Reviews of Recent Progress and Proposals on Chilean

Transcription

SATREPS Chile Tsunami Project Publication Series Volume 6 Reviews of Recent Progress and Proposals on Chilean Tsunami Warning System SATREPS Chile Tsunami Research Project on Enhancement of Technology to Develop Tsunami‐Resilient Community March 2016 Reviews of Recent Progress and Proposals on Chilean Tsunami Warning System
Contents
Chapter 1. Introduction
1.1 Review of Pre-SATREPS and Current Status of Chilean Tsunami Warning System
Patricio CATALAN (UTFSM)
1.2 Improvement of Japanese Tsunami Warning System based on Lessons from the 2011 Great East
Japan Earthquake Tsunami
Tomoaki OZAKI (JMA)
Chapter 2. Proposal on Tsunami Warning System Based on Seismic Observation
2.1 Developing Tsunami Database
2.2 Rapid Magnitude Determination of Megathrust Earthquake
Akio KATSUMATA (MRI, JMA), Shigeki AOKI (JMA), Yasuhiro YOSHIDA (MC, JMA),
Hiroshi UENO (JMA), Kazuki MIYAOKA (MRI, JMA), and Takashi YOKOTA (former
JMA)
2.3 Application of Tsunami Forecast Chart
Yutaka Hayashi (MRI, JMA)
2.4 Instrumental Modified Mercalli Intensity
Akio KATSUMATA, Yutaka HAYASHI, Kazuki MIYAOKA, Hiroaki TSUSHIMA (MRI,
JMA), and Toshitaka BABA (Tokushima Univ.)
Chapter 3. Proposal on Tsunami Warning System Based on Tsunami Observation
3.1 Review on Measurement Technologies for Deep-Ocean Tsunami
Toshitaka Baba (Tokushima Univ.)
3.3 Methodology of Optimizing Tsunami Observation Array Using Tsunami Inversion
Hiroaki TSUSHIMA (MRI, JMA), Joaquín MEZA (UTFSM), César NÚÑEZ (SHOA), Patricio
CATALÁN (UTFSM), Cecilia ZELAYA (SHOA), Tomohiro TAKAGAWA (PARI), Toshitaka
BABA (Tokushima Univ.), and Yutaka HAYASHI (MRI, JMA)
3.4 Real-Time Inundation Estimation Using GPU Computing
Tomohiro TAKAGAWA (PARI)
Chapter 4. Proposal on Communication Protocol between Organizations Related to Tsunami Warning
4.1 Methods to Determine Thresholds to Issue Precaution Evacuation based on Modified Mercalli
Intensity
Yutaka HAYASHI, Akio KATSUMATA, Kazuki MIYAOKA, Hiroaki TSUSHIMA (MRI,
JMA), Patricio CATALAN, Jose BAQUEDANO (UTFSM), ZELAYA Cecilia (SHOA), Victor
ORELLANA (ONEMI), and Toshitaka BABA (Tokushima Univ.).
Chapter 1. Introduction
1.1 Review of Pre-SATREPS and Current Status of Chilean Tsunami
Warning System
Over the last few years, Chile has been affected by a unique sequence of large earthquakes, which
starting from the Mw8.8 Maule Earthquake, followed by the Mw8.2 Pisagua Earthquake, and the
recent Mw8.4 Illapel Earthquake. Adding to this, the relative contribution of these events to existing
gaps of seismic release, make it possible to occurrence of large magnitude earthquakes (Mw8.5< ) a
strong probability within the next few years, in areas such as northern and central Chile.
In addition, all these events created tsunamis, which varying degrees of destructiveness and damage.
On the other hand, the first of these events occurred after a relatively long period of relative
calmness where only moderate earthquake occurred in the period comprising 1960-2010. As a
consequence, while the country had systems that considered the possibility of tsunamigenic
earthquakes in their procedures and protocols, these systems had not been put to test.
All of that changed on February 27th, 2010, when the Maule earthquake and tsunami not only
brought damage and destruction, but also highlighted that the country was ill prepared to face a
challenging situation like this. Several errors occurred, both in terms of hazard and warning level
assessment, timely response, communications, and others.
Chile has a unique setting on the issue of tsunami warning systems, when compared with other
countries. Here, the seismic and tsunami assessment, and warning issuing are fragmented in three
different institutions, each with limited and clearly defined responsibilities, resources and goals.
Within the context of a Tsunami Warning System, The National Seismological Center (CSN) is
responsible of providing a quick assessment of low level earthquake characteristics, such as
hypocentral location and magnitude. This information is passed on to the National Hydrographic and
Oceanographic Service (SHOA), which within its National Tsunami Warning System division
(SNAM), should assess the tsunami hazard. Once the assessment is completed, the information is
passed on to the Office of National Emergency (ONEMI), which evaluates the situation and is the
only responsible of issuing evacuation alerts.
As of early 2010, the system was not prepared to work on a 24/7 basis at CSN, and communication
procedures were feeble. Regarding SNAM, the tsunami assessment relied on historical records and
expert criteria, which may have been distributed among people with varying degrees of expertise. On
the other hand, communication systems to the population were not well established, and relied on
systems that failed during the emergency itself. Adding to that, the overall tsunami awareness on the
Chilean population was low, with only people on certain coastal areas being specifically educated by
either oral tradition and/or ad hoc training programs carried out by SHOA and ONEMI. However,
the rest of the country was largely removed from the tsunami issue.
It is of note that despite of the deficiencies, these local education programs helped to reduce the
death toll since many people were capable of self evacuating. No casualties occurred over a long
stretch of coast with coastal dwellings, and of the 124 tsunami victims, many concentrated at
specific locations where evacuation was hampered by geography and local conditions, such as at Isla
Orrego, near Constitución.
Nevertheless, the significant conclusion was that something had to be done, quickly and efficiently,
to improve preparedness and response. As a result, several initiatives where put in place, with a wide
range of scopes and intervening actors. Among these, it can be mentioned

