Inazu et al. Progress in Earth and Planetary Science (2018) 5:38
https://doi.org/10.1186/s40645-018-0194-5

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open AccessMeasuring offshore tsunami currents using
ship navigation records
Daisuke Inazu1* , Tsuyoshi Ikeya1, Takuji Waseda2,3, Toshiyuki Hibiya2,4 and Yoshinori Shigihara5

Abstract
We investigated ship navigation records known as Automatic Identification System (AIS) data near the source
region of the 2011 Tohoku, Japan, tsunami. The AIS data of 16 ships in the offshore navigation could be compiled
by about 40 min after the tsunami generation. Most of the AIS data showed notable deviation of the ship headingfrom the course over ground during the tsunami passage. There was good agreement in terms of amplitude/phasebetween the ship velocity and the simulated tsunami velocity in the direction normal to the ship heading. An
equation of motion due to wave drag and inertia forces was examined for an offshore movable floating body. We
explain that the ship movement in the direction normal to the heading immediately responds to the tsunami
current, and relative velocity between the ship and the tsunami current asymptotically become zero. This indicatesthe movement velocity of navigating ships in the direction normal to the heading derived from AIS data will work
as an offshore tsunami current meter. We examined the AIS data during the 2011 Tohoku tsunami and showedthese data could be useful for tsunami source estimation and forecast. The AIS data in the current framework will
possibly be a crowd-sourced tool for monitoring offshore tsunami current and tsunami forecast.
Keywords: Tsunami current, Automatic identification system (AIS), Navigating ship

Introduction
Great earthquakes and tsunamis have caused significant
loss of life and property in coastal communities (ITIC2015). In the 2004 Sumatra-Andaman, Indonesia, earthquake and the 2011 Tohoku, Japan, earthquake, most casualties (200,000 for the 2004 disaster and 20,000 for the2011 disaster) were caused by the flooding due to the tsunamis (Satake 2014). Coastal mega cities have to prepare to
reduce these losses especially from the tsunamis. In
addition to constructing proper embankments in advanceof earthquake and tsunami occurrences (Tomita et al. 2012;
Suppasri et al. 2013; Raby et al. 2015), immediate actions ofindividuals and groups after the earthquake/tsunami occurrences are critical for reducing significant, early-stage lossof life (Mas et al. 2013; Makinoshima et al. 2016).
Real-time forecasting of great earthquakes and tsunamis
can be utilized for suitable decision-making for disaster mitigation. Real-time forecast systems should be robust andreliable. Current systems are based on real-time Earth* Correspondence: inazud@m.kaiyodai.ac.jp
1
Department of Marine Resources and Energy, Tokyo University of Marine
Science and Technology, 4-5-7 Konan, Minato, Tokyo 108-8477, Japan
Full list of author information is available at the end of the article

monitoring that uses onshore and offshore observations.
These observations include seismic waves (Allen et al. 2009;
Kamigaichi 2009), crustal deformation (Sagiya 2004; Ruhl
et al. 2017), and offshore sea level or the tsunami itself (Falck
et al. 2010; Kawai et al. 2013; Rabinovich and Eblé 2015).
Rapid earthquake-source estimation systems using
seismic-wave observations are popular. However, thesemethods estimate a point source that may be indirectly
related to tsunamis, and are somehow difficult toaccurately constrain the tsunami source dimension particularly for great earthquakes (Katsumata et al. 2013;
Hoshiba and Ozaki 2014). Earthquake-source estimation
using Global Positioning System/Global Navigation
Satellite System (GPS/GNSS) observations of land deformation will suitably constrain the source dimension
with moment magnitude of nearshore (< 100 km fromthe coast) earthquakes (Melgar and Bock 2015;
Kawamoto et al. 2017). For greater earthquakes (Mw >8.5), in addition to the fault slip, seafloor failure due tostrong ground motion likely causes a certain portion oftsunami (Kawamura et al. 2014; Løvholt et al. 2015). It isbetter to use direct observations of offshore sea level toestimate the resultant tsunami size.

© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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Inazu et al. Progress in Earth and Planetary Science (2018) 5:38

Real-time forecasting of great tsunamis based on offshore tsunami observations has been popular. The tsunami forecast system using the Deep-ocean Assessment
and Reporting of Tsunamis (DART) buoys is well known(Wei et al. 2013; Tang et al. 2016). The Japan Meteorological Agency (JMA) developed a tsunami forecast system using cabled seafloor pressure and GPS-buoy
observations (Tsushima et al. 2011, 2012). These forecast
systems will be enhanced using real-time geodetic monitoring as well (Tsushima et al. 2014; Wei et al. 2014).
Forecast methods using very dense seafloor observation
networks have been also proposed independently (Maeda
et al. 2015; Igarashi et al. 2016; Yamamoto et al. 2016).
These forecast systems using offshore tsunami observations will be robust and reliable.
The observatories have to be maintained and sustained atleast until the next devastating tsunami. Such an event may
occur once typically during tens to hundreds of years at a
certain place (McCaffrey 2008; Rong et al. 2014). In general,
offshore observatories require higher maintenance costs
than onshore observatories, and replacements will be necessary after endurance periods, typically several decades after
installation (Hirata et al. 2009). Tsunami forecast methods
especially measurement systems need sustainability as well
as reliability (NRC 2011; Bernard and Titov 2015). It is desirable to propose and develop other sustainable methods ofmonitoring and forecasting of great tsunamis.
Tsunami as sea-level change could be detected by shipborne GPS. A 10 cm tsunami caused by the 2010 Maule,
Chile, earthquake was detected by a vertical position change
derived from high-precision GPS of a navigating ship
(Foster et al. 2012). This indicates that navigating ship networks will serve as offshore tsunami observation networks(Inazu et al. 2016). However, this requires high-precision
GPS facilities on most of the vessels to precisely measure
their positions as well as GPS buoys (Terada et al. 2015).
On the other hand, ship positions are currently aggregatedas the Automatic Identification System (AIS) data underInternational Maritime Organization (IMO) regulations(IMO 2002; Earles et al. 2010). The ship positions in the
AIS data do not include vertical position, and they aremostly measured by ordinary GPS with meter-order precision which is employed in ordinary car navigation. Therefore, if we employ ship GPS/AIS data to measure tsunami
height, it is necessary to update current facilities so as to estimate the high-precision locations of almost all of the vessels and modify the regulations of the IMO.
During the 2011 Tohoku tsunami, a number of vessels
in ports drifted significantly and collisions occurred due
to strong tsunami currents (Suga et al. 2013; Suppasri
et al. 2014; Matsuda and Tomita 2015). The behaviors of
the ship horizontal drifts with collision in a port were
examined using AIS data with tsunami simulation(Matsuda et al. 2012). The ship drifts should be affected

Page 2 of 11

by tsunami currents; however, the relationship between
them still seems unclear due to a complicated situationin the port. On the other hand, AIS data were also available in offshore regions during the tsunami. It was reported that movement directions of several offshore
ships deviated during the tsunami (Makino 2013; Liuet al. 2015). However, these reports merely showed thefacts and hardly described any quantitative relationshipbetween the deviations and the tsunami. There is stillmuch room to be clarified regarding the expected relationship between ship drifts and tsunamis.
In the present study, we examine the AIS records ofmultiple ships navigating near the source region of the2011 Tohoku tsunami. The relationship is investigatedquantitatively between the ship horizontal movement ordrift and the tsunami current. We describe the usefulnessof the AIS data to measure offshore tsunami currents andto be applied to tsunami forecast.

