controls of seismogenic zone width and subduction velocity ... · 11 keywords: subduction...
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Controls of seismogenic zone width and subduction 1
velocity on interplate seismicity: insights from 2
analog and numerical models 3
FABIO CORBI1,*, ROBERT HERRENDÖRFER2, FRANCESCA FUNICIELLO3, YLONA 4
VAN DINTHER2 5
6
1 Géosciences Montpellier Laboratory, University of Montpellier, Montpellier, France. 7
2 Institute of Geophysics, ETH Zurich, Sonneggstrasse 5, CH-8092, Zurich, Switzerland. 8
3 Dipartimento di Scienze, University “Roma Tre”, L. S.L. Murialdo, 1, 00146, Rome, Italy. 9
*Correspondence to: [email protected] 10
Keywords: subduction megathrust earthquakes, analog modelling, numerical modelling, subduction 11
velocity, seismogenic zone width 12
13
Abstract 14
Correlations between geodynamic parameters and interplate seismicity characteristics in subduction 15
zones are generally weak due to the short instrumental record and multi-parameter influences. To 16
investigate the role of subduction velocity Vs and the width of the seismogenic zone W on 17
maximum magnitude Mmax, seismic rate τ, charateristic recurrence rate τc, and moment release rate 18
MRR we add synthetic data sets from simplified analog- and numerical models to a global database 19
of natural subduction zones seismicity. Our models suggest that Mmax increases with W and is 20
unaffected by Vs, τ increases with Vs, τc increases with Vs/W, MRR increases with Vs*W. In 21
nature, only the positive correlation between Vs and τ is significant. Random sampling of our time 22
series suggests that a minimum span of at least the characteristic recurrence time would be required 23
to witness Mmax and to observe similarly strong correlations in nature. 24
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25
1. Introduction 26
The world largest earthquakes occur along the subduction megathrusts: the large faults between the 27
subducting and overriding plates (Figure 1a). Along megathrusts, stress is locally built up as a 28
consequence of friction that acts against plate convergence. When the fault strength is overcome, 29
stress is released through a variety of slip modes that range from slow slip events to regular 30
earthquakes [e.g., Ide et al., 2007; Peng and Gomberg 2010]. During regular earthquakes, slip can 31
quickly reach tens of meters and involve hundreds of kilometers of the fault, resulting in moment 32
magnitude Mw 8 or larger earthquakes. These earthquakes, in combination with tsunamis that they 33
may trigger, can cause extensive human losses and severe damages in densely populated areas, as 34
for the 2011 Mw 9.0 Tohoku-Oki event (Japan). 35
The seismic signature differs between subduction zones under various aspects, including the 36
maximum magnitude and earthquake productivity [e.g., Ide 2013]. Such variability has been 37
initially attributed to the combination of age of subducting plate and plate-rate convergence [Uyeda 38
and Kanamori 1979; Ruff and Kanamori 1980]. However, this former idea failed in explaining the 39
occurrence of events like the 2004 Sumatra and the 2011 Tohoku-Oki, pushing the scientific 40
community to find other possible links between subduction interplate seismicity and long-term 41
geodynamic parameters [e.g., Heuret et al., 2011; Schellart and Rawlinson 2013]. 42
Two parameters, namely the width of the seismogenic zone W and subduction velocity Vs, have 43
been proposed to exert a key role on interplate seismicity for their first order control on deformation 44
rate and coupling area between plates, respectively [e.g., Kanamori and Brodsky 2001; Scholz and 45
Campos, 2012]. However, previous studies focusing on the possible relationships between 46
seismicity and W or Vs lead to contradicting conclusions. While Kelleher et al. [1974] concluded 47
that the maximum magnitude increases with the width of the contact zone between the converging 48
plates, Pacheco et al. [1993] and Heuret et al. [2011] found no significant correlation between these 49
two parameters. Similarly for Vs, Ruff and Kanamori [1980] and Jarrard [1986] noted that the 50
earthquake magnitude potential of subduction zones is positively correlated with relative plate 51
motions, but then it is not clear why the fastest subduction zones (i.e., Tonga and the New 52
Hebrides) have not experienced a Mw > 8.0 earthquake since 1900 [e.g., Heuret et al., 2011]. 53
These observational studies provide a snapshot of an intertwined truth, as they are confronted with a 54
too short instrumental time span within which multiple, intercorrelated geodynamic parameters 55
affect the seismicity at the same time. Our instrumental record dates back to 1900, while thousands 56