Improvement of Tsunami Monitoring capability

Improvement of Protocols, communications and required response times

Improvement of the Tsunami Hazard Assessment
1.1.1 Improvement of Tsunami Monitoring capability
As of 2010, Chile had a network comprising 18 sea level monitoring stations and 1 DART buoy. The
sea level monitoring stations were located at main ports, and its main goal was to measure tides. As
such, little redundancy was provided in terms of communications, and many stations only reported
once hourly. Moreover, most of the stations were pressure sensors, which had a range of deployment
depths but seldomly were placed at the sea bottom, hence they were prone to be clipped by the water
level falling below the instrument position during a tsunami.
As any tsunami warning based on tide gages, the system was only capable of detecting a tsunami
once it reached the coast, thereby reducing the response time to a minimum. Moreover, latitudinal
coverage of the country was sparse, and no stations existed between San Antonio and Talcahuano
(33o 50´S and 36o44´S ). Significantly, this was the area affected by the tsunami, and the
Talcahuano tide gage was destroyed shortly after the arrival of a second wave, when a wall collapsed
over the sensor. The San Antonio gage was under repairs, and the other sensor within the main
tsunami path, in the Juan Fernandez Archipelago, was destroyed within a minute of tsunami arrival.
On the other hand, the only tsunami DART buoy was located off the coast of Pisagua, in northern
Chile. Consequently, its main goal was to assess the occurrence of a transoceanic tsunami emanating
from Chile but not providing early warning to Chile. The tsunami arrived to it about on hour after
the event had caused massive destruction in Central Chile.
After this situation, the network has been improved significantly. By 2015, it now consisted of 42
stations on nearly all relevant coastal dwellings along the coast. Moreover, significant changes have
been made regarding robustness, including the use of two sensors transmitting and reporting in
nearly real time, one under water and one radar sea surface tracker. Communications have been
strengthened, using multiple systems such satellite (GOES or BGAN), cellular (GPRS), along with
solar panels for electrical supply. The locations of the sensors are shown on Fig. 1, where it can be
seen that a relatively uniform latitudinal coverage is now achieved. These sensors operated without
issue on the tsunamis of April 1st, 2014 and September 16th, 2015.
In addition, shortly after the 2010 event, SHOA had two DART tsunami buoys, located offshore the
northern part of the country. Again, these sensors were not enough to provide a timely warning for
the latest events. However, as recent as September 2015, SHOA has now deployed two additional
DART buoys, of a newer generation, which allows deployment in shallower water, hence reducing
the time for tsunami waves to arrive to them. In addition, they are located offshore Caldera and
Constitución (see Fig 1 for reference), hence its aim is more in line for a national warning whilst still
providing transpacific monitoring.
It is of note, however, that despite these improvements, the system is still a monitoring system for
evaluation once the tsunami has reached the coast, and no predictive ability is expected from this
network. Its main aim, within the context of a TWS, is to provide information regarding the
reduction in the tsunami threat, and warning and evacuation cancellation procedures.
1.1.2 Improvement of the Tsunami Hazard Assessment
コメントの追加 [y1]: 1.1.3 Improvement of
Protocols, communications and required
As mentioned before, the system as of now, still relies on an expert assessment and historical data to
provide the technical hazard evaluation that is passed on to ONEMI. However, in the intervening
period, some significant advances have been reached that are aimed to significantly improving these
capabilities.
response times
These have been reached within the context of the present SATREPS project, and through funding
from the Chilean Government through the National Science and Technology Council (CONICYT)
and its grant FONDEF D11I1119, Design and implementation of a tsunami database for early
warning based on high performance computing. The project was led by UTFSM, with active
collaboration of PUC and SHOA, and support and advising from JMA, Japan.
The goal of the project was to implement the early stages of a Decision Support System, based on
the implementation of tsunami catalog of modeled, hypothetic tsunamis, that would be queried upon
an emergency. In turn, these tsunamis considered propagation to the coast up to depths of 100 and
200 m, after which they are shoaled to the coast using Green’s linear approximation. Based on the
maximum amplitude at the coast, a four-level hazard categorization is achieved. Multiple filters are
set up to ensure a conservative assessment.
The system is a complex development, and included the design of not only the database, but also the
design and implementation of the hardware and software architecture, using open source software
which has been integrated allowing for flexibility and ease of expansion. In this way, the system not
only manages the emergency assessment, but also the modeling of the tsunami catalog, the
administration and issuing of evaluation bulletins, in protocol-compatible format.
The system also includes the flexibility of using different modeling modules. As of now, COMCOT
is being used as main tsunami model engine, but a GPU based software was developed and
implemented, with the expectation to be run in near real time (see section of 3.4 of this document).
The system is now under testing in SHOA, and early assessment has shown a good predicting
capability (see section 2.1). Moreover, the developing team has recently applied to an extension of
the funding, to turn the early database into a complete Decision Support System, including integrated
monitoring and graphical user interfaces.
1.2 Improvement of Japanese Tsunami Warning System based on
Lessons from the 2011 Great East Japan Earthquake Tsunami
Tomoaki OZAKI (JMA)
1.2.1. JMA tsunami warnings for the 2011 Great East Japan Earthquake
The Japan Meteorological Agency (JMA) issued a tsunami warning on March 11, 2011,
for the 2011 Great East Japan Earthquake at 14:49 based on a promptly estimated magnitude of 7.9.
It was about three minutes after the earthquake occurred, and a warning of "Major Tsunami", which
is the highest category of Japan's tsunami warning, was issued for Iwate, Miyagi and Fukushima
prefectures. Predicted tsunami heights were 3m, 6m and 3m respectively. After the initial warning,
the JMA upgraded tsunami height predictions to 10m or more for Miyagi prefecture and 6m for
Iwate and Fukushima prefectures at 15:14, and to 10m or more for Iwate and Fukushima prefectures
at 15:30. The upgrade was mainly based on offshore observations with GPS buoys that had been
deployed 10-20km off the Tohoku coast by the Ministry of Land, Infrastructure, Transport and
Tourism. On the other hand, cable-type sea-bottom pressure sensors deployed more offshore than
GPS buoys by the Earthquake Research Institute (ERI) of the University of Tokyo also detected
large tsunami arrival earlier than GPS buoys. However, these data could not be used for warning
upgrade because the method for utilizing them in tsunami warnings was still under development.
The above mentioned observation data and tsunami warning issuance timings are summarized in Fig.
1.2.1.
As for the magnitude, JMA magnitude (Mj) (Katsunata, 2004) was used for the initial
tsunami warning. Mj is based on the maximum displacement amplitude of strongmotion
seismograms and has the advantage of quick availability. However, due to dependence on the short
period (-10 sec.) wave component, Mj is prone to underestimation for gigantic or tsunami
earthquakes. The JMA, therefore, calculate moment magnitude (Mw) operationally within 15
minutes of an earthquake using domestic broadband seismic waveform data, and the initial warning
for the 2011 Great East Japan Earthquake would have been updated using Mw. However Mw could
not be obtained because of scale out records at most of the broadband stations in Japan, and Mw8.8
was calculated from unsaturated overseas broadband records about 55 minutes after the earthquake,
i.e., around 15:40. This value was not utilized for warning updates because the tsunami height
prediction based on Mw8.8 was almost as high as the already updated warning at 15:30.
1.2.2. Lessons learned and improvements
Considering the serious damage caused by the 2011 Great East Japan Earthquake, JMA improved its
tsunami warnings in March 2013 based on the lessons learned from this devastating event. The
major points of tsunami warning improvement are as follows.
1) Measures to avoid magnitude underestimation for huge earthquakes in an initial warning
Although an initial warning is required to be issued as early as possible, currently there is
no established method to estimate reliable huge magnitude value of much larger than 8.0 within
several minutes. To deal with this matter, the JMA introduced tools with which operators can
evaluate the probability of Mj underestimation before the initial tsunami warning issuance. If the
possibility of Mj saturation is detected with the tools, the JMA will issue a tsunami warning using
the maximum possible magnitude around the region close to the epicenter. The tools utilize various
methods such as analyzing long periods of seismic waves (Katsumata et al., 2013) and measuring the
length of a strong ground shaking area.
In addition, the JMA introduced qualitative expression, e.g., "huge", in tsunami height
prediction for such a warning that is based on the maximum possible magnitude, aiming at
conveying impending danger at an early stage when large uncertainty of magnitude estimation is
remained. Criteria and levels in numerical expression of tsunami height prediction were also revised
(Table 1.2.1).
2) More precise update of tsunami warning
At the time of the 2011 Great East Japan Earthquake, JMA could not calculate Mw within
15 minutes as with JMA's normal operation because of scale out records in most of the domestic
broadband seismometers. In addition, cable-type sea-bottom pressure sensors' data could not be
applied to warning update because such procedure and relevant technique did not established at that
time. To deal with these issues, the JMA deployed broadband strong motion seismometers to acquire
full scale of broadband seismic wave data and to obtain Mw robustly. The JMA also started the
operation of utilizing sea-bottom pressure sensors' data for warning update from March 2012. In
addition, the JMA deployed three buoy-type sea-bottom pressure sensors along the Japan Trench,
and the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) deployed 20 of
cable-type sea-bottom pressure sensors. These data started to be used for JMA's tsunami warning
operation from March 2013. Currently, in estimating a coastal tsunami height from offshore data,
the method of multiplying a coefficient defined for each location to an offshore tsunami height is
used. In the near future, the method of applying inversion technique (Tsushima et al., 2009, 2011,
2012) is planned to be introduced to JMA's tsunami warning system.
REFERENCES
.
Hoshiba, M., T. Ozaki, 2012, Earthquake Early Warning and Tsunami Warning of the Japan
Meteorological Agency, and their performances for the 2011 off the Pacific coast of Tohoku
Earthquake (Mw9.0).
Kamigaichi, O (2009), Tsunami Forecasting and Warning, En-cyclopedia of Complexity and System
Science, Springer., 9592-9618.
Katsumata, A. (2004), Revision of the JMA displacement magnitude, Quart. J. Seism., 67, 1-11 (in
Japanese with English abstract).
Ozaki, T. (2011), Outline of the 2011 off the Pacific coast of Tohoku Earthquake (Mw9.0) - Tsunami
warnings/advisories and observations -, Earth Planets Space, 63, 827-830.
Ozaki, T. (2012), JMA's Tsunami Warning for the 2011 Great Tohoku Earthquake and Tsunami
Warning Improvement Plan, Journal of Disaster Research, Vol.7 Special Edition.
Katsumata, A., H. Ueno, S. Aoki, Y.Yoshida and S. Barrientos (2013) ： Rapid magnitude
determination from peak ampli-tude at local stations, Earth Planet Space.
Tsushima, H., R. Hino, H. Fujimoto, Y. Tanioka and F. Imamura (2009), Near-field tsunami
forecasting from cabled ocean bottom pressure data, J. Geophys. Res., Vol.114, B06309,
doi:10.1029/2008JB005988.
Tsushima, H., K. Hirata, Y. Hayashi, Y. Tanioka, K. Kimura, S. Sakai, M. Shinohara, T. Kanazawa, R.
Hino and K. Maeda (2011), Near-field tsunami forecasting using offshore tsunami data from the
2011 off the Pacific coast of Tohoku Earthquake, Earth Planets Space, Vol.63, pp. 821-826,
doi:10.5047/eps.2011.06.052.
Tsushima, H., R. Hino, Y. Tanioka, F. Imamura and H. Fujimoto (2012), Tsunami waveform
inversion incorporating permanent seafloor deformation and its application to tsunami forecasting,
J. Geophys. Res., Vol.117, B03311, doi:10.1029/2011JB008877.
Fig. 1.2.1 Coastal and offshore sea level observation data for the period of the initial
and the following two upgraded tsunami warnings. Dotted lines in observation curves
indicate data recovered afterward through site surveys.
Table 1.2.1 Categories and criteria of tsunami warning and tsunami height prediction.
Current (March 2013-)
Previous
Category
Expression of
Tsunami height
Tsunami
Major Tsunami
Warning
Tsunami
Tsunami Advisory
10m or more, 8m,
6m, 4m, 3m
Expression of
Tsunami height
Numerical
Predicted height
Qualitative
Over 10m
10m
10m Huge
5m
5m
to 10m
3m
to 5m
2m, 1m
3m
High
1m to 3m
0.5m
1m
(Blank)
0.2m to 1m
Chapter 2. Proposal on Tsunami Warning System Based on Seismic
Observation
2.1.1 SIPAT Overview
One of the key developments of the last few years, and with a significant support of the SATREPS
project, was the development of the so called tsunami-database, which in reality turned out to be the
baseline of a Decision Support System, termed Integrated System of Tsunami Prediction and
Warning, SIPAT (in Spanish). The system was originally intended to be a simple database of
precomputed tsunami scenarios, but it evolved into a complete hardware and software system, which
allows modeling, pre and post processing, storage, administration and query of a large tsunami
catalog during the non-emergency state. This whole subsystem is called Creator, and is accessed
through a web interface on the SHOA servers.
Regarding the modeling, within the SATREPS project a study was carried out on which it could be
seen that reasonable modeling predictions at depths between 100 and 200m could be achieved. This
reduces the computing load, as linear implementations of the NSWE can be used. In consequence, a
total of 962 forecast points is used, which include also DART bouys and tide gages. In case of the
true forecast points, time series data are stored, which are then shoaled to the nearest coastal location
(shortest path) by using Green’s Law. Then, the shoaled time series are processed, estimating several
parameters as shown in Table 1. From these, the relevant hazard category level (0-3) is stored for
query when in emergency. The levels are defined as

0: No Hazard. 
1: Tsunami amplitude at the coast is between 0.3 and 1 m 
2: Tsunami amplitude at the coast is between 1 and 3 m. 
3: Tsunami amplitude exceeds 3 m.
in accordance with the SHOA ONEMI protocol.
Regarding the modeling, Creador allows for multiple modeling alternatives, which are controlled by
a Modeling Subsystem. As of now, only COMCOT and a GPU model are implemented, but it is
flexible enough to incorporate other models if needed by SHOA.
2.1.2 Tsunami Catalog
One of the key elements of the system is the earthquake catalog to be used in the analysis. A report
by the National Seismological Center showed that by varying 9 parameters (hypocentral latitude,
longitude and depth; aspect ratio relationship; dip, rake, strike angles; magnitude; shear modulus)
could lead to a total number of scenarios in excess of 1.8 million. However, in many cases, the
parameter space considered values not consistent with the fault geometry. Nevertheless, in order to
optimize the database populating process, a decision was taken in order to ensure

