Geophysical Research Letters
Supporting Information for
Local Tsunami Warning: Perspectives from Recent Large Events
Diego Melgar1*, Richard M. Allen1, Sebastian Riquelme2, Jianghui Geng3,4, Francisco
Bravo5, Juan Carlos Baez2, Hector Parra6, Sergio Barrientos2, Peng Fang3, Yehuda Bock3,
Michael Bevis7, Dana J. Caccamise II7+, Christophe Vigny8, Marcos Moreno9 and Robert
Smalley Jr.10
1
University of California Berkeley, Seismological Laboratory, Berkeley, CA, USA
2
Centro Sismológico Nacional, Universidad de Chile, Santiago, Chile.
3
Cecil H. and Ida M. Green Institute of Geophysics and Planetary Physics, Scripps
Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
4
GNSS Center, Wuhan University, Wuhan, China
5
Universidad de Chile, Departamento de Geofísica, Santiago, Chile.
6
Instituto Geográfico Militar, Santiago, Chile
7
The Ohio State University, School of Earth Sciences, Columbus, OH, USA.
8
9
Laboratoire de Géologie, Ecole Normale Supérieure, Paris, France.
Helmholz Centre, GFZ German Research Centre for Geosciences, Potsdam, Germany.
10
CERI, University of Memphis, Memphis, TN, USA.
+Now at the National Oceanographic and Atmospheric Administration, National
Geodetic Survey, Silver Spring, MD, USA.
Contents of this file
Figures S1 to S11
Introduction
There are 11 Supplementary Figures in this file. Figure S1 shows the results of the
computational speed-up (scaling) as more CPUs are added to the tsunami propagation
modeling for all 4 events. It demonstrates that it is reasonable to expect a tsunami
propagation model to be available within 2 minutes after OT. Figure S2 shows the
magnitude computation for the Illapel event using the OPGD scaling relationship of
1
Melgar et al. [2015]. Figure S3 shows the tsunami warning maps for the moment tensor
solutions of Figures S4-S7.
Figure s S4-S7 show the joint and land-only slip inversions for all 4 events as
well as the inferred rupture areas from PGD scaling and from the regional moment
tensors. Figure S8-S10 show the waveform fits for these slip inversions. Finally Figure
S11 shows the comparison between the tsunami survey measurements and the
predictions from the joint slip inversions.
Figure S1. Average wall time as a function of the number of CPUs used in the tsunami
model computation. For each data point (red circles) we show the average of 5 runs. The
black line is the reference 1/t curve, where t is time, which represents the ideal case
where doubling the number of CPUs results in half the run-time.
2
Figure S2. Magnitude computation for the 2015 Illapel, Chile earthquake from the
scaling of peak ground displacement (PGD) observations at high rate GPS sites [Melgar
et al., 2015]. The red line is the reference magnitude of Mw8.3. The blue dots are the
second by second magnitude results and the error bars represent an uncertainty of 0.27
magnitude units. The algorithm requires at least 4 stations to record a PGD value larger
than 5cm.
3
Figure S3. Tsunami warning maps and coastline amplitude predictions from
moment tensors. (A) The 2010 Maule, (B) 2011 Tohoku-oki, (C) the 2014 Mw 8.24
Iquique, Chile and (D) the 2015 Mw 8.34 Illapel, Chile earthquakes. The red or blue
rectangles represent the inferred source size from scaling laws [Blasser et al, 2010],
additionally for these models amplitude curves are calculated at 1 km intervals on coarse
coastlines and are computed in less than 2 minutes (Figure S1).
4
Figure S4. Slip inversions for the 2010 Maule, Chile earthquake (A) Land based slip
inversion from high rate GPS data. (B) Joint inversion of high-rate GPS data and tide
gauges. For both panels the red rectangle is the rupture area determined from the
magnitude scaling and the orange area form the W-phase moment tensor solution. The
moment tensor is shown.
Figure S5. Slip inversions for the 2011 Tohoku-oki, Japan earthquake (A) Land based
slip inversion from high rate GPS and strong motion data. (B) Joint inversion of high-rate
GPS, strong motion data, ocean-bottom pressure and GPS buoys. (C) Rapid simplified
geodetic source inversion [Colombelli et al., 2013]. For all panels the red rectangle is the
rupture area determined from the magnitude scaling and the orange area form the Wphase moment tensor solution. The moment tensor is shown. Red circles are collocated
GPS/strong motion stations used in the inversion. Green triangles are tsunami
measurement sites, these are a combination of ocean-bottom pressure and GPS buoys.
5
Figure S6. Slip inversions for the 2014 Iquique, Chile earthquake (A) Land based slip
inversion from high rate GPS and strong motion data. (B) Joint inversion of high-rate
GPS, strong motion data, and tide gauges. For both panels the red rectangle is the rupture
area determined from the magnitude scaling and the orange area form the W-phase
moment tensor solution. The moment tensor is shown.
Figure S7. Slip inversions for the 2015 Illapel, Chile earthquake (A) Land based slip
inversion from high rate GPS and strong motion data. (B) Joint inversion of high-rate
GPS, strong motion data, and tide gauges. For both panels the red rectangle is the rupture
area determined from the magnitude scaling and the orange area form the W-phase
moment tensor solution. The moment tensor is shown.
6
Figure S8. Data fits to the high-rate GPS and tide gauge data for the joint kinematic slip
inversion (Fig S4B) of the 2010 Maule earthquake. Black lines are observed data and red
are synthetics.
Figure S9. Data fits to the high-rate GPS, strong motion (integrated to velocity) and tide
gauge data for the joint kinematic slip inversion (Fig S6B) of the 2014 Iquique
earthquake. Black lines are observed data and red are synthetics.
7
Figure S10. Data fits to the high-rate GPS, strong motion (integrated to velocity) and tide
gauge data for the joint kinematic slip inversion (Fig S7B) of the 2015 Illapel earthquake.
Black lines are observed data and red are synthetics.
8
Figure S11. Comparison between observed inundation heights from post-event surveys
(blue) and modeled inundation heights at the same locations (orange). There is no survey
data yet for the 2015 Illapel earthquake. Not all the survey points are inundated in the
tsunami models, this is to be expected given we are using the shallow water
approximation which might unsuitable for flow over land and neglects potentially
important physics such as sediment entrainment and flow over the built environment.
Furthermore it must be noted that the SRTM3 (90m resolution) elevation models used
have an elevation error of the order of several meters [Farr et al., 2007] this can lead to
some points not being inundated and conversely to some survey points being overestimated. Significant improvements have been found from manually comparing the
SRTM3 measurements to local survey data and applying corrections pixel by pixel, we
have foregone this rather painstaking process. However, this shows that the simulations
derived from the slip inversions are capable, to first order, of replicating the inundation
pattern, and most importantly the amplitudes
9