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The traditional Hungarian method for determining soil phosphorus (P) status is ammonium-lactate acetic acid (AL) extraction. AL is an acidic solution (buffered at pH 3.75), which is also able to dissolve P reserves, so there is a need for extraction methods that also characterize the mobile P pool.

0.01 M CaCl2-P is considered to directly describe available P forms, because the dilute salt solution has more or less the same ionic strength as the average salt concentration in many soil solutions.

The amount of AL-P may be two orders of magnitude greater than that of CaCl2-P. Previous studies suggested that the relationship between AL-P and CaCl2-P was influenced by soil parameters. Regression analysis between AL-P and CaCl2-P showed medium or strong correlations when using soils with homogeneous soil properties, while there was a weak correlation between them for soils with heterogeneous properties.

The objective of this study was to increase the accuracy of the conversion between AL-P and CaCl2-P, by constructing universal equations that also take soil properties into consideration.

The AL-P and CaCl2-P contents were measured in arable soils (n=622) originating from the Hungarian Soil Information and Monitoring System (SIMS). These soils covered a wide range of soil properties.

A weak correlation was found between AL-P and CaCl2-P in SIMS soils. The amounts and ratio of AL-P and CaCl2-P depended on soil properties such as CaCO3 content and texture. The ratio of AL-P to CaCl2-P changed from 37 in noncalcareous soils to 141 on highly calcareous soils. CaCl2-P decreased as a function of KA (plasticity index according to Arany), which is related to the clay content, while the highest AL-P content was found on loam soils, probably due to the fact that a high proportion of them were calcareous.

The relationships between AL-P, CaCl2-P and soil properties in the SIMS dataset were evaluated using multiple linear regression analysis. In order to select the best model the Akaike Information Criterion (AIC) was used to compare different models. The soil factors included in the models were pHKCl, humus and CaCO3 content to describe AL-P, and KA, CaCO3 content and pHKCl to describe CaCl2-P. AL-P was directly proportional to pHKCl, humus and CaCO3 content, while CaCl2-P was inversely proportional to KA, CaCO3 content and pHKCl. The explanatory power of the models increased when soil properties were included. The percentage of the explained variance in the AL-P and CaCl2-P regression models was 56 and 51%, so the accuracy of the conversion between the two extraction methods was still not satisfactory and it does not seem to be possible to prepare a universally applicable equation. Further research is needed to obtain different regression equations for soils with different soil properties, and CaCl2-P should also be calibrated in long-term P fertilization trials.

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One option for adaptation to climate change is to grow a wider variety of plant species. Sorghum (Sorghum bicolor (L.) Moench) is known to tolerate unfavourable environmental conditions, so it may be feasible to grow it on areas with extreme conditions to replace other species such as maize. Nowadays, spatial decision supporting systems primarily support the crop production process rather than crop structure adjustment. In this study, potential sorghum production sites in the Great Hungarian Plain were selected based on soil characteristics including genetic soil type, parent material, physical soil type, clay composition, water management, pH, organic matter content, topsoil thickness and fertility, as well as climatic data, particularly precipitation. For all the parameters the aim was to find the extreme values at which sorghum, which is less sensitive than maize, may still give an acceptable yield. By combining map layers of soil characteristics, it could be concluded that although the soil is suitable for sorghum on 40.46% of the Great Hungarian Plain, maize is generally a better choice economically. On the other hand, the soil conditions on 0.65% of the land are still suitable for sorghum but unfavourable for maize. As regards the precipitation demand of sorghum, May is the critical period; on 698,968 ha the precipitation required for germination was only recorded once in the period 1991-2010, so these areas cannot be considererd for sorghum. As a consequence, in an alternative crop rotation system sorghum could be competitive with maize, but both the soil and climate conditions and the demands of the crop need to be assessed. The lack of precipitation in critical phenophases significantly decreases the area where maize can survive. Sorghum, however, may produce an acceptable yield, as it is a drought-resistant species.