Latitudinal coverage

Complete range of depths

Only one shear modulus
This led to a total of 7656 scenarios. However, given the time constraint imposed by the project, this
was further reduced to focus on only the range of earthquake magnitudes where tsunami generation
was less certain (Mw 7.5, 8.0, 8.5) and only one aspect ratio. This reduced the number of proposed
scenarios to 1528.
Table 2.1.1: Table output of the Extract, Transform and Load post processing algorithm
2.1.3 Tsunami Hazard Evaluation Procedure
With the database set up, SIPAT can now provide an effective tool for estimating the tsunami hazard.
To this end, it is assumed that seismic parameters are provided from several sources (CSN, USGS,
GEOFON). Hypocentral location data and magnitud are introduced in accordance to prescribed
formats, and with this information SIPAT begins its table look up procedure. Three filters are setup
in order to estimate the hazard
i) A spatial filter, by which catalog candidates are identified within an interrogation window
centered on the epicenter, with a length of 2L and width of 2W. L and W are the source
length and width, as estimated by scaling laws. The rationale is that given the uncertainty in
the location of the epicenter relative to the rupture, and the closeness of the tsunamigenic
zone to the coast, it is necessary to include all possible configurations.
ii) A magnitude filter, by which the candidates are reduced to those with magnitudes within [00,5] Mw of the estimated magnitude. With this, it is expected that possible variations in
tsunami hazard due to non uniformity of the slip is covered by the increased magnitude.
iii) Once all candidate scenarios are identified, the hazard at each coastal location is estimated
as the maximum hazard level among all candidates.
Figure 2.1.1: Offline assessment of SIPAT performance, for the Sep 16, Mw 8.4 Illapel Earthquake.
Colors correspond to the hazard level as predicted by the system.
It is of note that this procedure is increasingly conservative, and could led to an overestimation of the
hazard. However, at this stage, it is considered a reasonable compromise given all the epistemic
uncertainties involved. In addition, the final evaluation does not relate to a single tsunami model, but
rather to the hazard envelope of a set of candidates.
Testing of the system offline has shown the benefits of the system, as well as testing of its accuracy.
For instance, in Fig. 1, a sample output of the first hazard solution for the Mw 8.4 Illapel Earthquake
and Tsunami. It is of note that the first seismic estimation yield Mw 7.9 (provided by the USGS).
Nevertheless, despite the smaller magnitude, the conservative approach used leads to a very accurate
assessment of the hazard, on which on the region of Coquimbo and Valparaiso the hazard level reach
the Alarm state, that is, waves exceeding 3 m. This was indeed the real tsunami case, as measured by
tsunami survey efforts.
In addition, the benefits of having the system can be observed as well. The evaluation took less than
15 seconds, and is capable to provide a regional assessment, where different administrative regions
having different hazard levels. This is clearly an improvement over the existing procedure.
2.2 Rapid Magnitude Determination of Megathrust Earthquake
Akio KATSUMATA (MRI, JMA), Shigeki AOKI (JMA), Yasuhiro YOSHIDA (MC, JMA), Hiroshi
UENO (JMA), Kazuki MIYAOKA (MRI, JMA), and Takashi YOKOTA (former JMA)
2.2.1 Introduction
One of major problems in the tsunami warning for the 2011 off the Pacific coast of Tohoku
Earthquake (Mw 9.0) was a lack of awareness of underestimation of the earthquake magnitude
at the time soon after the occurrence.
The first tsunami warning should be issued within a few minutes after the detection of the
earthquake occurrence. Displacement magnitude (Katsumata, 2004) is used for the first tsunami
warning. Seismic moment tensor is also estimated after the first tsunami warning to estimate
size of the earthquake more accurately (Usui et al., 2010). At the time of the occurrence of the
2011 off the Pacific coast of Tohoku Earthquake (Mw 9.0), the magnitude was not estimated
properly due to too short cutoff period (six seconds) of the filter for the displacement magnitude
compared with the rupture duration (about three minutes [Yoshida et al., 2011a]).
Seismic
moment could not be estimated from the regional seismological network data due to over-range
of broadband sensor (STS-1 and STS-2) outputs, and it took longer time to estimate it from
global data.
Magnitude determination is the key to the proper tsunami warning. Some method had been
developed after the earthquake. Here those rapid magnitude estimation methods are described
based on Katsumata et al. (2012).
2.2.2 Magnitude Determination from Span of Strong Motion Area
Large earthquakes cause strong motion in a wide area. Span of strong-motion area is
related to earthquake magnitude. Seismic intensity distribution in Japan can be known in a few
minutes after earthquake occurrence owing to a dense on-line network of seismic intensity
meter in Japan. It is possible to estimate earthquake magnitude roughly from the area of strong
shaking.
Figure 1 shows the distributions of seismic intensity of the 2011 off the Pacific coast of
Tohoku Earthquake (Mw 9.0) and the 2003 Off-Tokachi Earthquake (Mw 8.0). The span of
5-lower or the greater of the JMA seismic intensity scale of the 2011 off the Pacific coast of
Tohoku Earthquake reached about 700 km. The span of the 2003 Off-Tokachi Earthquake was
about 300 km, which was much less than that of the 2011 off the Pacific coast of Tohoku
Earthquake.
Fig. 1. Distributions of seismic intensity of the 2011 off the Pacific coast of Tohoku Earthquake
and the 2003 Off-Tokachi Earthquake. The contours in maps denote slip distributions
estimated by Yoshida (2005) and Yoshida et al. (2011a).
2.2.3 Source Area Estimation from Seismic Intensity Distribution
The place of observed strong motion would be close to the seismic source. Source area can
be estimated from the area of strong-motion. At each station, distance from the fault is estimated
from the seismic intensity with the formula by Si and Midorikawa (1999). To estimate the
source area, grid points are set on the plate boundary. At each grid points, number of stations
where the distance between the station and the grid point is larger than the estimated fault
distance is counted. If the grid point is on the source area, the number of inconsistent stations
(where the distance between the station and the grid point is larger than the estimated fault
distance) should not exceed some number. To exclude far offshore areas, the grid points are
restricted to those at which more than ten seismic stations are expected to detect seismic
intensities equal to 4.5 or the greater. Figure 2 shows the estimated source area of the 2011 off
the Pacific coast of Tohoku Earthquake and the 2003 Off-Tokachi Earthquake from the number
of the inconsistent stations.
Fig. 2.
Estimated source area of the 2011 off the Pacific coast of Tohoku Earthquake and the
2003 Off-Tokachi Earthquake from seismic intensity distribution [Ueno et al., 2013].
2.2.4 Duration of Strong Motion
The duration of the strong motion becomes also longer for larger earthquakes. Durations of
strong motion were investigated for large earthquakes in and around Japan.
Figure 3 shows distribution of strong-motion durations for the 2003 Off-Tokachi Earthquake.
The colors denote strong-motion duration at stations. Data from K-NET [Kinoshita, 1998] and
KiK-net [Aoi et al. 2000] is used for this analysis. The durations in the north of the epicenter are
shorter than those in the other directions in the figure. The duration difference is considered due
to directivity effect. Figure 4 shows azimuthal distribution of the duration, which shapes a
sinusoidal curve. It is possible to estimate fault length and rupture direction from the
distribution (Izutani and Hiraswa, 1987). The arrow in Figure 3 indicates estimated fault length
and rupture direction. When the faulting is simple unilateral, this method is useful for rapid
estimation of the fault parameters.
Figure 5 shows relationship between moment magnitude and strong-motion duration of
earthquakes which occurred in and around Japan. Good correlation is seen between
strong-motion duration and earthquake magnitude. The duration of the earthquake in March,
2011 exceeded eighty seconds, which was the longest among those of the large earthquakes. It
is difficult to estimate the accurate moment magnitude on the basis of the duration due to the
large scatter. We can, however, judge possible magnitude underestimation by the duration.
Fig. 3. Distribution of strong-motion duration of the 2003 Off-Tokachi Earthquake [Aoki et al.,
2011]. The star indicates the epicenter of the earthquake, and the arrow indicates estimated
fault length and rupture direction by the method of Izutani and Hirasawa (1987).
Fig. 4. Azimuthal distribution of strong-motion duration of the 2003 Off-Tokachi Earthquake
[Aoki et al., 2011]. Blue curve indicates the theoretical distribution of the estimated fault.
Fig. 5. Relationship between moment magnitude (after the Global CMT Project) and
strong-motion duration Dobs
of earthquakes which occurred in and around Japan [Aoki, et
al., 2011]. The red circle denotes that of the 2011 off the Pacific coast of Tohoku
Earthquake.
2.2.5 Magnitude Estimation from P-wave
Some magnitude determination methods from P-wave have been proposed.
Yoshida
(1995) proposed a magnitude determination method from P-wave displacement amplitude, Mp.
Figure 6 shows the station magnitude Mp of the 2011 off the Pacific coast of Tohoku
Earthquake. The horizontal axis of Figure 6 denotes time of magnitude determination from the
origin time of the earthquake which corresponds to S-wave arrival time at the station. The
magnitude grows before about four minutes. This reflects the extension of the rupture area.
Tsuboi et al. (1995) developed a method of estimating moment magnitude, Mwp, from
integrated P-wave. Figure 7 shows the magnitude Mwp of the 2011 off the Pacific coast of
Tohoku Earthquake.
Ogawara et al. (2004) showed a magnitude determination method, Mwliss, based on squared
amplitude of broadband seismic wave. Figure 8 shows the magnitude Mwliss of the 2011 off the
Pacific coast of Tohoku Earthquake.
These magnitudes show similar variation along the time from the origin time, and reach
about nine in five minutes. It is possible to determine the magnitude of this earthquake properly
in five minutes with these methods. Dispersion of Mwp is smaller compared with other P-wave
magnitudes.
Fig. 6. Mp of the 2011 off the Pacific coast of Tohoku Earthquake (Mw 9.0) [Yoshida et al.,
2011b].
The red circle denotes station magnitude calculated from seismic wave between
P and S arrivals at each station. The blue small dot denotes that calculated from seismic
wave contaminated with S-wave. The horizontal axis shows the time when the magnitude
is estimated at each station measured from the origin time.
Fig. 7. Mwp [Tsuboi et al., 1995] of the 2011 off the Pacific coast of Tohoku Earthquake
[Yoshida et al., 2011b]. The symbols are the same as those in Figure 6.
Fig. 8. Mwliss [Ogawara et al., 2004] of the 2011 off the Pacific coast of Tohoku Earthquake
[Yoshida et al., 2011b]. The symbols are the same as those in Figure 6.
2.2.6 Magnitude Determination from Long-period Seismic Wave
It takes a long time to complete a rupture of a large earthquake.
Excitation of long-period
seismic wave is one of features of large earthquakes. The cutoff period (6 s) for the
displacement magnitude used for the first tsunami warning was too short for the 2011 off the
Pacific coast of Tohoku Earthquake. Usage of long period components of seismic wave
would help to estimate earthquake magnitude properly.
Figure 9 shows seismic waves processed with filters of different frequency responses. The
figure shows seismic waves of the 2011 off the Pacific coast of Tohoku Earthquake (the
left) and the 2003 Off-Tokachi Earthquake (the right). The upper of the figure shows seismic
waves with the same response for the displacement magnitude. The lower shows those
processed with a filter of 200-1000 second pass-band. While amplitudes of short-period seismic
wave are not so different between the two earthquakes, those of long-period seismic wave differ
very much. This is one method to distinguish the difference of these magnitudes.
Magnitude determination methods from various frequency ranges are developed. Peak
displacements of seismic waves of 1, 2, 5, 10, 20, 50 and 100 second cutoff periods are used to
determine magnitude. Phase type is not cared in this method, and peaks are possibly those of
S-waves.
Figure 10 shows estimated durations to determine magnitude only from P-wave and
from S-wave. It is necessary to wait for the completion of fault rupture to get enough length of
data. At a station close to source area, S-P time would be shorter than the rupture duration. It is
considered that magnitude determination from S-wave peak is more rapid than that only from
