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X-ray Bragg diffraction on an acoustically modulated La3Ga5SiO14 crystal (langasite, LGS) was studied in the saggital geometry where a surface acoustic wave (SAW) serves as diffraction grating of normal incidence. A set of parabolic compound Be refracting lenses was used to focus x-ray radiation in order to obtain good angular and spatial resolution of diffraction satellites. X-ray diffraction spectra analysis provided information on the acoustic properties of the LGS crystal and enabled the determination of SAW amplitudes and wavelengths.

The work deals with the application of neural networks residual kriging (NNRK) to the spatial prediction of the abnormally distributed soil pollutant (Cr). It is known that combination of geostatistical interpolation approaches (kriging) and neural networks leads to significantly better prediction accuracy and productivity. Generalized regression neural networks and multilayer perceptrons are classes of neural networks widely used for the continuous function mapping. Each network has its own pros and cons; however both demonstrated fast training and good mapping possibilities. In the work, we examined and compared two combined techniques: generalized regression neural network residual kriging (GRNNRK) and multilayer perceptron residual kriging (MLPRK). The case study is based on the real data sets on surface contamination by chromium at a particular location of the subarctic Novy Urengoy, Russia, obtained during the previously conducted screening. The proposed models have been built, implemented and validated using ArcGIS and MATLAB environments. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. MLRPK showed the best predictive accuracy comparing to the geostatistical approach (kriging) and even to GRNNRK.

Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area