Jiménez, C. et al.

Paper:

M w 8.1)
Tsunami Waveform Inversion of the 2007 Peru (MEarthquakeCésar Jiménez∗1,∗2 , Nabilt Moggiano∗2 , Erick Mas∗3 , Bruno Adriano∗3 ,Yushiro Fujii∗4 , and Shunichi Koshimura∗3∗1 Universidad Nacional Mayor de San Marcos

FENLAB Av Venezuela s/n, Lima, PerúE-mail: cjimenezt@unmsm.edu.pe∗2 Direccion de Hidrografia y Navegacion, DHN, Chucuito-Callao, Peru
∗3 International Research Institute of Disaster Science, Tohoku University, Sendai, Japan∗4 International Institute of Seismology and Earthquake Engineering,
Building Research Institute, Ibaraki, Japan[Received July 1, 2014; accepted September 29, 2014]

An earthquake shook the central-southern coast ofPeru on August 15, 2007, as a coseismic effect atsunami was generated, which flooded some villages
and beach resorts and killed 3 people. From theanalysis and signal processing of 10 tidal records, we
obtained the parameters of the seismic source andthe initial coseismic deformation through an inversion modeling, in which the synthetic signals are compared with the observed signals by a non-negative
least square method. The maximum slip located on
the southern part of the rupture geometry is 7.0 m.
The calculated scalar seismic moment is M 0 = 1 . 9 9 ×
1 0 2 1 Nm, equivalent to a magnitude of M w 8.1. We
used these parameters to obtain a heterogeneous seismic source model, which was used as initial condition
to simulate the tsunami propagation and inundation.
We used the field survey observations to validate oursource model.
Keywords: seismic source, inversion, numerical simulation

1. Introduction
The objective of the research is to obtain the slip distribution (or seismic source distribution) and deformation
field of the Pisco (Peru) 2007 earthquake from tsunami
waveform inversion [1] and the validation of this modelfrom the field survey observations [2].
One way to verify a seismic source model or initialcoseismic deformation model is by simulating the corresponding tsunami. The parameters such as arrival time,
amplitude and polarity of the first wave and in general thetsunami waveform simulated should have a good correlation with the corresponding values digitally processed oftidal gauge records for the available stations.
From a physical point of view, the tsunami waveform
inversion provides better results to obtain the initial deformation field, because the velocity of tsunami waves is954

relatively well known in this method (the velocity of thetsunami waves depends only on the bathymetry), unlike
in teleseismic waveform inversion the velocity of the seismic waves is not very well known, it is assumed a velocity
model that provides the best correlation between the observed signals and the simulated signals in the inversion
process. The goodness of the results obtained depends on
the quality of the tidal gauge recordings and quality ofbathymetry around the tidal stations.
The Pisco earthquake of 2007 (Mw 8.1) was generated
at the boundary between the Nazca and South America
tectonic plates. These two plates converge at a rate of 78 cm per year [3]. The quake is the type of thrust fault at
the interface between the two plates, the Nazca plate moving beneath the South American plate, a process known as
subduction. The dominant seismic activity along the coastof Peru is a direct consequence of subduction of the Nazca
plate beneath the South American plate, so that the coastof Peru has a history of great earthquakes. Over the past
five centuries have been many destructive earthquakes ofmagnitude greater than Mw 8.0 in some place of Peru [4].
As coseismic effect of the earthquake, a tsunami was
generated, which originated the greatest run-up south of
the Paracas Peninsula, this corresponds to the greater asperity. The focal mechanism for these earthquakes generally corresponds to a thrust fault with the nodal planeoriented in the NNW-SSE and dipping nearly horizontal
plane [5].

2. Data
2.1. Tidal Waves Recording
We used 12 digital tidal stations as shown in Fig. 1
with red triangles. Eleven belonging to Sea Level Station Monitoring Facility [6], usually located in a port orcoastal inlet, and one buoy DART (Deep-ocean Assessment and Reporting of Tsunamis) of the National Data
Buoy Center [7] located at deep sea. All these stations are
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Tsunami Waveform Inversion of the 2007 Peru (Mw 8.1) Earthquake

Table 1. The tidal stations are in order from north to south.
The arrival time (TA) is referenced with the origin time ofthe earthquake.
N
1
2
3
4
5
6
7
8
9
10
11
12

Fig. 1. Bathymetry and distribution of tidal wave stations.

located along the coast of Chile and Peru, except DART
buoy (Fig. 1). A good azimuthal geometric distribution of
stations with respect to the epicenter is obtained with additional stations to the north; however, it is not the case forthis event because the missing of tidal recordings of goodquality, free of noise from stations located in the near andregional field to avoid dispersion effects.