P-wave peaks when local seismic data are available. Figure 11 shows growth of the magnitude
with time from the origin time on the horizontal axis for the earthquakes of the 2010 Maule
Earthquake and the 2011 off the Pacific coast of Tohoku Earthquake. The averaged magnitudes
calculated from closest ten stations are shown in the figure. The magnitudes from long-period
seismic wave reach the final values within three minutes for the earthquake.
Fig. 9. Filtered Seismic waves of the 2011 off the Pacific coast of Tohoku Earthquake (the left)
and the 2003 Off-Tokachi Earthquake (the right) [Yoshida et al., 2011b]. The upper
shows seismic waves for the displacement magnitude used for the tsunami warning, and the
lower shows those processed with a filter of a 200-1000 second pass-pand.
Fig. 10.
Assumed time to estimate earthquake magnitude from P-wave (the green broken
curve) and S-wave (the red solid curve) [Katsumata et al., 2011]. The curves indicate
relationships between epicentral distance and sum of travel time and assumed rupture
duration. Sixty seconds is assumed as the rupture duration here.
Fig. 11. Growth of magnitude from seismic waves of various cutoff-periods [Katsumata et al.,
2013].
The horizontal axis shows time from the origin time of the earthquake. The
numerals indicate cutoff periods of the filters.
2.2.7 Estimation of Fault Plane with High-sample-rate GNSS Data
High sample rate (1 sample/second) GNSS data is currently available. Those data can be
used for rapid estimation of the seismic source parameters.
very-long period seismic data.
The GNSS data can be used as
Here we used the time-series GNSS data to obtain CMT
solutions with W-phase analysis (Kanamori and Rivera, 2008), and then a finite fault plane
model is estimated so that calculated static co-seismic displacements would be consistent with
the observed ones. (Fig. 12).
Fig. 12. Estimation steps of fault plane with high-sampling-rate GNSS data.
2.2.8 Conclusions
Several magnitude determination methods are being developed to estimate earthquake
magnitude soon after occurrence of a large earthquake for tsunami warning. Magnitude
estimation from span of strong-motion area, strong-motion duration, P-wave, and S-wave
amplitudes were examined.
Combination of these methods is expected to help us to issue a
proper tsunami warning for the next great earthquake.
Acknowledgements
Data from National Research Institute for Earth Science and Disaster Prevention (K-NET,
KiK-net, F-net), University of Chile, Geospatial Information Authority of Japan, and the Japan
Meteorological Agency were used in this study.
Hypocenter parameters of the unified seismic
catalog of Japan and Global CMT Project are referred to.
The unified seismic catalog of
Japan is based on data from the National Research Institute for Earth Science and Disaster
Prevention, Hokkaido University, Hirosaki University, Tohoku University, University of Tokyo,
Nagoya University, Kyoto University, Kochi University, Kyushu University, Kagoshima
University, the National Institute of Advanced Industrial Science and Technology, Tokyo
metropolitan government, Shizuoka prefectural government, Kanagawa prefectural government,
the City of Yokohama, the Japan Marine Science and Technology Center, and the Japan
Meteorological Agency. We thank Y. Kaida for many valuable discussions about source area
estimation from seismic intensity distribution.
REFERENCES
.
Aoki, S., Y. Yoshida, and A. Katsumata (2011), “The characteristics of rupture propagation
deduced from strong motion duration”, Programme and Abstract of The Seismological
Society of Japan Fall Meeting, P2-42.
Aoi, S., K. Obara, S. Hori, K. Kasahara, and Y. Okada, (2000), “New strong-motion
observation network: KiK-net”, EOS. Trans. Am. Geophys. Union, Vol. 81, F863.
Usui, Y., S. Aoki, N. Hayashimoto, T. Shimoyama, D. Nozaka, and T. Yoshida (2010),
“Description of and advances in automatic CMT inversion analysis”, Quart. J. Seism., Vol
73, 169-184.
Izutani, Y. and T. Hirasawa (1987), “Use of strong motion duration for rapid evaluation of fault
parameters”, J. Phys. Earth, Vol. 35, 171-190.
Kanamori, H. and L. Rivera (2008): Source Inversion of W phases: speeding up seismic tsunami
warning, Geophys. J. Int., 175, 222-238.
Katsumata, A. (2004), “Revision of the JMA displacement magnitude”, Quart. J. Seism., Vol.
67, 1-10. (in Japanese with English abstract)
Katsumata, A., S. Aoki, Y. Yoshida and K. Kimura (2011), “Quick magnitude determination
based on peak velocity and displacement”, Programme and Abstract of The Seismological
Society of Japan Fall Meeting, P2-67.
Katsumata, A., H. Ueno, S. Aoki, Y. Yoshida and S. Barrientos (2013): Rapid magnitude
determination from peak amplitudes at local stations, Earth Planet Space, 65, 843-853.
Kinoshita, S. (1998), “Kyoshin Net (K-NET)”, Seismological Research Letters, Vol. 69,
309-332.
Ogawara, T., T. Furudate and M. Okada (2004), “Preliminary empirical formula to estimate
moment magnitude of teleseismic event by using LISS data”, Technical Reports of the
Matsushiro Seismological Observatory, JMA, Vol. 21, 75-81.
Si, H. and S. Midorikawa (1999), “New attenuation relationship for peak ground acceleration
and velocity considering effect of fault type and site condition”, J. Struct. Constr. Eng., Vol.
523, 63-70. (in Japanese with English abstract and figure captions)
Tsuboi, S., K. Abe, K. Takano, and Y. Yamanaka (1995), “Rapid determination of Mw from
broadband P waveforms”, Bull. Seism. Soc. Am., Vol. 85, 606-613.
Ueno, H., A. Katsumata, Y. Kaida, and T. Yokota (2013): Rapid source area estimation based on
seismic intensity distribution, Japan Geoscience Union Annual Meeting 2015，SSS33-P26.
Yokota, T. and Y. Kaida (2011), “Estimation of magnitude using distribution of seismic
intensity”, Programme and Abstract of The Seismological Society of Japan Fall Meeting,
P2-22.
Yoshida, Y (1995), “Magnitude determination from P-wave amplitude”, JMA Technical Note of
Seismology and Volcanology, Vol. 71, 41-52.
Yoshida, Y. (2005), “Rupture process of the 2003 Off-Tokachi Earthquake”, Technical Report of
Japan Meteorological Agency，Vol. 126, 9-14.
Yoshida, Y., H. Ueno, D. Muto and S. Aoki (2011a), “Source process of the 2011 off the Pacific
coast of Tohoku Earthquake with the combination of teleseismic and strong motion data”,
Earth Planets Space, Vol. 63, 565-569.
Yoshida, Y., S. Aoki, A. Katsumata and T. Yokota (2011b), “Evaluation of rapid Mw
determination method”, Programme and Abstract of The Seismological Society of Japan Fall
Meeting, P2-69.
2.3 Application of Tsunami Forecast Chart
Yutaka HAYASHI (MRI, JMA)
2.3.1 Background
Tsunami database [Tatehata, 1997] is a tool to realize tsunami early warning based on seismic
observation. And as described in Section 2.1, Chilean prototype tsunami database has developped as
the key technique used for near-future near-field tsunami forecast system in Chile. It will enables us
to divide coast lines of mainland Chile into forecast areas. However, from the viewpoint of
reliability and robustness of Chilean tsunami forecast system, it also is preferable to develop a
couple of independent methods as backups in preparation to earthquakes beyond the scenario set
engaged in the database, the troubles of a system, and data transmission failure between
organizations of tsunami warning operation.
In this section, application potency of Tsunami Forecast Chart (hereafter TFC) is discussed as a
backup of oncoming Chilean tsunami database system and also as a benchmark for determining
thresholds to issue precaution evacuation based on Modified Mercalli Intensity (Section 4.1).
2.3.2 Outline of Tsunami Forecast Chart (TFC)
Various kind of TFC had been used in Japan until starting quantitative tsunami forecast by Japan
Meteorological Agency (JMA) in 1999 [Kusano and Yokota, 2011]. The latest version of the chart
which was used from 1987 to 1999 determines three boundary lines of tsunami advisory/warning
levels and x- and y-axis of the chart are distance from the epicenter to any forecast area, and seismic
magnitude, respectively (Fig. 1). JMA had been operating tsunami forecast for 18 tsunami forecast
areas in Japan using the focus location and magnitude determined by real-time.
2.3.3 Verification and Modification of TFC
Because the latest version of TFC (hereafter TFC-J) did not experienced large near-field
tsunami during its life time from 1987 to 1999, the chart has verified by maximum tsunami height
observed during a different period (Fig. 2). Verification suggests that using TFC-J would have
mislead underestimation of tsunami height by the 2011 off the Tohoku earthquake even under
assumption of availability of final moment magnitude (Mw9.0). In addition, TFC-J cannot be
applied to Chilean tsunami warning system as itself, because tsunami advisory/warning category for
larger than magnitude 9.0 and further than 700km in epicentral distance is not defined.
Then alternative two charts, or sets of threshold lines, are proposed. One is TFC-A, which is
derived from definition of tsunami magnitude [Abe, 1979] and equations for tsunami height
forecasting using seismic or tsunami magnitudes [Abe, 1989]; the other set TFC-M is the modified
by using scaling law on large earthquakes by Murotani et al. (2013) with consideration of the
standard aspect ratio of a seismic fault and a possible location of a hypocenter within its seismic
fault (Fig. 2). Three lines for each set mean maximum epicentral distance where maximum tsunami
height become 0.25m, 0.75m, or 2.5m, when the definition of tsunami magnitude is applied. Both
TFC-A and TFC-M better explain the maximum tsunami height by the 2011 off the Tohoku
earthquake than TFC-J (Fig. 2).
Finally, TFC-A and -M for Chilean use are redefined so that the boundary lines indicate the
epicentral distance for maximum tsunami height of 0.3m, 1m, and 3m, which are boundary values of
tsunami warning ranks (small or no tsunami, tsunami watch, warning, warning (major tsunami))
under planning by SHOA (Fig. 3). As the typical extent of an Magnitude 8 or larger earthquake is
estimated to exceed 100 km from scaling laws. TFC for large tunamigenic earthquakes is considered
to involve uncertainty more than a 100 km.
2.3.4 Conclusions
After modification of Tsunami Forecast Chart which used to be applied in Japan during
non-database period, new TFCs for Chilean use (Fig. 3) are proposed. Because of the accuracy of
TFC, in case of introducing tsunami forecast area in Chile, one or a few area for each state is
desirable. Usage of TFC in real-time is simple; it is just to look up the warning rank for a certain
forecast area in the chart using the Mw (or substitute seismic magnitude) as y-value and the
minimum distance from the epicenter to the concerning forecast area as the x-value.
Newly proposed TFCs can be used as one of backup methods (e.g. for unexpected earthquakes)
of oncoming Chilean tsunami database system (Section 2.1); and also they can be used as a
benchmark for determining thresholds to issue precaution evacuation based on Modified Mercalli
Intensity (Section 4.1). Selection between TFC-A and TFC-M by using tsunami observation data in
Chile, comparing to results of forecast experiments by using Chilean tsunami database (Section 2.1),
and/or exchanging the equation using local seismic scaling laws may more improve the reliability of
the chart.
REFERENCES
Abe, K. (1979): Size of great earthquakes of 1837-1974 inferred from tsunami data. J. Geophys. Res.,
Vol. 84, 1561-1568.
Abe, K. (1989): Estimate of tsunami heights from magnitudes of earthquakes and tsunami. Bull.
Earthq. Res. Inst., Vol. 64, 51-69. (in Japanese with English abstract)
Kusano, F., and T. Yokota (2011): History of tsunami warning services in Japan. Quart. J. Seism.,
Vol. 74, 35-91. (in Japanese)
Murotani, S., K. Satake, and Y. Fujii (2013): Scaling relations of seismic moment, rupture area,
average slip, and asperity size for M~9 subduction-zone earthquakes. Geophys. Res. Lett., Vol.
40, 5070-5074. doi:10.1002/grl.50976.
Tatehata, H. (1997): The new tsunami warning system of the Japan Meteorological Agency. in
“Perspectives on tsunami hazard reduction" ed. G. Hebenstreit, Kluwer Academic Publishers,
Dordrecht, The Netherlands, pp. 175-188.
Fig. 1. Tsunami Forecast Chart used by Japan Meteorological Agency from 1987 to 1999 (translated
from [Kusano and Yokota, 2011]).
level to each forecast area.
The chart indicates the guidelines for the tsunami warning
Fig. 2. Maximum Tsunami Height Observation Data and Tsunami Forecast Chart based on JMA's
categories.
Maximum tsunami heights observed at tidal stations along the Pacific coast of Japan from
earthquakes in the Pacific Ocean during 1999-2013 are classified into four and plotted (circles).
Data observed in Tokyo, Ise or Osaka Bay are excluded. Three sets (TFC-J, -A, and -M) of
boundary lines for neighboring tsunami forecast categories (0<h<0.25m, 0.25<=h<0.75m,
0.75m<=h<2.5m, 2.5m<=h) by JMA are indicated; TFC-J was used by JMA until 1999 [Kusano
and Yokota, 2011], TFC-A was derived from definition of tsunami magnitude and
corresponding equations in Abe (1989); and TFC-M which is modified by using the scaling law
