Fig. 2a). This low seismicity
region corresponds reasonably to a conductive region
detected by Audio-magnetotelluric (AMT) (Nurhasan
et al. 2006) and magnetotelluric (MT) surveys (Matsunaga et al. 2020; Tseng et al. 2020) at a depth of 1–3.5 km
from the surface. The conductor is interpreted as a hydrothermal fluid reservoir, supplying hydrothermal fluid forthermal springs, fumaroles, and lake waters at KusatsuShirane as inferred by geochemical studies (Hirabayashi
1999; Ohba et al. 2000; Ohwada et al. 2003). Therefore,a plausible interpretation for seismicity above the fluid
reservoir is that ascending hydrothermal fluid induces
small earthquakes (Nurhasan et al. 2006; Tseng et al.
2020). The Motoshirane cone had the latest lava effusion
at the Kusatsu-Shirane ~ 1500 years ago (Nigorigawa et al.
2016). In addition, some small craters at the summit areaof Motoshirane suggest the possibility of unconfirmed

phreatic eruptions after the last lava effusion (Fig. 1c,
Geospatial Information Authority of Japan, 2018). The
flank of the Motoshirane produces thermal springs witha total discharge energy of 110 MW (Ueki and Terada2012). Although this value is substantial for non-eruptive
volcanism (Kagiyama 1981), no specific seismic activities
are related to their features (Fig. 2a).
Focusing on volcanic activity of Kusatsu-Shirane in
the last few decades, the first unique event after the
1989–1991 unrest (Takahashi and Fujiwara, 2014; Terada2018) includes a transient ground inflation–deflation
event accompanying volcanic tremor on January 2011
(the 2011 unrest) beneath the Ainomine peak (Terada
et al. 2011; Fujiwara et al. 2011). From March 2014,
major seismic swarms started around Yugama (Kuwahara et al. 2016), along with ground inflation, changes
in the total magnetic field, temperature, and geochemical components in lake water (Terada et al. 2016; Ohba
et al. 2016). Figure 2b shows the time history of seismicity at Kusatsu-Shirane from June 2016 to December 2018.
The beginning of the time window in Fig. 2b corresponds
to unrest from 2014, and that has been gradually diminishing. Apart from the phreatic eruption on 23 January
2018, a new series of the seismic swarm began at Yugama
in April 2018. No eruption related to this unrest occurred
up to May 2020 (JMA, 2020).

The 2018 phreatic eruption
We plot seismic, tilt, and infrasound records from 09:58
to 10:05 on 23 January 2018 (all times in JST in thispaper), including signals accompanying the 2018 eruption in Fig. 3. Volcanic tremor appears on a raw ground
velocity record at KSW from 9:59:35 (Fig. 3a). Tremor
amplitude decreases from 10:01:30, and another wave
group follows with a larger amplitude (10:02:10). Fourier spectra of seismic signals in Fig. 3a (from 09:58:20)
and background signals before the onset of tremor (from09:50:00) are denoted in Fig. 4a. The frequency content

Table 1 Instruments details of seismic networks at Kusatsu-Shirane volcano
Station

Short period seismometer

SensorNaturalperiod/flat
response

Sampling

Data logger/resolution

KSE, KSS, and KSW (KSVO)

JTS-33 (Akashi)

1s

200 HzLS-7000XT (Hakusan) / 22 bit

KSYG and JIE (KSVO)

L4 (Mark Product)1s

200 HzLS-7000XT (Hakusan) / 22 bit

KSHV, KSNV, and KSYV (NIED)JTS-33 (Mitsutoyo)1s

100 HzHKS-9200/9300 (Keisokugiken) / 27 bit

KSAO (JMA)

JTS-23 (Mitsutoyo)1s

100 HzS502G (Meisei) / 24 bit

KSHA (JMA)

L4-C (Mark Product)1s

100 HzLS-7000XT (Hakusan) / 24 bit

Infrasound microphone

KSRH (KSVO)

SI-102 (Hakusan)

