SR 2013-032

GNS Science Consultancy Report 2013/32
July 2013
Sources of Information for Tsunami Forecasting
in New Zealand
A. Barberopoulou
E. D’Anastasio
J. Ristau
X. Wang

Sources of Information for Tsunami Forecasting in
New Zealand
A. Barberopoulou J. Ristau
E. D’Anastasio X. Wang
GNS Science Report 2013/32
July 2013

© Institute of Geological and Nuclear Sciences Limited, 2013
ISSN 1177-2425
ISBN 978-1-972192-71-9
A. Barberopoulou, GNS Science, PO Box 30368, Lower Hutt 5040, New Zealand
J. Ristau, GNS Science, PO Box 30368, Lower Hutt 5040, New Zealand
E. D’Anastasio, GNS Science, PO Box 30368, Lower Hutt 5040, New Zealand
X. Wang, GNS Science, PO Box 30368, Lower Hutt 5040, New Zealand
BIBLIOGRAPHIC REFERENCE
Barberopoulou, A.; Ristau, J.; D’Anastasio, E.; Wang, W. 2013. Sources of
Information for Tsunami Forecasting in New Zealand, GNS Science Report
2013/32. 64 p.

GNS Science Report 2013/32 i
CONTENTS
ABSTRACT ......................................................................................................................... IV
KEYWORDS........................................................................................................................ IV
1.0 INTRODUCTION ........................................................................................................1
2.0 SEISMIC SOURCE.....................................................................................................3
2.1 Tsunami early warning potential for local sources ........................................................ 4
2.2 References .................................................................................................................. 15
3.0 GPS DATA ...............................................................................................................17
3.1 Use of GPS instruments in forecasting ....................................................................... 17
3.2 Current status of GeoNet CGPS network and GPS data processing ......................... 19
3.3 Recommendations....................................................................................................... 22
3.4 References .................................................................................................................. 23
4.0 DEEP OCEAN TSUNAMI WAVEFORM DATA ........................................................25
4.1 Numerical Modelling of the Feb 27, 2010 Chile Tsunami ........................................... 26
4.2 Numerical Modelling of the Mar 11, 2011 Japan Tsunami.......................................... 32
4.2.1 Source Model 1 – USGS Finite Fault Model ................................................... 33
4.2.2 Source Model 2 – cGPS Inverse Solution....................................................... 34
4.3 Tsunami wave arrival times......................................................................................... 36
4.5 References .................................................................................................................. 38
5.0 GEONET TIDE GAUGE NETWORK ........................................................................39
5.1 Literature Review: use of tide gauges in tsunami warning.......................................... 39
5.2 References .................................................................................................................. 40
5.3 Tide Gauge Recordings of Significant Recent Tsunamis............................................ 41
5.3.1 27 February 2010 Chile tsunami ..................................................................... 41
5.3.2 Spectral Analysis............................................................................................. 45
5.3.3 Arrival times..................................................................................................... 47
5.3.4 September 29, 2009 Samoa Tsunami ............................................................ 48
5.5 References .................................................................................................................. 51
6.0 DISCUSSION............................................................................................................53
7.0 ACKNOWLEDGEMENTS.........................................................................................55
EQUATIONS
Equation 4.1 ...................................................................................................................................................36
Equation 4.2 ...................................................................................................................................................37
Equation 5.1 ...................................................................................................................................................46

ii GNS Science Report 2013/32
FIGURES
Figure 2.1 Location of earthquakes used for testing......................................................................................8
Figure 2.2 Squared velocity (red) and displacement (black) records for the first 55 s after the P-
arrival for the Dusky Sound and George Sound earthquakes. .....................................................9
arrival for the Solomon Islands Mw 8.1 and Solomon Islands Mw 7.0 earthquakes.....................10
arrival for the El Salvador Mw 7.3 earthquake.............................................................................11
Figure 2.5 TdT50 calculated over a frequency range of 0.02-0.075 Hz for all earthquakes tested................12
Figure 2.6 If TdT50 ≥ 10 when using a 0.075 Hz high-pass filter (red arrow) the earthquake is a
potential tsunamigenic earthquake.............................................................................................13
Figure 3.1 (Left): map of all the New Zealand Continuous GPS stations operated by GeoNet:
(Right): map of the 37 LINZ stations connected in real time......................................................19
Figure 3.2 GPS Time Series of the station MQZG.......................................................................................20
Figure 3.3 (top): Displacement field after the 2011 Christchurch earthquake from GPS data (blue
arrows, observed: red arrows: modeled); (bottom): slip distribution on the modeled fault
plane ..........................................................................................................................................21
Figure 4.1 Map of the global distribution of DART
®
buoys (red triangles)....................................................25
Figure 4.2 Maximum water level distribution in meters (zero-to-peak) in the Pacific Ocean (GNS
Model 2) for the Mw 8.8 February 27, 2010 Chile tsunami..........................................................27
Figure 4.3 Comparisons between the numerical results with GNS MODEL 2 and DART
®
measurements............................................................................................................................28
Figure 4.4 Comparisons between the numerical results for the improved source (InSAR GPS) and the
DART
®
buoy measurements. .....................................................................................................29
Figure 4.5 Comparisons between the numerical results and the DART® measurements at a spatial
resolution of 2 arc-minutes. ........................................................................................................30
Figure 4.6 Comparisons between the numerical results with INSAR GPS MODEL and the DART®
measurements at a spatial resolution of 1 arc minute. ...............................................................31
measurements at a spatial resolution of 1 arc minute. ...............................................................32
Figure 4.8 Initial vertical seafloor displacement in meters computed with USGS finite fault model for
the March 11, 2011 Japan earthquake.......................................................................................33
Figure 4.9 Comparisons between DART
®
measurements and modeled results with the USGS finite
fault model..................................................................................................................................34
Figure 4.10 Initial vertical seafloor displacement of cGPS inverse model for the March 11, 2011
Japan event................................................................................................................................35
®
measurements and modeled results with the cGPS
inverse model.............................................................................................................................35
Figure 4.12 Comparison between the modelled time history data and the measurements at DART
®
buoy locations throughout the Pacific for the 2010 Chile event..................................................37
Figure 5.1 Currently operating GeoNet tide-gauges (triangles)...................................................................39
Figure 5.2 Tide gauge records of the February 27, 2010 Chile tsunami before detiding. X-axis
shows time for the 24hr records starting at 06:00:00 UTC hrs ...................................................42
Figure 5.3 Detided tide gauge records of the February 27, 2010 Chile tsunami..........................................43

GNS Science Report 2013/32 iii
Figure 5.4 Signal spectra and background spectra for stations AUCT, CHIT, GIST, NAPT, OTAT,
TAUT and WLGT........................................................................................................................45
Figure 5.5 Ratio of tsunami signal of the February 27, 2010 Chile tsunami to background signal as
recorded by the AUCT station. ...................................................................................................47
Figure 5.6 Comparison between the modelled arrival times and the arrival times at the tide gauges .........48
Figure 5.7 Tide Gauge Records of the September 29, 2009 Samoa tsunami before detiding. X-axis
shows time for the 24hr records starting at 06:00:00 (UTC) hrs.................................................49
Figure 5.8 Detided tide gauge records of the Samoa September 29, 2009 Samoa tsunami. X-axis
shows time for the 24hr records starting at 06:00:00 (UTC) hrs (data is sampled at 10hz;
note the 10^4 scalar). Y-axis is amplitude in meters. .................................................................50
TABLES
Table 2.1 Earthquakes used to test tsunami early warning........................................................................14
Table 5.1 Tide gauge instruments operating during the February 27, 2010 Chile tsunami. .......................44
APPENDICES
APPENDIX 1: LOCATION OF GEONET TIDE GAUGES ....................................................59
APPENDIX FIGURES
Figure A 1.1 Location of AUCT (Auckland) tide gauge in the Auckland region (inset) and close up view
of the approximate location of the station within the port (green triangle at centre image).........59
Figure A 1.2 Location of GIST (Gisborne) tide gauge in the north island (see inset) and close up view
Figure A 1.3 Location of NAPT (Napier) tide gauge in the north island (see inset) and close up view of
the approximate location of the station within the port (green triangle at centre image).............61
Figure A 1.4 Location of TAUT (Port of Tauranga) tide gauge in the north island (see inset) and close
up view of the approximate location of the station within the port (green triangle at centre
image). .......................................................................................................................................62
Figure A 1.5 Location of OTAT (Dunedin) tide gauge in the south island (see inset) and close up view
Figure A 1.6 Location of WLGT (Wellington) tide gauge in the north island (see inset) and close up
view of the approximate location of the station within the port (green triangle at centre
image). .......................................................................................................................................64

iv GNS Science Report 2013/32
ABSTRACT
Tsunami science has evolved considerably in the last two decades due to technological
advancements which also helped push for better numerical modelling of the tsunami phases
(generation to inundation). The deployment of DART®
buoys has also been a considerable
milestone in tsunami forecasting. Tsunami forecasting is one of the parts that tsunami
modelling feeds into and is related to response, preparedness and planning. Usually tsunami
forecasting refers to short-term forecasting that takes place in real-time after a tsunami has
or appears to have been generated. In this report we refer to all types of forecasting (short-
term or long-term) related to work in advance of a tsunami impacting a coastline that would
help in response, planning or preparedness.
This report looks at the standard types of data (seismic, GPS, water level) that are available
in New Zealand for tsunami forecasting, how they are currently being used, other ways to
use these data and provides recommendations for better utilisation.
The main findings are:
• Current investigation of the use of seismic parameters quickly obtained after an
earthquake, have potential to provide critical information about the tsunamigenic
potential of earthquakes. Further analysis of this kind of method should be undertaken
to determine a path to full implementation.
• Network communication of the GPS array is not currently at a stage that can provide
data early enough for tsunami warning. It is believed that it has potential but changes
including additional staffing may have to happen before major changes are made to the
data that is currently provided.
• Tide gauge data is currently under-utilised for tsunami forecasting. Spectral analysis
(briefly presented in this report), modal analysis based on identified modes and arrival
times extracted from the records can be useful for forecasting.
• The current study is by no means exhaustive of the ways the different types of data can
be used. This publication is only presenting an overview of what can be done. More
extensive studies with each one of the types of data collected by GeoNet and other
networks should be investigated either through research or through follow-ups to this
report.
KEYWORDS
Tsunami, forecasting, tide gauge, tsunami warning, mitigation