between faults areas and moment magnitudes by Murotani et al. (2013) and consideration of a
bilateral fault rupture under assumption of standard aspect ratio (length / width = 2). Lines of
TFC-A and TFC-M for far-field earthquakes are depend on the location of the epicenter; the
uppermost lines (Mw=9.2, 8.7, 8.2) for earthquakes occurred in Aleutian-Alaska area, the
middle lines (Mw=8.9, 8.4, 7.9) for South America, and the lowermost lines (Mw=8.7, 8.2, 7.7)
for earthquakes along Kurile-Kamchatka.
Fig. 3. Proposal of Tsunami Forecast Chart (TFC) for Chile as a backup for Database System.
Data and threshold lines are based on SHOA's tsunami ranks. Maximum tsunami heights data
used in this figure are same as the Fig. 2. TFC-A and -M are shifted from those of Fig. 2 so that
lines indicate the threshold values (0.3m, 1m, 3m for the lower limit of tsunami alert, tsunami
alarm, and major tsunami) for tsunami warning ranks by SHOA.
2.4 Instrumental Modified Mercalli Intensity
Akio KATSUMATA, Yutaka HAYASHI, Kazuki MIYAOKA,
Hiroaki TSUSHIMA (MRI, JMA), and Toshitaka BABA (Tokushima Univ.)
2.4.1 Introduction
Seismic intensity is usually measured by sense of human or degree of damages. If seismic
intensities are reported through the network like as in Japan (Hachimine, 1989), it is considered that
rapid grasp of possible damaged areas can be done. Some methods have been proposed as
instrumental modified Mercalli scale (e.g., Trifunac and Brady 1975; Wald et al., 1999; Shabestaria
and Yamazaki, 2001). It is considered that low-coast equipment of current technology is enough to
get seismic intensity value.
The seismic intensity itself could be a trigger of evacuation from
tsunami (Hayashi et al., 2014). A former tsunami alarm in Japan was based on seismic intensity
observed by human. Similar way could be used for any country in the world.
2.4.2 Instrumental Seismic Intensity
Some formulas have been proposed to relate modified Mercalli scale to instrumentally obtained
values. Relationship among seismic intensity, peak ground acceleration and velocity were
investigated by Trifunac and Brady (1975) and Wald et al. (1999).
Shabestaria and Yamazaki
(2001) used a filter for Japan Meteorological Agency (JMA) seismic intensity scale to obtain the
value of the modified Mercalli scale.
Figure 1 shows observed seismic intensity and instrumentally obtained seismic intensities for
the earthquake on April 1, 2014 off Iquique.
Whereas effective intensity range is limited in each
method, attention is not paid to the applicable range of the formulas here. Since the seismic intensity
is usually affected by surface geology at the observation point very much and the observation sites
are different between observed and instrumental values, it would be not proper to compare
instrumental values with the observed values here.
Fig. 1. Comparison of various instrumental seismic intensities.
MMI:Yama, Shabestaria and
Yamazaki (2001); MMI:PGA-W, Wald et al. (1999) based on peak ground acceleration;
PGV-W, Wald et al. (1999) based on peak ground velocity; MMI-PGA-T, Trifunac and Brady
(1975) based on peak ground velocity; MMI-PGV-T Trifunac and Brady (1975) based on peak
ground velocity; OBS:MMI, observation by ONEMI.
2.4.3 Prototype of Instrumental Seismic Intensity Meter
The MEMS (micro electro mechanical systems) sensors are currently available with low coast.
Small computer systems are also available. We developed a low-cost equipment using a MEMS
sensor and a small computer which could tell instrumental seismic intensities with display and
synthetic voice.
PC
LCD monitor
USB accelerometer
Keyboard
Fig. 2. Prototype of instrumental seismic intensity meter.
Relatively low-noise MEMS sensors are available today.
lower limit of being felt.
0.2 gal.
Ground shaking of 1 gal is about the
In the prototype of the meter, we used one of which noise level is about
The noise level can be used to detect the ‘felt’ ground shaking.
2.4.4 Further Applications of the Seismic Intensity Meter
It is considered that one of the quickest ways of tsunami evacuation is movement to higher
place soon after a strong and long ground shaking. Information from mass media could be helpless
sometimes due to power failure. It would be helpful if there is a device which tells risk of coming
tsunami. Strong ground motion means that the source area of the event would be close to the current
location, and long ground shake means that the event magnitude would be large. Fig. 3 shows the
relationship between magnitude and various instrumentally observed values. Here we considered
strong-motion duration, ground velocity, ground displacement, and their combination.
The velocity
and displacement are obtained from acceleration with numerical integration. The period of the
high-pass filter is set at 20 seconds here.
Velocities or displacements corresponding to acceleration
lower than 0.2 gal are ignored assuming the usage of the MEMS sensors.
The strong motion
duration is good for distinguishing the huge earthquakes such as the 2011 off the Pacific coast of
Tohoku Earthquake.
However, the duration is not suitable to distinguish M7-class earthquake and
M8-class earthquake. If we use ratio of numbers of data plot as the index of suitability, the
displacement is the most suitable in the case of Fig. 3
Fig. 3. Relationship between magnitude and observed values.
Upper left, duration of the modified
Mercalli scale value calculated with Wald et al. (1999); upper center, PGV, upper right, PGD,
lower left, product between the duration and the PGV; lower right, product of the duration and
the PGD.
The MEMS sensor equipment is considered to be used also for the earthquake early warnings. For
example, it is possible to estimate S-wave amplitude from the amplitude of P-wave of a single sensor.
Other application is considered to be possible to install in the instrumental seismic intensity meter.
2.4.5 Conclusions
Instrumental seismic intensity meter is proposed here. If the instrumental seismic intensities are
reported through the network, it would be valuable for quick dispatch of rescue teams. Such
instrument can be used for standalone tsunami warning or earthquake early warning.
Acknowledgements
Data from University of Chile were used in Section 2.4.2 in this document.
Data of JMA seismic
intensity meters are used in section 2.4.3
REFERENCES
Hachimine, T., 1989, Instrumentation of the JMA seismic intensity scale, Quart. J. Seismol., 52,
43-68. (In Japanese)
Hayashi, Y., A. Katsumata, K. Miyaoka, H. Tsushima, P. Catalan, J. Baquedano, C. Zelaya, V.
Orellana, and T. Baba, 2015, A method to ensure consistency between tsunami forecast
chart-based warnings and instrumental Mercalli scale intensity-based evacuation, IUGG2015,
JP05p-021.
Shabestaria, K. T. and Fumio Yamazaki, 2001, A Proposal of Instrumental Seismic Intensity Scale
Compatible with MMI Evaluated from Three-Component Acceleration Records, Earthquake
Spectra, 17, 711–723.
Trifunac, M. D. and A. G. Brady, 1975, On the correlation of seismic intensity scales with the peaks
of recorded strong ground motion. Bull. Seism. Soc. Am., 65, 139-162.
Wald, D. J., V. Quintro, T. H. Heaton and H. Kanamori, 1999, Relationships between peak ground
acceleration, peak ground velocity and Modified Mercalli Intensity in California, Earthquake
Spectra, 15, 557-564.
Chapter 2. Proposal on Tsunami Warning System Based on Seismic
Observation
Chapter 3. Proposal on Tsunami Warning System Based on Tsunami
Observation
3.1 Review on Measurement Technologies for Deep-Ocean Tsunami
Toshitaka BABA1,2
1
Institute of Science and Technology, University of Tokushima
2
Japan Agency for Marine-Earth Science and Technology
Abstract
In recent years, deep-ocean tsunami measurements became to more play an important role in
understanding of the physics of tsunami generation and propagation and early warning. Currently
several types of measurement systems are working around the world for deep-ocean tsunami such as
Deep-ocean Assessment and Reporting of Tsunami, cabled connected bottom pressure recorders and
GPS buoys. These observation systems successfully provided high-precision tsunami data of the
2010 Chile and 2011 Tohoku tsunamis and others. Japanese GPS buoys contributed to modify the
tsunami warning level during the 2011 Tohoku tsunami issued by Japan Meteorological Agency.
New measurement technologies are also developed such as oceanographic radars and ocean-bottom
electromagnetometers. We here compare these measurement technologies for the deep-ocean
tsunami.
3.1.1 Introduction
Measurement technologies of tsunamis in the deep ocean were greatly developed in the last
decade so that we acquired many high-quality data of tsunami off shore. A satellite altimeter
attached to Jason-1, for example, has detected the 2004 Sumatra tsunami in the midst of the Indian
Ocean (Gower, 2005). Deep-ocean Assessment and Reporting of Tsunami (DART, e.g. Bernard and
Meinig, 2011) buoys distributed in the Pacific Ocean recorded inherent tsunamis caused by the 2010
Chile earthquake and the 2011 Tohoku earthquake. These high-quality records were incredibly
accurate enough to expose incompleteness in the numerical tsunami simulation using the
conventional shallow water theory. There were significant discrepancies between the observed and
computed tsunami waveforms in terms of the time of tsunami arrival and a small drawdown prior to
the first-arriving positive peak during the Chile and Tohoku tsunamis (e.g. Fujii and Satake, 2013).
But soon after, following studies (e.g. Allgeyer and Cummins, 2014, Watada et al., 2014)
successfully improved the tsunami calculation model by adding effects of deformation of the Earth
due to tsunami load and seawater density stratification to the equation of motion. We were able to
obtain the advanced model to simulate the far-field tsunami thanks to progress on the tsunami
measurement technologies in the deep ocean.
There are two advantages in the deep-ocean tsunami measurement. Tsunami is largely deformed
in the shallow sea region by effects such as nonlinearly, bottom friction, refraction, and reflection at
the coast. We can ignore them in the deep ocean so as to get inherent tsunami signals. It gives useful
information to tsunami source analysis. The second advantage is rapidness in tsunami detection. The
tsunami stations off shore can detect tsunami before it attacks to the coast. It must be very important
in terms of tsunami early warning. Tsunami early warning systems are basically based on analyses of
earthquake hypocenters and magnitude derived from seismic observations, because seismic waves
travel much faster than tsunamis. However, we have already known tsunami earthquakes generate
large tsunamis with weak seismic shaking (Kanamori, 1972). Submarine landslides generate tsunami
not accompanied with seismic wave (e.g., Tappin et al., 2001). For giant earthquakes such as the
2010 Chile and 2011 Tohoku earthquakes, the maximum amplitude of the short-period seismic wave
would be saturated, resulting in underestimation of the tsunami warning level via underestimation of
the earthquake magnitude (Hoshiba and Ozaki, 2012). Tsunami measurements off shore can predict
tsunami accurately even if that caused by tsunami earthquakes, submarine landslides or giant
earthquakes because it monitors tsunami signal itself, not seismic wave. It is clearly needed to
embed the real-time data monitored by the deep-ocean tsunami measurements in the tsunami early
warning systems.
Chapter 3 leads the readers to several new real-time basis methods for tsunami prediction that
effectively use the offshore tsunami data. But, for introduction, we here summarize the tsunami
measurement technologies in the deep ocean to discuss their merits and demerits.
3.1.2 Measurement Technologies for Deep-Ocean Tsunami
3.1.2.1 Bottom Pressure Based Technologies
Bottom pressure recorders (BPRs) are common for the tsunami observation in the deep ocean.
The hydrostatic bottom pressure fluctuations during tsunami are converted to the sea level change by
assuming a linear pressure-water height relationship. We have to resolve tsunami of 1-2 cm at least
in the open ocean because tsunami is grown up by approaching the coast so as to have a potential
casing damage in the coastal area. BPR is very sensitive to pressure change. It can resolve pressure
change corresponding to less than a millimeter of water column. DART developed by Pacific Marine
Environmental Laboratory (PMEL), National Oceanic and Atmospheric Administration uses the
BPR for tsunami observation. DART is composed of a BPR and a surface buoy which receives the
data from the BPR and send them to a land station via the Iridium satellite in real time (e.g. Bernard
and Meinig, 2011). This is a battery-powered system. The endurance of deployed system is two
years for the surface buoy and four years for the BPR. DART buoys are many distributed along the
Pacific Rim, near Hawaii, east off USA, Caribbean Sea and Indian Ocean (Figure 1). PMEL
assimilates the DART data in real-time into a tsunami forecast system for far-field tsunami (Titov,
2009).
