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Earth and Planetary Science Letters 266 (2008) 332 – 344
www.elsevier.com/locate/epsl
Seasonal variations of seismicity and geodetic strain in the
Himalaya induced by surface hydrology
Pierre Bettinelli a , Jean-Philippe Avouac b,⁎, Mireille Flouzat a , Laurent Bollinger a ,
Guillaume Ramillien c , Sudhir Rajaure d , Som Sapkota d
b
a
Laboratoire de Détection et de Géophysique, CEA, BP12, Bruyères-le-Châtel, 91680, France
California Institute of Technology, Tectonics Observatory, Geology and Planetary Sciences Division, MC 100-23, Pasadena, CA 91125, USA
c
LEGOS, 31401 Toulouse, France
d
National Seismological Centre, Department of Mines and Geology, Lainchaur, Kathmandu, Nepal
Received 11 July 2007; received in revised form 9 November 2007; accepted 11 November 2007
Available online 22 November 2007
Editor: C.P. Jaupart
Abstract
One way to probe earthquake nucleation processes and the relation between stress buildup and seismicity is to analyze the
sensitivity of seismicity to stress perturbations. Here, we report evidence for seasonal strain and stress (~ 2–4 kPa) variations in the
Nepal Himalaya, induced by water storage variations which correlate with seasonal variations of seismicity. The seismicity rate is
twice as high in the winter as in the summer, and correlates with stress rate variations. We infer ~ 10–20 kPa/yr interseismic stress
buildup within the seismicity cluster along the high Himalaya front. Given that Earth tides exert little influence on Himalayan
seismicity, the correlated seasonal variation of stress and seismicity indicates that the duration of earthquake nucleation in the
Himalaya is of the order of days to month, placing constraints on faults friction laws. The unusual sensitivity of seismicity to small
stress changes in the Himalaya might be due to high pore pressure at seismogenic depth.
© 2007 Elsevier B.V. All rights reserved.
Keywords: seismology; geodesy; tectonics; hydrology; Himalaya
1. Introduction
Background seismicity in the Himalaya is known to be
driven by the slow, presumably steady-state, process of
interseismic stress buildup in the period separating very
large (M N 8) earthquakes (Cattin and Avouac, 2000;
Bollinger et al., 2004). However, strong seasonal fluc-
⁎ Corresponding author. Tel.: +626 395 4239; fax: +626 395 1740.
E-mail address: avouac@gps.caltech.edu (J.-P. Avouac).
0012-821X/$ - see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.epsl.2007.11.021
tuations are observed, with winter seismicity twice as high
as summer seismicity, on average (Bollinger et al., 2007).
We analyze continuous geodetic data in order to investigate the cause of these variations, and derive implications for earthquake nucleation and fault rheology.
Indeed, the earthquake nucleation process determines
how seismicity responds to temporal or spatial variations
in strain, whether these are related to coseismic deformation, postseismic relaxation, interseismic stress buildup,
or periodic perturbations such as Earth tides (Dieterich
et al., 2000; Dieterich, 1994, 1987; Lockner and Beeler,
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1999; Beeler and Lockner, 2003; Stein, 1999; Toda et al.,
2002; Perfettini and Avouac, 2004).
2. Background seismicity, secular strain and crustal
structure
Local seismic monitoring in the Nepal Himalaya
has revealed very intense background seismicity along
the front of the high range (Pandey et al., 1995, 1999)
(Fig. 1). Most of the activity comes from thrust events
induced by north–south compression related to the ongoing convergence across the Himalaya. The secular
333
velocities derived from geodetic measurements across the
Nepal Himalaya show that the Main Himalayan Thrust
(MHT) is locked from beneath the high range to the
piedmont, where it surfaces (Fig. 1), and that it roots to the
north into a subhorizontal shear zone that is creeping at
about 2 cm/yr (Fig. 2a) (Bilham et al., 1997; Bettinelli et
al., 2006). Most of the seismicity clusters near the updip
edge of the creeping zone (Fig. 2b), in a region where,
according to the modeling of the geodetic data, Coulomb
stress builds up at a rate higher than about 6 kPa/yr
(Cattin and Avouac, 2000; Bollinger et al., 2004). This
midcrustal seismicity was also observed to coincide with a
Fig. 1. Map of study area with location of data analyzed in this article. Secular velocities relative to India (Bettinelli et al., 2006) determined from
campaign GPS measurements, continuous GPS measurements at SIMR, DAMA, NAGA, GUMB, and EVEB, and continuous DORIS measurements
at EVEB. Also indicated are locations where free water level was determined from TOPEX-POSEIDON altimetric measurements and where aquifer
levels were determined by GRACE gravimetric measurements. Seismicity was recorded by the National Seismic Center of Nepal between 04/01/
1995 and 04/11/2000 (Ml N 3), relocated with the double difference technique, and focal mechanism were compiled from the Harvard catalogue or
determined from regional waveforms and first motions (Sudhire Rajaure, DMG/NSC; de la Torre et al., 2007). The white arrow and the dashed lines
define the zone where a varying surface load, computed from the seasonal water level variations, is applied in the mechanical modeling.