GNS Science Report 2013/32 1
1.0 INTRODUCTION
Tsunami forecasting refers to the prediction of the destructive potential of tsunamis prior to
their arrival. Tsunami forecasts can be long term or short term. Long term forecasting is site-
specific and involves either a deterministic approach where representative scenarios are
considered, or a multitude of scenarios for a probabilistic approach. A well-known output of
long-term tsunami forecasts are inundation maps. A short term tsunami forecast is event
specific and involves an assessment of the source event and its subsequent tsunami in real-
time (http://nctr.pmel.noaa.gov/Pdf/Vasily_2011_Ports.pdf). It is clear that time is an
important factor in short-term or real-time forecasting and for this reason it is particularly
challenging. The focus of this report is on short-term forecasting and the sources of
information that are available in real-time (Titov, 2009). However, where possible we will also
look at data that are available and can provide us with useful information for long-term
forecasting.
There are a variety of data that can be used for tsunami forecasting in real-time. The most
common are seismic and water level data. Other types of data include displacements
measured by GPS. These types of information are available to GNS and other New Zealand
researchers through GeoNet (www.geonet.org.nz).
One of the products of GeoNet is to provide comprehensive coverage of geological hazards
as they unfold and essential information for effective mitigation and response. Although
planning and responding are both important in minimising impacts, this report focuses on the
real-time availability of data that are important in reliable tsunami forecasting. GeoNet uses a
variety of sensors located around New Zealand and the data collected includes:
1. Seismic data: earthquake location, magnitude, ground motion records through
more than 460 currently operating seismographs including (numbers are approximate):
˗ 63 broadband instruments
˗ 116 short period instruments
˗ 231 strong motion instruments
2. Tsunami Waveform data: water levels through
17 tsunami gauges
3. Geodetic data: Strain build up and release through
186 New Zealand continuous GPS stations
(temporary stations could be of use but are not fully discussed here)
Other data include information that GeoNet has access to, and is collected from international
networks e.g. NOAA DART®
buoys (see Figure 4.1) and USGS seismographs.
It is important to be able to determine accuracy, limitations of the data we collect and
corresponding uncertainty in approximating specific quantities (i.e. earthquake
magnitude/seismic moment, focal depth, focal mechanism). Improved understanding of the
above and their effect on tsunami forecasting also feeds into the benefits of GeoNet such as
improved scientific understanding of geophysical phenomena for the timely detection and
warning of hazards, and helps to assess the data collected from the monitoring network. The
ultimate goal is safer communities.

2 GNS Science Report 2013/32
In this report we describe the data that is related to the tsunamigenic source (i.e. seismic,
GPS) and water level data (i.e. DART®
buoys and tide gauge data) with emphasis on the tide
gauges. We discuss work that can be done to improve the estimates of seismic source
parameters through the review of current research. We also discuss recent water level data
and present comparisons between DART®
buoy records with modelling results. Factors that
improve on these comparisons are discussed. Since tide gauge data is more difficult to
interpret and gauge records do not always contain a clean tsunami signal, other types of
analyses are presented on gauge records. The analyses are not exhaustive but do offer
insight into how these kinds of data can be used in forecasting. We also review related
literature which complements this work. In summary, this work is trying to answer the
following questions:
• What types of data are available for use in forecasting?
• How can data be used to improve the estimates of the tsunami source within a short
time of the rupture?
• What factors contribute to discrepancies between water level records and numerical
modelling results?
• Utilising gauge records: What can tide gauge data tell us?
• Is there a way to determine rapidly (i.e. within minutes) whether a slow rupture
earthquake would be tsunamigenic or not? This is particularly important as such
earthquakes would generally not be felt and self-evacuation might not happen.

2.0 SEISMIC SOURCE
Undeniably the most important factor in tsunami forecasting is the source of the tsunami.
Without a good determination of the source even the most sophisticated numerical model will
make predictions of low reliability. For this work we consider seismic sources only; tsunamis
are generated primarily by tectonic sources for which more data is available. Submarine
landslide tsunamis are not only very difficult to detect but it is also very difficult to determine
the failure mechanism and scale of submarine landslides especially if they occur
coseismically.
Tsunami warning centres depend on earthquake information as it becomes available and
their initial warning messages are based on early and preliminary analyses. While distant
sources may provide ample time for sites far away, sites at regional or local distances will not
have this luxury. A satisfactory description of the source can help us to provide estimates of
important information (inundation distance, runup, arrival times etc.) prior to the arrival of the
tsunami waves for the appropriate response. For this reason we need to have some
understanding of the accuracy with which different quantities that are used in tsunami
forecasting are estimated.
To explain the current state of affairs in determining the necessary parameters for tsunami
forecasting we need to separate tectonic sources into local and/or regional sources that are
located less than 3 hours travel time away, and distant sources located further away. Local
and regional sources present a number of challenges that are of particular interest. The work
that can be done to improve current forecasting procedures in place in New Zealand can be
divided into two main categories:
• Work that can be completed in real time or near-real time that can help in evacuation
plans and/or response efforts.
• Work that can be completed prior to an event happening but can still help during the
unfolding of an event.
Local sources (less than an hour away) are more challenging as real-time simulations of
tsunami impact may not be possible given the short time lag between tsunami generation
and tsunami arrival. Accurate determination of various parameter values within a short time
window (i.e. 20-30 minutes) is quite challenging and has generated a great deal of research
(i.e. Kanamori, 1972; Kajiura, 1981; Lomax and Michelini, 2009a/2009b/2011).
Uncertainties to some extent are inherent in the process of tsunami simulations and forecast,
as assumptions on seismic source mechanisms, location, and rupture extent are inevitable
especially when a tight time window is required. Therefore it is not unusual to see many
approximations of the seismic source in an event. An example of this is the Samoa event on
the 29 September 2009 (17:48:11 UTC) whose source mechanism has been interpreted in a
couple of different ways (Beavan et al., 2010; Lay et al., 2010; Satake, 2010). Meanwhile, it
is also important to have reliable information in a short time and the seismic information
available early for tsunamigenic events. DART®
buoys can be and have been used more
recently to improve the source parameters as well (Satake, 1987; Wang and Liu, 2006). This
is possible when an event is far away and time allows for this processing in real time. It is
usually not possible prior to the tsunami arriving at the coast at regional or local distances.

For regional sources, it is also difficult but possible to perform quick calculations in a timely
manner in order to help in tsunami warning. We will discuss recent achievements and a
promising methodology that could lead to tsunami early warning in the next section.
2.1 TSUNAMI EARLY WARNING POTENTIAL FOR LOCAL SOURCES
In early September 2012 GeoNet began using SeisComP3 (SC3) for routine earthquake
analysis. SC3 analyses seismic data in real-time and provides rapid locations and
magnitudes of earthquakes. Preliminary locations are typically available within 1-2 minutes
for onshore earthquakes and 2-4 minutes for offshore earthquakes depending on epicentral
distance. Therefore, SC3 can potentially provide warning of a large, potentially tsunamigenic
offshore earthquake within 5 minutes. The main limitations with SC3 regarding tsunami early
warning are often poor automatic locations and magnitudes for earthquakes outside of the
network, in particular north of the North Island and south of the South Island. This can result
in false alarms or worse, no alarm for a potential tsunamigenic earthquake. SC3 also does
not currently determine focal mechanisms which are a crucial parameter for determining
whether an earthquake may have generated a tsunami. Work is ongoing to implement
moment tensor analysis in SC3; however, it will likely require an analyst to calculate a
moment tensor solution which will delay the release of focal mechanism information.
Preliminary work on developing and testing a tsunami early warning procedure for New
Zealand, based on techniques discussed in Lomax and Michelini (2012) (hereafter referred
to as LM2012) and Wu and Kanamori (2005) (hereafter referred to as WK2005), has been
carried out and the initial results show some promise. It uses the first 60 s of data after the P-
arrival which allows for a rapid assessment (~ 5 minutes) of the tsunamigenic potential of an
earthquake. The method is not dependant on focal mechanism or magnitude, although
LM2012 also discuss a technique for a rapid magnitude estimate. As the primary concern is
purely whether or not a tsunami may have been generated regardless of the magnitude, no
attempt is made here to estimate the magnitude.
There are two parts to the method, and when used in conjunction they can potentially provide
a rapid tsunami early warning.
The first part to the method relates to the work of LM2012. LM2012 (and references therein)
outline a technique for estimating the likelihood (T50) that the high-frequency apparent
rupture duration (T0) of an earthquake exceeds 50-55 s. Long rupture duration will be
indicative of a large earthquake with a long fault length and/or a slow rupture speed. T50 is
also sensitive to depth. Large shallow earthquakes which rupture the surface and generate
tsunamis will tend to have a fault length much greater than the fault width, and therefore long
rupture duration. Deep earthquakes will tend to have a fault length comparable to fault width,
and therefore shorter rupture duration. T50 can then discriminate between a potentially
tsunamigenic shallow earthquake and an earthquake of the same magnitude but too deep to
generate a tsunami.
To calculate T50 the data is bandpass filtered at 1-5 Hz. The RMS amplitude is calculated for
the first 25 s after the P-arrival (A25) and from 50-60 s after the P-arrival (A50). The ratio,
A50/A25, is the duration exceedance (DE) level designated T50. If T50 is greater than 1.0 then
the apparent rupture duration is likely to exceed 50-55 s and the earthquake is potentially
tsunamigenic.