2.2. Digital Tidal Wave Processing
Each tidal gauge signals are composed of a wide rangeof frequencies with 3 main components: oceanic tides(of astronomical origin), waves or surge (of atmosphericorigin) and tsunami (of tectonic origin, mainly). To analyze the tidal gauge records of tsunami we applied thesame algorithms of digital signal processing as in Jiménez(2007) [8]. Here a brief description of the method: firstthe data is interpolated (in time domain) for a sampling
rate of 1 minute (using the cubic interpolation method),this in order to homogenize the data, since each station
can have a different sampling rate (1 min, 2 min or 5 min).
Then, the mean value of the signal is removed. A highpass Butterworth filter ( fc = 6.94 × 10−5 Hz) and a lowpass filter ( fc = 0.0083 Hz) is applied to remove components of long period (tides) and of short period (surges).
Finally, the time is referenced with respect to origin timeof the earthquake [9]. However, for computational purposes of the inversion process, all records (observed andcalculated or Green functions) are synchronized so thatthe arrival time of the first wave corresponds around 20thminute, i.e., close to the lower arrival time corresponding
to Marcona station (Table 1).
The arrival time for Marcona station is less than the arrival time of Callao station, this is due to the bathymetry
Journal of Disaster Research Vol.9 No.6, 2014

StationCallaoMarconaArica
DART32401
Iquique
Antofagasta
CalderaCoquimbo
Valparaiso
San Antonio
Juan Fernandez
Talcahuano

Lat◦-12.071-15.343-18.476-19.287-20.205-23.653-27.065-29.950-33.027-33.580-33.620-36.695

Lon◦ TA (min)-77.17455-75.15721-70.32380-74.73151-70.14882-70.404110-70.825133-71.335164-71.626187-71.630
197-78.830181-73.106254

and directivity of the propagation of tsunami waves. Except Marcona station, all arrival times have some proportionality to the epicentral distance of each station.

2.3. Bathymetry
Because the speed and directivity of tsunami waves depend on the marine topography (or bathymetry), is essential to have a good model of bathymetry to calculate
the inversion. The global bathymetry model is taken fromGEBCO 30 [10], which has a grid resolution of 30 arc sec
or ∼927 m (Fig. 1). For Callao station, we used a finerbathymetry (3 arc sec or 92 m), corresponding to marine
soundings. However, for other stations we used a coarse
bathymetry.

2.4. Rupture Geometry from Aftershocks
Hypocentral parameters taken from the National Earthquake Information Center [11] are given in Table 2. NEIC
has recorded a total of 167 events of magnitude greaterthan Mw 4.0, since the occurrence of main earthquake(Aug. 15, 2007) until a month later (Sep. 15, 2007). The
strongest aftershock occurred on August 18, 2007 and had
a magnitude of Mw 6.0.
These aftershocks are distributed mainly between the
trench and the coast line, and to a lesser extent on the continent, along the geometry of rupture from Cañete (13◦ S)
to Paracas Peninsula (15◦ S) covering a surface with an effective length of around 180 km and an effective width ofaround 90 km (Fig. 2).

3. Methodology
If we know the parameters that characterize a system
and its initial (and boundary) conditions, then we can predict the behavior of the system at any instant of time. This
type of problem, to predict future behavior, is called the955

Jiménez, C. et al.

Table 2. Hypocentral parameters from NEIC.
Parameter
Value
Date
Aug. 15, 2007
GMT time
23:40:57
Latitude-13.39◦Longitude-76.60◦Depth
39 km
Magnitude
Mw 8.0Fig. 3. Slip distribution of unitary seismic sources.
Table 3. Global CMT focal mechanism parameters.
Focal mechanism Angle
Strike angle
325◦
Dip angle
18◦
Rake angle
63◦
Fig. 2. Aftershocks of the earthquake. The rupture geometry
is observed. Data is taken from USGS-NEIC.