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P. Bettinelli et al. / Earth and Planetary Science Letters 266 (2008) 332–344
zone of high electrical conductivity interpreted to reflect
an interconnected fluid phase that probably comes from
metamorphic dehydration of the underthrusting Indian
basement (Lemmonier et al., 1999; Avouac, 2003)
(Fig. 2c). This coincidence suggests some coupling between seismicity and fluids flow.
3. Seasonality of strain and seismicity
The geodetic time series (Figs. 2b, 3b and c) and
seismicity (Fig. 3a) both show strong seasonal variations. Interpretation of the variations requires some care,
as they could result from a variety of meteorological
Fig. 2. Secular interseismic strain and seasonal variation across Nepal Himalaya. a Secular velocities relative to India determined from campaign
measurements (black dots) and from the analysis of the time series recorded at the continuous geodetic stations (Bettinelli et al., 2006) (red dots,
uncertainties are smaller than the dots size). Continuous line shows the predicted horizontal velocity for a slip rate of 16 mm/yr, a shallow dipping
dislocation as indicated in cross section and derived from least-squares adjustments of all GPS and leveling data from central Nepal (Bettinelli et al.,
2006). b GPS time series de-trended by removing the secular motion. The seasonal variations increase in amplitude from south to north. Details on the
processing of the GPS data are given in Bettinelli et al., (2006). Red shaded areas show seasonal geodetic displacements computed from the variation
of surface load due to seasonal variation of water storage (2 sigma uncertainty) assuming an elastic half space with a elastic shear modulus of 40 GPa
and a Poisson coefficient of 0.25. c simplified geological cross section and geophysical constraints on the crustal structure across central Nepal. See
Fig. 1 for location of section. The conductivity section was obtained from a magnetotelluric experiment carried out along section AB across central
Nepal (Lemmonnier et al, 1999). Also reported are the INDEPTH seismic sections about 500 km east of section AB (Brown et al, 1996; Nelson et al,
1996; Zhao et al., 1993). All thrust faults are inferred to root at depth into a subhorizontal ductile shear zone that coincides with a prominent
midcrustal reflector. White circles show seismicity corresponding to events with well-constrained hypocentral depths (Cattin and Avouac, 2000).
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335
Fig. 3. Correlation between seasonal variation of seismicity, geodetic displacements and water level in the Ganges basin. a Seismicity (Ml N 3) is
shown in red for the period over which the geometry of the seismic network has not changed. It is shown in grey for the period over which the
detection level varied due to discontinuous operation of the seismic stations (the apparent drop in January 2002 in particular is an artifact of technical
failure of about 50% of the seismic stations during that winter). Variations from the secular trend of the geodetic position shown for stations DAMA
(b) and GUMB (c). d Water level variations of some major rivers in the Ganges basin measured by TOPEX-Poseidon (yellow dots) and equivalent
water thickness derived from GRACE (blue dots). See location of these measurements in Fig. 1. Red shaded areas in b and c show seasonal geodetic
displacements computed from the variation of surface load due to seasonal variation of water storage (2 sigma uncertainty) assuming an elastic half
space with a elastic shear modulus of 40 GPa and a Poisson coefficient of 0.25.
artifacts: the GPS time series might be biased by miscorrection of the tropospheric delays and seasonal variations in the seismic noise level could affect the detection
threshold of the seismic network.