0.05–100 Hz

200 HzLS-7000XT (Hakusan) / 22 bit

KSAO (JMA)ACO 3348 (Aco)

0.1–100 Hz

100 HzTYPE 7144 (Aco) / 16 bit

Tiltmeter

KSHV, KSNV, and KSYV (NIED)JTS-33 (Mitsutoyo)

DC–5 Hz

20 Hz

HKS-9200/9300 (Keisokugiken) / 27 bitYamada et al. Earth, Planets and Space(2021) 73:14

Page 4 of 15abFig. 2 a Map and vertical-NS cross-sections of hypocenters of volcanic earthquakes determined by KSVO. Vent locations of the 2018 eruptions are
represented by red color. A black star shows a reference point for the infrasound waveform plot in Fig. 5 and ASL analysis. b Space–time plot ofseismicity at Kusatsu-Shirane in the period from June 2016 to December 2018of seismic signals associated with the 2018 eruption has abroad peak range from 0.5 Hz to 30 Hz. A better view forunderstanding the frequency content is obtained from
amplitude ratios to background time window in Fig. 4b.
We see that there are broadly two major signal peaks infrequency bands of 1–2 Hz and 3–30 Hz. To examine
temporal intensity changes, we apply causal band-passfilters with two corresponding cut-off frequency bands(0.5–5 Hz, and 5–20 Hz) on raw vertical ground velocity in Fig. 3b. The dividing frequency range from 5 Hz in
Fig. 3b is related to our analysis in the following section.
A filtered waveform in the 0.5–5 Hz band has an almost
identical time history with a raw waveform. On the other

hand, another filtered (5–20 Hz) waveform shows a different time-series of amplitude; it has signals with a considerable amplitude on 09:59–10:01 as well as raw andthe 0.5–5 Hz filtered waveforms, but has fewer signals on
the second impulsive wave group from 10:02:10.
Tilt records at KSHV are shown in Fig. 3c. To emphasize dynamic ground deformation, we apply an acausal
low-pass filter with a cut-off frequency of 0.05 Hz on tilt
records in Fig. 3c. We see that an east-northward uplift
begins almost simultaneously with volcanic tremor. Since
the station KSHV is on the west-southwest side flank ofMotoshirane, the tilt change from 10:00 can be interpreted as inflation of the Motoshirane cone (Terada et al.

Yamada et al. Earth, Planets and Space(2021) 73:14

Page 5 of 15

a

b

c

d

Fig. 3 a Raw vertical ground velocity waveform at KSW from 09:58 on 23 January 2018. b Filtered (Gray: 0.5–5 Hz; Red: 5–20 Hz) vertical ground
velocity waveforms at KSW. c Tilt change records at KSHV. An acausal low-pass filter with a cut-off frequency of 0.05 Hz is applied to emphasize
dynamic tilt changes. The cumulative squared amplitude of filtered vertical ground velocity at KSW is also shown to compare tilt changes andexcitation of the 5–20 Hz band seismic signals. d Raw infrasound waveform at KSRH

2018). The polarity of tilt change turns to the opposite
at 10:01:50. This timing coincides with a reduction oftremor in the 5–20 Hz signals. We also plot the cumulative squared amplitude of filtered (5–20 Hz) verticalground velocity at KSW in Fig. 3c. Both volcanic tremor

in the 5–20 Hz band and the inflation occur withinalmost identical time windows.
Figure 3d shows the raw infrasound waveform at KSRH.
Although the waveform contains noise signals probably
derived from wind, there is a distinct wave group from

Yamada et al. Earth, Planets and Space(2021) 73:14

Page 6 of 15abFig. 4 a Fourier spectra for the vertical ground velocity at KSW (Red:for 500 s from 09:58:19; Green: for 100 s from 10:14:09; Gray: for 500 s
from 09:50:00). Pink color corresponds to the frequency band of
5–20 Hz, which is focused on our ASL analysis. b Spectrum ratios tobackground signals