“direct problem.” The “inverse problem” is to use the results of certain observations to infer the values of the parameters characterizing the system [12]. In this research,
the tidal gauge records of tsunami will be used to infer theseismic source parameters and slip distribution.
The process will be considered as a “Linear and TimeInvariant System,” which has two important properties:a) linearity, which allows using the superposition principle for Green’s functions and for unitary deformations
and b) invariance in time, that allows a shift in the time ofthe observed signals and their corresponding Green functions, synchronizing signals observed so that the tsunami
arrival time corresponds to 20th minute, only for calculation purposes. This last point makes possible the use of
tidal stations regardless of their distance from the epicenter. The solution of the inverse problem is not unique, but
the set of values that provide the best correlation between
observed and simulated data is chosen.

3.1. Source Parameters
To estimate the slip distribution, the inversion method
for 10 tsunami waveform of good quality was used [13].
The rupture geometry was divided into 8 sub-sources(fragments) of identical dimensions: of length L = 45 km
956

and of width W = 45 km, located at a depth H j with respect to the upper side (in the vertical coordinate) of eachsub-source (Fig. 3).
Hj = h +W
sin δ × [1 − mod ( j, 2)] , j = 1, . . . , 82. . . . . . . . . . . . . . . (1)h = he − [(xe − x0 ) cos θ − (ye − y0 ) sin θ ] tan δ(2)Where h is the depth of the upper side of the rupturearea, x0 and y0 are the coordinates of the lower left corner, j represents the position of the jth sub-source, δ isthe dip angle, mod( j, 2) represents the module or restof j with respect to 2, (xe , ye , he ) are the coordinates of
the hypocenter and θ is the azimuthal angle. It should
be noted that H j is not the focal depth, but is related by
Eq. (2).
The coseismic deformation is calculated for each subsource with a unitary slip using Okada (1985) [14] formulation and focal mechanism data. The parameters of the
focal mechanism are taken from Global CMT [15] and areequal for each sub-source (Table 3).

3.2. Green Functions Calculation
From the physical point of view, a Green’s function represents the response of the system (simulated signal in the
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Tsunami Waveform Inversion of the 2007 Peru (Mw 8.1) Earthquake

Table 4. Slip distribution for each of the 8 sub-sources.
The coordinates correspond to the lower left corner and thedepth corresponds to the upper side (on the vertical axis) of
the respective source.
N
1
2
3
4
5
6
7
8

Lat (◦ )-14.750-14.533-14.417-14.200-14.083-13.867-13.758-13.533

Lon (◦ ) H j (km) Slip(m)-76.300
14.00
2.3-75.983
27.91
5.7-76.53314.00
0.8-76.21727.91
7.0-76.767
14.00
1.7-76.450
27.91
3.1-77.000
14.00
4.1-76.683
27.91
0.1

ith station) when the system is disturbed by a unit impulse(deformation due to a unitary slip in the jth sub-source).
Using this deformation field as initial condition, the waveforms (or Green functions) are calculated at each station
simulating the propagation of tsunami waves by a finite
difference method for each of the sub-sources, using an
actual bathymetry. The tsunami waveforms observed are
expressed as a linear combination of the tsunami waveforms calculated, in tensor notation:
Gi j (t) ∗ m j = di (t). . . . . . . . . . . (3)where: Gi j (t) is the Green function in the ith station generated by the jth sub-source, m j is the value of slip at the jth
sub-source, di (t) is the observed signal at the ith station.
The linear tsunami propagation model TUNAMI in
spherical coordinates was used [16], with a single rectangular grid to obtain the Green’s functions corresponding
to the tidal stations of Chile and the DART buoy.

3.3. Inversion
The inversion technique is performed by the method of
non-negative least squares [17]. This method compares
the simulated signals with the observed signals and minimizes the squared residual:Gi j m j − di 2 → 0 . . . . . . . . . . . (4)with the condition: m j > 0. We must take only the partof the signal corresponding to the first period of the wave
to avoid contamination problems such as signal reflection
effects and local resonance due to bathymetry and morphology of the coast around each station. In this case, it
was taken the time window from the 20th minute to 60th
minute, for purposes of calculating the inversion.
This algorithm guarantees that all values of slip are positive, as a necessary condition from the physical point ofview. However, mathematically this model is not the most
optimal result with respect to the method of simple leastsquares. The results of the inversion process are shown in
Table 4.

