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  • Author or Editor: E. Szűcs x
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A new quasi-geoid model for Hungary was determined by combining gravity data, GPS/levelling and vertical deflections. Reduction of the measurements was performed by using Earth Gravitational Model 2008 (EGM2008) and Shuttle Radar Topographic Mission (SRTM) elevation data sets. Calculation method was Least Squares Collocation (LSC) with self-consistent planar logarithmic covariance model. In the computations the weights of GPS/levelling data were large, in this way normal heights obtained from levelling are consistent with GPS heights and with the quasi-geoid model. Astrogeodetic-gravimetric, pure astrogeodetic and pure gravimetric solutions have been calculated besides the combined solution to investigate the discrepancies among the different models. The combined quasi-geoid model fits to the GPS/levelling data with standard deviation of ±4.9 cm, nevertheless at some GPS/levelling sites large differences were indicated.

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Topographic masses have a strong impact on the medium and short wavelength components of the gravitational signal generated by the mass of the Earth, thus digital terrain models (DTM) are routinely involved in gravity field modelling. In this study the verification of the Shuttle Radar Topographic Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) which is a joint product of METI (Ministry of Economy, Trade and Industry of Japan) and NASA has been done by comparing them to the points of the horizontal and vertical control networks of Hungary. SRTM data fit better to geodetic ground control points than ASTER GDEM, since some artefacts have been found in ASTER elevation set which impede further use of the latter without any pre-processing. Since SRTM is an “unclassified” surface model including all those points which reflected the scanning radar signal thus tree canopy height has been compared to the differences of SRTM and DTM elevations in a hilly test area in Hungary where a local and accurate DTM having 20 m × 20 m horizontal resolution was available. Considerable agreement was indicated between forest height and model differences. Model differences were evaluated to determine their effect synthetically on gravity related quantities. Their influence on geoid height is insignificant, but the change of the investigated second derivatives of the potential is considerable.

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This paper introduces the alternating conditional expectation (ACE) algorithm of Breiman and Friedman (1985) in multiple regression problems in groundwater monitoring data analysis. This special inverse nonparametric approach can be applied easily for estimating the optimal transformations of different groundwater monitoring data from the Bükk Mountains to obtain maximum correlation between observed aquifer variables. The approach does not require a priori assumptions of a mathematical form, and the optimal transformations are derived solely based on the groundwater data set. The advantages and applicability of the proposed approach to solve different multiple regression problems in hydrogeology or in groundwater management are illustrated by means of case studies from a Hungarian karst aquifer. It is demonstrated that the ACE method has certain advantages in some fitting problems of groundwater science over the traditional multiple regression.In the past, different groundwater monitoring data (like groundwater level, groundwater temperature and conductance, etc.) had been used for groundwater management purposes in the Bükk Mountains. One of the difficulties in earlier approaches has been the need to make some kind of assumption of the expected mathematical forms among the investigated reservoir and petrophysical variables. By using nonparametric regression, the need to assume a specific form of model is avoided, and a clearer vision of the relationships between aquifer parameters can be revealed in the Bükk Mountains, where karst water is the main source of potable water supply. Complex monitoring data from the Bükk Mountains were analyzed using the ACE inverse method, and results were verified successfully against quantitative and qualitative field observations.

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Fractured fluid reservoirs are of key importance for recovering water and hydrocarbon supplies and geothermal energy, or in predicting the subsurface flow of pollutants. There are several fractured metamorphic-basement HC reservoirs in the Pannonian Basin; one of the largest among them is the Szeghalom Dome in SE Hungary. Previous production and fluid inclusion data infer that in this case several unconnected fluid regimes must coexist in the basement, making modeling of the fracture network essential. Because the representative volume of a fractured rock mass is usually too large to measure hydraulic properties directly, stochastic calculations should be carried out, which are consistent with observed deformation history and stochastic patterns. Input statistical data (orientation, length, distribution, fractal dimension for fracture seeds) were determined for amphibolite and gneiss samples representing the Szeghalom Dome. Data were measured simultaneously using binocular microscope and computerized X-ray tomography. Comparison of the two data sets suggests that they are comparable and both can be used for modeling. A new computer program, called REPSIM has been developed recently, which follows a fractal geometry-based discrete fracture network (DFN) algorithm to simulate the fracture network. The evaluation of simulated networks suggests that amphibolite and gneiss-dominated parts of the basement behave differently; large amphibolite bodies have a connected fracture network, while gneiss domains usually are well below the percolation threshold.

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Agrokémia és Talajtan
Authors:
Katalin Sárdi
,
P. Csathó
,
I. Sisák
,
E. Osztoics
,
P. Szűcs
, and
Á. Balázsy
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