Clément Narteau

Common dependence on stress for the two fundamental laws of statistical seismology.

Two of the long-standing relationships of statistical seismology are power laws: the Gutenberg–Richter relation describing the earthquake frequency–magnitude distribution, and the Omori–Utsu law characterizing the temporal decay of aftershock rate following a main shock. Recently, the effect of stress on the slope (the b value) of the earthquake frequency–magnitude distribution was determined by investigations of the faulting-style dependence of the b value. In a similar manner, we study here aftershock sequences according to the faulting style of their main shocks. We show that the time delay before the onset of the power-law aftershock decay rate (the c value) is on average shorter for thrust main shocks than for normal fault earthquakes, taking intermediate values for strike-slip events. These similar dependences on the faulting style indicate that both of the fundamental power laws are governed by the state of stress. Focal mechanisms are known for only 2 per cent of aftershocks. Therefore, c and b values are independent estimates and can be used as new tools to infer the stress field, which remains difficult to measure directly.

Setting the length and time scales of a cellular automaton dune model from the analysis of superimposed bed forms

[1] We present a new 3-D cellular automaton model for bed form dynamics in which individual physical processes such as erosion, deposition, and transport are implemented by nearest neighbor interactions and a time-dependent stochastic process. Simultaneously, a lattice gas cellular automaton model is used to compute the flow and quantify the bed shear stress on the topography. Local erosion rates are assumed to be proportional to the shear stress in such a way that there is a complete feedback mechanism between flow and bed form dynamics. In the numerical simulations of dune fields, we observe the formation and the evolution of superimposed bed forms on barchan and transverse dunes. Using the same model under different initial conditions, we perform the linear stability analysis of a flat sand bed disturbed by a small sinusoidal perturbation. Comparing the most unstable wavelength in the model with the characteristic size of secondary bed forms in nature, we determine the length and time scales of our cellular automaton model. Thus, we establish a link between discrete and continuous approaches and open new perspectives for modeling and quantification of complex patterns in dune fields.

Two modes for dune orientation

Earth’s sand seas (dune fields) experience winds that blow with different strengths and from different directions in line with the seasons. In response, dune fields show a rich variety of shapes, from crescentic barchans to star and linear dunes. These dunes commonly exhibit complex and compound patterns with a range of length scales and various orientations, which up to now have remained difficult to relate to wind cycles. Here, we develop a model for dune orientation that explains the coexistence of bedforms with different alignments in multidirectional wind regimes. This model derives from subaqueous experiments, which show that a single bidirectional flow regime can lead to two different dune orientations depending on sediment availability, i.e., the erodibility of the bed. Sediment availability selects the overriding mechanism for the formation of dunes: increasing in height from the destabilization of a sand bed (with no restriction in sediment availability) or elongating in a finger on a non-erodible surface from a localized sand source. These mechanisms drive the dune orientation. Therefore, dune alignment maximizes dune orthogonality to sand fluxes in the bed instability mode, while dunes are aligned with the mean sand transport direction in the fingering mode. Applied to Earth’s deserts, the model quantitatively predicts the orientation of rectilinear dunes and their superimposed patterns. This field study suggests that many linear dunes on Earth elongate from sources, and are simply aligned with the mean sand transport direction.

Scaling organization of fracture tectonics (SOFT) and earthquake mechanism

Abstract In this paper we use an energy splitting combined with a renormalization group approach to model the behaviour of a fault zone subject to earthquakes. After developing the formalism we explore through numerical experiments the case of a single domain and the case of several interactive domains. This approach is a link between physical approaches, multiblock approaches (like Burridge-Knopoff) and scaling approaches to earthquakes.

Growth mechanisms and dune orientation on Titan

Dune fields on Titan cover more than 17% of the moons surface, constituting the largest known surface reservoir of organics. Their confinement to the equatorial belt, shape, and eastward direction of propagation offer crucial information regarding both the wind regime and sediment supply. Herein, we present a comprehensive analysis of Titans dune orientations using automated detection techniques on nonlocal denoised radar images. By coupling a new dune growth mechanism with wind fields generated by climate modeling, we find that Titans dunes grow by sediment transport on a nonmobile substratum. To be fully consistent with both the local crestline orientations and the eastward propagation of Titans dunes, the sediment should be predominantly transported by strong eastward winds, most likely generated by equinoctial storms or occasional fast westerly gusts. Additionally, convergence of the meridional transport predicted in models can explain why Titans dunes are confined within ±30° latitudes, where sediment fluxes converge.