Journal of Disaster Research Vol.9 No.6, 2014

4. Results
4.1. Deformation Field
From the slip distribution (Table 4), the total deformation field is obtained as a linear combination of unitary
deformations, which will be used as initial condition fortsunami propagation. The maximum slip located at thesouthern part of the rupture geometry in the sub-source
No.04 is 7.0 m. This maximum slip explains the fact that
the Lagunillas bay area is the one that suffered the greatest impact due to inundation because it lies within the
sub-source 04. Additionally, according to geodetic datataken by the Geophysical Institute of Peru after the earthquake, a geodetic point in Punta Paracas moved up 0.50 m
and 1.60 m to the west [18]. The maximum value of thevertical component of the initial coseismic deformation is1.53 m, located SW from Paracas, on the sea.
Table 5 shows the vertical deformation in some locations near the seismic source. For example, the cities ofCañete, Chincha and Tambo de Mora have suffered subsidence or downfall. Unfortunately, we don’t have GPS
measurements in these locations.
After the analysis of the position of the epicenter andthe geometry of the rupture area, we can infer that therupture process propagated in one way: from the epicenter(in the northern region of the geometry of rupture) to thesouth where there was the greatest deformation (Fig. 4).
This is consistent with the results obtained by other methods such as teleseismic inversion [19] and teleseismicgeodetic inversion [20].
4.2. Scalar “Tsunami” Moment
Taking the slip distribution and dimensions of each subsource, it can be calculated the scalar seismic moment,
defined as:
M0 = μ LW D

. . . . . . . . . . . . . (5)where: M0 = seismic moment (Nm), μ = 4.0×1010 N/m2
average rigidity of elastic medium, L = 180 km total
length of the source, W = 90 km total width of the source,
D = 3.07 m, slip average. The scalar seismic moment is
M0 = 1.99 × 1021 Nm, according to the relation:2log(M0 ) − 6.07 . . . . . . . . . (6)3
A moment magnitude scale of Mw 8.1 is obtained. Thismeans that from the tsunami waveforms recordings yields
a similar magnitude Mw that with the seismic records.
Mw =4.3. Tsunami Waveforms from Seismic Source Inversion
Figure 5 shows the results of the inversion by comparing simulated and observed waveforms. Note that for purposes of calculating the inversion, all signals are displacedin time with respect to time of arrival at the respective
station, so that all arrival times are synchronized on 20thminute. We noticed that observed and simulated waves
957

Jiménez, C. et al.

Table 5. Vertical deformation Uz in some locations near theseismic source: uplift (+) or subsidence (−). We don’t have
geodetic observations for these locations, except for Punta
Paracas (+50 cm).
LocationCañeteChinchaTambo de Mora
Pisco
ParacasIndependencia

Lat (◦ )-13.0777-13.4048-13.4604-13.7099-13.8353-14.2791

Lon (◦ ) Uz (cm)-76.3874-04.3-76.1270-22.1-76.1768-14.8-76.2031
38.9-76.2546
105.3-76.1982
111.8

Fig. 5. Comparison of tide gauge records. In blue: observed signals, in red: simulated signals. Height (m) vs.
Time (min). The time is synchronized for all records withrespect to arrival time on 20th minute.
Table 6. Correlation coefficient between observed and simulated signals.
N
1
2
3
4
5
6
7
8
9
10

Fig. 4. Vertical component of the initial coseismic deformation.

are well correlated (as shown in Table 6). Table 6 shows
the correlation coefficients between the observed signals
and the simulated signals for each station. The stationsof Coquimbo, Arica and Calderas have the best correlation. These correlations would improve if DART buoys(located at deep sea) were included in the inversion process, because there not would be problems of reflection
and coastal resonance. However, the lack of coverage ofthese buoys in the western South Pacific and the large epicentral distance from the rest of these buoys would cause
computing problems when working on far field simulation. For example, the dispersion effects are appreciable
for far field simulation and this is a non linear problem.
We have bypassed stations of Marcona and Juan Fernandez because they have a low correlation between simulated and observed signals.