10:02:13 with a greater amplitude than the backgroundlevel. To examine the infrasound origin time, we plot raw
infrasound records at KSRH and KSAO from 10:02:00 in
Fig. 5. Although the signal-to-noise ratio (SNR) of infrasound record at KSAO is relatively low, we find coherent signals on both infrasound waveforms from 12 s in
Fig. 5. Arrival times of signals in both waveforms can be
explained assuming that the signals propagate from thecenter of active vents of the 2018 eruption (Fig. 2a) with
an apparent velocity of 340 m/s, which is consistent withthe sound velocity near the ground. Hence, we consider
the origin time of infrasound is 10:02:09. This estimationis consistent with a report by ERI (2018), which examinedthe coherence of seismic and infrasound signals (Ichihara
et al. 2012). The infrasound origin time also implies thatthe second wave group on seismic record (Fig. 3a) is associated with surface activity with ash and gas emissions.

Fig. 5 Raw (black) and filtered (5–20 Hz, gray) infrasound records at
KSRH and KSAO. The origin time of the horizontal axis is 10:02:00. The
vertical offset of each waveform denotes horizontal distances fromthe center of active vents of the 2018 eruption denoted in Fig. 2a. Red
broken lines show an apparent propagation velocity (Vi ) of 340 m/s

ASL method for the 2018 eruption
Our inspection of observed data associated with the 2018
eruption shows that volcanic tremor precedes the onsetof eruption for 2 min. Now, we apply the ASL techniqueto seismic records to estimate volcanic tremor source
locations. Here, analyzed seismic signals are assumed asbody waves from a point source having an isotropic radiation pattern with wave scattering in the medium. From
this assumption, ground velocity amplitude at ri km awayfrom the seismic source, A(ri ), can be expressed as:
A(ri ) =

A0 Si
πf
,
exp(−Bri ), B =
ri
βQ

(1)

where A0 is source amplitude, Si is site amplification factor, f is the center frequency of analyzed signals, β is the
S-wave velocity in the medium, and Q is the quality factor for medium attenuation (Aki and Richards 2002). The
subscript i corresponds at the ith station in the network.
The assumption for isotropic S-wave radiation is valid
for signals with frequencies higher than 5 Hz (Takemura
et al. 2009; Kumagai et al. 2011). Hence, we focus on
ground velocity in the band of 5–20 Hz, which is almost
identical to one of the dominant frequency ranges inobserved seismic records (Fig. 4). A homogeneous S-wave
velocity of β = 2.42 km/s is referred from Kuwahara
et al. (2016), who examined seismic velocity structuresat Kusatsu-Shirane with travel times of volcanic earthquakes. The quality factor Q is set at 50 as a representative value at active volcanoes (e.g., Koyanagi et al. 1995;
Battaglia and Aki 2003; Morioka et al. 2017). We adopt
seismic records at KSE, KSS, KSW, KASO, and KSHAYamada et al. Earth, Planets and Space(2021) 73:14

Page 7 of 15

in the ASL based on the observed SNR, distances from
the Motoshirane cone, and operational history, including
the 2011 unrest. The ASL determines the source location
by minimizing residuals between observed and modeled
amplitudes by Eq. 1. However, since no seismic station
covers the south of Motoshirane, the grid search for minimum residual needs to be modified to suit our network
configuration. Hence, we follow an improved version of
the ASL proposed by Ichihara and Matsumoto (2017) for
searching the minimum residual and Si determination.
Their method evaluates residuals with amplitude ratiosof each station pair. Ratios of observed ( Rijobs) and modeled ( Rijmodel ) amplitudes from Eq. 1 of each station pairare defined as:

Rijobs =Si ri−1 exp(−Bri )
model
=
,
R

,
ij
Aobs
Sj rj−1 exp −Brj
j
Aobs
i

(2)

where Aobs
i is a band-passed Root-Mean-Squared (RMS)
amplitude in each time window for 10 s. By adopting
Eq. 2 for the grid search, observed seismic traces withsmaller amplitudes could minimize residuals equallyalong with traces with larger amplitudes. Diminishinga parameter of A0 in Eq. 2 improves the robustness of
the analysis. Therefore, we regard the error evaluation
with amplitude ratio as an improvement over the amplitude only approach (Battaglia and Aki 2003). The residual between observed and modeled amplitude ratios isdefined as:
RES = N −1


