Soil acidity adaptive control problem

Problem of soil acidity regularization is modeled as stochastic adaptive control problem with a linear difference equation of the dynamics of a field pH level. Stochastic component in the equation represents an individual time variability of soil acidity of an elementary section. We use Bayesian approach to determine a posteriori probability density function of the unknown parameters of the stochastic transition process. The Kullback–Leibler information divergence is used as a measure of difference between true distribution and its estimation. Algorithm for the construction of an adaptive stabilizing control in such a linear control system is proposed in the paper. Numerical realization of the algorithm is represented for a problem of a field soil acidity control.     

Mineral systems and the thermodynamics of selenites and selenates in the oxidation zone of sulfide ores – a review

Mineralogy and Petrology - Contemporary mineralogy and geochemistry are concerned with understanding and deciphering processes that occur near the surface of the Earth. These processes are...     

Feasibility of joint 1D velocity model and event location inversion by the Neighbourhood algorithm

Using a set of synthetic P‐ and S‐wave onsets, computed in a 1D medium model from sources that mimic a distribution of microseismic events induced by hydrofrac treatment to a monitoring geophone array(s), we test the possibility to invert back jointly the model and events location. We use the Neighbourhood algorithm for data inversion to account for non‐linear effects of velocity model and grid search for event location. The velocity model used is composed of homogeneous layers, derived from sonic logging. Results for the case of one and two monitoring wells are compared. These results show that the velocity model can be obtained in the case of two monitoring wells, if they have optimal relative position. The use of one monitoring well fails due to the trade‐off between the velocity model and event locations.     

Environmental Radioxenon Levels in Europe: a Comprehensive Overview

Activity concentration data from ambient radioxenon measurements in ground level air, which were carried out in Europe in the framework of the International Noble Gas Experiment (INGE) in support of the development and build-up of a radioxenon monitoring network for the Comprehensive Nuclear-Test-Ban Treaty verification regime are presented and discussed. Six measurement stations provided data from 5 years of measurements performed between 2003 and 2008: Longyearbyen (Spitsbergen, Norway), Stockholm (Sweden), Dubna (Russian Federation), Schauinsland Mountain (Germany), Bruyères-le-Châtel and Marseille (both France). The noble gas systems used within the INGE are designed to continuously measure low concentrations of the four radioxenon isotopes which are most relevant for detection of nuclear explosions: ^131mXe, ^133mXe, ¹³³Xe and ¹³⁵Xe with a time resolution less than or equal to 24 h and a minimum detectable concentration of ¹³³Xe less than 1 mBq/m³. This European cluster of six stations is particularly interesting because it is highly influenced by a high density of nuclear power reactors and some radiopharmaceutical production facilities. The activity concentrations at the European INGE stations are studied to characterise the influence of civilian releases, to be able to distinguish them from possible nuclear explosions. It was found that the mean activity concentration of the most frequently detected isotope, ¹³³Xe, was 5–20 mBq/m³ within Central Europe where most nuclear installations are situated (Bruyères-le-Châtel and Schauinsland), 1.4–2.4 mBq/m³ just outside that region (Stockholm, Dubna and Marseille) and 0.2 mBq/m³ in the remote polar station of Spitsbergen. No seasonal trends could be observed from the data. Two interesting events have been examined and their source regions have been identified using atmospheric backtracking methods that deploy Lagrangian particle dispersion modelling and inversion techniques. The results are consistent with known releases of a radiopharmaceutical facility.     

A statistically significant signature of multi-decadal solar activity changes in atmospheric temperatures at three European stations

We study the effect of solar variability on temperatures recorded in three European stations with the longest gapless series available (Prague, Bologna and Uccle). Following a pattern recognition approach, we partition daily temperature “indices” (minimum, maximum and range) in two separate classes with respect to the level of solar activity (high H vs low L 11 year cycles). Using the two-sample Kolmogorov–Smirnov statistics, multiple shuffles of data, and other partitions, we demonstrate that the separation between the probability distribution functions of H and L temperatures is statistically significant and robust. We find that average annual variations for the H and L classes display common and site-specific patterns. For practically all series considered, differences between graphs of annual change for the two classes H and L are large (～1 °C). Solar activity accounts, at least in part, for the multi-decadal variations in temperature observed at these European sites in the past two centuries.     

Modelling ice conditions in the easternmost Gulf of Finland in the Baltic Sea

To model ice conditions in the eastern Gulf of Finland, a high-resolution three-dimensional hydrodynamic model is coupled with the advanced sea-ice model HELMI (Haapala et al., 2005). To test the model in extreme situations, the ice pattern in the eastern Gulf of Finland was simulated for a mild ice winter (2007–2008) and for a moderate one (2003–2004). The reference runs were performed on the assumption that the ice in the model domain is fast ice if the sea depth is less than 10 m. Using this assumption, the ice thickness averaged over the Neva Bay (the easternmost part of the Gulf of Finland) is overestimated by the model for almost the entire wintertime in the mild winter and during the ice formation and melting periods in the moderate winter, as compared with the thickness reported in ice charts.     

On the use of JMA intensity in earthquake early warning systems

The estimation of strength of shaking at a site from the initial P-wave portion of ground motion is the key problems for shortening the alert time of the earthquake Early Warning (EEW). The most of the techniques proposed for the purpose utilize (a) ground motion models based on the estimated magnitude and hypocentral distance, or (b) the interim proxies, such as initial vertical displacement P _d . We suggest the instrumental Japan Meteorological Agency (JMA) intensity (JMA_I) as a characteristic for fast estimation of damage potential in the EEW systems. We investigated the scaling relations between JMA_I measured using the whole earthquake recordings (overall intensity) and using particular time intervals of various duration (2.0–8.0 s) starting from the P-wave arrival (preliminary intensity). The dataset included 3,660 records (K-NET and the KiK-net networks) from 55 events (M _W 4.1–7.4) occurred in 1999–2008 in Japan. We showed that the time interval of 4–5 s from the P-wave arrival can be used for reliable estimations of the overall intensity with the average standard error of about 0.5 JMA units. The uncertainty in the prediction may be reduced by consideration of local site conditions or by development of the station-specific models.