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121.
W. N. Mussel E. Murad J. D. Fabris W. S. Moreira J. B. S. Barbosa C. C. Murta W. P. Abrahão J. W. V. De Mello V. K. Garg 《Physics and Chemistry of Minerals》2007,34(6):383-387
We studied a chalcopyrite from a Cu ore deposit in Rio Grande do Sul, Brazil, by Mössbauer spectroscopy at room temperature and 110 K. Supporting methods to check for sample purity and to characterize further sample properties were slow-scanning X-ray powder diffraction and optical and microprobe analyses of polished sections of selected grains. Chemical analyses obtained using a scanning electron microscope equipped with an energy-dispersive X-ray spectrometer showed the sample to consist of homogeneous and essentially stoichiometric chalcopyrite (CuFeS2). Mössbauer spectra taken at both the above temperatures consist of asymmetric magnetically ordered patterns with unequal intensities of the line pairs 1–6 and 2–5, pointing to the existence of non-equivalent or multiple Fe sites. Least-squares fittings evidenced that the resonance intensity ratio of subspectrum with lower quadrupole shift (indicative of a more symmetric environment) to that with higher shift is 69:31, at room temperature, and 68:32, at 110 K. Rietveld refinement of the XRD data indicates the existence of tetragonal [a = 0.52855(1) and c = 1.0412(1) nm] and cubic [a 0 = 0.5273(2) nm] modifications in a proportion of 74:26, in good agreement with the Mössbauer data. The saturation magnetization of the sample was 32.7 J/(T kg), confirming the oxidation state of Fe as trivalent and pointing to little to no spin canting. 相似文献
122.
Boris Kargoll Mohammad Omidalizarandi Ina Loth Jens-André Paffenholz Hamza Alkhatib 《Journal of Geodesy》2018,92(3):271-297
In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student’s) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model. 相似文献
123.
Landslides - The accurate prediction of landslide tsunami amplitudes has been a challenging task given large uncertainties associated with landslide parameters and often the lack of enough... 相似文献
124.
Landslides - Natural soils often exhibit an anisotropic fabric pattern as a result of soil deposition, weathering, or filling. This paper aims to investigate the effect of soil interdependent... 相似文献
125.
Bulletin of Earthquake Engineering - Making a decision on the post-earthquake functionality of structures has always been one of the most challenging issues in earthquake engineering. There is an... 相似文献
126.
Eskandari Damaneh Hadi Khosravi Hassan Habashi Khalil Eskandari Damaneh Hamed Tiefenbacher John P. 《Natural Hazards》2022,110(3):2185-2205
Natural Hazards - Analysis of long-term land use and land cover (LULC) changes requires up-to-date remotely sensed data to assess their effects on erosion. This is a particularly important... 相似文献
127.
Natural Hazards - Extreme weather events such as heat waves and cold spells affect people’s lives. This study develops a probabilistic framework to evaluate heat waves and cold spells. As... 相似文献
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This paper discusses a geostatistical approach to model a groundwater aquifer in 3-D. The study aims at utilizing geostatistics as a tool for characterizing zones of better-water quality in a brackish-saline aquifer. In particular, the geostatistical model was constructed to characterize the aquifer’s salinity, represented by total dissolved solids (TDS), using logs of porosity and resistivity. Quality-checked estimated TDS vertical profiles were employed to construct and model horizontal and vertical semivariograms. Parameters of semivariogram models were used to develop both the kriging plan and the generated model. Results of this modeling process are shown in the form of horizontal salinity distribution maps. The aquifer was sliced into 20 layers, each 20 m thick, to represent its overall thickness. Salinity layers maps reflect vertical stratification of TDS concentrations in the aquifer and show that water quality deteriorates with depth and toward the northern part of the aquifer. Relatively better-quality water (TDS ≤10,000 mg/l) can be found at depths between 100 and 250 m below the aquifer’s top in both eastern and southeastern parts. Water in the same interval to the western and southwestern parts reflects the presence of higher TDS concentration. From a planning point of view, it is more feasible to target the eastern part of the aquifer for pumping and desalination purposes. In addition, the generated model could be utilized as an initial condition for flow simulation. 相似文献