Analysis of a catalogue of ~ 10,000 earthquakes
(Bollinger et al., 2007) shows that the seismicity rate in
the winter is twice as high as in the summer at all
magnitudes above the detection threshold (estimated to
be Ml = 2.0 (Pandey et al., 1999)), which rules out the
possibility that the pattern arises solely from seasonal
variations in seismic noise. This analysis also shows an
extremely low probability—only 10− 15 when computed
with the Schuster test as applied to the declustered
catalogue (Schuster, 1897; Heaton, 1975)—that this
seasonality is due to chance. We have analyzed potential
tropospheric bias in the GPS measurements. The details
of this analysis is presented elsewhere (Bettinelli, 2007)
and only summarized briefly here. We have first
compared the troposphere delays estimated from the
GPS data with those predicted from the only in situ
meteorological data, and verify that, although these
latter are not representative of the whole atmospheric
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column, there is no significant discrepancy in the overall
shape and amplitude (Bettinelli, 2007) (Fig. S2). The
vertical positions, which are known to be particularly
sensitive to tropospheric effects, do however show some
correlation with the seasonal variations of tropospheric
delays suggesting possible atmospheric bias. By contrast, the seasonal variations of horizontal positions are
in phase neither with the tropospheric delays, the phase
shift being of about 4 months, nor with the variations of
vertical positions (Bettinelli, 2007). Temporal variations
in the horizontal gradient of the tropospheric delay
cannot be either the cause of the observed seasonal
variations of horizontal positions since they also vary in
phase with the tropospheric delays (Bettinelli, 2007).
The horizontal strain variations derived from our GPS
measurements must thus represent real ground deformation. Variations of the vertical component are less
reliable. They probably reflect a combination of real
ground displacement and troposheric artifacts.
The amplitude of the horizontal seasonal geodetic
displacements increases northwards, so that the shortening rate across the range is lower in the summer than
in the winter. This points to a local source capable of
producing horizontal contraction in the winter and horizontal extension in the summer, superimposed on the
secular shortening rate across the Himalaya.
4. Potential causes of seasonal strain in the Himalaya
Seasonal variations not related to tropospheric miscorrections are commonly observed in geodetic time
series and may have a variety of causes (Blewitt and
Lavallee, 2002; Bawden et al., 2001; Dong et al., 2002;
Ben-Zion and Leary, 1986) but only few can produce the
kind of seasonal strain variation reported in this study,
and explain the correlated variations of geodetic strain
and seismicity. One possibility is that the creep rate on
the MHT, which drives interseismic shortening, varies
with time due to a resonance with atmospheric seasonal forcing (Perfettini and Schmittbuhl, 2001). Such
a mechanism has been invoked to explain the periodicity of slow events (Lowry, 2006; Shen et al., 2005).
Another possibility is that the creep rate on the MHT
is constant, but a seasonal source of loading produces
stress variations capable of modulating the seismicity and geodetic strain. Thermoelastic strain has been
shown to be a dominant source of seasonal strain in
California (Ben-Zion and Leary, 1986; Prawirodirdjo
et al., 2006) and could be invoked in the Nepal Himalaya as well. Atmospheric pressure, hydrology and snow
cover can also all produce seasonal variations in surface
oading, which could in turn cause seasonal variations
in geodetic strain and seismicity. In Japan, for example, snow load variations produce observable seasonal
geodetic displacements, and seasonal seismicity (Heki,
2003). In California, atmospheric pressure has been proposed to explain seasonal variations in seismicity following the Landers earthquake (Gao et al., 2000). In the
Himalaya, snow load and atmospheric pressure are both at
a maximum in the winter (Putkonen, 2004). These effects
should, contrary to what is observed, inhibit thrust earthquakes in the winter (Bollinger et al., 2007).
Variation of surface loading associated with the hydrological cycle is another possible mechanism that has
been shown to be capable of producing measurable
geodetic strain at a regional scale (Blewitt et al., 2001;
van Dam et al., 2001). We have explored more particularly this potential effect using satellite altimetry measurements from TOPEX-Poseidon and geoid data from
the GRACE mission (Fig. 3d) to estimate the temporal
variations of land water storage (Frappart et al., 2006).