4.4. Kinematic Effect of Seismic Source
The kinematic effects of the seismic source are appreciable if the rupture process is very slow (vr < 1.5 km/s)
958

StationCorrelation
Arica
0.82
DART 32401
0.79
Iquique
0.78
Antofagasta
0.70
Callao0.01
Calderas
0.82
Coquimbo
0.85
Valparaı́so
0.80
San Antonio
0.70
Talcahuano
0.56

and if the effective length of rupture is high, as the caseof the Sumatra tsunami of 2004 that had a unidirectional
rupture mode from south to north, with an effective rupture length of Le f ≈1000 km [21].
In the case of Pisco 2007 tsunami, the rupture processlasted a time τ around 2 minutes [5], so we can estimate
the average rupture velocity from:Le f. . . . . . . . . . . . . . . (7)τAs the rupture process mode is unidirectional from
north to south: Le f = L, then Le f ≈180 km, therefore:vr ≈1.5 km/s. However, Sladen et al. (2010) [20] argues
that the Pisco event should not be considered a “tsunami
vr: =Journal of Disaster Research Vol.9 No.6, 2014

Tsunami Waveform Inversion of the 2007 Peru (Mw 8.1) Earthquake

Table 7. Maximum horizontal inundation obtained from numerical simulation compared with field survey observations
from DHN [22].

Fig. 6. Tsunami inundation map for Lagunillas and Yumaque (southern part of Paracas). In red: maximum inundation line from DHN tsunami survey [2].

earthquake” despite the apparent slow rate of rupture, inthis case should be interpreted that the earthquake consistsof 2 sub-events, each one with a conventional speed (2 to
4 km/s), separated by a time of 60 seconds.
The rupture velocity is a parameter used to take into account the kinematic effect of the seismic source, so in thiscase, we will not take into account this effect, otherwise,
we would simulate the seabed deformation sequentiallyand this would affect the propagation and inundation process.

4.5. Tsunami Propagation and Inundation
To simulate the propagation process of the tsunami,
TUNAMI model was used. This model integrates theequations of linear shallow water theory by central finite
difference numerical scheme.
With the seismic source obtained, the simulation was
performed again for the nonlinear model, after performing the inversion, to verify that simulated signals obtainedwith the inversion process is correlated with the observed
signals.
With respect to the directivity of the tsunami waves: themost of the energy is directed to the west, towards New
Zealand and Australia. This directivity is determined by
the perpendicular direction to the length of the fault plane
and the bathymetry of the Pacific Ocean.
The most remarkable horizontal inundation took placein Lagunillas and was around 2 km (Fig. 6). The inundation distances (black arrows in Fig. 6) belong to the fieldsurvey observations of DHN [22]. According to the report of the DHN, the people mentioned that the first wave
would have arrived 15 to 20 min after the earthquake, thesecond wave (which arrived after 10 to 15 min) was thehighest and destructive. People report that the first wave
came up to his knees when they were inside their homes
Journal of Disaster Research Vol.9 No.6, 2014

Lat (◦ )Lon (◦ )-13,4611-13,4691-13,6975-13,7100-13,7177-13,7272-13,7361-13,7452-13,8325-13,8455-13,8927-13,8858-13,8919-13,9080-76,1861-76,1902-76,2203-76,2183-76,2222-76,2230-76,2258-76,2283-76,2486-76,5330-76,3138-76,3227-76,3119-76,2843

Observed
distance (m)
171
103
759
317
309
120
156
141
90.4
191
2000
360
174
70

SimulatedError
distance (m)
(%)
173
1.2
101
1.6
758
0.1
318
2.6
310
1.6
126
5.3
150
3.8
140
0.7
96
6.2
218 14.0
1963
1.8
375 3.32
150 13.5
60 14.3

and the second wave covered the roof of the houses. In
this area three adults died. For Yumaque beach, the maximum run-up was reported by Woodman (2007) [23] andwas around 10 m above the highest tide of the day; while
the maximum inundation distance was around 150 m.
The maximum inundation distances and run-up can beused for validation of the seismic source model. If thedifference between the observed and modeled results is
too large, then either the model or the input data (in mostcases) should be improved. This could include obtaining
a better resolution bathymetric and topographic data anda better model of the seismic source. The requirements to
reduce errors between observed and simulated parameters
vary from one model to another.
Table 7 shows the comparison of the simulation results
versus field survey observations from DHN. For example, in the case of Lagunillas, the maximum inundation
observed was 2000 m (according to DHN) and the simulated distance was 1963 m with an error of 1.8%. This isa very good correlation and shows that the seismic source
model works very well.