of years of data are required to cover several complete seismic cycles and reveal each subduction 57
zone seismic characteristics [e.g., McCaffrey, 2008]. Unfortunately paleoseismological 58 AUTHOR’SPOST-PRIN
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investigations [e.g., Cisternas et al., 2005; Sieh et al., 2008] do not provide enough spatio-temporal 59
coverage to extend our global analysis that far back in time. Additionally, contemporary variations 60
of several geodynamic parameters within tens of subduction zones makes it difficult to identify one-61
to-one correlations and cause-effect relationships [e.g., Heuret et al., 2011]. 62
To overcome these limitations, we use complementary analog- and numerical models to investigate 63
how W and Vs control the seismic behavior of subduction interplate events. Both analog and 64
numerical models have recently become a robust tool of investigation [e.g., Corbi et al., 2013; van 65
Dinther et al., 2013; van Dinther et al., 2014a,b; Funiciello et al., 2013]. Their main advantage is the 66
capability to simulate tens to hundreds of seismic cycles per model. Properly scaled analog models 67
are physically self-consistent (i.e., stresses and strain evolve spontaneously in response to applied 68
boundary conditions), while numerical models have also the advantage of being more flexible and 69
efficient for a parametric study. A previous benchmark ensures the similarity between the two 70
modelling techniques [van Dinther et al., 2013]. To describe the seismic behavior of our models we 71
use the following parameters: maximum magnitude Mmax, seismic rate τ, characteristic seismic 72
rate τc and moment release rate MRR (Supporting information Text S1). These parameters allow us 73
to answer the following questions: a) how large is the strongest event? b) how many events occur in 74
a given time period? c) what is the recurrence time of the largest events; d) how fast is the release of 75
seismic energy for a given (geometric and kinematic) subduction configuration in a given time 76
window? We finally compare the modelling results with a database that includes geometrical, 77
mechanical and seismological properties of worldwide subduction zones [Heuret et al., 2011]. The 78
aim of this comparison is to identify potential cause-effect relationships among the investigated 79
parameters that may be flawed by the short observation interval and multi-parameter influence. 80
81
2. Methods 82
We use three complementary sources of information to analyze the role of W and Vs. Besides a 83
global database of natural subduction zones [Heuret et al., 2011], new insights are provided by a 84
systematic parameter study executed with an analog and numerical models that have been described 85
in detail by Corbi et al. [2013] and van Dinther et al. [2013], respectively. Here we recall their 86
basics, while a summary of model performance and scaling is provided in Supporting information 87
Text S2. The setup of our models (Figure 1b,c) mimics the subduction environment (Figure 1a). A 88
10° dipping, flat and undeformable plate, analog of the downgoing slab, underthrusts a visco-elastic 89
wedge (i.e., the overriding plate). The frictional interaction between converging plates leads to a 90
stress build up, which is episodically released by frictional instabilities propagating along the base 91
of the model (i.e., the analog earthquakes). A velocity-weakening zone (analog of the seismogenic 92 AUTHOR’SPOST-PRIN
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zone) is confined between two velocity-strengthening zones at its updip and downdip limits [e.g., 93
Scholz 1998; Marone and Saffer, 2007]. Vs is applied as a boundary condition in the numerical 94
model and via a stepping motor in the analog models. W is varied together with the depth Dz of the 95
downdip limit according to the worldwide geometry of subduction zones (Supporting information 96
Figure S1 and Table S1). The numerical models systematically investigate the role of Dz at constant 97
Vs. Since Dz plays only a secondary role on the selected seismological parameters, related results 98
are shown in Supporting information Text S3. The numerical models are 2D and the analog models 99
are quasi-2D as the geometric, kinematic and frictional properties of the setup are constant along the 100
width. Both analog and numerical models are monitored for a time scaled to nature of > 105 yr, 101
allowing to recognize tens of seismic cycles per model. 102
Correlations between the investigated parameters (i.e., W and Vs) and seismological ones (i.e., 103
Mmax, τ, and MRR) are provided by means of Spearman correlation coefficient R and p-value 104
[Press et al., 1996] both for our models and for natural subduction zones. The Spearman correlation 105