We selected TOPEX-Poseidon altimetric measurements
over major rivers within the Ganges basin (Fig. 1). These
measurements provide an estimate of the free surface
water level to within a few tens of cm. They show that
during the monsoon season the free water level over the
whole Gangetic basin rises by about 4 m, starting in midMay, reaches a maximum in early September, followed
by a steady decrease until the next monsoon. Strong, inphase, seasonal variations in seismicity, geodetic displacements and water levels in the Ganges basin suggest
a striking causal relationship (Fig. 3).
The GRACE satellite measurements of the geopotential complemented the TOPEX-Poseidon observations,
which ended in 2002. In addition the GRACE measurements provide an estimate of the mass surface loading due
to free surface water, groundwater and soil moisture. The
GRACE geoid data set consists of monthly estimates of
time series of spherical harmonics up to degree and order
50, corresponding to a spatial resolution of 400 km at the
surface of the Earth (Tapley et al., 2004; Tapley et al.,
2004; Schmidt et al., 2006). In the process, atmospheric
and oceanic tide effects are removed from the geoid
harmonic coefficients. The coefficients of the contribution of continental water mass storage, including surface
waters, soil moisture and groundwater, are extracted from
the 10-day sampled GRACE solutions (Lemoine et al.,
2007) using an iterative inverse method (Ramillien et al.,
2006, 2005). Strong seasonal variations of surface load
are revealed. Variation of water storage in the Ganges
basin dominates the signal with a peak-to-peak amplitude
of about 50 cm (Figs. 3d and 4).The radar altimetry and
GRACE-derived land water time series do not overlap,
but a similar phase is found in the seasonal cycle and the
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P. Bettinelli et al. / Earth and Planetary Science Letters 266 (2008) 332–344
amplitude of the equivalent water thickness derived from
GRACE is about ten times smaller than the variations of
rivers surface elevation (Fig. 3). The free surface within
the major rivers and the groundwater level vary jointly
over the seasonal cycle, probably due to the relatively
high permeability of the clastic sediments of the Ganges
basin (Fig. 3). The surface load variations over the whole
period can thus be estimated by combining the radar
altimetry and the GRACE-derived land water time.
5. Influence of water storage variations on strain
and stress in the Himalaya
We propose that the seasonal geodetic displacements
reflect a lithospheric response to the seasonal variation
of hydrological surface loads (Fig. 5). This mechanism
is qualitatively consistent with our geodetic observa-
337
tions since the surface load rises during the summer
monsoon is expected to induce extension across the
Himalaya, while compression should follow in the winter when the surface load decreases. For a more quantitative assessment, we modeled this effect using two
different approaches. The first is based on the analytical
solution of Boussinesq (1885) for deformation of an
elastic half-space submitted to a surface point load (Fig.
6), an approach used in previous similar study (Grapenthin et al., 2006). This model ignores the effects of
topography and of viscous deformation. The second
approach uses a finite element model in which a purely
elastic plate is assumed to overly a nonviscous fluid
(Figs. S1 and S2). This modeling takes topography into
account. In both cases, a load mimicking the effects of
water level variation is applied at the surface. The spatial
distribution of surface load was estimated from the
Fig. 4. Map of the seasonal peak-to-peak amplitudes of total water storage in the Southeast region, which were estimated from the GRACE Land
Waters solutions up to harmonic degree 50 by linear leastsquare inversion (units: meters of equivalent-water thickness). Note the important annual
variations of water mass over the Mekong and the Ganges basins that reach ± 2.4 m of water height (http://www.legos.obs-mip.fr/en/equipes/gohs/
resultats/c1_grace1). The dots show the locations of the GPS stations analyzed in this study.
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Fig. 5. Schematic diagram showing the effect of an increased water level in the Ganges basin on geodetic displacements and strain in the Himalaya. a
Water level rise due to the summer monsoon increases the surface load (grey arrows), inducing elastic deformation of the crust. Subsidence and
southward horizontal displacements occur north of the Ganges basin. Horizontal displacements south of the Ganges basin should be northward. b In
the summer, this seasonal strain implies horizontal extension at seismogenic depth (2–15 km, blue arrows) in the Himalaya, which reduces the effect
of secular horizontal compression due to interseismic strain buildup (red arrows). c The opposite occurs in the winter so that unloading, as water level
drops, implies some horizontal compression (green arrows) that adds to the secular interseismic contraction (red arrows).