Japan has conducted cabled BPR observations since 1979. Japan Meteorological Agency (JMA)
installed two BPRs off Tokai region where a large subcudtion earthquake is anticipated to occur
(Meteorological Research Institute, 1980). This was followed by installations of cabled systems off
Boso (Fujisawa, 1986), in Sagami Bay (Eguchi et al., 1999), off Sanriku (Kanazawa and Hasegawa,
1997), off Muroto (Monma et al., 1999), off Tokachi (Hirata et al., 2002). JMA again deployed a
new cable system equipped with 3 BPRs in Tokai region in 2008. Two large-scale comprehensive
cabled systems has been deployed in recent years. One of them named as “Dense Ocean Network
system for Earthquakes and Tsunamis (DONET, Kawaguchi et al., 2012) was deployed on the sea
bottom in the Nankai subduction zone. DONET has 51 BPRs in total to cover the seismic source
regions of 1944 Tonankai and half of the 1946 Nankai earthquakes (Fig. 2). “Seafloor observation
network for earthquakes and tsunamis along Japan Trench (S-net)” will install about 150 BPRs on
the sea bottom where the 2011 Tohoku earthquake occurred (Uehira et al., 2012). The connection of
those BPRs by a submarine fiber-optic cable to stations on land enables us to feed power to the
systems and acquire submarine data in real time. Many ocean-bottom seismometers are also attached
to these cable systems. We will be able to acquire precious data recorded directly above the source
regions of the subduction zone earthquakes by a large number of cabled BPRs and seismometers.
The cabled BPR observations are also carried out in Canada (Barnes et al., 2008) and Taiwan (Hsiao
et al., 2014).
3.1.2.2 GPS Based Technology
The GPS buoy (Fig. 3, Kato et al., 2005) employs the real-time kinematic (RTK) GPS technique
to detect a tsunami before it reaches the coast. It measures the buoy altitude above the Earth ellipsoid
WGS84 every second using RTK-GPS instruments on the buoy and at the base station on land. The
GPS buoy system relies on an approximation that the height from the water surface to the GPS
receiver on the top of the buoy is constant, but this height actually changes owing to the tilting of the
buoy. The system therefore measures also the tilting angle. The 17 GPS buoys were deployed around
Japan (as of July, 2015). The GPS buoys have successfully observed many tsunami including the
2010 Chile and 2011 Tohoku tsunami. During the 2011 Tohoku tsunami, the GPS buoys played an
important role to modify the tsunami warning level issued by JMA. However, the observation
accuracy is lower than BPR measurements. The root-mean-squares of the errors in vertical
positioning is a few centimeters in the case of the fix solution for a distance of 20 km between the
buoy and the base station, which is also affected by several factors, such as the GPS satellite number
and geometry. This is the reason why deployment location is limited within 20 km from the coast.
Displacement of the base station due to crustal deformation during the earthquake also decrease the
accuracy of observation. A new GPS buoy system using precise point positioning technique is under
development in order to solve these problems. If it is accomplished, the base station is not needed,
we can install it anywhere you want in the ocean. Real-time tsunami forecast methods using the data
recorded by the GPS buoy cooperating with BPRs are developed by, for example, Tsushima et al.
(2014) and Takagawa (This issue, Chapter 3.4).
3.1.2.3 Rader Based Technology
Oceanographic radar remote sensing technology may have great potential for tsunami
observation in the open ocean, which uses radio waves to measure the flow directions and velocities
of the surface layer. We would be able to compute tsunami height distribution form surface current
velocities by assuming the long wave theory. It has a wide field of view, which extends
approximately ±45° in direction and beyond 50km in distance while the BPRs and GPS buoys
conduct point-based measurements. Installation and maintenance of oceanographic radar is relatively
easy because of land-based so as to make continuous observations of broad area over long periods.
The first observation of tsunami by the oceanographic radar was carried out during the 2011 Tohoku
tsunami. The radars captured the 2011 Tsunami not only in Japan (Hinata et al., 2011), and but also
in California and Chile (Lipa et al., 2011, HELZEL, 2011).
A satellite altimeter can measure sea surface height with resolution of a few centimeters for the
average of a 5-km circle ocean. By using this instrument, Jason-1 has detected the 2004 Sumatra
tsunami in the midst of the Indian Ocean (Gower, 2005). It showed that the ocean raised up above
the height measured in previous cycles, to a maximum of about 70 cm, and the subsequent 30- to
40-cm drop below the average. Because satellites are continuously moving, timing and location of
the observation strongly depends on satellite tracks. It is unlikely an optimal instrument for the
tsunami early warning. However, it must provide important data for the study of propagation of
trans-ocean tsunami as well as BPRs.
3.1.2.4 Electromagnetic Based Technology
A flow of electrically conducting seawater induces the secondary magnetic field on the Earth’s
primary magnetic field. It has been discussed if tsunami flows can produce measureable magnetic
field (e.g. Manoj et al., 2010). Sugioka et al. (2014) developed high-precision ocean-bottom
electromagnetometer (OBEM) array network deployed in the mid of the Pacific Ocean and reported
on the detection of deep-seafloor electromagnetic perturbations of 10-micron-order induced by the
2010 Chile tsunami. BPRs were also attached to their OBEMs. The time series of recorded vertical
magnetic field indicated very good agreement with that recorded by BPRs. The resolution limit of
OBEM of 0.01 nT can resolve tsunami with cm-order heights. Theoretical relationship also
demonstrated that observation of the EM field with two electric and three magnetic field components,
even at a single station, can extract information about variations in not only sea level, but also the
propagation directions of tsunami waves, the phase velocity of tsunami propagation and its
frequency dependency.
3.1.3 Discussion and Conclusion
The BPRs and GPS buoys are operationally used in tsunami early warning. The BPR can detect
tsunami of a few millimeters and more, while the GPS buoy extract tsunami larger than a few
centimeters. Currently, the installation position of GPS buoy should be located in about 20 km or
less from the coast because the positioning accuracy becomes much worse if the baseline length
exceeds this distance, although they have been improving it to remove the distance limit. BPRs with
DART can be installed in anywhere in the ocean. For the versatility, the BPR observation sounds like
better than the GPS buoy.
However, we have to again notice that BPR observes the water pressure at the bottom of ocean,
not sea level. The BPR data is also disturbed by hydro-dynamic pressure vibrations from seismic
Rayleigh waves and so on. When the station is located near the source, the short-period pressure
vibration overlaps on the long-period hydrostatic components (tsunami). We have to apply an
appropriate filter to extract tsunami component on the pressure data. When the station is located just
above the source, a more serious problem is seen in the hydrostatic component. BPR can detect
changes hydrostatic bottom pressure, which is equivalent to total depth (depth of the sea + tsunami
height). We consider the hydrostatic pressure change at a point on the seafloor uplifted by an
earthquake. Almost no hydrostatic pressure change is expected during earthquakes because the sea
surface and the ocean bottom are dislocated equally, the result being no change in the total depth.
The hydrostatic pressure suddenly decreases afterward as the tsunami propagates. A change in
hydrostatic pressure corresponding to the vertical displacement of the seafloor remains after the
tsunami has passed. Thus, the hydrostatic pressure is simultaneously affected by the tsunami and the
vertical seafloor displacement. It would be difficult to monitor sea surface fluctuations associated
with a near-field tsunami based on hydrostatic pressure changes immediately after the occurrence of
an earthquake. On the other hand, the GPS buoy doesn’t matter where it is relative to the tsunami
source because of direct monitoring of the height of sea surface.
For early warning of near-field tsunamis, we accordingly emphasis necessity of further
development of the tsunami measurement technologies in the deep ocean that enable to observe the
initial sea surface deformation due to the earthquake, that is tsunami initial condition. We look
forward to the new GPS buoy system using precise point positioning technique that doesn’t have the
distance limit. We can distributed it in the region far from the coast where is just above the large
subduction zone earthquakes could occur accompanied by tsunami so that the initial tsunami can be
directly observed immediately after the earthquake. On the other hand, there may be good methods
to retrieve the sea level change from the BPR data even for near-field earthquakes. It might be
possible to the estimate amount of uplift at the seafloor by analyzing the short-period pressure
fluctuations recorded by BPRs or data recorded by hydrophone, or ocean-bottom seismometer. These
kind of analysis methods should also be investigated and developed more.
The point-based measurements in the ocean using the BPRs and the GPS buoys require on-going
maintenance and management with great efforts and cost because we need anyway research vessels
to maintain them. Oceanographic radar, on the other hand, uses only land-based instruments
expecting easy access and maintenance. It can monitor the wide region which extends approximately
±45° in direction and beyond 50km in distance by one station. We would like to ask the
oceanographic radar measurements and the data analysis methods to be developed for the
operational use of the tsunami early warning.
Acknowledgements
Figure 1 was provided by NOAA/PMEL via internet (http://www.ndbc.noaa.gov/dart.shtml).
Figure 3 was provided by the Ministry of Land, Infrastructure, Transport and Tourism. We thank for
their kindness.
REFERENCES
Allgeyer, S. and P. Cummins (2014), “Numerical tsunami simulation including elastic loading and
seawater density stratification”, Geophys. Res. Lett., 41, 2368-2375, doi:10.1002/2014GL059348.
Bernard, E., C. Meinig (2011), “History and future of deep-ocean tsunami measurements”, in
Proceedings of Oceans' 11 MTS/IEEE, Kona, IEEE, Piscataway, NJ, 19–22 September 2011, No.
6106894, 7 pp.
Eguchi, T., Y. Fujiwara, E. Fujita, S. Iwasaki, I. Watanabe, and H. Fujita (1998), “A real-time
observation network of ocean-bottom-seismometers deployed at the Sagami trough subduction
zone, central Japan”, Mar. Geophys. Res., 20, 73–94.
Fujii, Y., K. Satake (2013), “Slip distribution and seismic moment of the 2010 and 1960 Chilean
earthquakes inferred from tsunami waveforms and coastal geodetic data, Pure Appl. Geophys.,
170, 1493-1509, doi:10.1007/s00024-012-0524-2.
Fujisawa, K., S. Tateyama, A. Funasaki (1986), “Permanent Ocean-Bottom Seismograph
Observation System off the Boso Peninsula (in Japanese)”, Sokkou jihou, 53, 127-166.
Gower, J. (2005), “Jason 1 detects the 26 December 2004 Tsunami”, EOS. Trans. Am. Geophys.
Union, Vol. 86, 37-38.
HELZEL (2011), “WERA in Chile observed tsunami signatures”, WERA Newsletter, August, 2011,
2-3.
Hinata, H., S. Fujii, K. Furukawa, T. Kataoka, M. Miyata, T. Kobayashi, M. Mituzani, T. Kokai, and
N. Kanatsu (2011), “Propagating tsunami wave and subsequent resonant response signals
detected by HF radar in the Kii Channel, Japan”, Est. Coast. Shelf Sci., 95, 268-273.
Hirata, K., et al. (2002), “Real-time geophysical measurements on the deep seafloor using submarine
cable in the southern Kurile subduction zone”, IEEE J. Oceanic Eng., 27, 170– 181.
Hoshiba, M., T. Ozaki (2012), “Earthquake early warning and tsunami warning of JMA for the 2011
off the Pacific Coast of Tohoku earthquake (in Japanese with English Abstract)”, Zisin, 64, 155–
168.
Hsiao, N.C., T.W. Lin, S.K. Hsu, K.W. Kuo, T.C. Shin, P.L. Leu (2014), “Improvement of
earthquake locations with the Marine Cable Hosted Observatory (MACHO) offshore NE
Taiwan”, Mar. Geophys. Res., 35, 327-336, doi:10.1007/s11001-013-9207-3.
Kanamori, H. (1972), “Mechanism of tsunami earthquake”, Phys. Earth Planet. Inter., 5, 129-139.
Kanazawa, T., and A. Hasegawa (1997), “Ocean-bottom observatory for earthquakes and tsunami
off Sanriku, north-east Japan using submarine cable”, International Workshop on Scientific Use
of Submarine Cables, Inst. of Electr. and Electr. Eng., Okinawa, Japan.
Kawaguchi, K., Y. Kaneda, E. Araki et al. (2012), “Reinforcement of seafloor surveillance
infrastructure for earthquake and tsunami monitoring in western Japan”, Proc. Oceans2012
Mts/IEEE, Yeosu, May 21-24, 2012.
Lipa, B., D. Barrick, S. Saitoh, Y. Ishikawa, T. Awaji, J. Largier and N. Garfield (2011), “Japan
tsunami current flows observed by HF radars on two continents”, Remote Sensing, 3, 1663-1679.