2N (N − 1)N

i=1 j=i+1a

Rijobs − Rijmodel
Rijobs(3)Each time window is shifted for 10 s without overlapping each other.
Equation 1 shows that Si at each station is critical forsource location determination with the ASL. Si values
are often determined with a coda-normalization method
using far-field earthquakes (Phillips and Aki, 1986;
Mayeda et al. 1991). However, amplitude variations ofobserved coda-wave affect the estimation of Si , causing
potential errors for source location estimates (Ogiso et al.
2016; Walsh et al. 2017). Following Ichihara and Matsumoto (2017), the present study determines Si setting areference time window that the source location is fixed ata certain point. Tremor source location is often estimatedat the surface of the vent during eruptions (Battaglia et al.
2005; Yukutake et al. 2017). Therefore, our reference time
window is set from 10:02:10, including the origin time ofdominant infrasound signals, and with source location
at the surface of the center of active vents (Fig. 2a). The
maximum infrasound amplitude in the 5–20 Hz band isabout ~ 1 Pa (Fig. 6). Following Ichihara et al. (2012), thispressure change can induce ground velocity on the orderof ­10–7 m/s, which is one order of magnitude smaller
than the observed tremor amplitude in the referencetime window (Fig. 3). Seismometers at KSE, KSS, KSW,
and KSAO are borehole-type and installed at a depth of
50–200 m from the surface (Mori et al. 2006; Saito et al.
2013; Tanada et al. 2017). Therefore, we consider that theeffect of infrasound propagation on seismic records is
negligible. Figure 6a shows RES values for the referencetime window with different reference source elevations
beneath the vent without considering Si (setting Si as 1).
The minimum value of RES is obtained at the groundsurface (an elevation of 2050 m). Hence, we consider thebFig. 6 a Residual values (RES ) with different sour elevations beneath the crater setting Si as 1. b Modeled amplitude at an elevation of 2050 m (theground surface) for the reference time window. Red bars correspond to values of Si at each station

Yamada et al. Earth, Planets and Space(2021) 73:14

assumption of a fixed reference source location at thesurface as acceptable, although our network configuration is not ideal with insufficient stations at the Motoshirane and Ainomine peaks. Corresponding modeled
amplitudes by Eq. 1 are denoted in Fig. 6b with observed
filtered RMS amplitudes of the reference time window.
The source amplitude of A0 is calculated to explain theobserved RMS amplitude at KSW for each grid node andtime window. This choice of A0 is based on the borehole
instrument at KSW, which is installed in the host rock
(Uto et al. 2004). Here, the vertical seismic records show
the lowest amplification in the network based on thecoda-normalization method (Additional file 1: Table S2).
Hence, Si at other stations is determined to satisfy themodeled amplitudes in the reference time window in
Fig. 6b.
Searching for the minimum RES is conducted with agrid size of 10 m. We set a search range from elevationsof − 1500 m to the surface, latitudes of 36.618–36.651,
and longitudes of 138.524–138.558, which covers therelevant area of the Motoshirane and Shirane cones.
The 2018 eruption affected the local electricity supply
network with ashfall, and it caused data loss at KSE, JIE,
KSYG, and KSHA after 10:02:40. Therefore, we focus on
the time window for 210 s from 09:59:00.