The second part to the method is calculating the dominant period (Td) of the earthquake
which is based on WK2005 (and references therein). The tsunami discriminant is then given
as TdT50 with LM2012 defining TdT50 ≥ 8 s as a potential tsunamigenic event.
The primary motive was to test if this method could work for tsunamigenic events at
distances less than ~ 3000 km from New Zealand, and particularly less than 1000 km. If the
tsunami travel time is relatively short, e.g. < 3 hours, and no DART buoy information is
available, then a method which can rapidly assess the potential tsunami threat is of high
importance. A number of events were chosen to test the proposed method and were
selected to represent different categories: large earthquakes that generated measurable
tsunamis, earthquakes that were too small to generate tsunamis, and large earthquakes that
did not generate tsunamis (Figure 2.1). As the number of potential tsunamigenic events close
to New Zealand is limited, events from other parts of the world were also used. For events at
local-to-regional distances from New Zealand, with the furthest events being in the Solomon
Islands and Vanuatu, GeoNet data was used. For other events data was retrieved from IRIS
(Incorporated Research Institutions for Seismology), or the Canadian National Seismograph
Network (CNSN) operated by the Geological Survey of Canada in order to only use data with
source-receiver distances of a few hundred kilometres to less than 3000 km.
An important area of concern is the ability to rapidly detect a slow-rupture tsunamigenic
earthquake. In March 1947 a moment magnitude (Mw) ~ 7.0-7.1 earthquake off the east
coast of the North Island generated a tsunami with local run-up of > 10 m. This earthquake is
known to have been a slow-rupture tsunamigenic earthquake. In May 1947 a second slow-
rupture tsunamigenic earthquake struck off the east coast of the North Island with local run-
up of 4-5 m. The ability to rapidly detect a slow-rupture earthquake is of high importance as it
may not be felt strongly, and the initial magnitude would likely be relatively low (M < 7.0-7.5)
to imply a tsunami has been generated. Earthquakes which are known to be slow-rupture
tsunamigenic events are not common; however, three events which are known to be, or
thought to be slow-rupture earthquakes are included here.
For the method to be useful it also needs to avoid false detections, i.e. triggering an alarm for
a non-tsunamigenic earthquake. A number of events which did not generate tsunamis were
tested to ensure they were not identified as potential tsunamigenic events. These included
local events (source-receiver distances of several hundred kilometres) with Mw < 7, and more
distant events (source-receiver distances ~2000-3000 km) with Mw ~ 7.0-7.5. Table 2.1 lists
all of the events tested; a brief description of the tsunami generating events is given here.
1. 23/12/2004 Macquarie Ridge Mw 8.1: This was a strike-slip earthquake ~ 500-600 km
south of the South Island. Despite being a strike-slip event it generated a measurable
tsunami.
2. 01/04/2007 Solomon Islands Mw 8.1: This earthquake generated a destructive local
tsunami and a measurable tsunami in New Zealand. A tsunami alert was issued for
coastal areas in New Zealand.
3. 15/07/2009 Dusky Sound Mw 7.8: This was the largest earthquake in New Zealand in
nearly 80 years and generated a 2 m local tsunami. Although not considered a slow-
rupture earthquake, it did have a relatively slow rupture velocity and produced very little
high-frequency energy.
4. 28/10/2012 Haida Gwaii Mw 7.7: This earthquake occurred off the west coast of
Canada and generated a small tsunami.

5. 27/08/2012 El Salvador Mw 7.3: A known slow-rupture earthquake. It generated a > 5
m local tsunami and had a long rupture time compared with its magnitude. It had an
energy magnitude (Me) 6.4 and body-wave magnitude (mb) 6.0.
6. 25/10/2010 Sumatra Mw 7.7: Generated a 7 m local tsunami and may have been a
slow-rupture earthquake.
7. 17/07/2006 Java Mw 7.7: A known slow-rupture earthquake which generated a tsunami
with tsunami heights exceeding 6 m at locations along the south coast of Java.
8. 06/02/2013 Solomon Islands M7.0 This earthquake followed a larger earthquake of
M8.0. This earthquake did not generate a tsunami.
The Mw 7.3 El Salvador earthquake is of particular interest as it produced a large tsunami
compared with its magnitude. This event may be similar to the 1947 earthquakes off the east
coast of the North Island. When integrating the velocity records to displacement WK2005
applied a 0.075 Hz high-pass Butterworth filter to remove the low-frequency drift after
integration. The choice of the low-frequency cut-off for the high-pass filter was found to be an
important part of the tsunami warning method proposed here. By experimenting with a range
of low-frequency cut-offs from 0.02-0.075 Hz, it showed that the choice of low-frequency cut-
off can have a significant effect on Td which relates to the tsunamigenic potential of the
earthquake.
The 2009 Mw 7.8 Dusky Sound earthquake and 2007 Mw 6.7 George Sound earthquake can
be used as an example as they had similar locations in the Fiordland region. Figure 2.2
compares squared displacement and squared velocity records for the first 55 s after the P-
arrival from station HIZ in the North Island for both earthquakes. In Figure 2.2a and
Figure 2.2c a 0.02 Hz high-pass filter was used for Dusky Sound and George Sound
respectively, and in Figure 2.2b and Figure 2.2d a 0.075 Hz high-pass filter was used for
Dusky Sound and George Sound respectively. It is clear that the amplitudes for Dusky Sound
when filtered using a low-frequency cut-off of 0.02 Hz are much larger than the other three
cases. Therefore, the integral of the displacement record in Figure 2.2a will be much greater
than for the other three cases.
Similar to Figure 2.2, Figure 2.3 compares a 2007 Mw 8.1 Solomon Islands earthquake which
generated a destructive earthquake (Figure 2.3a and Figure 2.3b), and a Mw 7.0 Solomon
Islands earthquake which did not generate a tsunami (Figure 2.3c and Figure 2.3d). For the
Mw 8.1 event the displacement record amplitudes when filtered using a 0.02 Hz high-pass
filter are much larger than the velocity record amplitudes. For the other three cases the
displacement and velocity amplitudes are similar. Figure 2.4 shows the results for the 2012
Mw 7.3 El Salvador slow-rupture event. Figure 2.4a compares the squared displacement and
velocity records using a 0.02 Hz high-pass filter, and Figure 2.4b using a 0.075 Hz high-pass
filter. When using a 0.02 Hz high-pass filter the displacement record amplitudes are much
larger than the velocity record amplitudes. When using a 0.075 Hz high-pass filter the
velocity record amplitudes are larger than the displacement record amplitudes.
Figure 2.5 plots TdT50 for all of the earthquakes listed in Table 2.1 using a range of low-
frequency cut-offs from 0.02-0.075 Hz. The solid and dotted lines are tsunamigenic
earthquakes with the dotted lines being the slow-rupture events. Dashed lines are large
magnitude earthquakes which did not generate tsunamis and the dash-dot-dash lines are the
smaller (M≤ 7) events. There is a clear distinction between the tsunamigenic events and non-
tsunamigenic events as the low-frequency cut-off becomes lower. Some of the tsunamigenic
events (e.g. Solomon Islands Mw 8.1) have a larger TdT50 than the non-tsunamigenic events

at higher frequencies, but others (e.g. Dusky Sound Mw 7.8; Macquarie Ridge Mw 8.1) do not.
However, at lower frequencies (e.g. < 0.04 Hz) these events clearly distinguish themselves
from the non-tsunamigenic events. It is particularly encouraging that the slow-rupture events
have large TdT50 values which suggest they can be detected rapidly. The El Salvador Mw 7.3
earthquake had Me 6.4 and mb 6.0, and therefore its local magnitude (ML) would likely have
been similar (ML ~ 6.0-6.5). However, Figure 2.5 clearly distinguishes it from events such as
George Sound Mw 6.7 or Gisborne Mw 6.7 which were not tsunamigenic.
A tsunami warning can then be defined as shown in Figure 2.6:
• If TdT50 ≥ 10 at 0.075 Hz the earthquake is a potential tsunami threat.
• If TdT50 ≥ 15 at frequencies < 0.075 Hz and > 0.04 Hz the earthquake is a potential
tsunami threat.
• If TdT50 ≥ 20 at frequencies ≤ 0.04 the earthquake is a potential tsunami threat.
• If TdT50 < 20 and ≥ 15 at frequencies ≤ 0.04 the earthquake is likely not a potential
tsunami threat but should be monitored.
• If TdT50 remains < 10 at frequencies 0.02-0.075 Hz there appears to be no tsunami
threat.
For SC3 with real-time data streaming and reliable automatic event detection it should be
relatively straightforward to implement this method. An important point is that this method is
based on the frequency content of the waveforms and is independent of focal mechanism.
Therefore, it can only provide information as to whether the frequency content of the
earthquake is characteristic of a tsunamigenic earthquake, but not whether it generated a
tsunami or how large the tsunami might be.

Figure2.1Locationofearthquakesusedfortesting.Redstarsareeventswhichgeneratedmeasurabletsunamisandbluecirclesareeventswhichdidnotgeneratetsunamis.

Figure 2.2 Squared velocity (red) and displacement (black) records for the first 55 s after the P-arrival for the
Dusky Sound and George Sound earthquakes. (a) Velocity and displacement records using a 0.02 Hz high-pass
filter for Dusky Sound. (b) Velocity and displacement records using a 0.075 Hz high-pass filter for Dusky Sound.
(c) Velocity and displacement records using a 0.02 Hz high-pass filter for George Sound. (d) Velocity and
displacement records using a 0.075 Hz high-pass filter for George Sound. In (a) the amplitudes of the squared
displacement record are much larger than for the velocity record, whereas the displacement and velocity
amplitudes are similar in (b)-(d). Note also the change in scale in (b)-(d).

Solomon Islands Mw 8.1 and Solomon Islands Mw 7.0 earthquakes. (a) Velocity and displacement records using a
0.02 Hz high-pass filter for Solomon Islands Mw 8.1. (b) Velocity and displacement records using a 0.075 Hz high-
pass filter for Solomon Islands Mw 8.1. (c) Velocity and displacement records using a 0.02 Hz high-pass filter for
Solomon Islands Mw 7.0. (d) Velocity and displacement records using a 0.075 Hz high-pass filter for Solomon
Islands Mw 7.0. In (a) the amplitudes of the squared displacement record are much larger than for the velocity
record, whereas the displacement and velocity amplitudes are similar in (b)-(d). Note also the difference in scale
in (a), (b) compared to (c) and (d).

El Salvador Mw 7.3 earthquake. (a) Velocity and displacement records using a 0.02 Hz high-pass filter for El
Salvador Mw 7.3. (b) Velocity and displacement records using a 0.075 Hz high-pass filter for El Salvador Mw 7.3.
In (a) the amplitudes of the squared displacement record are much larger than for the velocity record, whereas
the displacement and velocity amplitudes are similar in (b). Notice also the difference in scale in (a) and (b).

Figure 2.5 TdT50 calculated over a frequency range of 0.02-0.075 Hz for all earthquakes tested. There is a
clear distinction between the tsunamigenic earthquakes (solid and dotted lines) and non-tsunamigenic
earthquakes (dashed and dash-dot-dash lines) as the low-frequency cut-off becomes lower. This becomes
particularly evident at frequencies <~ 0.04 Hz.

Figure 2.6 If TdT50 ≥ 10 when using a 0.075 Hz high-pass filter (red arrow) the earthquake is a potential
tsunamigenic earthquake. Else, if TdT50 enters the red area in the 0.02-0.075 Hz range it is a potential
tsunamigenic earthquake. If TdT50 is in the yellow area in the 0.02-0.04 Hz range it likely is not a potential
tsunamigenic threat but should be monitored. If TdT50 < 10 over the 0.02-0.075 Hz range it is unlikely to be a
potential tsunami threat.