5. Conclusions
The solution of the inverse problem is not unique, but
we used slip values that provide the best correlation between observed and simulated tsunami waveforms.
The maximum amplitude of the initial coseismic deformation model is 1.53 m. The maximum slip is 7.0 m and it
is located in the southern part of the rupture area, specifically, south of the Paracas Peninsula. This fact and thehorizontal slope topography in Caleta Lagunillas explain
the great horizontal inundation of around 2 km.
The results of the deformation field show that the cities
of Cañete, Chincha and Tambo de Mora have sufferedsubsidence or downfall, while the cities of Pisco and Para959

Jiménez, C. et al.

cas have suffered uplift.
It is important to have good azimuthal distribution oftide gauge stations around the seismic source geometry.
Furthermore, the result will depend on the quality of thetide gauge data and quality of the local bathymetry aroundeach station.
The rupture process, whose beginning was at the epicenter, presented a unidirectional directivity along the azimuth from north-west to south-east. There are two principal asperities, the greater one is concentrated on southern of the Paracas Peninsula. This is in agreement withthe teleseismic body waveform inversion.
The scalar seismic moment calculated is M0 = 1.99 ×
1021 Nm and the magnitude of the earthquake inferredfrom tide gauge records is Mw 8.1. Same as the magnitude
obtained from teleseismic waveform inversion.
One way to verify the seismic source model proposed isby comparing the observed and simulated tsunami waveform. The tidal stations in Coquimbo, Arica, Calderas
and Valparaiso have the highest correlation values, while
Callao, Marcona, Juan Fernandez and Talcahuano have a
low correlation value.
The proposed model is not good in the near field region because the correlation values are low in Callao andMarcona. So, additional studies are required to improve
the seismic source model and bathymetry data for tsunami
inversion in near-field region.
The most remarkable horizontal inundation took placein Lagunillas and was around 2 km. Comparing with thefield survey observation, there is an error of 1.8%, so, theseismic source model works very well.

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Name:

Acknowledgements
The authors acknowledge support from: the project of Science andTechnology Research Partnership for Sustainable Development
supported by JST-JICA, the Dirección de Hidrografı́a y Navegación of the Peruvian Navy and the Universidad Nacional Mayorde San Marcos – FENLAB.
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Center.
Web:
http://www.ndbc.noaa.gov/dart.shtml [accessed Dec. 2012][8] C. Jiménez, “Procesamiento digital de señales sı́smicas con Matlab,” Revista de Investigación de Fı́sica, UNMSM, Vol.10, No.2,
pp. 23-28, 2007.
[9] A. Rabinovich, R. Thomson, and F. Stephenson, “The Sumatra
Tsunami of 26 December 2004 as observed in the North Pacific andNorth Atlantic oceans,” Surv Geophys, Vol.27, pp. 647-677, 2006.

960

Cesar Jimenez Tintaya

Affiliation:
Fenlab, Universidad Nacional Mayor de San
Marcos (UNMSM)
Dirección de Hidrografı́a y Naveagacı́on (DHN)

Address:Av. Venezuela s/n, Lima, PerúCalle Roca 116, Chucuito-Callao, Perú

Brief Career:
2000-2007 Assistant Research, Instituto Geofisico del Perú IGP
2008-2014 Research Scientist, Dirección de Hidrografı́a y Naveagacı́on
2009-2014 Assistant Professor, UNMSM, Perú
2014- Master in Geophysics, UNMSM, Perú

Selected Publications:• M. Ioualalen, H. Perfettini, S. Yauri, C. Jimenez and H. Tavera,“Tsunami Modeling to Validate Slip Models of the 2007 Mw 8.0 Pisco
Earthquake, Central Peru,” Pure and Applied Geophysics. 2012.
• E. Mas, S. Koshimura, A. Suppasri, M. Matsuoka, M. Matsuyama, T.
Yoshii, C. Jimenez, et al. (2012). “Developing Tsunami fragility curves
using remote sensing and survey data of the 2010 Chilean Tsunami in
Dichato,” Natural Hazards and Earth System Science, 12, 2689-2697.
doi:10.5194/nhess-12-2689-2012.
• T. Yoshii, M. Imamura, M. Matsuyama, S. Koshimura, M. Matsuoka, E.
Mas and C. Jimenez. “Salinity in Soils and Tsunami Deposits in Areas
Affected by the 2010 Chile and 2011 Japan Tsunamis,” Pure and AppliedGeophysics, 2012.

Academic Societies & Scientific Organizations:• Peruvian Society of Physics (SOPERFI)

Journal of Disaster Research Vol.9 No.6, 2014