Combining Seismic Noise Techniques for Landslide Characterization

A strong topographic relief and the presence of weakly consolidated sediments create favorable conditions for the development of landslides around the eastern rim of the Fergana Basin (Central Asia). In summer 2012, a field experiment employing small aperture seismic arrays was carried out on an unstable slope, using ambient vibration recordings. The aim of the study was to constrain the seismic response of a potential future landslide and to map lateral and vertical changes in the shear-wave velocity of the surficial soil layers. Strong variations of horizontal-to-vertical spectral ratios in terms of amplitude and directionality indicated clear differences in local site effects, probably reflecting the stability of different sections of the slope. Results further showed resonant frequencies of both the entire unstable block, as well as for smaller, individual parts. The use of an ad hoc, passive seismic tomography approach based on noise correlograms allowed for the mapping of the shear-wave velocities of the sliding material, even in cases of significant topography relief. Based on the recording of seismic noise only, we clearly identified a low-velocity body of weakly consolidated claystone and limestone material, which can be interpreted as the landslide body, with laterally varying thickness.     

A new bivariate Gamma distribution generated from functional scale parameter with application to drought data

Univariate and bivariate Gamma distributions are among the most widely used distributions in hydrological statistical modeling and applications. This article presents the construction of a new bivariate Gamma distribution which is generated from the functional scale parameter. The utilization of the proposed bivariate Gamma distribution for drought modeling is described by deriving the exact distribution of the inter-arrival time and the proportion of drought along with their moments, assuming that both the lengths of drought duration (X) and non-drought duration (Y) follow this bivariate Gamma distribution. The model parameters of this distribution are estimated by maximum likelihood method and an objective Bayesian analysis using Jeffreys prior and Markov Chain Monte Carlo method. These methods are applied to a real drought dataset from the State of Colorado, USA.     

Support Vector Machines for Classification of Aggregates by Means of IR-Spectra

The increasing physical and technical demands placed on construction materials, especially as they are being used more and more up to the limits of their mechanical strength, has led the aggregates industry to search for more efficient methods of quality control. Information from theoretical work on rock spectra in near-infrared and mid-infrared light as well as achievements gained in signal processing can all be used to improve quality control in an economically acceptable manner. As engineering properties of aggregates are to a great extent determined by the petrological composition of the rock aggregates, the question is, whether a statistical classification rule for identification of rock aggregates can be developed. However, the classification of rocks is complicated by the fact that the optical behavior of minerals forming the rock often appears muted. In addition, minor constituents may dominate the spectrum. Furthermore, the relevant spectra form high dimensional data that are extremely difficult to analyze statistically, especially when curves are very similar. In this paper, support vector machines for classification of rock spectra are investigated, since they are appropriate in classifying highly dimensional data such as spectra.     

Why do we need and how should we implement Bayesian kriging methods

The spatial prediction methodology that has become known under the heading of kriging is largely based on the assumptions that the underlying random field is Gaussian and the covariance function is exactly known. In practical applications, however, these assumptions will not hold. Beyond Gaussianity of the random field, lognormal kriging, disjunctive kriging, (generalized linear) model-based kriging and trans-Gaussian kriging have been proposed in the literature. The latter approach makes use of the Box–Cox-transform of the data. Still, all the alternatives mentioned do not take into account the uncertainty with respect to the distribution (or transformation) and the estimated covariance function of the data. The Bayesian trans-Gaussian kriging methodology proposed in the present paper is in the spirit of the “Bayesian bootstrap” idea advocated by Rubin (Ann Stat 9:130–134, 1981) and avoids the unusual specification of noninformative priors often made in the literature and is entirely based on the sample distribution of the estimators of the covariance function and of the Box–Cox parameter. After some notes on Bayesian spatial prediction, noninformative priors and developing our new methodology finally we will present an example illustrating our pragmatic approach to Bayesian prediction by means of a simulated data set.     

On the performance of a new bivariate pseudo Pareto distribution with application to drought data

A new bivariate pseudo Pareto distribution is proposed, and its distributional characteristics are investigated. The parameters of this distribution are estimated by the moment-, the maximum likelihood- and the Bayesian method. Point estimators of the parameters are presented for different sample sizes. Asymptotic confidence intervals are constructed and the parameter modeling the dependency between two variables is checked. The performance of the different estimation methods is investigated by using the bootstrap method. A Markov Chain Monte Carlo simulation is conducted to estimate the Bayesian posterior distribution for different sample sizes. For illustrative purposes, a real set of drought data is investigated.