Table 2.1 Earthquakes used to test tsunami early warning.
Date Name Lat Lon Mw Tsunami Me mb
23/12/2004 Macquarie Ridge -50.145 160.365 8.1
Small tsunami recorded in
New Zealand.
8.2 n/a
1/04/2007 Solomon Islands -8.481 156.978 8.1
2 m tsunami recorded in
Papua New Guinea.
7.7 n/a
15/07/2009 Dusky Sound -45.751 166.577 7.8 Yes 7.3 n/a
06/02/2013 Solomon Islands -10.479 165.772 7.0 No
28/10/2012 Haida Gwaii 52.742 -132.131 7.7 Yes 7.6 n/a
27/08/2012 El Salvador 12.278 -88.528 7.3
5 m local tsunami - slow-
rupture earthquake.
6.4 6.0
25/10/2010 Sumatra -3.484 100.114 7.7
7 m local tsunami - possible
slow-rupture earthquake.
7.2 n/a
17/07/2006 Java -9.222 107.320 7.7
1.8 m local tsunami - slow-
rupture earthquake.
6.8 n/a
21/08/2003 Fiordland -45.193 166.830 7.1 Landslide induced tsunami. 6.9 n/a
9/30/2007 Auckland Islands -49.420 163.840 7.3 No 7.2 n/a
10/21/2011 Raoul Island -28.998 -176.183 7.4 No 7.2 n/a
7/10/2009 Vanuatu -13.052 166.187 7.7 No 7.3 n/a
30/01/2007 Macquarie Ridge -54.888 145.733 6.9 No 7.3 n/a
15/10/2007 George Sound -44.721 167.302 6.7 No n/a n/a
20/12/2007 Gisborne -38.890 178.537 6.7 No n/a n/a
18/02/2009 Kermadec Islands -27.464 -176.324 7.0 No 7.1 n/a

2.2 REFERENCES
Weblinks
SeisComP3 http://www.seiscomp3.org
Articles
Beavan, J., Wang, X., Holden, C., Wilson, K., Power, W., Prasetya, G., Bevis, M. and Kautoke, R.
(2010). Near-simultaneous great earthquakes at Tongan megathrust and outer rise in
September 2009. Nature, Vol 466. 19 August 2010, doi:10.1038/nature09292.
H. Kanamori, (1972). Mechanism of tsunami earthquakes, Phys. Earth Planet. Inter., 6, pp. 356–359
K. Kajiura, (1981). Tsunami energy in relation to parameters of the earthquake fault model, Bull.
Earthq. Res. Inst., 56, Univ. Tokyo, pp. 415–440
Lay, T., Ammon, C. J., Kanamori, H., Rivera, L., Koper, K. D. and Hutko, A. R. (2010). The 2009
Samoa–Tonga great earthquake triggered doublet. Nature, Vol 466. 19 August 2010,
doi:10.1038/nature09214
Lomax, A. & Michelini, A., (2009a). Mwpd: a duration-amplitude procedure for rapid determination of
earthquake magnitude and tsunamigenic potential from P waveforms, Geophys. J. Int., 176,
200–214, doi:10.1111/j.1365-246X.2008.03974.x.
Lomax, A. & Michelini, A., (2009b). Tsunami early warning using earthquake rupture duration,
Geophys. Res. Lett., 36, L09306, doi:10.1029/2009GL037223.
Lomax, A. and Michelini, A. (2011). Tsunami early warning using earthquake rupture duration and P-
wave dominant period: the importance of length and depth of faulting. Geophysical Journal
International, 185: 283–291. doi: 10.1111/j.1365-246X.2010.04916.x
Lomax, A., and A. Michelini (2012). Tsunami early warning within five minutes, Pure and Applied
Satake, K. (2010). Double trouble at Tonga. Nature, Vol.466, 19 August 2010.Geophysics,
doi: 10.1007/s00024-012-0512-6.
Satake, K. (1987). “Inversion of tsunami waveforms for the estimation of fault heterogeneity: Method
and numerical experiments,” J. Phys. Earth 35, 241–254.
Wang, X. and Liu, P.L.-F. (2006). An analysis of 2004 Sumatra earthquake fault plane mechanisms
and Indian Ocean tsunami. Journal of Hydraulic Research Vol. 00, No. 0 (2006), pp. 1–8
Wu, Y.-M, and H. Kanamori (2005). Experiment on an onsite early warning method for the Taiwan
early warning system, Bulletin of the Seismological Society of America, 95, 347-353, doi:
10.1785/0120040097.

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3.0 GPS DATA
3.1 USE OF GPS INSTRUMENTS IN FORECASTING
Near field GPS stations can record the static (permanent coseismic offset) and dynamic
(waveform) displacement of the surface during an earthquake. With the increasing number of
permanent GPS stations worldwide, the use of GPS data in seismic source inversion is now
widely adopted, and, when combined with tsunami modeling and sea level data, GPS helps
to improve the earthquake source parameters and rupture process (see, e.g., Pietrzak et al.,
2007, Vigny et al., 2005).
One of the main problems of tsunami early warning is the rapid determination of the
earthquake source parameters (magnitude, slip distribution, geometry), and thus of its
tsunamigenic potential. The earthquake size of the two most deadly tsunamis that occurred
in recent time (the Mw 9.2 2004 Indian Ocean Sumatra-Andaman earthquake and the Mw 9.0
2011 Tohoku-Oki earthquake) was underestimated within the first hours, leading to
underestimation of the size of the tsunami. The Sumatra earthquake of 26 December 2004
was estimated at Mw 8.0 within the first minutes. Similarly the Japan earthquake of March 11,
2011 was initially estimated at 7.9. It thus appears that in very large earthquakes (~Mw9.0)
this is not uncommon. With respect to the Sumatra-Andaman earthquake in particular, it took
9 hrs for the magnitude to be estimated at Mw9.0 and the rupture length to be suggested as
450km. It was several days later that the magnitude and rupture length of the source were
finally available.
The tsunami potential of an earthquake is estimated through its seismic moment, M0. The
problem with the use of this quantity for the rapid assessment of the tsunami potential of an
earthquake is that it uses the longest period surface waves (300s-500s). In the case of
earthquakes with longer duration than those periods, the rapid moment magnitude estimate
can be problematic. As showed by Blewitt et al., 2006 for the 2004 Sumatra-Andaman
earthquake, if GPS data are available, they could be automatically processed and inverted to
provide initial surface displacement fields within 15-30 min, and to infer the earthquake
moment magnitude and tsunamigenic potential (Blewitt et al., 2006, Pietrzak et al., 2007;
Sobolev et al., 2007) with a timing useful for tsunami early warning.
As described by Hoechner et al. (2008): “Of first order importance as initial condition for the
tsunami is the static deformation of the sea bed resulting from the coseismic relative motion
between the subducted oceanic and the overriding continental plate. It can be computed if
the slip distribution at the fault zone is known. While teleseismic inversions yield a detailed
picture of rupture timing and extent (Kruger and Ohrnberger, 2005), GPS-inversions provide
a more direct measure for slip (Banerjee et al., 2007), are available shortly after an
earthquake (Blewitt et al., 2006), and could even be used to follow rupture propagation in
near-real time (Sobolev et al., 2007).”
Currently, some efforts are done worldwide to set up tsunami early warning systems that use
the contribution of GPS data.
1. The German Indonesian Tsunami Early Warning System (GITEWS) is a 24/7 warning
center operating in Jakarta and specifically designed for Indonesia, where tsunami
early warning systems have to deal with very little warning times (30-40 minutes or
less) and thus the provision of reliable arrival times and inundation information to local
tsunami warning centers in a short time is rather challenging given the distance of

coastlines to the trench. Within this project, a network specifically designed for
Indonesia has been build up following the idea of Sobolev et al., 2007 (the “GPS Shield
approach”), as well as a specific system for near real time GPS data analysis. The
network is constituted by seismological, GPS, tide gauges and buoys instrumentation.
Falck et al. (2010) in his article give an end-to-end overview of the GITEWS GPS
component system. The GPS early warning system uses four types of GPS sensors
with varying functions that include ground motion detection and measurements on the
ocean, among many. Sensors operate in two modes (normal mode and tsunami mode)
and are on onshore and offshore locations.
Real time GPS data are collected and processed to calculate the displacement
occurred on the GPS station in the case of an earthquake. This system is still not
implemented for tsunami pre-alert from GPS displacement calculation (Falck et al.,
2010), but the idea is to use displacement values within a simulation module (GITEWS
SIM), which reads pre-calculated scenario results and picks the most probable tsunami
scenario based on a matching process.
2. The Great (GPS Real Time Earthquake and Tsunami) Alert project, is a NASA-
sponsored project whose main effort is to develop a global tsunami early warning
system based on available real-time GPS infrastructure. This project is still developing
a “prototype operational system to enable more accurate and timely assessment of the
magnitude and mechanism of large earthquakes, as well as the magnitude and
direction of resulting tsunamis” (http://www.gdgps.net/products/great-alert.html).
The main idea underlying this project is that surface displacements detected from a global
network of real-time GPS tracking instruments, could be used to rapidly model the
earthquake and thus initialize parameters for real-time modeling of tsunami generation
(Blewitt et al., 2006). The GPS static displacement could be used together with the
information available within few minutes after the earthquakes (seismic epicenter, subduction
zone geometry) and be compared to static displacements “predicted from a suite of
physically realistic rupture models, which could in principle be calculated and stored before
the earthquake. [….] Then, a fingerprint approach is used to estimate the magnitude of the
earthquake directly from the GPS time series” (Blewitt at al., 2006).
These two different projects cover the two main aspects of a tsunami early warning system:
a) a “distant earthquake” early warning (that is the main purpose of the Great Alert project,
which use the global GPS network and is thus potentially capable to detect very large
earthquakes worldwide, depending only on network infrastructure), and b) a “near field
earthquake” tsunami early warning (GITEWS project), which is the most challenging for a
New Zealand tsunami early warning system.
Two important challenges of early warning systems are to be able to model in nearly real
time the seismic source and also to distinguish between similar events (similar rupture
parameters) with different geometry or slip distribution, that could result in rather differing
tsunamis. This is demonstrated by Sobolev et al. (2007) for two very similar earthquakes with
the same seismic moment but very different tsunamis demonstrating that it is not enough to
know the magnitude, location of the earthquake and the fault geometry. Slip distribution at
depth appears to be the controlling factor of the difference shown in maximum tsunami
amplitude distribution. This seems to become more critical as we move from distant to local
events. In this study, Sobolev et al. (2007) suggest two models with differing static
displacements at the surface. Both models were selected as they fit uplift data from
Indonesia associated with the 1833 offshore earthquake near Sumatra The two models

produce very different tsunamis with model 2 producing maximum wave heights at least 5
times larger than model 1. The authors suggest the use of the GPS Shield method discussed
above (see GITEWS) to areas lying in close proximity to subduction zones i.e. New Zealand.
3.2 CURRENT STATUS OF GEONET CGPS NETWORK AND GPS DATA PROCESSING
The New Zealand Continuous GPS Network, operated and maintained by GeoNet, is made
of more than 180 continuous GPS sites including 37 sites of the Land Information
New Zealand (LINZ) PositioNZ network (Figure 3.1). The GeoNet data centre downloads
hourly GPS raw data at 30 s sampling rate, and produces hourly and daily 30 s rinex files
available via anonymous ftp at ftp.geonet.org.nz/gps/rinex.
The 37 LINZ sites are connected in real time to the GeoNet data centre, and acquire GPS
(and Glonass) observations at 1 second sampling rate. These latter sites are then used by
LINZ to support positioning activities in New Zealand. The PositioNZ network, as currently
configured, could provide real-time position estimations with an accuracy of a few
centimetres.
Figure 3.1 (Left): map of all the New Zealand Continuous GPS stations operated by GeoNet: (Right): map of
the 37 LINZ stations connected in real time.
(figure from http://info.geonet.org.nz/display/equip/New+Zealand+Continuous+GPS+Network).
The daily position of GeoNet stations are automatically calculated each day for the preceding 24
hours, and GPS time series are available via anonymous ftp at ftp://ftp.geonet.org.nz/gps/solutions.
For a comprehensive review of the processing scheme, please refer to the GeoNet website
(http://info.geonet.org.nz/display/appdata/GPS+Processing+Notes, and references therein).
Currently, 1Hz real time GPS data collected from the 37 LINZ stations are not processed
real-time at GeoNet, and only daily solutions are available. However, it is likely to be
implemented at GeoNet in the future.

