PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Estimation of infrasound source positions using multipoint
observation
Zhenglie CUI1, Ryosuke SASAHARA1, Ryouichi NISHIMURA2, Yôiti SUZUKI1
1
2

Research Institute of Electrical Communication, Tohoku University, Japan

Resilient ICT Research Center, National Institute of Information and Communications Technology, Japan

ABSTRACTInfrasound can be excited by global phenomena such as tsunamis and volcanic eruptions. Because infrasoundtravels significantly faster in the atmosphere than tsunami gravity waves, early detection of tsunamis can beachieved by monitoring the infrasound. To accurately identify a tsunami source (i.e., the position of theinfrasound source), using numerous sensors distributed with long baselines is important. We developed alow-cost infrasound observation device using micro-electro-mechanical systems-based atmospheric pressure
sensors. To test its usability, indoor and outdoor tests were conducted using multipoint observations. In theindoor experiments, the infrasound was excited by opening and closing a door and by bursting a large balloon.
The accuracy of time synchronization was examined by setting the infrasound observation devices acrossdifferent points in a room. The results demonstrated that the errors in time synchronization are tolerable. In
the outdoor field test, the infrasound excited by fireworks during a festival was observed at multiple points in
Sendai, Japan. Furthermore, as a large-scale infrasound observation experiment, we measured the infrasoundexcited by volcanic eruptions in Sakurajima, Japan. The results showed that the source position can be
estimated with reasonable accuracy for an infrasound with an amplitude of over 5 Pa (waveform noise).
This suggests the potential suitability of the device in estimating the source position of tsunami-scale
earthquakes.
Keywords: Infrasound, Atmospheric pressure sensor, Source position estimation

1. INTRODUCTION
Large-scale disasters such as strong earthquakes, huge oceanic gravity waves (tsunamis), andvolcanic eruptions often generate sound waves, the frequency spectra of which have components
below the human audible frequency range (i.e., below 20 Hz) [1]-[3]. Such a low-frequency sound iscalled infrasound, which is analogous to infrared radiation emitted in the form of low-frequency
light. Infrasound travels at approximately the speed of sound, i.e., in the range of 310–343 m/s, at
20°C in the atmosphere, whereas a tsunami wave travels at a speed of 221 m/s at an oceanic depth of5000 m along the ocean surface [4], and its speed reduces as it approaches the coast because the speed
is proportional to the square root of the ocean depth. Consequently, the infrasound waves generated bytsunamis will reach the coast earlier than the tsunami waves. Furthermore, the infrasound generated
by tsunamis provides information on the shape and wavelength of the tsunami waves [5][6].
As infrasound in atmosphere travels significantly faster than tsunamis, early detection of tsunamis
can be achieved by monitoring the infrasound [7][8]. To accurately identify the source positions oftsunamis as those of infrasound sources, installing numerous sensors distributed along the coast withlong baselines is necessary. However, constructing new infrasound monitoring networks will be
expensive since traditional infrasound sensors (micro-barometers) are expensive.
Therefore, in our previous study [9], we developed a small infrasound observation device using a
low-cost atmospheric pressure sensor (BME280 BOSCH) based on micro-electric mechanicalsystems (MEMS) technology and a microcomputer (Raspberry Pi 3, Raspberry Pi Fund). A field test
was carried out to evaluate the usability of the device by deploying it 13 km away from an active
volcano (Sakurajima in Japan). The results showed that the low-cost device can be used to observe1
2

sai@ais.riec.tohoku.ac.jp, yoh@riec.tohoku.ac.jp
ryou@nict.go.jp

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Figure 1 – New infrasound observation device comprising eight MEMS pressure sensors(a)(b)
Figure 2 – Noise test waveform results of (a) old device and (b) new device
infrasound waves at frequencies down to 0.01 Hz, similar to how a conventional micro-barometer isused. Moreover, it enables the detection of a signal with a peak-to-peak amplitude of around if thesignal level is sufficiently high, e.g., more than 10 Pa.
However, the noise level and thus the error were much higher in the higher frequency range.
Therefore, in the present study, to improve the signal-to-noise ratio (S/N), eight MEMS pressure
sensors were stacked on a single board to assemble a new infrasound monitoring device. To test its
performance, indoor and outdoor experiments were conducted using multipoint observations. In thisarticle, we introduce the outline of the new device and report the experimental results.