coefficient is a non-parametric measurement that indicates how well two variables follow a 106
monotonic function. With respect to the more common Pearson correlation coefficient, the 107
Spearman correlation coefficient has the advantage of dealing with skewed data or outliers. This is 108
particularly important for the natural dataset where correlations may be flawed by few fast 109
subduction zones (i.e., North Tonga and New Hebrides; Heuret et al., 2011). 110
2.1 Analog models 111
The wedge is made of 2.5%wt Pig Skin gelatin (see Di Giuseppe et al. [2009] for details on 112
rheological properties and preparation and Corbi et al. [2011] for frictional behavior of gel against 113
sandpaper). The seismogenic zone is modeled with sandpaper while the updip and downdip 114
velocity-strengthening zones are modeled with acetate plastic sheets. The setup is designed 115
maintaining Dz constant during the experimental run. Monitoring is performed via particle image 116
velocimetry PIV method [MatPIV; Sveen, 2004]. The velocity field is used to calculate model 117
deformation time series and earthquake source parameters [Corbi et al., 2013]. 118
2.2 Numerical models 119
The continuum-mechanics based numerical model solves for the conservation equations of mass 120
and momentum for an incompressible medium with a visco-elasto-plastic rheology [Gerya and 121
Yuen, 2007]. The governing equations are discretized on a fully-staggered finite-difference grid in 122
combination with a marker-in-cell technique, in which advected markers carry material properties. 123
The interface between the wedge and the subducting plate is modelled as a frictional boundary 124
layer, in which a non-associative Drucker-Prager plastic flow law is applied with a pressure-125 AUTHOR’SPOST-PRIN
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dependent yield strength. To simulate analog earthquakes, van Dinther et al. [2013] introduced a 126
velocity dependent friction and the inertial term to the momentum equations. 127
2.3 Natural database 128
We compare modelling results with the natural global database compiled by Heuret et al. [2011]. 129
This database includes Mw≥5.5 interplate events occurred worldwide in the 1976-2007 interval 130
sampled from the Harvard CMT catalog [Dziewonski and Woodhouse, 1981] and Mw>7 in the 131
1900-1975 interval sampled from the Centennial catalog [Engdahl and Villasenor, 2002]. Besides 132
seismological information, the database includes W and Vs of worldwide subduction zones. As our 133
models have a subduction angle of 10°, we highlight the subsample of the original database with 134
shallowly dipping seismogenic zones (dip angle <15°). 135
136
3. Results and Analysis 137
3.1 Control on Mmax 138
In our models, Mmax overall increases with W, while it is mostly insensitive to Vs (Fig. 2a,b, Fig. 139
3a,b). The correlations between W and Mmax are high for both the numerical and analog models 140
(i.e., R= 0.93 and R=0.73 for numerical and analog models, respectively), while the correlations 141
between Vs and Mmax are low (R=0.15 and R=0.22 for numerical and analog models, 142
respectively). The largest events rupture at least the entire seismogenic zone width (i.e., a rupture 143
width equal or larger than W, dashed line in Fig. 2a,b) and also propagate into the up- and downdip 144
velocity-strengthening regions. This penetration distance depends on plates interface frictional 145
properties and downdip location of the seismogenic zone with respect to the trench and backstop. 146
For W>180 km, Mmax saturates at Mw~ 9.5. 147
Statistically insignificant correlations between Mmax and W (R=0.15, p>0.05) and between Mmax 148
and Vs (R=0.09, p>0.05) are observed in nature. 149
3.2 Control on τ and τc 150
The seismic rate τ increases mainly as a function of Vs (Fig. 2c,d; Fig 3c), which leads to a high 151
correlation between Vs and τ (R= 0.82 and R=0.95 for numerical and analog models, respectively). 152
Note that τ is lower in the analog models than in the numerical models (Fig. 3c). This difference is 153
due to the lower interseismic locking of the analog models (~40%) with respect to the numerical 154
ones (~100%), which reduces the effective strain rate. Almost identical τ is obtained taking into 155
account this reduced interseismic locking of the analog models (Fig 3c). W has no consistent effect 156 AUTHOR’SPOST-PRIN
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on τ in our models (R=-0.4 and R= 0.09 for the numerical and analog models, respectively; Fig. 157
2c,d). 158
τc, in contrast to τ, clearly decreases as a function of both W and Vs (R=-0.75 and R=-0.14 for 159
numerical and analog models, respectively; Fig. 2g,h). Higher correlation is observed when 160
comparing τc to the Vs/W ratio (R=0.95 and R=087 for numerical and analog models, respectively; 161