ResultFigure 7a represents horizontal tremor locations obtainedby our ASL analysis. The color of each plot indicates
the time, as shown in a color palette of Fig. 7b. Filtered(5–20 Hz) vertical ground velocity at KSW, estimatedsource elevation, and the minimum RES value for eachtime window are denoted in Fig. 7b. A source location
plot surrounded by a green square (190 s) is the reference
window that the source is fixed at the surface of the vent.
Inferred tremor locations up to 170 s are ranging about
500 m east of the Ainomine peak. Corresponding elevations are in the range of 1.0–1.5 km, equivalent to a depthof 0.5–1 km from the ground surface. Inferred source
locations suddenly move to the south in 180 s. Afterthe reference window in 190 s, source locations are estimated at near the ground surface around Motoshirane.
For evaluating the reliability of source location estimates,
Fig. 8 shows a representative example of RES values inhorizontal and vertical cross-sections of a time windowof 120 s (Fig. 7). If we set an error range of 0.1 > RES , therange corresponds − 110–130 m in the NS directionand − 50–60 m in the EW direction in horizontal crosssection. On the other hand, the error range in verticalcross-sections is − 200–300 m. One possible explanationof relatively poor constraint on source elevation may bederived from the station configuration, which is not ideal.

Page 8 of 15abFig. 7 a Locations of estimated tremor source. The color of each plotcorresponds to a contour bar in b. Gray dots also show epicentersof volcanic earthquakes in Fig. 2. A plot with a green square is the
fixed source location for the reference time window. b Analyzed
filtered (5–20 Hz) vertical ground velocity at KSW, estimated source
elevations, and the minimum RES value of each time window. The
origin time of the horizontal axis is 09:59:00

Therefore, inferred tremor source elevations may include
potential errors with several hundred meters.
We also conduct the ASL analysis with different S-wave
velocity and Q values to assess how our ASL locations areaffected by the related elastic media parameters. If we
adopt β as 1.43 km/s from Ida et al. (1989), the ASL yields
source location at an elevation of 40 m higher and 70 m
south compared to the result in the window of 120 s in

Yamada et al. Earth, Planets and Space(2021) 73:14

Page 9 of 15

Fig. 8 Spatial distributions of RES values in map and cross-sections through the point (star) with the minimum RES value for a time window of 120 s
in Fig. 7. Red plots represent vent locations of the 2018 eruptions

Fig. 8. Adopting different Q values (40 and 60) changes
inferred source locations for ± 30 m. Therefore, we conclude that the location dependence on the elastic media
parameters is limited.
To validate our determination of Si at each station, we
also conduct the ASL with Si obtained with the codanormalization method with different station configuration (Additional file 1: Fig. S2). Additional file 1: Table S2compares estimated Si values with different methods.
A higher amplification at KSHA, where the sensor isinstalled on the surface, is recognized in both results.
However, a considerable disagreement is that the largest
Si is obtained at KSW among the borehole-type stations
if we fix the reference source at the surface of the vent(Fig. 6b), while Si at KSW is the lowest by the coda-normalization method (Additional file 1: Table S2). A similar
disagreement has been reported by Walsh et al. (2017),
who examined the difference of Si estimates between thecoda-normalization and active seismic source experiment results. Although Si values potentially include such
uncertainties in estimation methods, the horizontal
tremor locations in Additional file 1: Fig. S2 are identicalto those of the main result in Fig. 7. Therefore, we consider our ASL approach to be robust.

Discussion
Interpretation for examined volcanic tremor and eruption
process

Seismic signals in the 5–20 Hz band are usually excitedby VT events with faulting mechanisms at active volcanoes (Nishimura and Iguchi, 2011). Figure 4b shows theconsiderable agreement of frequency content between
the examined volcanic tremor and a VT event after the
eruption (10:14). We interpret the 5–20 Hz band seismic signals of volcanic tremor as a superposition ofsmall shear fractures based on this frequency content