AIS data
We can browse almost real-time ship distributions derived
from AIS data on web sites such as MarineTraffic (https://www.marinetraffic.com/). According to the IMO
regulations, ships exceeding 300 gross tonnage and all passenger ships have to send their AIS information via
very-high-frequency (VHF) radio transmission (Tetley andCalcutt 2001; IMO 2002). The AIS data include dynamic
and static information. The dynamic information includes
latitude/longitude (without vertical height), speed overground (SOG), course over ground (COG), and ship heading (HDG). The static information includes draft, Maritime
Mobile Service Identity (MMSI), ship type, and ship name.
The AIS data from nearshore vessels (< ~ 100 km fromcoast) are received by coastal stations, and those from ships
farther offshore can be also received by low-Earth-orbit satellites such as ORBCOMM (Carson-Jackson 2012). The
number of ships recognized with AIS data is increasing year
by year with increasing seaborne trade and number of satellites (Tournadre 2014; Willick 2014).
Ship distributions during the Tohoku tsunami
The 2011 Tohoku earthquake occurred at 05:46 (UTC) on
11 March 2011, and the resultant tsunami reached thecoasts with significant wave heights (> 10 m) 40–60 min
after the tsunami generation (Tsushima et al. 2011; Weiet al. 2013; Saito et al. 2014). The tsunami currents were expected to be greater than 1 m/s near the coastal areas where
sea depth is shallower than 200 m (Sugawara and Goto2012). Our tsunami simulation also shows such tsunami
heights and currents (Fig. 1). Due to such strong tsunami
currents, as mentioned above, a number of vessels in theports drifted significantly, resulting in collisions and severe
damages to buildings and structures there. On the other
hand, there were actually many offshore vessels navigating

Inazu et al. Progress in Earth and Planetary Science (2018) 5:38

aPage 3 of 11bcFig. 1 a Tsunami source as sea-surface elevation due to the 2011 Tohoku tsunami (Saito et al. 2011). b Simulated maximum tsunami height with
its arrival time (contours in minutes). c Simulated maximum tsunami current velocity with its arrival time (contours in minutes). The tsunami
simulation is based on non-linear long-wave equations (Saito et al. 2014)

near the earthquake/tsunami source. Just after the earthquake occurrence, although most of the AIS data offshoreof Miyagi were lost because coastal stations failed to receive
the AIS data probably due to near-source strong groundmotion, some data were obtained until the tsunami devastated the coast (Fig. 2). The offshore navigating ships probably experienced tsunami currents of around 1 m/s (Fig. 1).

Ship drifting and tsunami current
AIS records

It was found that AIS messages were successfully received from 16 vessels in offshore regions (depth > ~100 m) during about 40 min after the earthquake occurrence. These ships were typical commercial ships suchas cargo ships and tankers. In the present study, the AIS
data of 13 vessels (#1–#13) were used for the tsunami
analysis, and 3 other vessels (*1–*3) were not used due
to data quality issues (Fig. 2 and Table 1).
We examined the speed over ground (SOG), thecourse over ground (COG), and the heading (HDG)of the offshore ships (Fig. 3). COG is the direction oftrue ship movement. HDG denotes the direction ofship heading that is measured by an ordinary magnetic/gyro compass. SOG and COG are measured by
the time evolution of ordinary GNSS positioning.
Though COG and HDG are expected to be consistentunder normal weather/ocean conditions, it was reported that there were significant deviations between

COG and HDG during this tsunami as mentioned
above (Makino 2013; Liu et al. 2015).
We show examples of time evolution of AIS records ofnavigating ships (Fig. 4). One ship offshore of Iwate (#3)
was moving northward (HDG ~ 10°) before and after theearthquake occurrence. Just after the earthquake, thecaptain may have reduced the ship velocity (from 6.8 to
5.7 m/s) probably because of receiving an early tsunami
warning. Although there was no significant deviation
between COG and HDG until 15 min after the earthquake, there was significant deviation (> 5°) after that.
Another ship offshore of Fukushima (#5) was moving
southward (HDG ~ 185°). While this ship was going withconstant speed (~ 6.0 m/s) during this earthquake andtsunami, the deviation between COG and HDG was evident (> 5°) after 20 min after the earthquake. Other examples that were used or not for the analysis are shown
in Additional file 1: Figure S1.
We investigate the relationship between the deviation and the tsunami current. Ship propulsion duringordinary offshore navigation is imposed mostly in the
HDG direction. The deviation between COG and
HDG is reasonably caused by the normal componentof external force against the HDG. Here, ship velocity
and tsunami current are examined in the direction
normal to the HDG (Fig. 3).
The ship velocity component in the direction normal
to the HDG is:Inazu et al. Progress in Earth and Planetary Science (2018) 5:38