GRACE data and the temporal variations were estimated
from combining the TOPEX-Poseidon and GRACE data
(Fig. 3). The predicted seasonal displacements of both
modeling approaches match the observations remarkably
well (Figs. 3, 6 and S2). Although other factors might
contribute to some degree, variations of water storage in
the Ganges basin turns out to be the primary cause of the
observed seasonality of geodetic displacements.
6. Relation between seasonal variations of stress and
seismicity
This mechanism is also qualitatively consistent with
the higher seismicity rate in the winter, suggesting that
the gradual decrease of surface load in the Ganges
induces a gradual increase of compression in the Himalaya, which adds to the effect of interseismic stress
accumulation at the front of the creeping portion of the
MHT (Fig. 6). To quantify the effect of stress variations
on seismicity, we use the Coulomb stress, defined as
S = τ − μ′ · σ, where τ is the shear stress, σ is the normal
stress and μ′ is an effective friction coefficient, here
set to 0.3. The Coulomb stress is commonly used to
evaluate whether stress variations should promote or
inhibit seismicity (King et al., 1994). We find that
seasonal variations in the water level of the Ganges
basin induce fluctuations in the Coulomb stress computed for the zone of seismicity along the front of the
high range, with a peak-to-peak amplitude of 2–4k Pa
(Fig. 7). In fact, the seismicity rate is found to correlate
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P. Bettinelli et al. / Earth and Planetary Science Letters 266 (2008) 332–344
best with stress rate (Figs. 8 and 9), indicating that
triggering of earthquakes depends on the stressing rate
rather than on the absolute stress level, as has been
339
observed in a number of previous case studies (Toda
et al., 2002; Perfettini and Avouac, 2004; Hsu et al.,
2006).
Fig. 6. Observed and predicted NS, EW and vertical displacements at stations SIMR, DAMA, GUMB, NAGA, EVEB and LHAS. Elastic half-space.
The theoretical displacements were computed by convolving the distribution of surface load with the analytical solution for the deformation of an
elastic half space submitted to a surface point load assuming a Young modulus of 40 GPa. [The equations originally from Boussinesq (1885) are given
in standard text books on linear elasticity and can be found for example at http://www.engin.brown.edu/courses/en224/halfspace/halfspace.html]. The
effect of the topography is neglected in this computation, as well as that of viscous deformation. The blue shaded areas show the seasonal geodetic
displacements computed from the surface load exerted by seasonally varying water storage taking into account the spread in the GRACE and
TOPEX-Poseidon measurements (shown in Fig. 3d).
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Fig. 7. Effect of seasonal variation of surface load on Coulomb stress variation. Coulomb stress is defined as S = τ − μ · σn, where μ = 0.3 (King et al.,
1994) and is computed on thrust fault dipping 30° to the north, consistent with most focal mechanisms in the seismicity cluster along the Himalayan
front (Fig. 1). a Difference of Coulomb stress due to the seasonal ~ 4 m drop of the water level within the Ganges basin. White circles shows seismicity
(Cattin and Avouac, 2000). b Temporal evolution of Coulomb stress computed at depths of 10 km (grey line) and 15 km (blue line) within the
seismicity cluster. The Coulomb stress is maximum in the winter when the stored water is at minimum. It is minimum at the end of the summer
monsoon, when the stored water is at maximum. The peak to peak amplitude of Coulomb stress variation within the seismicity cluster is estimated to
between 2 kPa and 4 kPa.
Assuming that this linear correlation has some physical significance the sensitivity of seismicity rate to
seasonal stress fluctuations can be used to estimate the
secular interseismic stress rate independently of the
modeling of interseismic strain. To do so, we have
solved for the secular stress rate which yields the best
linear correlation between the stress rate and the seismicity rate and obtained a value between 10 and 20 kPa/yr
(Fig. 9). This rate represents the average stress rate
within the seismicity zone along the front of the high
Himalaya where stress builds up in the interseismic
period around the downdip end of the locked portion of
the Main Himalayan Thrust fault (Cattin and Avouac,
2000; Bollinger et al., 2004). It is likely, however, that
the stress rate within this zone is highly variable depending on the geometry and complexity of the transition between the locked and creeping fault zones.