GeoNet GPS stations have already been used in earthquake source modelling. Here we
show some examples from the Canterbury earthquakes that show how GPS recordings of
static coseismic offset help to constrain earthquake source parameters.
In Figure 3.2, the GPS time series of the permanent station MQZG, located near the city of
Christchurch, is shown. Daily displacements (in mm) with respect to an initial position are
shown for the North, East and Up components. The daily position time series of MQZG,
show three clear offsets, due to the 2010 Darfield and 2011 Christchurch earthquakes.
These displacements, together with the ones recorded by other temporary GPS stations in
the epicentral area, have been modeled to estimate the fault location and slip distribution of
the largest earthquakes in the Canterbury earthquake sequence (see, among many others,
Beavan et al., 2011; Figure 3.3).
Figure 3.2 GPS Time Series of the station MQZG.
(figure from http://magma.geonet.org.nz/resources/gps/timeseries).

Figure 3.3 (top): Displacement field after the 2011 Christchurch earthquake from GPS data (blue arrows,
observed: red arrows: modeled); (bottom): slip distribution on the modeled fault plane (figure from Beavan et al.,
2011).
As can be seen for the 2011 Christchurch example, GPS stations in the near-field are able to
record the coseismic offset of major earthquakes with high accuracy. In the case of a great
earthquake (M > 8) occurring off-shore of New Zealand on the subduction interface, the
GeoNet GPS stations located on the coast would be able to catch accurately the static and
dynamic displacement field. The challenge for an early warning system will be thus to timely
collect and process these recordings.

3.3 RECOMMENDATIONS
The GeoNet continuous GPS network is presently far from being readily available for a
tsunami early warning system. Although the network is currently constituted by more than
180 CGPS sites, only 37 of them provide data in real time and a real time processing
procedure is not currently available. The network has the potential to become an active part
of a tsunami early warning monitoring system, but some improvements are required for that
scope:
1. Depending on the “target” earthquake size, the current number of real time GPS
stations may be insufficient to define a reliable displacement field. Therefore, an
increase of the number of real time GPS stations is recommended. This would require
a substantial effort from GeoNet staff and a significant increase of available funds,
since the transmitting equipment should be upgraded to real time.
2. A real time processing procedure should be developed, tested and made automatic in
order to be useful for an early warning system.
3. A “fingerprint” approach (Blewitt et al., 2006), or some other system to model near real-
time displacements obtained from GPS stations should be developed as well.
4. Identify the factors controlling tsunami impact similar to the work by Sobolev et al.
(2007). Studies addressing the above should be pursued further.
A brief review by Ken Gledhill of the role of GeoNet for a tsunami early warning system is
available on these two web pages:
http://geonet-scienceinaction.blogspot.co.nz/2012/10/geonet-and-tsunami-part-one.html
http://geonet-scienceinaction.blogspot.co.nz/2013/01/geonet-and-tsunami-part-two.html

3.4 REFERENCES
Web links
GITEWS: http://www.gitews.de
Great Alert: http://www.gdgps.net/products/great-alert.html
New Zealand Continuous GPS Network (GeoNet):
http://info.geonet.org.nz/display/equip/New+Zealand+Continuous+GPS+Network
http://info.geonet.org.nz/display/appdata/GPS+Resources
http://magma.geonet.org.nz/resources/gps/timeseries/
LINZ PositioNZ network
http://apps.linz.govt.nz/positionz/
Articles
Beavan J., E. Fielding, M. Motagh, S. Samsonov, N. Donnelly (2011), Fault location and slip
distribution of the 22 February 2011 Mw 6.2 Christchurch, New Zealand, Earthquake from
Geodetic Data, Seismological research letters, 82(6): 789-799; doi: 10.1785/gssrl.82.6.789
Blewitt, G., C. Kreemer, W. C. Hammond, H.-P. Plag,, S. Stein, and E. Okal (2006), Rapid
determination of earthquake magnitude using GPS for tsunami warning systems, Geophys.
Res. Lett., 33, L11309, doi:10.1029/2006GL026145.
Boebel, O., M. Busack, E. R. Flueh, V. Gouretski, H. Rohr, A. Macrander, A. Krabbenhoeft, M. Motz,
and T. Radtke, The GITEWS ocean bottom sensor packages, Nat. Hazards Earth Syst. Sci.,
10, 1759–1780, 2010
C. Falck, M. Ramatschi, C. Subarya, M. Bartsch, A. Merx, J. Hoeberechts, and G. Schmidt (2010) -
Near real-time GPS applications for tsunami early warning systems, Nat.Hazards Earth Syst.
Sci., 10, 181-189
Hoechner, A., A. Y. Babeyko, and S. V. Sobolev (2008), Enhanced GPS inversion technique applied
to the 2004 Sumatra earthquake and tsunami, Geophys. Res. Lett., 35, L08310,
doi:10.1029/2007GL033133.
Pietrzak J., Socquet A., Ham D., Simons W., Vigny C., Labeur R., Schrama E., Stelling G. Vatvani D.
(2007) Defining the source region of the Indian Ocean Tsunami from GPS, altimeters, tide
gauges and tsunami models, EPSL, 261, 1-2, doi: http://dx.doi.org/10.1016/j.epsl.2007.06.002
Sobolev, S. V., A. Y. Babeyko, R. Wang, A. Hoechner, R. Galas, M. Rothacher, D. V. Sein, J.
Schro¨ter, J. Lauterjung, and C. Subarya (2007), Tsunami early warning using GPS-Shield
arrays, J. Geophys. Res., 112, B08415, doi:10.1029/2006JB004640.
C. Vigny, W. J. F. Simons, S. Abu, Ronnachai Bamphenyu, Chalermchon Satirapod, Nithiwatthn
Choosakul, C. Subarya, A. Socquet, K. Omar, H. Z. Abidin & B. A. C. Ambrosius, (2005).
Insight into the 2004 Sumatra–Andaman earthquake from GPS measurements in southeast
Asia, Nature (434), doi:10.1038/nature03937

4.0 DEEP OCEAN TSUNAMI WAVEFORM DATA
Successful tsunami forecasts depend on water level measurements from tsunameters, e.g.,
DART®
buoys. DART®
(Deep-ocean Assessment and Reporting of Tsunamis) real-time
tsunami monitoring systems, developed by PMEL (Pacific Marine Environmental Laboratory),
are positioned at strategic locations throughout the ocean and play a critical role in tsunami
forecasting. Since their installation DART®
buoys have been used to provide water level data
for tsunami warnings and improving tsunami models while more recently they have also been
used for the improvement of source models through inverse and other methods (i.e. Percival
et al., 2009/2011; Okal and Titov, 2007).
In this section we look at data collected by DART®
buoys and demonstrate how these data
can be used to improve our modelling predictions. A map of the operating DART®
s as of
2012 is shown in Figure 4.1. DART®
s are located far from coasts and therefore the signal
they record will be largely free of the frequency content typical of harbours, bays and that
along coastlines which are usually comparable to the frequencies of tsunamis. This type of
deployment allows for better comparisons between data and synthetic records than tide
gauge records which are affected by their proximity to land and their signal is quite complex.
In this chapter tsunamographs are compared with synthetic records using variations in the
tectonic sources approximated during and after recent events. Other parameters have also
been varied to demonstrate the effects of their variation to comparisons between recordings
and simulation results.
Two recent tsunami events are presented here: one is the February 27, 2010 Chile tsunami
and the other is the March 11, 2011 Japan tsunami. For the South America case, we present
two sources; one is a preliminary source model developed during the February 27, 2010
Chile event and the second is a more refined source model developed later.
Figure 4.1 Map of the global distribution of DART
®
buoys (red triangles).

4.1 NUMERICAL MODELLING OF THE FEB 27, 2010 CHILE TSUNAMI
On 2010 February 27 06:34:14 UTC (03:34am local time), a massive Mw 8.8 earthquake
struck Chile with the epicentre off the coast of the Maule Region of Chile. The earthquake
took place on the boundary between the South American and Nazca tectonic plates, the
second subducting beneath the west coast of Chile at a rate of 80mm/year.
According to media collected information, the earthquake caused over 800 casualties and
severe damage on buildings and infrastructure. A tsunami was triggered and recorded by
DART®
buoys and tidal gauges throughout the Pacific Ocean and induced a Pacific-wide
tsunami warning from PTWC (Pacific Tsunami Warning Center). A tide gauge record from
Valparaiso, Chile showed water level was raised by 2.6 meters. In New Zealand, water level
was raised by up to 1.0m as recorded by tide gauges at Chatham Islands (about 12 hours
after the main shock) as well as at several other locations along the east coast of New
Zealand. The water continued to oscillate for many hours after the first wave arrived.
This tsunami event was modelled in real-time with in-house tsunami model – COMCOT
(Cornell Multi-grid Coupled Tsunami) (Liu et al., 1995; Wang and Liu, 2006), at the Institute
of Geological and Nuclear Sciences, New Zealand, and the modelled results were used to
evaluate the threat to the coasts of New Zealand, hours before the first arrival of the tsunami.
Shortly after the main earthquake shock and after several adjustments USGS fixed the
magnitude to Mw8.8 and the following fault parameters were initially posted on its webpage
with the wphase solution:
Time: 2010 02 27 06:34:14 UTC
Epicenter: (35.826o
S, 72.668o
W)
Depth: 35 km
Strike: 14
Dip: 16
Strike: 104
Within one hour of the maishock, the tsunami scientists at GNS Science used the above
approximation of the source, to construct a preliminary source model, called GNS Model 1, to
initiate a real-time tsunami simulation with COMCOT. In this source model, the rupture area
was assumed to be 420km x 130km with a homogeneous slip amount of 7.3 meters. A 2-
level nested grid configuration was implemented to simulate the tsunami propagation and
interactions with the coast of New Zealand with 2 arc-minute grids covering the Pacific
(ETOPO2) and 30 arc-second grids (from GEBCO30) covering the entire New Zealand
including Chatham Islands. The computed tsunami elevations were obtained within three
hours after the main shock and were used to evaluate the impact on the coasts of
New Zealand.
Approximately one hour later (i.e. four hours after the mainshock) and when the first
measurement at DART buoy 32412 became available, GNS Model 1 was further improved to
GNS Model 2 in which the rupture width was narrowed down to 100km and the slip amount
was increased to 9.5 meters, to better match the measurement at DART®
buoy 32412. The
simulated domain was also extended to the whole Pacific, ranging from 120E to 60W
degrees in longitude and from 65S to 60N degrees in latitude, at a spatial resolution of 4 arc
minutes for the whole Pacific and 30 arc seconds around New Zealand. Here we only