Yamada et al. Earth, Planets and Space(2021) 73:14

correspondence. Voight (1988) proposed the material Failure Forecast Model (FFM) that explains accelerating precursory signals of volcanic eruptions with ageneral material failure law. Following the approach ofChardot et al. (2015), we conduct the FFM analysis forthe 5–20 Hz signals at KSW to assess our interpretation. Additional file 2 : Fig. S3 shows the FFM predicts
the failure in 10:00:10–10:01:00. Although this predictionis ~ 2 min earlier than the onset of the eruption (10:02:09),
it can be interpreted alternatively as the time of the subsurface cap-rock failure: when the cap-rock layer is nolonger capable of restricting the over-pressurized fluid
and proceeds to the vertical fluid migration. The predicted timing relates to the beginning of inflation (Fig. 3).
Sudden expansion by boiling of the migrated hydrothermal fluid is a plausible scenario for inducing the grounddeformation preceding phreatic eruptions. We estimate
fluid advection velocity is about ~ 10 m/s by considering
the lapse time (~ 2 min) and the tremor source–vent distance (1.1–1.4 km). This estimate is almost comparablewith VT hypocenter migration (~ 5 m/s) prior to the 2014
phreatic eruption at Ontake (Kato et al. 2015) and slightly
faster than numerical investigation (0.25–2.9 m/s) at
White island by Jolly et al. (2018).
The hydrothermal fluid behaviors can also excite seismic activity (Chouet 1986; Julian 1994; Rust et al. 2008).
Geysers and fumaroles can excite seismic signals withfrequencies higher than 10 Hz (Motoya and Nogoshi,
1962; Kedar et al. 1996). Hence, a superposition of fault
slips may not be a unique mechanism for the examinedseismic signals. However, seismic wave excitation fromhydrothermal fluid behaviors are often related to signals
with lower frequency ranges, such as < 0.5 Hz (e.g., Fujita
and Ida, 2003; Ohminato, 2006; Maeda et al. 2013). Thissignal range is also similar to another frequency peak
range of observed ground velocity (Fig. 4), which has asignificantly larger amplitude around the eruption onset(Fig. 3b). Hence, we prefer small shear fractures as amechanism of examined tremor signals.
Our ASL method estimates volcanic tremor source
locations at about 500 m east and the depth of 0.5–1 km
beneath the Ainomine cone. This region is slightly east
and within the upper portion of the Ainomine cluster(Fig. 2). Hypocenters of volcanic earthquakes with a different station configuration and seismic velocity modelfrom the present routine of KSVO are reported by Moriet al. (2006). Their epicenter distributions match closelywith tremor locations from our ASL. Note that inferred
tremor locations are relative to the reference point fixedat the surface of the vent. However, the comparison
between our result and the earlier hypocenter distributions by Mori et al. (2006) suggests that the tremor
is related to the seismogenic zone beneath Ainomine.

Page 10 of 15

According to AMT (Nurhasan et al. 2006) and MT
soundings (Matsunaga et al. 2020; Tseng et al. 2020), theeastern flank of Kusatsu-Shirane is covered by a conductive layer with a thickness of 0.3–1 km. Drilling surveys
reveal that the conductive layer consists of smectite with
an impermeable characteristic (Kurasawa 1993). A low
permeable structure, often known as a cap-rock, is essential to seal vertical fluid flow for hosting a hydrothermal
system. The focal depth of volcanic earthquakes can support the sealing structure by the cap-rock beneath Ainomine. Most earthquakes in the Ainomine cluster occur
below an elevation of 1 km (Fig. 2). Pre-eruption conditions may be interpreted as a result of the low flow rate ofhydrothermal fluid into a steady-state impermeable caprock, resulting in intermittent seismic events beneath the
Ainomine. Based on the above signal characteristics andbackground, it is plausible to assume that sudden andunusual hydrothermal fluid injection for the cap-rocklayer beneath Ainomine occurred, which induced thesmall shear fractures in the cap-rock observed as volcanic
tremor (Fig. 9).
Notably, the pre-eruption seismic signals in the
5–20 Hz band are also accompanied by the rapid inflation of Motoshirane. This correspondence is also widelyreported associated with phreatic eruptions and unrests
at other volcanoes, such as Kuchinoerabujima (Tameguri
et al. 2016; Nakamichi et al. 2016), Ontake (Kato et al.
2015), Hakone (Honda et al. 2018; Yukutake et al. 2018),
Meakan-dake (Aoyama and Oshima 2008, 2015), andHokkaido-Komagatake (Usu Volcano Observatory, 1997).
In volcanic settings, sudden ground inflation is often
inferred as an expansion of super-heated undergroundwater, which may trigger phreatic eruptions (Maeda et al.
2017; Stix and de Moor 2018). Himematsu et al. (2020)detected syn-eruptive and post-eruptive subsidence ofMotoshirane with Synthetic Aperture Radar (SAR) data.
Although we do not estimate the deformation source
location with tilt records in this paper, ground deformation obtained with the spaceborne observation by
Himematsu et al. (2020) suggests that the deformation
source beneath the Motoshirane induced the tilt change.
Therefore, a possible scenario to explain observed volcanic tremor and ground inflation is that injected hydrothermal fluid beneath Ainomine migrated laterally at
a certain depth and induced the deformation beneath
Motoshirane (Fig. 9).