aPage 4 of 11bFig. 2 Ship distributions derived from AIS data during the 2011 Tohoku
tsunami. Distributions at a 0 min, b 2 min, c 24 min, and d 45 min after
the earthquake occurrence are shown. In c and d, the right panel
enlarges the rectangular area in the left panel, and blue lines trace ship
tracks of 13 offshore vessels during 0–45 min after the earthquake
occurrence (Table 1). Dashed isobaths of 100 and 500 m are addedv ¼ SOG sinðCOG−HDGÞ:

c1

c2

ð1Þ

The tsunami current component in the HDG-normal direction is calculated at respective ships by using a tsunami
simulation (Saito et al. 2014) with a validated tsunami
source (Saito et al. 2011; Baba et al. 2017). We confirm notably good agreement between the ship velocity and the tsunami current in the HDG-normal direction for almost all
the ships navigating offshore (Fig. 5). The velocity amplitudes are ~ 1 m/s or more and agree within the ship andthe tsunami. Lag time of the velocity between them aremostly shorter than ± 2 min. Thus, these AIS data are aclear proxy for tsunami currents with the equivalent amplitude in the HDG-normal direction.
Measurable tsunami current

We evaluate the relationship between the ship velocity
and the tsunami current (Fig. 5) using an equation of
motion of a movable floating body (Reddy and Swamidas2013). The floating body is the navigating vessel. Since
ship propulsion is likely imposed in the HDG directionin ordinary offshore navigation with autopilot, the equation of motion is applied in the HDG-normal directionin which only the external force works:
m

d1

d2

∂v
¼ FD þ FI;∂t1
F D ¼ C D ρDLjvc −vjðvc −vÞ;2
F I ¼ C M ρDLB

∂vc
∂v
−ðC M −1ÞρDLB :
∂t
∂t

ð2aÞ
ð2bÞ
ð2cÞ

Variables are summarized in Table 2. The driving
forces are the drag force (FD) and the inertia force (FI)(O’Brien and Morison 1952; Dean and Dalrymple 1985).
The ship velocity of Eq. (1), v = SOG sin(COG − HDG),
is derived from observational AIS records (Fig. 5). D, L,
and B are given by static information from AIS data
(Table 1). The equation is often rewritten as (Reddy andSwamidas 2013):
ðm þ m0 Þ∂v 1¼ C D ρDLjvc −vjðvc −vÞ∂t 2∂vcþ C M ρDLB;∂t

m ¼ ρDLB;

ð3aÞ
ð3bÞ

Inazu et al. Progress in Earth and Planetary Science (2018) 5:38

Page 5 of 11

Table 1 Static information of vessels derived from AIS data, except for gross tonnage, which was taken from MarineTraffic(https://www.marinetraffic.com/)
IDa

Vessel type

Gross tonnage

Draught (m)Length (m)Breadth (m)

#1

Cargo

1658

5.0

98.52

15.0

#2

Cargo

499

4.5

74.71

12.0

#3

Tanker2998

6.4

104.81

15.5

#4

Cargo

8566

5.3

119.5

20.4

#5

Tanker

3319

4.5

99.8

15.8

#6

Cargo

14,790

7.5

167

30.2

#7

Tanker

748

4.5

74.99

11.5

#8

Tanker3869

4.8

104.94

16.0

#9

Cargo

12,526

6.7

199

24.5

#10

Tanker

414

N/A

57.29

10.0

#11

Cargo

3692

6.3

115

20.0

#12

N/A

499

4.0

76.2

13.0

#13

Cargo

748

5.0

83

14.0

*1

Cargo

77,871

9.9

273

47.0

Clock of HDG may have an offset

*2

N/A

N/A

4.1

79

14.0

COG = HDG always declared

*3

N/A

9510

5.8

133.16

19.6

Cable-laying vessel, mostly stationary

Note

a

Serial IDs are used in this paper instead of MMSIs to protect vessel privacy

m0 ¼ ðC M −1ÞρDLB:

ð3cÞ

m is the mass of the ship that is equal to the watermass of the submerged portion of the vessel, but this is
not always equal to the declared gross tonnage (Table 1).
m′ is the so-called added mass. Formulations similar toEq. (3) were proposed to evaluate ship motion due totsunami currents in ports and harbors (Ikeya et al.
2006). Here, we obtain:∂v0¼ −λjv0 jv0 ;
∂t

ð4aÞ

v0 ¼ vc −v;

ð4bÞ

λ¼

1 CD 1:
2 CM B

ð4cÞ

This differential equation can be solved as:
v0 ¼

1 1
for v0 > 0;
λ t þ t rþ

ð5aÞ

1 1
for v0 < 0;
λ t þ t r−

ð5bÞ

v0 ¼ −

where tr+ and tr− are arbitrary time constants. We suppose a motionless ship is forced by transient tsunami
current (vc) in the HDG-normal direction, and consider
the response of the ship velocity (v) to the tsunami
current. A proper condition is v′ = vc at t = 0, and v′ = 0
at t = ∞, which determines the constants. Then,

Fig. 3 Schematic of speed over ground (SOG), course over ground(COG), heading (HDG), and HDG-normal components of shipvelocity and tsunami current

v0 ¼

vc
for v0 > 0;
1 þ vc λt

ð6aÞ

v0 ¼

vc
for v0 < 0:
1−vc λt

ð6bÞ

The profile of v′ = vc − v is shown in Fig. 6. It is understandable that ∣vc − v∣ will shrink to zero with an orderof t−1. We estimate λ = 1/40–1/120 m−1 from representative values of CD ~ 1 with CM ~ 2 (Dean and Dalrymple
1985), and B = 10–30 m (Table 1), and consider theInazu et al. Progress in Earth and Planetary Science (2018) 5:38

Page 6 of 11

a

a

b

b

c

c

Fig. 4 a SOG, b COG/HDG, and c COG minus HDG of two selected ships (#3 and #5 in Fig. 2)

tsunami current as vc ~ 1 m/s (Fig. 5). Once T1/2 is thetime that v becomes greater than half of vc (Fig. 6), we
obtain T1/2 = 1/vcλ = 40–120 s, indicating that the response time is effectively less than a few minutes.
An estimation of the ratio of drag force (FD) to inertia
force (FI) known as the Keulegan-Carpenter number(KC) (Keulegan and Carpenter 1958; Dean and Dalrymple 1985) is:K C ¼ vc

T FD
∝
:
D FI

ð7Þ

We obtain a value as large as KC = 103 from the reasonablecondition of vc = 1 m/s (tsunami current), T = 103 s (tsunami
period), and D = 100 m (submerged depth of vessel) (Fig. 5
and Table 1). Tsunamis involve long waves with longer periods than ordinary wind waves. It is reasonable that ships
quickly respond to the drag force due to great tsunamis withsuch amplitudes and time scales (Heo et al. 2015).
Thus, we can understand the result of equivalent amplitude without a significantly biased lag time (less than ±2 min) between the ship and tsunami velocity (Fig. 5).
This interpretation is very useful since Eq. (1) will be agood proxy for measuring tsunami velocity. We may consider that the short lag time (< ~ 120 s) is mostly negligible
compared to the great tsunami periods of 103 s. Then,
SOG sinðCOG−HDGÞ ¼ v ≈ vc :

ð8ÞRegarding this approximation, we need to consider λand vc instead of ship mass for the response time of theship drift to the tsunami current (Eq. (6)). If vc is muchsmaller than 1 m/s, the response time will be effectively
long. But what is important is great tsunamis as much as~ 1 m/s or more. In such severe cases, the response will
be more immediate and the approximation is more robust. If B is larger, the response time will be longer. But alarge portion (~ 80%) of the total number of vessels is occupied by small- and/or medium-sized (< 25,000 gross
tonnage) vessels that typically involve breadths of < ~30 m (EMSA 2017). Thus, most AIS data in the current
framework are already suitable as tsunami current meters,
especially for great tsunamis.