2. NEW INFRASOUND OBSERVATION DEVICE
Figure 1 shows the external appearance of the new infrasound observation device, which consistsof eight MEMS barometric pressure sensors (BME 280, BOSCH), a microcomputer (Raspberry Pi,
Raspberry Pi Fund), and a GPS base. The device uses the eight MEMS sensors to improve the SN
ratio by averaging the eight output signals. To make the device compact, the modules were designed
to fit in a case of dimensions 12 cm (width) × 12 cm (depth) × 10 cm (height). For the measurement,
after synchronizing the time with GPS, an infrasound is recorded as the temporal change in theatmospheric pressure at a sampling rate ranging from 1 to 50 Hz with 16-bit quantization.
Figure 2 shows the results of the noise test waveform obtained using the device at a sampling rateof 50 Hz. Compared with a previous test waveform based on only one MEMS pressure sensor [9],the waveform noise is reduced by approximately 10 dB (low-pass filter of 256 order and a cutoff
frequency of 20 Hz). The results show that the new device enables the detection of a signal with apeak-to-peak amplitude in the range of approximately 5–6 Pa. This means that it is possible to sense
infrasound if the peak-to-peak sound pressure level is approximately 108 dB or more.

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Figure 3 – Comparison of infrasound waveforms(Comparison of infrasound waveforms generated
by door opening/closing between two devices)

Figure 4 – Experiment environment(Comparison of time waveforms between
two devices)

3. EXPERIMENT 1: INDOOR EXPERIMENTS
3.1 Verification of time synchronization
The time synchronization between the devices was tested using two devices when the infrasoundwas generated. The experiment was conducted in a soundproof room at the Research Institute ofElectrical Communication, Tohoku University. The infrasound was excited by opening and closing adoor to change the atmospheric pressure in the room. The device was set adjacent on a desk placed in
the soundproof room, and the measurement was conducted by opening and closing the door twice in
10 s.
The DC component was removed by subtracting the average value of the signal. The timedifference between the sensors was calculated from the phase restriction correlation after the
resampling was carried out at 100 Hz (the original sampling frequency was 50 Hz). As a result, thetime difference ranged from a minimum of 3 ms to a maximum of 152 ms. Figure 3 shows theinfrasound waveforms when the time difference is maximum. This confirms an accuracy of less than
one sample point between two devices.
3.2 Estimation of distance between devices
To accurately estimate the sound source position, the infrasound signals should be recorded withcorrect time differences between the devices. Therefore, we checked the arrival time differences of
the infrasound when opening and closing the door using the multiple observation devices andestimated the distance differences between these devices. As shown in Figure 4, 14 devices are
distributed in line at intervals of 1 or 2 m in a quiet room at the Research Institute of ElectricalCommunication, Tohoku University (room size is 22.0 m × 9.7 m). As the infrasound generated by
opening and closing the room door did not exhibit clear peaks, in this experiment, a pulsive
infrasound was generated by bursting a large balloon (90 cm in diameter at the inflated state).
The peak signal was confirmed by bursting large balloons in multiple devices. The speed of soundwas calculated using the relationship 331.5 + 0.6 t [m/s], where t is the temperature in degree
Celsius; the temperature was 16°C in the experiment. The distance between the observation devices
was estimated from the arrival time difference between two of the 14 devices. As the sampling
timings of each device are different, the difference in the arriving times was calculated by
performing resampling at 100 Hz, and the phase difference of each peak signal was then calculated.
Table 1 lists the experimental results. For an actual interval of 17 m between M1 and M14, theestimated distance was 14.6 m as the arrival time difference was 0.04 s, resulting in an error of 2.4 m.
The results showed that the time errors are tolerable because these devices are used at a sampling
rate of 50 Hz. On the contrary, the estimated distance between M1 and M11 showed a large error of
49.8 m. This might be an artifact due to the reflected waves in the room and/or asynchronization due
to the lack of GPS radio waves; this is a subject for future study. Except for this case, the distances
between the devices could be acquired with acceptable accuracy when the distance between thedevices is 15 m or more. Therefore, the device exhibits an acceptable accuracy for sensing
large-scale natural disasters such as tsunamis and volcanic eruptions.

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Table 1 – Examples of estimation results for distance between devices
ActualComparison data
Arrival time
Estimated distance
Error (m)interval (m)(Device number)difference (s)(m)
M1-M14
M1-M13
M1-M11
M6-M13
M7-M12