Methane storms as a driver of Titan/'s dune orientation

Titan’s equatorial dunes propagate eastwards, whereas Titan’s surface winds blow towards the West. Atmospheric simulations suggest that tropical methane storms generate strong eastward gusts that may dominate sand transport on Titan’s surface.

Measuring bedload in gravel-bed mountain rivers: averaging methods and sampling strategies

A dataset of more than 1,000 individual bedload samples coupled with hydraulic flow variables (water depth and velocity) was collected on two high mountain rivers the torrent de Saint Pierre, a proglacial gravel-bed river in the French Alps, in July 2002 and the Urumqi River, in the Chinese Tianshan mountains during summer 2005 and 2006. Analysis of the dataset leads to question the usual section averaged sampling procedure of bedload using Helley-Smith type bedload sampler. It is shown that this procedure is inadequate to catch the full range of flow conditions. Comparison between moving averages on individual datasets and section averages furthermore show that this technique can lead to significantly different rating curves with predictions differing by more than an order of magnitude. Single point sampling is shown to be much more adequate than multiple point sampling and section averaging provided the dataset is sufficiently large.

Sediment flux from the morphodynamics of elongating linear dunes

Although dunes are very common bedforms in terrestrial sand seas, the description of linear dune growth, either by extension or lateral accretion, is still hindered by our limited understanding of the underlying mechanisms. Therefore, sand flux estimates from remote imagery rely essentially on the migration speed of barchan dunes, but not on the dynamics of linear dunes. Here we use ∼50 yr of high-resolution aerial and satellite imagery of the Tenere desert (Niger), the worlds largest source of mineral aerosols, to demonstrate that linear dunes can elongate in the direction of the resultant sand flux with no lateral migration. As they elongate from topographic obstacles in a zone of low sediment availability with multimodal winds, these elongating lee dunes are ideal to isolate and quantify linear dune growth only by extension. Using similar conditions in a numerical model, we show how deposition downstream of low hills may result in nucleation and development of bedforms. From elongation we derive the local sand flux parallel to the linear dune crests. This study shows that the morphodynamics of linear dunes under complex wind regimes can also be used for assessing sediment flux and wind conditions, comparably to the more-established method of using sand flux estimates perpendicular to the barchan dune crests in zones of unidirectional wind.

Combining earthquake forecasts using differential probability gains

We describe an iterative method to combine seismicity forecasts. With this method, we produce the next generation of a starting forecast by incorporating predictive skill from one or more input forecasts. For a single iteration, we use the differential probability gain of an input forecast relative to the starting forecast. At each point in space and time, the rate in the next-generation forecast is the product of the starting rate and the local differential probability gain. The main advantage of this method is that it can produce high forecast rates using all types of numerical forecast models, even those that are not rate-based. Naturally, a limitation of this method is that the input forecast must have some information not already contained in the starting forecast. We illustrate this method using the Every Earthquake a Precursor According to Scale (EEPAS) and Early Aftershocks Statistics (EAST) models, which are currently being evaluated at the US testing center of the Collaboratory for the Study of Earthquake Predictability. During a testing period from July 2009 to December 2011 (with 19 target earthquakes), the combined model we produce has better predictive performance - in terms of Molchan diagrams and likelihood - than the starting model (EEPAS) and the input model (EAST). Many of the target earthquakes occur in regions where the combined model has high forecast rates. Most importantly, the rates in these regions are substantially higher than if we had simply averaged the models.

Short-Term Earthquake Forecasting Using Early Aftershock Statistics

Abstract We present an alarm-based earthquake forecast model that uses the early aftershock statistics (EAST). This model is based on the hypothesis that the time delay before the onset of the power-law aftershock decay rate decreases as the level of stress and the seismogenic potential increase. Here, we estimate this time delay from 〈 t g 〉, the time constant of the Omori–Utsu law. To isolate space–time regions with a relative high level of stress, the single local variable of our forecast model is the E a value, the ratio between the long-term and short-term estimations of 〈 t g 〉. When and where the E a value exceeds a given threshold (i.e., the c value is abnormally small), an alarm is issued, and an earthquake is expected to occur during the next time step. Retrospective tests show that the EAST model has better predictive power than a stationary reference model based on smoothed extrapolation of past seismicity. The official prospective test for California started on 1 July 2009 in the testing center of the Collaboratory for the Study of Earthquake Predictability (CSEP). During the first nine months, 44 M ≥4 earthquakes occurred in the testing area. For this time period, the EAST model has better predictive power than the reference model at a 1% level of significance. Because the EAST model has also a better predictive power than several time-varying clustering models tested in CSEP at a 1% level of significance, we suggest that our successful prospective results are not due only to the space–time clustering of aftershocks.