Tsunami inversion/forecast
Once the tsunami velocity can be measured by the ship velocity component in the direction normal to the ship heading, it is worth carrying out inversion for real-time tsunami
forecast to estimate source and tsunami height. Fuji et al.
(2013) examined a tsunami inversion/forecast experimentusing pseudo observation of tsunami current derived fromcoastal ocean radars (Hinata et al. 2011; Lipa et al. 2012;
Benjamin et al. 2016). They employed the tsunami current
instead of tsunami height for the inversion for the tsunami
source. In the present study, we employ tsunami currents
derived from the observational ship drifting (Fig. 5). Green’s
functions are composed by tsunami current components in
the HDG-normal directions of the respective moving vesselby calculating from unit sources. Referring to our previous
study (Inazu et al. 2016), we allocate unit sources with thesame size/spacing and carry out the inversion/forecast.

Inazu et al. Progress in Earth and Planetary Science (2018) 5:38

Page 7 of 11abFig. 5 Observed ship velocity (green) and simulated tsunami current(red) components in the HDG-normal direction. The ship velocity
component is calculated from Eq. (1)Table 2 Notation used in equations
SymbolNotationm

Mass of shipm′

Added massv

Ship velocity component in the HDG-normal directionvc

Tsunami current component in the HDG-normal direction

t

Time

FD

Drag force due to the tsunami current relative to the shipvelocityFI

Inertia force due to water acceleration with respect to theship accelerationCD

Coefficient of drag force

CM

Coefficient of inertia forceρ

Density of seawater

D

Draught of ship

L

Length of ship

B

Breadth of ship

Fig. 6 Time evolution of a the solution of Eq. (6), and b tsunami
velocity (red) and associated response of ship velocity (green). T1/2
is time when v = vc/2

The inversion/forecast is carried out using the AIS
data for 25 min after the earthquake occurrence(Fig. 7). The result can show mostly similar featuresin terms of both the source and the maximum tsunami height at the coast, compared to those from thevalidated source (Fig. 1). This indicates that ship velocity components in the HDG-normal direction ofmultiple ships derived from AIS records will be possibly useful for tsunami source estimation and forecast in addition to conventional tsunami heightobservations at fixed stations.

Summary and remarks
The AIS records of offshore navigating vessels were examined during the 2011 Tohoku tsunami. The AIS records from 13 vessels near the tsunami source showedInazu et al. Progress in Earth and Planetary Science (2018) 5:38

aPage 8 of 11bcFig. 7 Result of inversion using AIS data during 0–25 min after the earthquake occurrence: a observed ship velocity (green) and estimatedtsunami current (red) in the HDG-normal direction, b estimated tsunami source, and c estimated maximum wave height at the coast. In b, dots
are allocated unit sources. Red and gray curves in c are derived from the estimated source and from Fig. 1b, respectively