present results from GNS Model 2. Figure 4.2 illustrates the distribution of modelled
maximum tsunami heights (zero-to-peak) throughout the Pacific. It is clear that a major part
of the tsunami energy was directed across the Pacific toward the coast of Japan. The impact
on the coast of New Zealand is also obvious largely due to the amplification effect of
Chatham Rise.
Figure 4.2 Maximum water level distribution in meters (zero-to-peak) in the Pacific Ocean (GNS Model 2) for
the Mw 8.8 February 27, 2010 Chile tsunami. Grid resolution used was 4 arc-minutes.
Figure 4.3 shows the comparison between the computed time history data with GNS Model 2
and the DART measurements. It is obvious that although this fault model was developed with
very limited information shortly after the earthquake, a fairly good agreement is obtained with
the measurements throughout the Pacific.
After the event, as more information became available, more advanced source models were
developed by various researchers worldwide. This event was also simulated with COMCOT
using a source model from a joint inversion of InSAR and GPS data, referred to as InSAR
GPS model here. The computed time history data at DART®
locations were also compared
with the DART®
measurements as shown in Figure 4.4.
It is easily seen that certain buoys compare well with both sources. Predicted water levels on
locations close to the source (i.e. 32412) and on the US West Coast (i.e. 46412) compare
well. Stations further away (i.e. Western Pacific buoys 21413 and 52403) show a mismatch
with respect to the arrival times and frequency content.

Figure 4.3 Comparisons between the numerical results with GNS MODEL 2 and DART
®
measurements.
Vertical axis corresponds to amplitude of water levels in meters and horizontal axis is in hours after the
earthquake. Note that a high-pass filter was applied to remove tidal background. Spikes in DART
®
records are
glitches in the records.

Figure 4.4 Comparisons between the numerical results for the improved source (InSAR GPS) and the DART
®
buoy measurements. Vertical axis corresponds to amplitude of water levels in meters and horizontal axis is in
hours after the earthquake. Note that a high-pass filter was applied to remove tidal background. Spikes in DART
®
records are glitches in the records.
Comparisons between simulations and DART®
records show a small improvement for the 2nd
source model (InSAR GPS). However, it is a common characteristic in all comparisons that
the long period motions are fairly well predicted but higher frequency oscillations do not
compare well. The Pacific basin DEM (Digital Elevation Model) may largely account for this
as reflections off small islands would not be modelled at the 4 arc-minute resolution of the
numerical grid; therefore, the higher frequencies in the signal would not be present. The fact
that COMCOT slightly underestimates the arrival of the first waves may be another artefact
of the low resolution adopted for the Pacific basin as the modelled wavefronts do not
encounter smaller features. A few of the instruments appear to have malfunctioned during
the event (i.e. 54401, 46408).
In order to investigate the effects of grid resolution on simulation results, two more runs were
carried out with higher spatial resolution (2 arc-minutes and 1 arc-minute).

Figure 4.5 Comparisons between the numerical results and the DART® measurements at a spatial resolution
of 2 arc-minutes. Vertical axis corresponds to amplitude of water levels in meters and horizontal axis is in hours
after the earthquake. Note that a high-pass filter was applied to remove tidal background. Spikes in DART
®

measurements at a spatial resolution of 1 arc minute. Vertical axis corresponds to amplitude of water levels in
meters and horizontal axis is in hours after the earthquake. Note that a high-pass filter was applied to remove tidal
background. Spikes in DART
®
Improvements in the spatial resolution around the source have resulted in better fits between
observed and synthetic waveforms as shown in Figure 4.5 and Figure 4.6. For example at
location 51406 higher spatial resolution has resulted in a better match of the observed record
with the numerical results. A visual comparison of the results at 1 and 5 arc minutes indicate
that the availability of higher resolution leads to improved matching in amplitudes and periods
as can be seen particularly for DART®
buoys 32412, 51406, 43412.

measurements at a spatial resolution of 1 arc minute. Vertical axis corresponds to amplitude of water levels in
meters and horizontal axis is in hours after the earthquake. Note that a high-pass filter was applied to remove tidal
background. Spikes in DART
®
4.2 NUMERICAL MODELLING OF THE MAR 11, 2011 JAPAN TSUNAMI
At 05:46:23 on March 11, 2011 UTC, a Mw 9.0 earthquake occurred about 130 km off the
coast of Sendai, Japan that triggered a catastrophic tsunami, sweeping the whole Pacific
basin. The tsunami caused tremendous devastation along the northeast coast of Japan and
was widely recorded by DART®
buoys and coastal buoys throughout the Pacific Ocean (Fujii
et al., 2011). Casualties were 18,000 deceased and nearly 3000 missing as reported by the
National Police Agency in each affected prefecture in Japan. Field survey results indicate
that the highest runup exceeded 38m (http://www.coastal.jp/tsunami2011/).The tsunami
caused damage to boats and harbor facilities across the Pacific along the coast of Oregon
and California after more than 9 hours travel time.
Within the first two days following this event, the tsunami generation and propagation was
modelled with COMCOT over a 4 arc-minute grid interpolated from NGDC’s ETOPO2
database. The modelled results from the two source models are presented next.

4.2.1 Source Model 1 – USGS Finite Fault Model
By using a finite fault algorithm by Ji et al. (2002), Gavin Hayes of USGS developed the slip
history of this earthquake based on the USGS Wphase solution with Mw 8.9 (an updated
version with Mw 9.0 is also available at
http://earthquake.usgs.gov/earthquakes/eqinthenews/2011/usc0001xgp/finite_fault.php). The
finite fault model contains 240 patches in total with maximum slip of 17.9 meters. In this
model, each patch ruptures at a different time with different uplift duration.
The seafloor deformation was computed via an elastic fault plane model (Okada, 1985) built
in COMCOT. Figure 4.8 shows the final stage of the vertical seafloor displacement:
Figure 4.8 Initial vertical seafloor displacement in meters computed with USGS finite fault model for the March
11, 2011 Japan earthquake.
The modeled tsunami time histories were compared with the measurements at DART®
locations (Figure 4.9).

®
measurements and modeled results with the USGS finite fault
model. Vertical axis corresponds to amplitude of water levels in meters and horizontal axis is in hours after the
earthquake. Spikes in DART
®
4.2.2 Source Model 2 – cGPS Inverse Solution
Dr. Laura Wallace of GNS Science, New Zealand developed a co-seismic slip distribution
from inverted cGPS site displacements in Japan with a capped maximum slip of 30 meters. A
simplified version of her model, with the total number of subfault patches reduced to 999,
was also used to model the wave field of the tsunami. The initial seafloor displacement of her
model is shown in Figure 4.10 (vertical component, computed via Okada’s theory with
COMCOT).
Both USGS finite fault model and cGPS model do a fairly good job at the prediction of arrival
times and amplitudes of tsunami waves. However, the USGS model appears to produce
more high frequency oscillations at the DART locations than the cGPS or the observed
records show. Arrivals of the peak energy at the various locations of the DART®
s are very
good with a few exceptions where arrival times are slightly underestimated. Amplitudes are
also better resolved for the USGS model than the cGPS model. An interesting observation
that appears prominently in the south Pacific, along an axis that runs west to east from east
of Phillipines and north of Papua New Guinea to Central America, is the leading depression
which appears in 6 DART®
s very clearly (mostly on the right column of Figure 4.11).
However, this was not picked up by the numerical simulations of either source model. In the
Chile event a leading depression appears mainly in the US West Coast DART®
buoys (i.e.
46407, 46412, 46419, 46410, 46409).

Figure 4.10 Initial vertical seafloor displacement of cGPS inverse model for the March 11, 2011 Japan event.
®
measurements and modeled results with the cGPS inverse model.
Vertical axis corresponds to amplitude of water levels in meters and horizontal axis is in hours after the
earthquake. Spikes in DART
®

4.3 TSUNAMI WAVE ARRIVAL TIMES
One of the most robust pieces of information is the tsunami arrival time (usually referring to
the first wave arriving at a point on a coast). Arrival times are important in tsunami
forecasting as they are associated with warning and emergency planning.
For the 2010 Chilean event and 2011 Japan event, numerical simulations with the linear
shallow-water-equations in COMCOT predicted slightly early arrivals for the leading tsunami
waves in comparison with DART®
buoy measurements at locations further away from the
source area. Specifically, for the 2010 Chilean event, the discrepancy on the tsunami arrival
times is roughly estimated as 0.5-0.7% at DART®
buoys on the Western Pacific. The
difference becomes larger the further the buoys are located from the source.
The discrepancy likely comes from cumulative effects either due to frequency dispersion
which is not physically included in these tsunami simulations or the universal parameters
used in the model, e.g., gravitational acceleration (g) and Earth radius (R).
In the simulations of the 2010 Chilean and 2011 Japan events, the values of g = 9.807m/s2
and R = 6378000.0m were used. As the Earth is an ellipsoidal body rather than a sphere, the
radius and gravity vary spatially too. However, numerical tests show that the uncertainty in
the Earth radius and the gravitational acceleration is not big enough to cause the arrival time
difference ~0.5-0.7% noted between numerical results and observations.
Therefore, the cumulative effect of frequency dispersion is likely a major cause of the arrival
time discrepancy. For the classic Boussinesq equations with the weak dispersion assumption
for water waves, the linear dispersion relationship gives
𝐶2
= 𝑔ℎ
1 +
1
6
(𝑘ℎ)2
1 +
1
2
(𝑘ℎ)2
Equation 4.1
where C is the phase speed of waves, g is the gravitational acceleration, k is the
wavenumber (k = 2π/L, L is characteristic wave length) and h stands for characteristic water
depth (Mei, 1989). For kh=0, the above relationship degrades to that given by Shallow Water
Equations which describes the phase speed of non-dispersive waves. We see that when
considering the arrival time calculation of long waves with certain wavenumber k, the
frequency dispersion effect will slow down the propagation of water waves in comparison to
that with a non-dispersive assumption (i.e., kh=0).
For the 2010 Chilean event and the 2011 Japan event the dispersion effect is very weak due
to the very large wave lengths in comparison to ocean depth, but it may still cause
discrepancies between the computed and measured arrival times in the far field after being
accumulated over a long distance.
Considering a large tsunami event in the Pacific, a wave length of ~200km is typical as
shown in past events, e.g., 1960, 2010 Chile and2011 Japan events. Then kh is evaluated as
kh = π/20 by assuming a typical ocean depth of 5 km in the Pacific.
Using kh = π/20 instead of kh = 0, we may quickly estimate that the dispersion effect, is
equivalent to modifying the gravitational acceleration as