4.3 × 10–2
5.3 × 10–2
17.8 × 10–2
3.9 × 10–2
2.0 × 10–2

14.6
15.8
60.8
13.2
6.8

17
15
11
10
7

–2.4
+0.8
+49.8
+3.2
–0.2

4. EXPERIMENT 2: OUTDOOR FIELD TEST
4.1 Observation of infrasound due to explosion of fireworks
Using the 18 new devices, we measured the infrasound excited by the explosion of fireworks during
a festival at multiple points in Sendai, Japan, from 19:00 to 20:30 on August 5, 2018. As shown in
Figure 5, two devices are set at each of the nine observation points from A to I around the fireworks
venue. In the figure, the observation point is indicated by two-dimensional coordinates, the origin of
which is the fireworks launch site. Table 2 lists the distance coordinate values of the nine points.
Figure 6 shows the observation results of the two devices C1 and C2 set at the measurement point
C, as an example of the experimental results. The most significant peaks are observed at around
19:24:30 at point C, as shown in Fig. 6. Similar peak signals are also observed at both points D and
E. Moreover, this peak signal, which was judged as an infrasound generated by an explosion of afirework, could not be observed at points other than C, D, and E.
The source position was thus estimated using the arrival time difference between each device,using only six data points from the observation points C, D, and E, where the peak could be observed.
Specifically, with Equation (1), which was used in a previous study [10], the sound source position(u, v, d) was estimated using the sound source position (u, v), distance d (distance from the soundsource to the shortest observation point), and sound speed c. In Equation (1), T ij (i, j = 1-6) indicatesthe peak time difference between devices i and j, and (X i, Y j ) indicates each observation point. As aresult, the coordinate (u, v) was found to be (–288, –92), located 290 m away from the actual
fireworks launch position. This error may be attributable to the two-dimensional estimation while athree-dimensional sound source estimation would be necessary to obtain better estimates for the
explosion points in the air a few hundred meters above the ground.

Figure 5 – Position of nine observation points A–I (the triangles represent the launch position, andthe numerical values represent the distance from the launch point [m]Table 2 – Position of observation points

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Measurement
point

Coordinates
(m)

A
B
C
D
E

(–336, 99)(–214, 144)(–208, –48)(–191, –114)(–276, –191)

Distance from
Measurement Coordinatessound source (m)
point
(m)
350
258
207
222
336

F
G
H
I

(–138, –250)(403, –254)(345, –370)(176, 87)

Distance fromlaunch point (m)
286
476
506
196

Figure 6 – Observation results for the two devices C1 and C2

4.2 Observation of infrasound due to volcanic eruptions
As a large-scale infrasound observation experiment, we measured the infrasound excited byvolcanic eruptions in Sakurajima, Japan. Sakurajima is an active volcano located in the southern partof Kyushu Island, Japan, and it is the best infrasound observation environment in Japan because it
erupts frequently.
As shown in Figure 7, four devices are set at each of the four observation points (A to D) aroundthe Sakurajima area. The Japan Meteorological Agency announced that an explosive eruption wasobserved at 18:27 on January 9, 2019, in Sakurajima. Figure 8 shows the observation results of thefour devices near the time when the eruption was reported. The figure was obtained by subtracting
the DC component for easy comparison.
The most significant peaks were observed with an amplitude in the range of approximately 30–
40 Pa in all the devices. This peak signal could be judged as infrasound generated by the volcanic
eruptions. For each data, after passing a finite impulse response low-pass filter of order 256 and acutoff frequency of 20 Hz, only the frequency range below 20 Hz (the bandwidth of infrasound) was
extracted. Thereafter, the arrival time difference between each device was estimated using thephase-limited correlation method, and the sound source position was estimated. As in the experimenton estimating the sound source position in the fireworks case, the sound source position (u, v) was
calculated using Equation (1). As a result, the coordinate (u, v) was found to be (5412, -1012),
located approximately 93 m away from the actual sound position.

Figure 7 –Position of four observation points

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Figure 8 – Observation results for four devices
In this experiment, all devices could detect the infrasound, unlike that in the fireworks
experiment, in which the peak signal amplitude was low, and the separation of noise and infrasoundwas an issue. The error in the infrasound source localization was found to be 93 m. Nevertheless,considering that volcanic eruptions have a scale of several thousand meters, the estimation of thesound source position is acceptably accurate. This suggests that estimating the sound source position
in a real environment is possible for an infrasound with an amplitude of over 5 Pa (waveform noise).

5. CONCLUSIONSIn this study, we developed a new infrasound observation device stacked with eight low-cost
MEMS atmospheric pressure sensors to improve the signal-to-noise ratio. We conducted multipoint
observation experiments with indoor and outdoor field tests. In the indoor experiments, the accuracyof time synchronization was examined by positioning the infrasound observation devices at different
points in a room. The results showed that the errors are within tolerable values.
In the outdoor field test, the infrasound excited by the explosion of fireworks during a festival
was observed at multiple points in Sendai, Japan. The results showed that the source positions can be
estimated with reasonable accuracy; however, a three-dimensional sound source estimation would benecessary to obtain better estimates. Furthermore, as a large-scale infrasound observation experiment,
we measured the infrasound excited by the volcanic eruptions in Sakurajima, Japan. The results
demonstrated that the source position can be estimated with reasonable accuracy for an infrasoundwith an amplitude of over 5 Pa (waveform noise). These results suggest the usability of the device inestimating the source position of tsunami-scale earthquakes.

ACKNOWLEDGMENTS
A part of this study was supported by the Ministry of Internal Affairs and Communications SCOPE(17150200).

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