significant deviation of COG from HDG during the tsunami passage.
We compared the observed ship velocity component tothe simulated tsunami velocity in the direction normal to
the ship heading and confirmed good agreement in termsof amplitude (~ 1 m/s) with short lag times (< ~ 2 min) between them. Using an equation of motion of an offshore
movable floating body, we obtained an analytical solution
and understood that the ships immediately respond to thegreat tsunami current with the equivalent velocity amplitude in the HDG-normal direction.
This study indicates AIS data in the current framework can be used as a tsunami current meter in one direction for each vessel. We also showed that the observed
tsunami velocity derived from the AIS data of multiple
vessels during the 2011 Tohoku tsunami could be usefulfor tsunami source estimation and forecast.
The current AIS data are already provided via almost
real-time successive transmission. The number of ships recognized by the AIS has been increasing year by year
(UNCTAD 2017). We hope to exploit this large amount ofdata and expect the use of AIS data will become a new
method for measuring and forecasting great tsunamis. Similar concepts of diverting information from crowd-sourced
agents to Earth monitoring recently have been also applied
to grasp earthquakes (Kong et al. 2016), winds, and ocean
currents (Miyazawa et al. 2015).
Suitable methods for forecast will need to be developed.
Assimilation/inversion of tsunami wave/current fields using
moving point observations will be a possible way (Fujii and
Satake 2007; Mulia et al. 2017). Such methods will be
adopted also for assimilating wave height fields derived
from satellite altimetry (Hamlington et al. 2012) and GNSS
reflectometry (Stosius et al. 2011; Clarizia et al. 2016).
Efforts to ensure a good quality of AIS data should bemade so that sophisticated forecast methods will work
well (Table 1 and Additional file 1: Figure S1). The minimum requirement is to obtain the right records of the
HDG and the GNSS ship position. SOG and COG arederived from GNSS. Ship officers can make adjustments
so that HDG and COG are consistent and there is no
substantial bias between them during offshore autopilotInazu et al. Progress in Earth and Planetary Science (2018) 5:38

navigation in calm weather/ocean conditions. As long as
based on Eq. (1), tsunami current evaluation may be difficult when SOG is very slow. We may easily pick up
and omit very-slow-SOG vessels (< ~ 0.5 m/s) when agreat earthquake occurs. Further discussion will be necessary for automatically obtaining high-quality AIS data
in real-time operation.
Although Japan already has dense offshore observatories for real-time tsunami monitoring and forecasting(Kawai et al. 2013; Kaneda et al. 2015; Yamamoto et al.
2016), most countries, in particular developing countries, will not be able to install and maintain similar
dense offshore observatories mainly for economic reasons. Hopefully, the use of the AIS data will play a rolein new tsunami monitoring/forecasting methods especially for developing countries such as those in Southeast Asia and South America. The density of vessels
increases in coastal mega cities with highly economic activities. Tsunami disaster mitigation is especially important for such coastal mega cities due to their large
populations. Maritime activities will continue as long as
populations remain large. The use of the AIS data fortsunami current monitoring may be a sustainable way oftsunami disaster mitigation.

Additional file
Additional file 1: Figure S1. (a) SOG, (b) COG/HDG, and (c) COG minus
HDG of selected ships (#8 and #11 for upper panels and *1 and *3 forlower panels). #8 and #11 are examples that were used for the tsunami
current analysis (See also Fig. 4). *1 and *3 were not used due to thedata quality issues noted in Table 1. (PDF 353 kb)

Abbreviations
AIS: Automatic Identification System; COG: Course over ground;
HDG: Heading; IMO: International Maritime Organization; SOG: Speed overgroundAcknowledgements
We thank two anonymous reviewers for their constructive comments. The
AIS data used in the present study were compiled by the IHS Maritime &Trade.
Funding
This study was a part of the Mega-Tsunami Project of the University ofTokyo-Ocean Alliance supported by the Nippon Foundation. This study
was partly supported by JSPS KAKENHI Grant Number 18K04654.
Availability of data and materials
Please contact the corresponding author for data requests.
Authors’ contributions
DI designed and carried out the study and wrote the paper. TI, TW, TH, andYS contributed to the discussion on the behavior of drifting ships andtsunamis. TW provided detailed information on the AIS data. All authors
read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interest.

Page 9 of 11

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in publishedmaps and institutional affiliations.
Author details
1
Department of Marine Resources and Energy, Tokyo University of Marine
Science and Technology, 4-5-7 Konan, Minato, Tokyo 108-8477, Japan.
2
UTokyo Ocean Alliance, The University of Tokyo, 7-3-1 Hongo, Bunkyo,
Tokyo 113-0033, Japan. 3Graduate School of Frontier Sciences, The Universityof Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8563, Japan. 4Graduate
School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo
113-0033, Japan. 5Department of Civil and Environmental Engineering,
National Defense Academy, 1-10-20 Hashirimizu, Yokosuka, Kanagawa
239-8686, Japan.
Received: 25 February 2018 Accepted: 2 July 2018

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