𝑔 𝑒 = 𝑔
1+
1
6
(𝑘ℎ)2
1+
1
2
(𝑘ℎ)2
~0.991876𝑔. Equation 4.2
Consequently, without dispersion, the phase speed will be overestimated by ~0.4% which
roughly explains the early arrivals found in the simulations of the 2010 Chile and 2011 Japan
events.
Using the above dispersion-adjusted gravitational acceleration ge to model the 2010 Chile
event, a fairly good agreement on the arrival times can be obtained at most of the far-field
DART®
locations (see Figure 4.12).
Figure 4.12 Comparison between the modelled time history data and the measurements at DART
®
buoy
locations throughout the Pacific for the 2010 Chile event. Solid black lines show the measurements (filtered); solid
red lines represent the modelled time history data with the modified gravity and the solid blue lines denote the
modelled time history data without gravity modification (g=9.807m/s
2
).
4.4 RECOMMENDATIONS
The timeframe of the real-time numerical simulations at GNS Science during the emergency
response to the 2010 Chile tsunami event demonstrates that real-time numerical simulations
are useful for tsunami forecasting during distant events. Performing real-time numerical
simulations is currently challenging for regional events and not possible for local events.
The analysis also shows that even for very large tsunamis, a numerical model without
dispersion will tend to predict arrival times slightly earlier than the measurements (e.g., by
~0.4% for a tsunami whose wave length is 40 times the typical water depth). The
discrepancy will become larger at locations further away from the source.

Boussinesq-type equation models will perform better as the dispersion effect is physically
included in the governing equations. However, modelling transoceanic tsunami propagations
with Boussinesq-type equation models is proven to be computationally expensive. In general,
Shallow Water Equation models (without frequency dispersion) are at least an order of
magnitude faster and still produce fairly good agreements in both amplitudes and arrivals for
most of events. Some of the Shallow Water Equations (SWE) tsunami models (e.g.,
COMCOT, MOST) are able to use numerical dispersion effect, inherent with their particular
numerical discretization schemes, to mimic the physical dispersion effect to some extent with
carefully arranged spatial resolution and time step size.
4.5 REFERENCES
Web Links
DART buoys: http://nctr.pmel.noaa.gov/Dart/
March 11, 2011 earthquake and tsunami:
http://www.ngdc.noaa.gov/hazard/tsunami/pdf/2011_0311.pdf
Fujii, Y.; Satake, K.;Sakai, S.; Shinohara, M. and T. Kanazawa (2011).Tsunami source of the 2011 off
the Paciﬁc coast of Tohoku Earthquake. Earth Planets Space, 63, 815–820, 2011.
Ji, C., D.J. Wald, and D.V. Helmberger (2002). Source description of the 1999 Hector Mine, California
earthquake; Part I: Wavelet domain inversion theory and resolution analysis, Bull. Seism. Soc.
Am., Vol 92, No. 4. pp. 1192-1207, 2002.
Liu, P.L.-F.; Cho, Y.-S.; Briggs, M. J.; Kanoglu, U. and C. E. Synolakis (1995). Runup of solitary waves
on a circular island. J. Fluid Mech., vol. 302, pp. 259-285.
Mei, C. C. (1989). The Applied Dynamics of Ocean Surface Waves. Singapore: World Scientific. ISBN
9971-5-0773-0.
Okal, E.A., and V.V. Titov (2007). MTSU: Recovering seismic moments from tsunameter records. Pure
Appl. Geophys.,164(2–3), doi: 10.1007/s00024-006-0, 355–378.
Percival, D.B., D.W. Denbo, M.C. Eble, E. Gica, H.O. Mofjeld, M.C. Spillane, L. Tang, and V.V. Titov
(2011). Extraction of tsunami source coefficients via inversion of DART® buoy data. Nat.
Hazards, 58(1), doi: 10.1007/s11069-010-9688-1, 567–590.
Percival, D.B., D. Arcas, D.W. Denbo, M.C. Eble, E. Gica, H.O. Mofjeld, M.C. Spillane, L. Tang, and
V.V. Titov (2009). Extracting tsunami source parameters via inversion of DART
®
buoy data.
NOAA Tech. Memo. OAR PMEL-144, 22 pp.
Wang, X. and Liu, P.L.-F. (2006). An analysis of the 2004 Sumatra earthquake fault plane
mechanisms and Indian Ocean tsunami. Journal of Hydraulic Research Vol. 44, No. 2, pp.
147–154.

5.0 GEONET TIDE GAUGE NETWORK
In New Zealand, a network of approximately 20 tsunami monitoring stations is planned.
Currently 17 stations are operating (Figure 5.1). Stations 3005 (Macquarie Islands), ANIT
(Antipodes Island), WHAT (Whareongaonga) and WIAT (Wellington South Coats) are not
currently installed. Station GLKZ is located on an inland lake so it has not been used for
tsunami studies. Stations are located around offshore islands to monitor regional and distant
events, at-risk coasts to detect the first arrival of tsunami waves to the main islands and at
population centres with vulnerability to tsunamis.
At each tsunami monitoring station relative sea level and wave heights are measured by two
submerged pressure sensors. Tsunami data is transmitted in near real-time to the GeoNet Data
Management Centre in Lower Hutt for dissemination and use by emergency crisis management
both locally and internationally (http://info.geonet.org.nz/download/attachments/950735/tsunami-
brochure.pdf; Power and Gale, 2011).
Figure 5.1 Currently operating GeoNet tide-gauges (triangles).
5.1 LITERATURE REVIEW: USE OF TIDE GAUGES IN TSUNAMI WARNING
Prior to the installation of DART®
buoys, the only direct records of tsunamis came from tide
gauge measurements. There are advantages and disadvantages to using tide gauge
instruments. As the only tsunami sensors available from the New Zealand coast, it is not only
necessary to use the information they provide, it is obligatory. Probably the most common
use of tide gauge records is the comparison of these with modeling predictions for the
calibration of numerical models. Tide gauges are located within harbours and near narrow
entrances in ports and/or near channels and the signal they record is not necessarily
representative of the tsunami signature as usually found in DART®
s. Therefore, finding
robust quantities in gauge records and performing a spectral analysis of those is necessary
in order to obtain useful information about the tsunami or the source which caused it.
Historic tide gauge records can reveal useful information about past tsunamis and their
generating sources. A prime example of how tide gauge records of historical events can be

used, is the work by Eva & Rabinovich (1997) on the February 23, 1887 earthquake and
tsunami in the Ligurian Sea near Genoa, Italy. A first negative wave polarity on the two tide
gauge records helped define the source as a normal fault earthquake. Using a digitized chart
of the 1887 Genoa port and a tsunami record of the 1887 earthquake combined with
numerical modeling it was shown that the dominant frequency in the spectrum of the tsunami
record is the fundamental frequency of the port excited during the event. Later modifications
to the port lengthened the fundamental period of the port from 22.5 mins to approximately 29
mins. The effects of physically modifying ports is another subject that may have important
implications with regards to tsunami vulnerability of ports and it is a subject that has to be
investigated separately. A spectrogram of the tsunami record revealed strong evidence of
frequency dispersion and edge wave generation which was responsible for the long ringing
observed in the Genoa harbour record.
Large subduction earthquakes are known to generate a significant fraction of their total
energy in low-order normal modes of the earth” (Lomnitz et al. (2005)). According to the
same article this should be detectable in tide gauge records. Fourier spectra with significant
maxima at frequencies near 0.31 and 0.46 mhz from Jackson Bay, NZ following the Indian
Ocean tsunami correspond to the frequencies of the lowest-order spheroidal modes. It is
stated in the paper “a warning strategy based on tide-gauge readings in the path of the
tsunami is unsatisfactory”. As many will argue it may be too late to wait for the waves to
reach the coast before issuing a warning. The paper suggests the use of low-order
spheroidal modes which can be detected within minutes for issuing a tsunami alert. The
technique presented in the paper should be further investigated to determine its promise in
local and regional events and whether it can improve current warning procedures.
5.2 REFERENCES
Eva C., A. B. Rabinovich (1997). The February 23, 1887 tsunami recorded on the Ligurian coast,
western Mediterranean, Geophys.Res. Lett. (24) 17, P2211-2214.
Lomnitz, C. and Nilsen-Hofseth (2005). The Indian Ocean Disaster: Tsunami Physics and Early
Warning Dilemmas, EOS, Vol.86, no.7.
Merrifield, M. A., et al. (2005). Tide gauge observations of the Indian Ocean tsunami, December 26,
2004, Geophys. Res. Lett., 32, L09603, doi:10.1029/2005GL022610.