Fig. 3e). 162
In nature, the correlation between τ and Vs is the highest observed and significant (R=0.57; p<0.05 163
Fig. 3h). 164
3.3 Control on MRR 165
MRR increases both with Vs and W. Similarly to Mmax, MRR increases with W for W<180km 166
due to the strong influence of the increasing size of the largest earthquakes in wider seismogenic 167
zones. For W>180km MRR approaches our setup’s limit of Mmax (Fig. 2g,h). The correlations 168
between MRR and Vs are higher (R=0.73 and R=0.72 for numerical and analog models, 169
respectively) than correlations between MRR and W (R=0.45 and R=0.54 for numerical and analog 170
models, respectively). Correlations are higher when comparing MRR with the product W*Vs 171
(R=0.93 and R=0.90 for numerical and analog models, respectively; Fig. 3d), while in nature the 172
MRR versus W*Vs correlation appears to be insignificant (R=0.22; p>0.05 Fig. 3i). 173
3.4 Observational time window length for capturing Mmax 174
In our models as well as in nature, Mmax evolves with observation length due to earthquake 175
magnitude variability. Short observation intervals may cause an underestimation of Mmax, while 176
long enough time series contain with high probability at least one event with magnitude close or 177
equal to the “real” Mmax. 178
Aiming to quantify the observation interval for capturing Mmax, we recorded the maximum rupture 179
widths mRw in 104 time windows of certain durations with random initial starts. We proceed in two 180
steps: 181
First we check for the evolution of the temporary maximum rupture width. We normalize the 182
temporary mRw by the final mRw, and the time window length Tw, by the characteristic recurrence 183
time Tc (where Tc=1/τc). In most models, a plateau is reached for rupture widths Rw> ~0.8 * mRw 184
(Figure 4a) justifying the use of this threshold (i.e., 0.8*mRw) as the criterion for random time 185
window search. 186
Then, we determined the percentage of time windows that contain at least one event with rupture 187
width Rw>0.8*mRw (Fig. 4a). Figure 4b shows the percentage of successful time windows as a 188 AUTHOR’SPOST-PRIN
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function of Tw/Tc. In the case of perfect periodicity, we would expect a 100% probability of 189
finding the largest earthquake when Tw/Tc=1. For the majority of our analog and numerical 190
models, which have a stronger variability in seismic behavior, the percentage of successful time 191
windows is around 50% for Tw/Tc=1 and approaches a plateau at 90% for 1≤Tw/Tc≤5. For the 192
analog models with a clear outlier in the time series, a time window is unlikely (i.e. <50%) to be 193
successful with a length of Tw=5*Tc. In this cases, the time series is not long enough to experience 194
events of similar size. 195
196
4. Discussion 197
The combination of our models with natural observations, each with their own non-overlapping 198
uncertainties and limitations (Supporting information Text S4), leads to the following discussion 199
points. 200
Our models show that Mmax is mainly controlled by W as the rupture potential increases with W. 201
This is in agreement with studies suggesting that a large downdip extent of the seismogenic zone is 202
required for the occurrence of mega-events [Kelleher et al., 1974; Uyeda and Kanamori, 1979; Lay 203
et al., 1982]. In our models, the largest ruptures saturate the entire seismogenic zone and propagate 204
into the velocity-strengthening regions, resulting in Mmax larger than those predicted by simple 205
Mw-Rw scaling for Rw=W [Blaser et al., 2010]. The location of several subduction zones (e.g., 206
East Alaska, Japan; Figure 3f) above this Mw-W scaling suggests that the occurrence of larger than 207
expected Mmax may also occur in nature. Regarding the rupture potential to propagate outside the 208
seismogenic zone, an alternative mechanism to the inefficient arresting effect of velocity 209
strengthening regions observed in our models is dynamic fault weakening, as suggested for the 210
2011 Mw9.0 Tohoku earthquake [Noda and Lapusta, 2013]. 211
Random sampling of our time series suggests that an observation history > 5 Tc would provide high 212
chances (i.e., > 90%) to observe at least one event that ruptured the 80% of the downdip of the 213
megathrust, and in turn displaying nearly the full seismic potential. A similar conclusion (i.e., Tw > 214
5 Tc) for determining recurrence time of Mw>9 events was constrained statistically for natural 215
megathrust earthquakes [McCaffrey, 2008]. This limited observation span with respect to the 216
seismic cycle duration might explain: a) why Mmax for the majority of natural subduction zones is 217
smaller than Mmax predicted on the base of Mw-W scaling (Fig. 3e); and b) why the observed 218
