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Register a new userStochastic modelling of regional archaeomagnetic series
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SUMMARY We report a new method to infer continuous time series of the declination, inclination and intensity of the magnetic field from archeomagnetic data. Adopting a Bayesian perspective, we need to specify a priori knowledge about the time evolution of the magnetic field. It consists in a time correlation function that we choose to be compatible with present knowledge about the geomagnetic time spectra. The results are presented as distributions of possible values for the declination, inclination or intensity. We find that the methodology can be adapted to account for the age uncertainties of archeological artefacts and we use Markov Chain Monte Carlo to explore the possible dates of observations. We apply the method to intensity datasets from Mari, Syria and to intensity and directional datasets from Paris, France. Our reconstructions display more rapid variations than previous studies and we find that the possible values of geomagnetic field elements are not necessarily normally distributed. Another output of the model is better age estimates of archeological artefacts.
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Numerical transport models based on the advection-dispersion equation (ADE) are built on the assumption that sub-grid cell transport is Fickian such that dispersive spreading around the average velocity is symmetric and without significant tailing on the front edge of a solute plume. However, anomalous diffusion in the form of super-diffusion due to preferential pathways in an aquifer has been observed in field data, challenging the assumption of Fickian dispersion at the local scale. This study develops a fully Lagrangian method to simulate sub-grid super-diffusion in a multi-dimensional regional-scale transport. The underlying concept is based on previous observations that solutions to space-fractional ADEs, which can describe super-diffusive dispersion, can be obtained by transforming solutions of classical ADEs. The transformations are equivalent to randomizing particle travel time or relative velocity for each model time step. Here, the time randomizing procedure known as subordination is applied to flow field output from MODFLOW simulations. Numerical tests check the applicability of the novel method in mapping regional-scale super-diffusive transport conditioned on local properties of multi-dimensional heterogeneous media.
We report on a novel stochastic analysis of seismic time series for the Earths vertical velocity, by using methods originally developed for complex hierarchical systems, and in particular for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced change of the shapes of the probability density functions (PDF) of the series increments. Before and close to an earthquake the shape of the PDF and the long-range correlation in the increments both manifest significant changes. For a moderate or large-size earthquake the typical time at which the PDF undergoes the transition from a Gaussian to a non-Gaussian is about 5-10 hours. Thus, the transition represents a new precursor for detecting such earthquakes.
A minimalist stochastic model of primary soil salinity is proposed, in which the rate of soil salinization is determined by the balance between dry and wet salt deposition and the intermittent leaching events caused by rainfall events. The long term probability density functions of salt mass and concentration are found by reducing the coupled soil moisture and salt mass balance equation to a single stochastic differential equation driven by multiplicative Poisson noise. The novel analytical solutions provide insight on the interplay of the main soil, plant and climate parameters responsible for long-term soil salinization. In particular, they show the existence of two distinct regimes, one where the mean salt mass remains nearly constant (or decreases) with increasing rainfall frequency, and another where mean salt content increases markedly with increasing rainfall frequency. As a result, relatively small reductions of rainfall in drier climates may entail dramatic shifts in long-term soil salinization trends, with significant consequences e.g. for climate change impacts on rain-fed agriculture.
Experimental microstylolites have been observed at stressed contacts between quartz grains loaded for several weeks in the presence of an aqueous silica solution, at 350 8C and 50 MPa of differential stress. Stereoscopic analysis of pairs of SEM images yielded a digital elevation model of the surface of the microstylolites. Fourier analyses of these microstylolites reveal a self-affine roughness (with a roughness exponent H of 1.2). Coupled with observations of close interactions between dissolution pits and stylolitic peaks, these data illustrate a possible mechanism for stylolite formation. The complex geometry of stylolite surfaces is imposed by the interplay between the development of dissolution peaks in preferential locations (fast dissolution pits) and the mechanical properties of the solid-fluid-solid interfaces. Simple mechanical modeling expresses the crucial competition that could rule the development of microstylolites: (i) a stress-related process, modeled in terms of the stiffness of springs that activate the heterogeneous dissolution rates of the solid interface, promotes the deflection. In parallel, (ii) the strength of the solid interface, modeled in terms of the stiffness of a membrane, is equivalent to a surface tension that limits the deflection and opposes its development. The modeling produces stylolitic surfaces with characteristic geometries varying from conical to columnar when both the effect of dissolution-rate heterogeneity and the strength properties of the rock are taken into account. A self-affine roughness exponent (Hz1.2) measured on modeled surfaces is comparable with natural stylolites at small length scale and experimental microstylolites.
The Stochastic Series Expansion (SSE) technique is a quantum Monte Carlo method that is especially efficient for many quantum spin systems and boson models. It was the first generic method free from the discretization errors affecting previous path integral based approaches. These lecture notes give a brief overview of the SSE method and its applications. In the introductory section, the representation of quantum statistical mechanics by the power series expansion of ${rm e}^{-beta H}$ will be compared with path integrals in discrete and continuous imaginary time. Extensions of the SSE approach to ground state projection and quantum annealing in imaginary time will also be briefly discussed. The later sections introduce efficient sampling schemes (loop and cluster updates) that have been developed for many classes of models. A summary of generic forms of estimators for important observables are also given. Applications are discussed in the last section.
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