The agreement between the estimate of the stress rate
derived from the seasonality of seismicity and that
estimated from the modeling of secular interseismic
strain (N 6 kPa/yr (Cattin and Avouac, 2000; Bollinger
et al., 2004)) is remarkable in light of the uncertainties
and approximations made in these two independent
approaches.
7. Implications for earthquake nucleation
The sensitivity of Himalayan seismicity to seasonal
stress and strain variations has important implications
for earthquake nucleation.
We first consider a simple Coulomb failure model
in which rupture occurs instantaneously when S first
reaches some critical value Sc and then drops to some
dynamic value Sd. In this case, if the initial Coulomb stress
on various faults is random distributed between Sd and Sc,
and if the fault population is subjected to a constant stress
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P. Bettinelli et al. / Earth and Planetary Science Letters 266 (2008) 332–344
341
Fig. 8. Seasonal variations in seismicity rate and Coulomb stress rate. Top graph shows the average seasonal variation of seismicity between 04/01/
1995 and 31/12/2001 (for Ml N 3 and Ml N 4) based on the declustered catalogue of (Bollinger et al., 2007). The number of events with magnitude
greater than 4 has been multiplied by 5. The bottom graph shows variations in Coulomb stress rate computed at 10 km (green) and 15 km (blue)
depths within the seismically active area at the front of the high range.
:
:
rate S 0 , then the seismicity rate r0 is proportional to S 0 .
Let's now consider that the system is perturbed by
Coulomb stress oscillations with period T and amplitude
Sm small
enough that the stress rate is always positive
:
(i.e. S 0 z2p STm ), as is the case in this study. The seismicity
rate is
: then expected to vary in proportion to the stress
rate S, with periodic variations of amplitude Rm given by:
Rm ¼ 2p
Sm
: r0
T S0
ð1Þ
: This model makes it explicit that the secular stress rate,
S 0 , can be estimated from observations of periodic
variations in seismicity, and, as argued in the previous
section, accounts reasonably well for the response of
Himalayan seismicity to annual stress variations. Eq. (1)
also suggests that the sensitivity of seismicity to periodic
stress variations should increase as the period decreases,
such that seismicity should be extremely sensitive to Earth
tides. Indeed, Earth tides induce variations in stress on the
order of 3–4 kPa, an amplitude similar to those related to
the seasonal variation of surface water storage, but at a
much shorter dominant period of 12 h. As already
reported (Cochran et al., 2004), no clear correlation
between seismicity and Earth tides is actually observed in
the Himalaya however. This suggests that the response of
seismicity to periodic stress perturbations is frequency
dependent. This is most probably due to the fact that, at
the time scale of Earth tides, the duration of earthquake
nucleation is not negligible and a simple Coulomb failure
model therefore does not apply (Dieterich, 1987; Lockner
and Beeler, 1999; Beeler and Lockner, 2003). The
duration of nucleation of earthquakes in the Himalaya is
thus probably short compared to the seasonal time scale
but long compared to the semi-diurnal and 14 days
dominant periods of Earth tides.
Some quantitative constraints on the constitutive laws
of faults might be derived from this analysis. Within the
theoretical framework of rate-and-state friction (Dieterich,
1994, 1987; Beeler and Lockner, 2003; Perfettini et al.,
2003), seismic rupture is preceded by a nucleation phase
of self-accelerating slip with a duration characterized by
ar
ta ¼ : ;
S
ð2Þ
where σ is the normal stress and a is a constitutive
parameter reflecting the dependence of the friction
coefficient, A ¼ rs , on slip velocity, V, according to
AA
:
ð3Þ
AlnV
If the period of an oscillating stress perturbation, T, is
such that T ≫ ta, a fault system responds as if it were
obeying a simple Coulomb failure model.
a¼
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Fig. 9. Coulomb stress rate variations induced by seasonal variations of
land water loading as function of seismicity rate. Purple and blue symbols
correspond to earthquakes with local magnitude respectively larger than
3 and 4 (the number of Ml N 4 earthquake per month was multiplied by
10). The approximately linear relationship implies a secular Coulomb
stressing rate between 10 and 20 kPa/yr on average within the seismicity
cluster (the secular rate reads as the intercept at the origin).