Mmax of the majority of subduction zones are more similar to the median magnitude of our models 219
(Fig 3a). A striking example is the 2011 M9.0 Tohoku earthquake with a rupture width of 200 km 220
[Romano et al., 2014]. Before this event the instrumentally recorded Mmax on the Japan segment 221
was 8.3, which is lower than expected from its W=161km [Heuret et al., 2011; Fig. 3e]. It should be 222 AUTHOR’SPOST-PRIN
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noted, however, that by using the scaling law [Blaser et al., 2010], we assume that Mmax scales 223
with Rw. Although a large downdip Rw is linked to a wide along-strike rupture lengths [e.g., Wells 224
and Coppersmith, 1994], it is debatable whether this scaling law holds at very high Mw since 225
unrealistically high widths would be expected for Mw>9.2. Such great megathrust earthquakes have 226
a large along-strike component (e.g., the 2004 Sumatra-Andaman earthquake; Shearer and 227
Burgmann, 2010), which is not taken into account in our 2D models. Therefore, Mmax might be 228
controlled by other factors that have been suggested to control the along-strike rupture propagation, 229
such as the trench-parallel extent of a subduction zone segment, the upper plate strain [Heuret et al., 230
2012] and interplate roughness [e.g., Wang and Bilek 2014]. 231
In our models as well as in natural subduction zones, no significant correlation between Mmax and 232
Vs is obtained (Fig. 2a,b; Fig 3b,g). Based on natural observations, it is unclear whether Vs 233
individually controls Mmax. On one hand, Heuret et al. [2011] identified a feedback between slow 234
subduction zones, shallow dipping slab and wide seismogenic zones, which is proposed to lead to 235
the generation of the largest Mmax. On the other hand, Uyeda [1983] associated the largest 236
magniudes with fast subduction zones due to their high strength of the mechanical coupling of the 237
subduction megathrust. In our models high Vs is associated to high τ and τc increasing the 238
probability to observe the largest events. Therefore, previously suggested Mmax-Vs correlations for 239
nature might have been affected by this potential observational bias. This is in agreement with 240
statistical simulations suggesting that the occurrence of Mw>9 earthquakes cannot be excluded at 241
any subduction zone, independently of Vs [McCaffrey, 2008]. However, it should be noted that our 242
models do not take into account the thermal evolution of a subduction zone, which is suggested to 243
control the vertical extent of the seismogenic zone [e.g., Heuret et al., 2011; Dal Zilio et al. 2016]. 244
More complete physical models self-consistently resolving both subduction dynamics and 245
seismogenesis are thus required to provide further insights. 246
Vs is found to exert a primary control on τ and τc both in analog and in numerical models, which 247
supports the direct correlation between Vs and τ that is found when considering subduction 248
megathrust events only [e.g., Heuret et al., 2011] and the whole convergent margin seismicity [Ide 249
2013]. When taking the different interseismic locking in the analog and numerical models into 250
account, the τ versus Vs and MRR versus Vs relationships of both models tend to overlap (Figure 251
3c-d). The reduced interseismic locking in the analog models may be explained by creeping that 252
occurs at the base of the gelatin wedge, which in turn reduces the seismic coupling. Natural 253
subduction zones display a wide range of seismic coupling [e.g., Scholz and Campos, 2012], whose 254
calculation is, however, affected by the short observation time. Moreover, interseismic locking may 255
also change through subsequent seismic cycles as suggested for the Mentawai segment of the Sunda 256
megathrust [Philibosian et al., 2014]. Aseismic slip transients belonging to the slow slip phenomena 257 AUTHOR’SPOST-PRIN
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observed in subduction zones [e.g., Peng and Gomberg, 2010], release periodically a fraction of the 258
accumulated elastic energy of convergent margins, and, thereby reduce the long-term locking [e.g., 259
Radiguet et al, 2016]. Therefore, different sources affecting the amount of locking in subduction 260
zones may explain the scatter of τ and Vs in the natural database (Fig. 3h). 261
In our models, MRR is controlled by both Vs and W (Fig 2g,h). In nature, MRR is mainly 262
influenced by the contribution of the largest event. For example, the great 1960 Mw9.5 Chile 263
earthquake alone accounts for about 30% of the total seismic energy released during the last century 264
[Heuret et al., 2011]. Our models furthermore demonstrates that the frequency of the largest events 265