5.3 TIDE GAUGE RECORDINGS OF SIGNIFICANT RECENT TSUNAMIS
For the work that follows we assume that the following is true:
• The resonant modes of any location remain the same1
˗ Local bathymetry will determine the peaks in the spectra of tide gauges
• Different disturbances such as tsunamis can induce different modes of resonance in
harbors and other semi-enclosed basins
The implications of the above can be one or more of the following as also pointed out by
Rabinovich (1997)
• Common peaks appear in both background and signal spectra in tide gauge records
• The tsunami waves observed in records are those preferentially observed due to local
bathymetric features rather than a signal characteristic of the source
˗ That should be noted as common peaks in spectra of tsunamis recorded in the
same location from different events
˗ Difficult to extract the source spectrum from coastal stations
The following sections present examples of recent tsunamis observed in New Zealand and
which also confirm the above.
5.3.1 27 February 2010 Chile tsunami
Although GEONET network has 17 stations, not all were operating during the February 2010
Chile tsunami. Stations GBIT, KAIK, MNKT, NORT, and PUYT were installed after this event
occurred. Station PUYT appears to have been in normal operation at the time but no record
has been obtained. From the remaining 16 stations only 7 stations recorded a clear signal
above noise. The 7 recordings of the Chile tsunami before de-tiding appear in Figure 5.2.
The data that were downloaded from the GeoNet database were sampled at 10 Hz and
amplitudes are in cm. Records are not detided if events are older than 6 months. Data were
high pass filtered with a 2 pole butterworth filter at 0.0005Hz and lowpass filtered with a
butterworth filter at 0.01Hz (Figure 5.3), in order to remove the tide and very high frequency
content (noise). Arrivals of first tsunami waves were manually picked using the program
SAC.
1
Obviously this is not always true especially when harbors are modified but for this work we can
make this assumption.

Figure 5.2 Tide gauge records of the February 27, 2010 Chile tsunami before detiding. X-axis shows time for
the 24hr records starting at 06:00:00 (UTC) hrs (data is sampled at 10hz; note the 10^4 scalar). Y axis shows
amplitude in centimeters.

Figure 5.3 Detided tide gauge records of the February 27, 2010 Chile tsunami. Records are shown at GMT
(UTC) time (x-axis). Vertical red lines represent arrival times of first tsunami waves (manually picked). Y-axis
shows amplitude in meters. Earthquake occurred at 06:34:14 (UTC) hrs.
As it can be seen from the records of the Chile 2010 event, even though tide gauges are
located in various settings it is common in all records that initial waves are not the largest
arrivals. The largest oscillations are recorded at Chatham Islands (CHIT) and reach just
below 1 m (0-peak).
Simulations are usually run in real-time or near real-time -to estimate potential impact to
coastlines once a tsunami is known or believed to have been generated –with efficiency and
timeliness in mind. This usually means lower bathymetric resolution than needed for model
calibration using gauge records. Tide gauge records also contain quite complex signals that
do not match well simulation results even when higher resolution bathymetry is used.
Because of the above we have not made any attempts to calibrate COMCOT using gauge
records in this report.
The list of stations that recorded the Chile 2010 tsunami is provided next:

Table 5.1 Tide gauge instruments operating during the February 27, 2010 Chile tsunami.
Tide Gauge
Name
Lat/Lon Location Depth, m
Approximated
Location (Lat/Lon)
Prediction
2
AUCT -36.8314371,
174.7865372
Auckland (*Devonport
Naval Base)
6 -36.835, 174.787 N/A
CHIT -44.024042464,
176.36747635
Chatham Islands 4 N/A
GIST -38.6754097,
178.0228774
Gisborne 8 -38.675,178.022
NAPT -39.4756612,
176.920066
Port of Napier 3 -39.472,176.92
OTAT -45.8143493,
170.62939
Dunedin 5 -45.814,170.629
TAUT -37.6410885,
176.1811791
Port of Tauranga 5 -37.641,176.181
WLGT -41.2844758,
174.7798536
Queen’s Wharf Shed 1,
Wellington
5 -41.284, 174.782 N/A
2
When resolution is low the location of the tide gauge may be approximated on a location that is on
land. At this case then there is no record available.

5.3.2 Spectral Analysis
Figure 5.4 Signal spectra and background spectra for stations AUCT, CHIT, GIST, NAPT, OTAT, TAUT and
WLGT. The most striking common peaks in both background and signal spectra, characteristic of a location are
designated with a light blue ellipse. Red arrows show the most striking common peaks in neighboring sites GIST
and NAPT which are likely characteristic of the continental shelf.
Tide gauge records were split into two types of signals: background signal and tsunami
signal based on the manual picks of tsunami arrivals shown in Figure 5.3. Fourier spectra of
both were plotted against each other following an approach described by Rabinovich (1997).
It is clear from the plots that not all spectra decay similarly. For example AUCT spectra
decay much faster than CHIT or GIST. Stations GIST and NAPT show similar spectra for
both tsunamis and background signals, which may be explained by similar local bathymetry.
In particular tsunami spectra at both of those stations show prominent peaks at 52 mins and
41.7mins (0.00032Hz and 0.0004 Hz). Another prominent peak appears at 0.0015 Hz (11
minutes). Red ellipses indicate common peaks in neighbouring plots (Figure 5.4).
Wellington tide gauge (WLGT) has a prominent peak at ~ 25.6 mins and strong energy at ~
13 mins which also appear in the background spectrum. At this location the background
spectrum and the tsunami spectrum share at least two peaks. At frequencies > 0.002Hz the
spectra appear identical. Common peaks in the spectra of the background and tsunami

signals indicate excited modes of oscillation. In the case of Wellington the peak at 25 mins
corresponds to mode 3 (Butcher and Gilmour, 1987; Abraham, 1997) and has been also
identified in spectrograms of gauge records from the Solomon Islands and Peru tsunamis
(Power, 2007).
5.3.2.1 Spectral Ratios
Spectral ratios are frequently used in seismology to separate the site effects on ground
motions from source and path effects. Ground motions contain information from the source,
the path the seismic waves have travelled to reach a site and local conditions as well as
instrument response. For site response studies which requires knowing how local topography
affects ground motions, the source, path and instrument effects have to be removed. This is
accomplished with the so called Standard Spectral Ratio (SSR) through the division of
ground motion spectra at selected sites with spectra of ground motions at rock sites (i.e. sites
which contribute nothing or very little to incoming waves and are therefore flat over the
frequency range).
More recently tsunami scientists have used the idea of spectral ratios as shown by
Rabinovich (1997). The idea behind the use of spectral ratios in tsunamis has been to isolate
the contributions of the source, path and local bathymetry to water level oscillations as
recorded by tide gauges. One of the motivations behind the use of spectral ratios has been
the reconstruction of the tsunamigenic source as records of near source water level records
were not available prior to DART®
installation.
The approach is based on the assumption that the spectrum S(ω) of both the tsunami and
background sea oscillations can be represented as follows (Rabinovich, 1997):
S(ω)=W(ω)E(ω) Equation 5.1
where, W(ω)=H2
(ω) with H(ω) representing the linear transformation of waves (also referred
to as the topographic admittance function) due to local topography and E(ω) is the source
spectrum.
Using the above we can estimate the topographic admittance functions for individual sites.
Assuming W(ω) is the same before and after a tsunami arrives at a site and the spectrum
prior to a tsunami and after is of the form (Equation 5.1), we can calculate the spectral ratio
using background and tsunami signals. This ratio will be free of the topographic influence
and will give the amplification of the excitation source (tsunami waves near the site) during
the tsunami event and relative to background conditions. An example using the Chile 2010
tsunami as recorded by the AUCT station is shown next:

Figure 5.5 Ratio of tsunami signal of the February 27, 2010 Chile tsunami to background signal as recorded by
the AUCT station.
5.3.3 Arrival times
Arrival times for this event extracted from gauge records were plotted in a map against
predicted arrival times from simulations (Figure 5.6). For all locations except Tauranga
(TAUT), arrival times are underestimated in our simulations.

Figure 5.6 Comparison between the modelled arrival times and the arrival times at the tide gauges (arrival
times were manually picked as shown in Figure 5.3).
5.3.4 September 29, 2009 Samoa Tsunami
Eleven of the sixteen tsunami instruments appear to have recorded the tsunami of the
September 29, 2009 Samoa earthquake. The pre-tided records are shown in
Figure 5.7. Figure 5.8 shows the detided records. Due to the magnitude of the event not all
records are of high enough quality for comparison purposes with numerical predictions. Even
after filtering, significant high frequency content is still present in the records.

Figure 5.7 Tide Gauge Records of the September 29, 2009 Samoa tsunami before detiding. X-axis shows time
for the 24hr records starting at 06:00:00 (UTC) hrs (data is sampled at 10hz; note the 10^4 scalar). Y axis shows
amplitude in centimeters.

Figure 5.8 Detided tide gauge records of the September 29, 2009 Samoa tsunami. X-axis shows time for the
24hr records starting at 06:00:00 (UTC) hrs (data is sampled at 10hz; note the 10^4 scalar). Y-axis is amplitude in
meters. Earthquake occurred at 17:48:11 (UTC) hrs.
As can be seen from Figure 5.8 it is very hard to pick arrival times. The larger arrivals that
are recorded at more than 15hrs after the start of the records and more than 5 hrs after the
earthquake must be either later arrivals or amplified oscillations within the bays as the first
tsunami waves from Samoa should arrive shortly 3 hrs after the earthquake rupture. Because
of this spectra for this event are not further analysed. It is unclear when the event starts on
the records.

5.4 RECOMMENDATIONS
The main question that we are trying to answer is how tide gauges can help improve our
advice to MCDEM for tsunami warning.
When analysing water level data, one type of information that is usually “extracted” are the
modes of oscillation present in the signal. Some of the ports in NZ (i.e. Wellington) have
been studied more thoroughly than others and excited modes of oscillations have been
identified during past events (i.e. Wellington; Butcher and Gilmour, 1987; Abraham, 1997,
Power, 2007). Although this is a very important piece of information, an important question
that is raised is what those modes of oscillation mean for the harbour. Are there modes of
oscillation that are more threatening than others? What exactly do certain modes mean for
the harbour? This can only be determined through modal analysis combined with identified
peaks in gauge spectra.
Arrival times are useful in advising MCDEM as they are associated with evacuation plans but
in the case of the Samoa event it appears that large arrivals were not associated with the first
waves which may have been too small to separate from background noise. It is
recommended that all available records of past significant events are analysed and the
findings summarized appropriately for reference during the unfolding of a tsunami event and
for use with the Tsunami Experts Panel (TEP).
5.5 REFERENCES
Abraham, E.R.C. (1997). Seiche modes of Wellington Harbour, New Zealand. Journal of Marine and
Freshwater Research, 31, 191-200.
Butcher, C.N. and Gilmour, A.E. (1987). Free oscillations of Wellington and Lyttelton Harbours. DMFS
Reports (NZ) 1(1), 1-8.
Power, W.L. (2007). Response of Wellington Harbour to the 2007 Solomon Islands and Peru
tsunamis. Geological Society of New Zealand Miscellaneous Publication 123A: p.133
Power, W., N. Gale (2011).Tsunami forecasting and monitoring in New Zealand. Pure Appl. Geophys.,
168, 1125-1136, DOI 10.1007/s00024-010-0223-9.
Titov, V.V. (2009). Tsunami forecasting. Chapter 12 in The Sea, Volume 15: Tsunamis, Harvard
University Press, Cambridge, MA and London, England, 371–400.

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