Our observations of seasonal correlation between
seismicity and stress rate thus imply that ta has to be
significantly shorter than a year. Equation (3) then
places an upper bound on aσ of about 8 kPa. Due to the
nucleation process, the seismicity rate should actually
not respond instantaneously to a stress rate change, but
instead follow a characteristic lag time on the order of ta
(Toda et al., 2002). The small lag time between
seismicity rate and stress rate seasonal changes (Fig.
7) suggests that ta is actually significantly less than 1 yr,
implying a value of aσ is less than 8 kPa.
When T b ta, the sensitivity of the seismicity to stress
rate variations is approximated by (Dieterich, 1987;
Lockner and Beeler, 1999).
Rm Sm
¼
r0
ar
ð4Þ
Eqs. (1) and (4) cannot be simultaneously valid
except in the particular regime in which T ≈ 2π · ta. In
any case, Eq. (4) always provides a lower estimate of the
seismicity variations expected to result from stress
variations with amplitude Sm. Given that the observed
seasonal variations correspond to a ratio Rr0m on the order of
1/3 and that Sm is on the order of 1–2 kPa, we infer that
aσ is probably larger than 3–6 kPa.
These lines of reasoning both point to a value of aσ
on the order of 3–8 kPa. This value is about one order of
magnitude less than the 0.5–10 MPa range of values
estimated from postseismic creep (Perfettini and
Avouac, 2004; Hsu et al., 2006; Miyazaki et al., 2004;
Hearn et al., 2002). Assuming a lithostatic normal stress,
the 5–15 km depth range of the seismicity implies that a
is on the order of 10− 5, several orders of magnitude
lower than the 10− 2–10− 3 range of laboratory estimates
(Marone, 1998). The low value of aσ might be due to
high pore pressure rather than a particularly low value of
a. This possibility is supported by the high conductivity,
suggesting the presence of fluids, observed in the area of
seismicity (Fig. 2c). High pore pressure has also been
proposed to explain the seasonal variation of seismicity
following the Landers earthquake, which was observed
mainly at sites with hydrothermal activity (Gao et al.,
2000), and the small value of aσ, about 35 kPa, which
was estimated from the temporal evolution of seismicity
induced by a magmatic intrusion (Toda et al., 2002).
8. Conclusion
Permanent geodetic monitoring in the Himalaya has
revealed that secular interseismic strain is modulated by strong seasonal variations dominantly driven by
surface load variations of hydrological origin. Himalayan background seismicity is extremely sensitive to
the small seasonal stress perturbations with a peak-topeak amplitude of a few kPa induced by this mechanism. Given a weak sensitivity to Earth tides and an
insignificant lag time between stress rate and stress rate
variations, we infer that earthquake nucleation in the
region takes place on a time scale longer than days but
probably shorter than a few months. A nucleation phase
is predicted by rate-and-state friction models of earthquake nucleation, but the short nucleation inferred here
AA
requires either that a ¼ AlnV
on natural faults be on
−5
the order of 10 —orders of magnitude smaller than
the values estimated from laboratory experiments—or,
more probably in view of the correlation of seismicity
with crustal conductivity, that earthquakes along the
Himalayan front nucleate in areas of high pore pressure.
Acknowledgements
We are grateful to M.R. Pandey and all our collaborators, at NSC and DMG, and DASE at CEA, for
their dedicated effort, which permitted the deployment,
maintenance, and operation of the Nepal seismic network and the CGPS stations. This study has benefited from discussions with Hugo Perfettini, Jean-Paul
Ampuero, Rodolphe Cattin, and Frederic Perrier, and
from insightful reviews by Roland Burgman and Roger
Bilham. Elisabeth Nadin is thanked for her help in
editing this manuscript.
Author's personal copy
P. Bettinelli et al. / Earth and Planetary Science Letters 266 (2008) 332–344
Appendix A. Supplementary data
Supplementary data associated with this article can
be found, in the online version, at doi:10.1016/j.epsl.
2007.11.021.
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