is a crucial factor to estimate the long-term MRR. This frequency, τc, is shown to be controlled by 266
the ratio of Vs/W, which means that larger W reduces the recurrence rate of the largest events. This 267
weakens the positive impact of W on MRR (Fig 3e). Consequently, the fastest subduction zones 268
with medium to large W are expected to have the largest MRR. A comparison of the correlations 269
with respect to MRR and τc would require multiple cycles of the largest events, which is clearly 270
beyond the available data. Paleoseismological studies [e.g. Cisternas et al., 2005; Sieh et al., 2008] 271
might provide such estimates in some subduction zones to allow for a comparison in future studies. 272
273
5. Conclusions 274
The following conclusions are drawn linking our models to natural observations: 275
- Mmax is strongly influenced by W. Subduction zones where the largest observed rupture 276
widths are smaller than W, may have shown only a fraction of their seismic potential. 277
However, the role of lateral megathrust extent (not taken into account in our models) might 278
become dominant for ruptures with Mw>9. 279
- Mmax is likely independent from Vs. The previously observed Mmax versus Vs correlation 280
in nature is likely due to the short observation interval and/or feedbacks between Vs and W. 281
- τ is mainly influenced by Vs. Differences in interseismic locking may explain the scattered 282
relationship between Vs and τ in nature. 283
- W does not play a relevant role in controlling τ in our models and in nature, but our models 284
suggest that larger W is associated with lower τc. 285
- MRR is both strongly influenced by Vs and W due to their control on τc and Mmax. Time 286
series larger than τc may be needed to observe the full seismic potential of a megathrust and 287
posibly improve correlations in nature. 288 AUTHOR’SPOST-PRIN
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Figures and captions 289
290
Figure 1: Schematic representation of a subduction zone in nature (a) compared to our analog (b) 291
and numerical models (c). The red lines highlights the seismogenic zone. The dashed purple 292
polygon in (b) highlights the cross sectional area analyzed by PIV. The numerical setup, including 293
gravity vector, is rotated by the subduction angle of 10˚ with respect to the analog setup. Boundary 294
conditions are given on the respective sides. 295
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Figure 2: Modelling results: Maximum magnitude Mmax (a,b), seismic rate τ (c,d), characteristic 297
rate τC (e,f), and moment release rate MRR (g,h) as a function of W and Vs (colorbar) in numerical 298
models (top row) and analog models (bottom row). The black dashed line in panels a and b 299
represents the Mw-Rw scaling [Blaser et al., 2010] for Rw=W. 300
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Figure 3: Summarized role of W and Vs on the maximum (squares) and medium magnitude 303
(triangles; a,b). Role of Vs on τ (c). Control of Vs*W on MRR (d) and control of Vs/W on τc (e). 304
The roles of W and Vsn in the dataset of natural subduction zones [Heuret et al., 2011] is shown for 305
comparison in terms of Mmax (f,g), τ (h), and MRR (i). Each panel report the Spearman correlation 306
coefficient R and the p-value. Subduction zones with a dip angle≤15˚: Sumatra (SUM), Cocos 307
(CO), Andaman (AN), Java (JV), E-Alaska (EA), Western Aegean (WA), Antilles (AT), Timor 308
(TI), Nankai (NA), Southern Chile (SC). Green stars in (f) represents Mmax for the Japan 309
subduction zone segment before (i.e., Mw=8.3) and after the 2011 Mw=9.0 Tohoku earthquake, 310
respectively. 311
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Figure 4: Random sampling of the model time series. Evolution of the temporary maximum rupture 314
width Rw normalized by the final maximum rupture width mRw as a function of normalized time 315
window length Tw/Tc (a). Percentage of successful time windows sampling an event with 316
Rw>0.8*mRw as a function of Tw/Tc (b). 317
318
Acknowledgements 319
FC and RH ran and analyzed the analog and numerical models, respectively, while together taking the lead in 320paper writing. All authors contributed into concept development and further writing of the paper. Riccardo 321Lanari is acknowledged for his support in the laboratory. Serge Lallemand is acknowledged for fruitful 322discussions on a preliminary version of the manuscript. FC received funding from the European Union’s 323Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 324658034 (AspSync). RH was supported by SNSF grant 200021-153524. Analog and numerical modelling data 325are available contacting the corresponding author and RH ([email protected]), respectively. 326
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