Manoj, C., A. Kuvshinov, S. Neetu, T. Harinarayana (2010), “Can undersea voltage measurements
detect tsunamis?”, Earth Planet Space, 62, 353-358, doi:10.5047/eps.2009.10.001.
Meteorological Research Institute (1980), “Permanent ocean-bottom seismograph observation
system (in Japanese with English Abstract)”, Tech. Rep. MRI 4, pp. 1 – 233, Tsukuba, Japan.
Monma, H., N. Fujiwara, R. Iwase, K. Kawaguchi, S. Suzuki, and H. Kinoshita (1997), “Monitoring
system for submarine earthquakes and deep sea environment”, MTS/IEEE OCEANS’97, Mar.
Technol. Soc., Columbia, Md.
Sugioka, H., Y. Hamano, K. Baba, T. Kasaya, N. Tada, D. Suetsugu (2014), “Tsunami: Ocean
dynamo generator”, Sci. Rep., 4, 3596, doi:10.1038/srep03596.
Tappin, D.R., P. Watts, G.M. McMurtry, Y. Lafoy, T. Matsumoto (2001), “The Sissano, Papua New
Guinea tsunami of July 1998-offshore evidence on the source mechanism”, Mar Geol. 175, 1–23.
Titov, V.V. (2009), “Tsunami forecasting”, In; The sea, Vol. 15, Tsunamis, (Eds. A. Robinson and E.
Bernard), Harvard University Press, Cambridge, USA, pp. 371-400.
Tsushima, H., R. Hino, Y. Ohta, T. Iinuma, S. Miura, (2014) “tFISH/RAPiD: Rapid improvement of
near-field tsunami forecasting based on offshore tsunami data by incorporating onshore GNSS
data”, Geophys. Res. Lett., 41, 3390-3397, doi:10.1002/2014GL059863.
Uehira, K., T. Kanazawa, S.I. Noguchi et al. (2012), “Ocean bottom seismic and tsunami network
along the Japan Trench”, AGU 2012 Fall meeting, OS41C-1736.
Watada, S., S. Kusumoto and K. Satake (2014), “Traveltime delay and initial phase reversal of
distant tsunamis coupled with the self-gravitating elastic Earth”, J. Geophys. Res. Solid Earth,
119, 4287-4310, doi:10.1002/2013JB010841.
Figure 1. Present DART station distribution (as of July, 2015). The map was provided by
PEML/NOAA (http://www.ndbc.noaa.gov/dart.shtml)
Figure 2. Schematic map of Dense Oceanfloor Network system for Earthquakes and Tsunamis
(DONET).
Figure 3. A GPS buoy.
3.3 Methodology of Optimizing Tsunami Observation Array Using
Tsunami Inversion
Hiroaki TSUSHIMA (MRI, JMA), Joaquín MEZA (UTFSM), César NÚÑEZ (SHOA),
Patricio CATALÁN (UTFSM), Cecilia ZELAYA (SHOA),
Tomohiro TAKAGAWA (PARI), Toshitaka BABA (Tokushima Univ.),
and Yutaka HAYASHI (MRI, JMA),
3.3.1 Introduction
As mentioned in the previous sections, two ocean-bottom pressure (OBP) sensors have been
deployed in deep sea off the Chilean coast, and new ones plan to be deployed. Tsunamis can be
observed at those offshore stations earlier than in the near-field coasts, and therefore real-time
tsunami forecasting based on the data will contribute to improvement of tsunami early warning.
Tsunami forecasting methods based on offshore tsunami data have been developed by several
researchers, including Japanese WG3 members [e.g., Baba et al., 2004, 2013; Tsushima et al., 2009,
2012a, 2012b; Hayashi, 2010, Takagawa and Tomita, 2012a, 2012b, 2014] (more details are
reviewed by Tsushima and Ohta, 2014). Here we focus on a method based on estimation of tsunami
source (tsunami inversion) from the OBP data, because the tsunami data acquired in deep ocean are
not distorted by complex topography, and thus they keep information of tsunami source signature.
One of the key factors for tsunami forecasting based on offshore tsunami data is array
configuration of tsunami stations, because time when sufficient tsunami data can be observed at
offshore stations strongly depends on spatial relationship between a tsunami source and the stations.
If tsunami sensors are located near a tsunami source, tsunami can be detected very quickly. In
addition, when the many sensors surround the tsunami source area densely, we can obtain sufficient
data for knowing detail of the tsunami. However, it may not be realistic to densely deploy OBP
sensors densely over the whole seismogenic zone along the Chilean coast, because the seismogenic
zone along the coast is very long (~3000 km) and an offshore tsunami sensor is not cheap. In
perspective of cost-performance ratio, it is very important to improve forecasting performance by
adding the small number of new offshore tsunami stations. In this section, we propose a
methodology of optimization of offshore tsunami observation array for effective improvement of
tsunami forecasting based on tsunami inversion.
3.3.2 Real-time tsunami forecasting method based on tsunami inversion
A tsunami forecasting method we use was developed by Tsushima et al. [2009]. The method
consists of two steps to predict tsunamis using offshore tsunami data. Firstly, we perform
waveform-inversion analysis to estimate spatial distribution of sea-surface displacement soon after
an earthquake occurrence (i.e. initial sea-surface displacement distribution) by using offshore
tsunami data. Then, we synthesize tsunami waveforms to forecast arrival times and wave heights of
the first tsunamis along the near-field coast (Fig. 1). In the successive calculation, seismic magnitude
and fault geometry are never used, and therefore it can be applicable to tsunami earthquakes
[Kanamori, 1972] and non-interplate earthquakes such as outer-rise normal faulting events. Since the
real-time calculations are based on linear approximation, the computation time is about a few
minutes. In advance of an earthquake occurrence, we calculate tsunami waveforms from unit
tsunami sources (i.e. Green’s functions) to perform inversion and waveform synthesis based on
linear combination of those Green’s functions. The good performances of the method have been
shown by its application to actual tsunami observation data from large earthquakes around Japan
[Tsushima et al., 2011, 2012].
Fig. 3.3.1 Schematic diagram of tsunami forecasting method based on tsunami inversion.
3.3.3 New methodology for optimization of offshore tsunami observation array
We propose a new methodology to determine an optimal array configuration of offshore
tsunami sensors for tsunami forecasting. The methodology is based on numerical simulations of
tsunami forecasting by assuming various offshore tsunami arrays. The outline of the methodology is
as follows: (1) we assume a certain array configuration of offshore tsunami stations; (2) tsunami
waveforms at the offshore and coastal observing points were computed numerically by assuming
fault motion of an earthquake of interest. The resultant waveforms are regarded as observation data
at each stations (hereafter, synthetic observations); (3) tsunami forecasting calculation (inversion and
waveform synthesis) is carried out by using the synthetic observation data at the offshore stations;
(4) evaluation of forecasting accuracy is made by comparison between synthetic observations
(results from step 2) and predictions (results from step 3) at coastal points. For the evaluation, we
define forecasting-accuracy indicators relating to wave heights, arrival times, and waveform of the
first tsunamis in the near-field coasts (here we call score), because these tsunami parameters are very
important for tsunami disaster mitigation; (5) we return to step 1 and assume different offshore
tsunami array configurations, and then steps 2 and 3 are repeated to investigate score for each array
configuration. After a lot of these simulations, we compare the scores to determine an optimal array
configuration.
3.3.4 Effectiveness of our new methodology
To know performance of our new optimization methodology, we applied it to the northern
Chile sea area. Here, we assume a tsunamigenic earthquake that is located in the north of the 2014
Mw 8.2 Pisagua earthquake (Fig. 3.3.2a). In the region, no large earthquakes occur since the 1877
great earthquake: i.e. seismic gap. We produced synthetic observation data by using a fault model
that was proposed by XXXX et al. as a possible earthquake scenario in the seismic gap.
For the inversion analysis, synthetic observation data at two OBP sensors were used in all the
tsunami-forecasting simulations, but a combination of offshore stations were changed every
simulation. One OBP sensor is fixed at outer-rise region off Iquique. The other is different in each
simulation, chosen from the pre-defined offshore observing points inside or near the source (triangle
symbols in Fig. 3.3.2a). Here we focus on tsunami forecasting 10 min after an earthquake occurs,
because at XX min after the earthquake the resulting tsunamis arrive at the coasts close to the source,
and thus the forecast at this elapsed time is very important for tsunami disaster mitigation.
Figure 3.3.2b shows spatial distribution of score for costal site Arica (shown by AR in Fig.
3.2.2b), which is located near the tsunami source. High score, shown by light red in Fig. 3.3.2b, is
distributed around the epicenter (star symbol) and perpendicular to the trench. This means that
deployment of the two OBP sensors, one is inside the high-scored area and the other is off Iquique
(i.e. fixed location in our simulations), contributes to improvement of forecasting accuracy for Arica.
Fig. 3.3.2 Application of our new methodology to a tsunamigenic earthquake in the northern Chile.
(a) Coseismic seafloor deformation due to fault motion. Star indicates an epicenter. Triangles
show observing points at offshore (gray) and coast (red). (b) Distribution of forecasting score
obtained by our optimization methodology for coastal site Arica (AR). Color variation indicates
forecasting score.
3.3.5 Discussion and conclusion
We proposed a methodology to obtain spatial distribution of tsunami-forecasting indicators, i.e.
scores, for optimal placement of offshore tsunami observatories. The results of its test application
show its effectiveness and we can know preferred locations of offshore tsunami sensors for
improvement of tsunami forecasting (Fig. 3.2.2b).
In future, more applications of our optimization methodology to other tsunamigenic
earthquakes are important, because disastrous earthquakes often occur everywhere along the
spatially long seismogenic zone off Chile. Also, investigation assuming various types of offshore
sensors may be important (Fig. 3.3.3), because measurement physical values are different with
sensor type. For example, when OBP sensors are located inside a tsunami source area, it is difficult
to find tsunami signal in the OBP records soon after the tsunami occurs. This is because the
measured OBP records are affected not only due to sea-surface movement (i.e. tsunami), but also
due to permanent seafloor deformation, and thus these two effects cancel out the OBP fluctuation
immediately after an earthquake [e.g., Tsushima et al., 2012; Baba et al., 2013]. On the other hand,
buoy-type sensors such as GPS buoy [e.g., Kato et al., 2005] is not affected by the permanent
seafloor deformation on the records, and thus tsunami signal will appear on the record soon after the
earthquake occurrence. Thus, the score distributions based on OBP sensors and those based on GPS
buoys may be different, even though the deployment locations are same. These detailed
investigations based on our new methodology will contributes to determining placement of offshore
tsunami sensors, resulting in great improvement of tsunami early warning in Chile.
Fig. 3.3.3 Comparison of tsunami-forecasting results between different types of sensors (OBP
sensors and GPS buoys in the same locations).
Acknowledgements
Bathymetric data off Chile and information of Chilean forecasting points were provided by SHOA.
Tsunami Green’s functions applied in tsunami forecasting were calculated by using JAGURS [Baba
et al., 2014]. Figures were prepared using Generic Mapping Tools (GMT) [Wessel and Smith, 1998].
REFERENCES
Baba, T., K. Hirata, and Y. Kaneda (2004), Tsunami magnitudes determined from ocean bottom
pressure gauge data around Japan, Geophys. Res. Lett., 31, L08303, doi:10.1029/2003GL019397.
Baba, T., N. Takahashi, and Y. Kaneda (2013), Near-field tsunami amplification factors in the Kii
Peninsula, Japan for Dense Oceanfloor Network for Earthquakes and Tsunamis (DONET), Mar.
Geophys. Res., doi:10.1007/s11001-013-9189-1.
Baba, T., N. Takahashi, Y. Kaneda, Y. Inazawa, and M. Kikkojin (2014), Tsunami inundation
modeling of the 2011 Tohoku earthquake using three-dimensional building data for Sendai,
Miyagi Prefecture, Japan, in Y. A. Kontar, V. S.-Fandi˜no, and T. Takahashi (Eds.) Tsunami
Events and Lessons Learned, Environmental and Societal Significance, Advances in Natural and
Technological Hazards Research, 35, 89-98, doi:10.1007/978-94-007-7269-43, Springer,
Amsterdam.
Hayashi, Y. (2010), Empirical relationship of tsunami height between offshore and coastal stations,
Earth Planets Space, 62, 269-275, doi:10.5047/eps2009.11.006.
Kanamori, H. (1972), Mechanism of tsunami earthquakes, Phys. Earth Planet. Inter., 6, 346–359,
doi:10.1016/0031-9201(72)90058-1.
Kato, T., Y. Terada, K. Ito, R. Hattori, T. Abe, T. Miyake, S. Koshimura, and T. Nagai (2005),
Tsunami due to the 2004 September 5th off the Kii peninsula earthquake, Japan, recorded by a
new GPS buoy, Earth Planets Space, 57, 297–301.
Takagawa, T. and T. Tomita (2012a), Effects of rupture processes in an inverse snalysis on the
