The acoustic signature of fluid flow in complex porous media

Effective medium approximations for the frequency-dependent and complex-valued effective stiffness tensors of cracked/ porous rocks with multiple solid constituents are developed on the basis of the T-matrix approach (based on integral equation methods for quasi-static composites), the elastic - viscoelastic correspondence principle, and a unified treatment of the local and global flow mechanisms, which is consistent with the principle of fluid mass conservation. The main advantage of using the T-matrix approach, rather than the first-order approach of Eshelby or the second-order approach of Hudson, is that it produces physically plausible results even when the volume concentrations of inclusions or cavities are no longer small. The new formulae, which operates with an arbitrary homogeneous (anisotropic) reference medium and contains terms of all order in the volume concentrations of solid particles and communicating cavities, take explicitly account of inclusion shape and spatial distribution independently. We show analytically that an expansion of the T-matrix formulae to first order in the volume concentration of cavities (in agreement with the dilute estimate of Eshelby) has the correct dependence on the properties of the saturating fluid, in the sense that it is consistent with the Brown-Korringa relation, when the frequency is sufficiently low. We present numerical results for the (anisotropic) effective viscoelastic properties of a cracked permeable medium with finite storage porosity, indicating that the complete T-matrix formulae (including the higher-order terms) are generally consistent with the Brown-Korringa relation, at least if we assume the spatial distribution of cavities to be the same for all cavity pairs. We have found an efficient way to treat statistical correlations in the shapes and orientations of the communicating cavities, and also obtained a reasonable match between theoretical predictions (based on a dual porosity model for quartz-clay mixtures, involving relatively flat clay-related pores and more rounded quartz-related pores) and laboratory results for the ultrasonic velocity and attenuation spectra of a suite of typical reservoir rocks. (C) 2003 Elsevier B.V. All rights reserved.

Population performance of collembolans feeding on soil fungi from different ecological niches

The potential reproductive value of arbuscular mycorrhizal fungi (Gloinus intraradices and Glomus invermaium), root pathogenic fungi (Rhizoctonia solani and Fusarium culmorum) and saprotrophic fungi (Penicillium hordei and Trichoderma harzianum) were examined for the collembolans Folsomia candida Willem and Folsomia fimetaria L. Dried baker's yeast (Saccharomyces cerevisiae) was used as a reference standard food in laboratory cultures. Collembolan performance was determined as final size, fecundity and population growth rate after when fed the fungal food sources for 31 days. The mycorrhizal fungi gave the least growth and fecundity compared with the other fungi, but G. intraradices gave good fecundity for F. candida. In terms of growth, Baker's yeast was a high-quality food for both adults and juveniles of both species, but it was a poorer food in terms of fecundity of F. candida. Preference of the fungi in all possible pairwise combinations showed that although F. fimetaria did not perform well on Glomus spp. and F. candida did not grow well on Glomus spp. their preference for these fungi did not reflect this. The highest fecundity was seen with the root pathogen F. culmorum. Different quality indicators such as the C:N ratio of the fungal food sources as well as other biological parameters are discussed in relation to their reproductive value and Collembola preferential feeding. (c) 2007 Elsevier Ltd. All rights reserved.

Likelihood-free estimation of model evidence

Statistical methods of inference typically require the likelihood function to be computable in a reasonable amount of time. The class of “likelihood-free” methods termed Approximate Bayesian Computation (ABC) is able to eliminate this requirement, replacing the evaluation of the likelihood with simulation from it. Likelihood-free methods have gained in efficiency and popularity in the past few years, following their integration with Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) in order to better explore the parameter space. They have been applied primarily to estimating the parameters of a given model, but can also be used to compare models. Here we present novel likelihood-free approaches to model comparison, based upon the independent estimation of the evidence of each model under study. Key advantages of these approaches over previous techniques are that they allow the exploitation of MCMC or SMC algorithms for exploring the parameter space, and that they do not require a sampler able to mix between models. We validate the proposed methods using a simple exponential family problem before providing a realistic problem from human population genetics: the comparison of different demographic models based upon genetic data from the Y chromosome.

Linkages between property asset returns and interest rates: evidence for the UK

This paper considers the effect of short- and long-term interest rates, and interest rate spreads upon real estate index returns in the UK. Using Johansen's vector autoregressive framework, it is found that the real estate index cointegrates with the term spread, but not with the short or long rates themselves. Granger causality tests indicate that movements in short term interest rates and the spread cause movements in the returns series. However, decomposition of the forecast error variances from VAR models indicate that changes in these variables can only explain a small proportion of the overall variability of the returns, and that the effect has fully worked through after two months. The results suggest that these financial variables could potentially be used as leading indicators for real estate markets, with corresponding implications for return predictability.

Bayesian model comparison with un-normalised likelihoods

Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalizing constant, such as Markov random field models, are used widely in computer science, statistical physics, spatial statistics, and network analysis. However, Bayesian analysis of these models using standard Monte Carlo methods is not possible due to the intractability of their likelihood functions. Several methods that permit exact, or close to exact, simulation from the posterior distribution have recently been developed. However, estimating the evidence and Bayes’ factors for these models remains challenging in general. This paper describes new random weight importance sampling and sequential Monte Carlo methods for estimating BFs that use simulation to circumvent the evaluation of the intractable likelihood, and compares them to existing methods. In some cases we observe an advantage in the use of biased weight estimates. An initial investigation into the theoretical and empirical properties of this class of methods is presented. Some support for the use of biased estimates is presented, but we advocate caution in the use of such estimates.