Implications for the hydrothermal system structureof Kusatsu‑Shirane volcano
The Yugama and Ainomine clusters of volcanic earthquakes have been recognized from seismic observation in
the 1980s (Ida et al. 1989). Ida et al. (1989) interpreted
that the Ainomine cluster can be a part of the fluidYamada et al. Earth, Planets and Space(2021) 73:14

Page 11 of 15

Fig. 9 Schematic illustration of the interpretation for examined volcanic tremor in the 2018 eruption and related hydrothermal system structure at
Kusatsu-Shirane volcano. Hydrothermal fluid ascends (gray broken arrows) beneath Shirane and Ainomine induces surrounding seismic activity. A
red arrow corresponds to inferred hydrothermal fluid injection to the cap-rock layer and that induced volcanic tremor

pathway from source to beneath Yugama, considering the
focal depth differences. Along with seismic records of the2018 eruption, we also apply the same ASL analysis to the2011 unrest (Additional file 3: Fig. S4). Inferred tremor
locations are almost identical to that of the 2018 eruption, which does not overlap the Yugama cluster. This
coincidence of tremor locations suggests that the 2011
unrest shared the same triggering mechanism as the 2018
eruption. The relation between both seismic clusters canbe discussed from the time-series of seismicity. Figure 2
shows no evident changes in the Yugama cluster related

to the 2018 eruption at Motoshirane. The Ainomine
cluster also shows no response to the series of unrest at
the Yugama since April 2018. Therefore, it is reasonable
to consider that both seismic clusters reflect independent hydrothermal fluid pathways from the fluid reservoir(Fig. 9). Such a perspective can contribute evaluation of
volcanic activity at Kusatsu-Shirane.
Our result reveals a major triggering system of the2018 eruption was located not beneath Motoshirane but Ainomine. Low seismicity beneath Motoshirane in the time window in Fig. 2b is consistent with

Yamada et al. Earth, Planets and Space(2021) 73:14

our interpretation. However, the present study does
not show the nature of the lateral distance between
the vent (Motoshirane) and triggering hydrothermal
fluid injection (beneath Ainomine). We are also blind
regarding the conditions beneath the Motoshirane
before the eruption, because a few ground observations
had focused on the Motoshirane cone. Previous studies at Kusatsu-Shirane have revealed some fundamental aspects of the hydrothermal system focusing on the
Shirane cone. Further investigations and continuous
observation targeting Motoshirane, as well as Shirane,
will contribute to better understanding of the hydrothermal system beneath the Kusatsu-Shirane volcano.