tsunami source of the 2011 Off the Pacific coast of Tohoku Earthquake, Proc. of the
Twenty-second (2012) Int. Offshore and Polar Engineering Conf., Rhodes, Greece, June 17-22.
Takagawa, T. and T. Tomita (2012b), Tsunami Source Inversion with Time Evolution and Real-time
Estimation of Permanent Deformation at Observation Points, J. Jpn. Soc. Civil Eng., Ser. B2
(Coastal Engineering), 68(2), I_311-I_315, doi:10.2208/kaigan.68.I_311 (in Japanese with
English abstract).
Takagawa, T. and T. Tomita (2014), Simultaneous inference of credible interval of inverted tsunami
source and observation error by a hierarchical Bayes model, J. Jpn. Soc. Civil Eng., Ser. B2
(Coastal Engineering), 70(2), I_196-I_200, doi:10.2208/kaigan.70.I_196 (in Japanese with
English abstract).
Tsushima, H., R. Hino, H. Fujimoto, Y. Tanioka, and F. Imamura (2009), Near-field tsunami
forecasting from cabled ocean bottom pressure data, J. Geophys. Res., 114, B06309,
doi:10.1029/2008JB005988.
Tsushima, H., R. Hino.Y. Tanioka, F. Imamura, and H. Fujimoto (2012a), Tsunami waveform
inversion incorporating permanent seafloor deformation and its application to tsunami
forecasting, J. Geophys. Res., 117, B03311, doi:10.1029/2011JB008877.
Tsushima, H., K. Hirata, Y. Hayashi, K. Maeda, and T. Ozaki (2012b), Effect of offshore tsunami
station array configuration on accuracy of near-field tsunami forecast, J. Jpn. Soc. Civil Eng., Ser.
B2 (Coastal Engineering), 68(2), I_211-I_215, doi:10.2208/kaigan.68.I_211, (in Japanese with
English abstract).
Tsushima, H. and Y. Ohta (2014), Review on near-field tsunami forecasting from offshore tsunami
data and onshore GNSS data for tsunami early warning, Journal of Disaster Research, 9(3),
339-357.
Wessel, P., and W. H. F. Smith (1998), New, improved version of generic mapping tools released,
Eos Trans. AGU, 79(47), 579, doi:10.1029/98EO00426.
3.4 Real-Time Inundation Estimation Using GPU Computing
Tomohiro TAKAGAWA (PARI)
Rapid estimation methods of tsunami sources are discussed in the previous section. If we simulate
tsunami propagation and inundation from the estimated source rapidly enough, the simulation result
would be used as a precise forecast of tsunami inundation. In this section, GPU computing is
introduced as one of the potential solutions of a rapid tsunami simulation. GPU is a short expression
of Graphic Processing Unit. It is originally developed for computer graphics, coming to be used for
numerical simulations due to the high performance of parallel computing. In the latest
supercomputer ranking of TOP500 lists June 2015, the No.2 system, Titan, and the No. 6 system, Piz
Daint, use NVIDIA GPUs to accelerate computation (TOP500.Org, 2015). Here, the performance of
a GPU accelerated tsunami simulation (GTS) code made by the author will be shown as a reference
of the GPU performance for tsunami simulation.
The GTS model calculates the tsunami propagation and inundation on the basis of non-linear
long-wave equations (Goto et al., 1997). Comparing with a legacy single thread CPU model, the
GPU code accelerated more than 700 times (Takagawa et al., 2013). A model simulation was
conducted by using GTS. A target site is Iquique (Fig. 1). The computational domain is nested by
grids with the different intervals of 10, 30, 90, 270, 810m. Two-way interaction between the grids is
considered. We performed three calculations with different finest grid intervals of 10, 30, 90 m for
comparison of the accuracy and computational efficiency. The number of grid cells and time steps
are shown in Table. 1. The time step is selected to satisfy the CFL condition. A potential tsunami
source due to the earthquake off of Iquique proposed by Yagi (xxxx) is assumed (Fig. 1).
Calculation result and short discussion for real-time use will be described here.
This figure will be replaced in
the case of Iquique
Fig. 1. Nested grids of the computational domain and the assumed tsunami source
Table 1. Computational conditions and the results about calculation efficiency and accuracy
Grid interval
Number of
Maximum
Time step (s)
Calculation
Inundation
(m)
grid cells
depth (m)
efficiency*
Area (m2)
810
XXX
XXX
N/A
N/A
270
XXX
XXX
N/A
N/A
90
XXX
XXX
XXX
XXX
XXX
30
XXX
XXX
XXX
XXX
XXX
10
XXX
XXX
XXX
XXX
XXX
* Calculation efficiency indicates the ratio of computational time to real time.
the case of Iquique
Fig. 2
the case of Iquique
Fig. 3
REFERENCES
Goto, C., Y. Ogawa, N. Shuto, and F. Imamura (1997) IUGG/IOC time project: Numerical method of
tsunami simulation with the leap-frog scheme, Manuals and guides, 35, UNESCO,
Intergovernmental Oceanographic Commission, Paris.
Takagawa, T., T. Tomita, D. Tatsumi (2013) Development and validation of real-time tsunami hazard
mapping system, American Geophysical Union, Fall Meeting 2013, abstract #NH41B-1716.
TOP500.Org. (2015) Top 500 Supercomputer Sites. Available on the WWW, August 2015.
http://www.top500.org/.
Yagi Y. (XXXX)…..
Chapter 4. Proposal on Communication Protocol between
Organizations Related to Tsunami Warning
4.1 Methods to Determine Thresholds to Issue Precaution Evacuation
based on Modified Mercalli Intensity
Yutaka HAYASHI, Akio KATSUMATA, Kazuki MIYAOKA, Hiroaki TSUSHIMA (MRI, JMA),
Patricio CATALAN, Jose BAQUEDANO (UTFSM), ZELAYA Cecilia (SHOA), Victor
ORELLANA (ONEMI), and Toshitaka BABA (Tokushima Univ.)
4.1.1 Background
Three national organizations in Chile cooperate for tsunami early warning operations. Centro
Sismológico Nacional, Universidad de Chile (CSN) operates real-time seismic analysis, Servicio
Hidrográfico y Oceanográfico de la Armada (SHOA) evaluates the necessity of tsunami alerts or
alarms by using CSN data, and Oficina Nacional de Emergencia del Ministerio del Interior y
Seguridad Pública (ONEMI) is the only responsible in disseminating warnings and prompting
residents to evacuate directly (Fig. 4.1.1).
For example, after the Pisagua earthquake on April 1, 2014 (moment magnitude (Mw) 8.2 by
CSN; Fig. 4.1.2a), a tsunami warning was issued for all coastlines of mainland Chile. Another
example is the case of the earthquake on July 13, 2014 (at 4:56 p.m. local time; local magnitude (M)
5.6 by CSN; Fig. 4.1.2b); based on Modified Mercalli Intensity (MMI) report from coastal cities,
ONEMI issued precautionary evacuation of the coastal edges of Iquique, in which high MMI was
reported (Fig. 4.1.2). However, the evacuation was soon canceled. The false alarm of the first
example for far areas from the seismic source is inevitable because tsunami forecast areas have not
been defined in Chile yet; and the second example exposed an inadequate MMI-based procedure.
Seismic intensity is the rapidest information obtainable in real-time and corresponding to the
tsunami potential. ONEMI's real-time use of seismic intensity for decision of tsunami potential is
considered to be a strong point of Chilean tsunami warning operation from the viewpoint of
rapidness (Fig. 4.1.3). However, as the above-mentioned examples show, there are difficulties due to
the necessity of consistency to the tsunami warning methodology based on magnitude and focus
location.
This section shows that deriving an empirical relationship among MMI, epicentral distance, and
seismic moment will enable us to optimize the standard of decision of MMI-based precautionary
evacuation, so that consistency between Tsunami Forecast Chart (TFC)-based warnings and
MMI-based evacuation is assured. TFC is a tool to decide tsunami warning rank for each tsunami
forecast area by using epicentral distance and seismic magnitude. Possible utilization of TFC for
Chilean tsunami warning system with tsunami forecast area is proposed in Section 2.3.
4.1.2 Methodology
The recipe to derive the adequate standards for improved determination of the area to issue
precautionary evacuation is as follows [Hayashi et al., 2015].
(1) First, moment magnitude (Mw) data from the earthquake catalog by CSN, past tsunami
observation data at the tide gauges by SHOA are collected; some tsunami numerical calculation
results should be added if observation data is not sufficient. These data can derive TFC for Chilean
use as proposed in Section 2.3.
(2) Then, previous empirical equations between MMI, and peak ground acceleration or velocity
(PGA or PGV) are validated by using MMI data collected by ONEMI, and PGV or PGA data by
CSN's observatory and others. Equations in Wald et al. (1999b) which are used for rapid estimation
of instrumental intensity in USA [Wald et al., 1999a] are examples of equations to be tested.
(3) Next, previous empirical equations between a distance (Δ) from a seismic source to an
observation point, and PGA or PGV are validated or modified by using strong motion data and focus
location collected data by CSN for several large earthquakes. The empirical equation considering a
fault size (e.g. Si and Midorikawa (1999, 2000)) should be selected for this purpose, because a fault
size of a large earthquake estimated by scaling law (e.g. Murotani et al., 2013) is too large to be
neglected.
(4) After validation process of (2) and (3), the integrated empirical equation among MMI, Moment
magnitude (Mw), and distance (Δ) can be derived.
(5) Therefore, the lines indicating the condition where specific intensities, e.g. MMI=VII, VIII, and
IX, should be observed can be superscribed on the TFC by using the equations derived in (4).
(6) Finally, by comparing the lines in TFC, the minimum MMI and area for issuing precaution
evacuation can be defined, so that the condition of MMI-based precautionary evacuation are
expected to be most equivalent to TFC-based tsunami warning. For example, the condition for
issuing precautionary evacuation can be defined as "within R km from any coastal city in which S or
higher in MMI is observed", where R and S are parameters to be optimized.
4.1.3 Optimizing Parameters
Among six steps indicated in 4.1.2, tentative results for validation steps (2) and (3) are indicated
in Fig. 4.1.4. and Fig. 4.1.5, respectively. Other steps are under research now.
4.1.4 Conclusions
This section indicates that optimization of parameters of the MMI-based method for consistency
to the TFC method is promising approach to improve the reliability of early tsunami warnings in
Chile. In addition, as described in Section 2.4, intensity-meter can observe objective instrument
intensity together with estimated magnitude from duration of strong instrumental intensity.
Combination use of objective intensity data might improve the reliability of MMI-based precaution
evacuation.
REFERENCES
Hayashi, Y., A. Katsumata, K. Miyaoka, H. Tsushima, P. Catalan, J. Baquedano, C. Zelaya, V.
Orellana, and T. Baba (2015): A method to ensure consistency between tsunami forecast
chart-based warnings and instrumental Mercalli scale intensity-based evacuation. Abstract of
26th General Assembly of the International Union of Geodesy and Geophysics, JP05P-021.
Murotani, S., K. Satake, and Y. Fujii (2013): Scaling relations of seismic moment, rupture area,
average slip, and asperity size for M~9 subduction-zone earthquakes. Geophys. Res. Lett., Vol.
40, 5070-5074. doi:10.1002/grl.50976.
Si, H., and S. Midorikawa (1999): Attenuation relations for peak ground acceleration and velocity
considering effects of fault type and site condition, J. Struct. Construct. Eng., no. 523, 63-70 (in
Japanese).
Si, H., and S. Midorikawa (2000): New attenuation relations for peak ground acceleration and
velocity considering effects of fault type and site condition. Proceedings of 12th World
Conference on Earthquake Engineering, CD-ROM.
Wald, D. J., V. Quitoriano, T. H. Heaton, H. Kanamori, C.W. Scrivner, and C. B. Worden (1999a) :
TriNet "Shake Maps" : Rapid generation of instrumental ground motion and intensity maps for
earthquakes in southern California, Earthquake Spectra, Vol. 15, 537-555.
Wald, D. J., V. Quitoriano, T. H. Heaton, H. Kanamori (1999b): Relationships between peak ground
acceleration, peak ground velocity, and Modified Mercalli Intensity in California. Earthquake
Spectra, Vol. 15, 557-564.
Fig. 4.1.1. Roles of Three National Organizations in Chile Cooperating for Tsunami Early
Warning Operations, and Comparison to Japan.
Fig. 4.1.2. Distribution of Modified Mercalli Intensity for Two Earthquakes with Issuing
Tsunami Evacuation Operations.
Mw, hypocenter, and origin times are analyzed by
CSN, MMIs are collected by ONEMI.
Fig. 4.1.3. Summary of Bases and Timings of Tsunami Warning in Chile and Japan.
Fig. 4.1.4. Comparison of Attenuation Relation between Fault Distance and Peak Ground
Velocity [Si and Midorikawa, 2000], and observed PGV data by seismometer during the
2010 Maule earthquake. (by J. Baquedano)
Fig. 4.1.5. Comparison of Empirical Relation between PGV and Modified Mercalli Intensity
[Wald et al., 1999b], and Observed MMI and PGV data from earthquakes in Chile. (by J.
Baquedano)

Reviews of Recent Progress and Proposals on Chilean

Transcription

Similar documents

TSU-1000 Data Sheet

TSU-AT1000 Data Sheet

Kampu`s poem - Bangkok Patana School

Tsunami Evacuation Map for Rockaway Beach, Oregon

the french tsunami warning center for the mediterranean

poster - Department of Earth and Planetary Sciences

Tsunami Disaster Management in the Philippines