Conclusion
We have examined seismic, tilt, and infrasound records
accompanying the 2018 phreatic eruption at KusatsuShirane volcano to understand the source process of aunique eruption after a long dormancy of the Motoshirane cone. The onset of eruption is preceded by ~ 2 minof volcanic tremor and tilt deformation. The broadspectrum of volcanic tremor is consistent with continuous microfractures accompanied by rapid inflation. We
applied the ASL method for the 5–20 Hz band signals
to determine tremor source locations. Our analysis
estimates the tremor sources at 1 km north from Motoshirane and at a depth of 0.5–1 km from the surface.
Inferred source locations correspond to an impermeable cap-rock layer and an upper portion of the seismogenic region beneath the Ainomine cone. Seismic
signals in the 5–20 Hz band correspond to dominantsignals accompanying VT events with faulting mechanism. Therefore, we interpret that the examined volcanic tremor reflects small shear fractures induced bysudden hydrothermal fluid injection for the cap-rocklayer, which triggered the 2018 eruption. This interpretation is supported by the FFM adopting the volcanic
tremor signals, which predicts consistent the cap-rockfailure timing. Our investigation also suggests that thehydrothermal fluid injection occurred at an independent pathway from beneath the Shirane cone, which has
driven a series of unrest at Yugama since 2014.
Supplementary Information
The online version contains supplementary material available at https​://doi.
org/10.1186/s4062​3-020-01349​-1.
Additional file 1. Site amplification factor estimates by the coda-normalization method and corresponding ASL result.
Additional file 2. The FFM application for the 5-20 Hz band ground velocity at KSW.
Additional file 3. ASL result for the 2011 unrest at Kusatsu-Shirane.

Page 12 of 15

Abbreviations
ASL: Amplitude Source Location; KSVO: Kusatsu-Shirane Volcano Observatory;
NIED: National Research Institute for Earth Science and Disaster Resilience;
JMA: Japan Meteorological Agency; VT: Volcano Tectonic; LP: Long Period; JST:Japan Standard Time; RMS: Root-Mean-Squared; GSI: The Geospatial Information Authority of Japan; AMT: Audio-magnetotelluric; MT: Magnetotelluric;
SNR: Signal-to-noise ratio.
Acknowledgments
Staffs of KSVO are grateful for their contribution to the continuous operation of the observation network. Discussions with Profs. Junichi Hirabayashi
and Masato Iguchi were valuable for giving interpretation for our result.
Suggestions by Dr. Mie Ichihara improve the ASL analysis. JMA and GSI are
acknowledged for providing observation data and digital elevation map
at Kusatsu-Shirane volcano. Details of instruments information of JMA wasprovided by Dr. Ryohei Kawaguchi in Meteorological Research Institute, JMA.
Constructive comments and suggestions by two anonymous reviewers andeditorial handling by A.D. Jolly improved the quality of the manuscript.
Authors’ contributions
TY and AK conducted the ASL analysis for seismic data. AT, WK, and YO arein charge of continuous observation of KSVO. HA and TO have assisted theobservation and data processing of KSVO. HU, TT, and TY are involved in operating of V-net at Kusatsu-Shirane. The initial outlines of this study have been
discussed by TY, AT, and HA. All authors read and approved the manuscript.
Funding
The present study was partly supported by the Ministry of Education, Culture,
Sports, Science, and Technology (MEXT) of Japan, under its Earthquake andVolcano Hazards Observation and Research Program, Integrated Program for
Next Generation Volcano Research and Human Resource Development.
Availability of data and materials
Seismic and tilt data from NIED and JMA can be downloaded from https​://www.vnet.bosai​.go.jp.
Ethics approval and consent to participate
Not applicable.
Competing interests
All authors have no competing interests.
Author details
1
Disaster Prevention Research Institute, Sakurajima Volcano Research Center,
Kyoto University, 1722‑19 Sakurajima‑Yokoyama, Kagoshima 891‑1419, Japan.
2
National Research Institute for Earth Science and Disaster Resilience, Tennodai 3‑1, Tsukuba, Ibaraki 305‑0006, Japan. 3 Volcanic Fluid Research Center,
School of Science, Tokyo Institute of Technology, 2‑12‑1 Ookayama, Meguro,
Tokyo 152‑8551, Japan. 4 Institute of Seismology and Volcanology, Facultyof Science, Hokkaido University, N10W8, Kita‑ku, Sapporo, Hokkaido 060‑0810,
Japan. 5 Aso Volcanological Laboratory, Kyoto University, 3028 Sakanashi,
Ichinomiya‑machi, Aso, Kumamoto 869‑2611, Japan.
Received: 9 September 2020 Accepted: 24 December 2020

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