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Assessing the uncertainty in reservoir performance is a necessary step during the exploration phase. To examine the uncertainty in flow response, a large set of realizations must be processed. There are several stochastic geostatistical algorithms capable of simulating multiple equiprobable realizations. Although these can show us the possible realities highlighting the spatial uncertainty, their handling is time- and CPU-consuming during the later processes, such as flow simulations. Consequently, only a small number of realizations can be post-processed in industrial practice. The purpose of this work is to develop a method, which will reduce the huge number of realizations in a way that the remaining ones retain the spatial uncertainty of a reservoir’s flow behavior, as would be demonstrated by a larger set of realizations. To solve this problem, ranking methods can be applied. Traditional ranking techniques, such as probability selection, are highly dependent on the applied static properties. In this paper, an alternative selection method is parameterized for measuring the pairwise dissimilarity between geostatistical models, with a distance function based on the hydrodynamic properties of the hydrocarbon reservoirs. The effectivity of the method is highly dependent upon the selected criteria. Thus, the distance function refers to the flow responses and allows visualizing the space of uncertainty through multidimensional scaling. A kernel transformation of the MDS data set is required to obtain a feature space where the K-means algorithm can discover non-linear structures in the basic data set. The final step of the method is the selection of the Earth models closest to the cluster centers. This tool allows for the selection of a subset of representative realizations, containing similar properties to the larger set.

Open access

The present article discusses the applicability of thermoanalytical methods in the analysis of Hungarian soils formed on carbonate rocks. Up to now only limited mineralogical and soil chemical research has been done on these soils. Soils from the Bükk Mountains, the most varied limestone region in Hungary, were used for the investigations. The aim was to extend our incomplete knowledge on the mineral composition and formation processes of these soils and to demonstrate the possibilities and evaluation potential of thermoanalytical techniques. All the soils investigated were formed on limestone and had different surface soil thickness, influenced by the accumulation of silicate debris and the microterrain. The results of soil mineralogical analysis revealed an extraordinarily high proportion of quartz compared to that of other minerals (especially calcite), indicating that these soils could not have originated solely from the weathering of the limestone bedrock. The results also showed that thermoanalytical methods could complement classical chemical and instrumental (XRPD) methods in research on the genesis of soils formed on limestone.

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In this study, the joint shear strength of low-strength Hungarian sandstones of different grain size and surface roughness was investigated. The direct shear tests along discontinuities were performed under constant normal load. Previously, the direct shear test basic rock mechanic parameters of the investigated intact rocks were determined, such as the UCS value. The goal of the investigation is to determine the effect of the surface properties, such as surface roughness, grain size, and surface quality, on the joint shear strength of Hungarian sandstones. The failure curves derived from the experimental results of direct shear tests under laboratory conditions, and the empirical results according to Barton and Choubey (1977) were compared.

Open access

Depth of reservoirs of Hungarian oil fields and related oil density data were collected from the database of the Hungarian Mineral Resource Inventory. The purpose of the investigation was to point out the correlation between oil density and reservoir depth in some of the Hungarian hydrocarbon productive regions. Oil density related to reservoir depth in a particular area is generally linked to the migration mechanism. Zala Basin trends show a different migration process regionally and locally; tertiary migration by overflow mechanism can be supposed for the latter case. In the case of the Szeged–Kiskunság region, locally and regionally, migration along carrier beds through semipermeable sediments is present, with faults playing a significant role. In the Nagykunság region, the migration processes are similar to those in Zala, but the presence of faults seems more important. At depths below 2,000 m, the Bihar region trends are similar to those of the Szeged–Kiskunság region. In the shallower zone, hydrodynamic effects are recognizable. In two studied regions, the Battonya–Pusztaföldvár High and the Hungarian Paleogene Basin, the density of crude oil data does not show any significant variability and trend. Biodegradation and water washing were recognizable in the depth sections shallower than 2,000 m below surface. In karstic reservoirs of the Zala Basin (Nagylengyel, Sávoly), alteration is presumed at greater depths due to the karst water flow. The presented results show several trends of oil migration in the explored areas, which can be used for future estimation of the hydrocarbon potential in the Hungarian part of the Pannonian Basin.

Open access

The aim of our research was to better understand the spectral characteristics of precipitation variability, because through infiltration, this is the most important source of groundwater recharge. To better understand the periodicity of the rainfalls, we used monthly and annual rainfall data. We examined precipitation time records over a 110-year period from two different cities in the Carpathian Basin, obtained from the Hungarian Meteorological Service. With discrete Fourier-transformation (DFT) and wavelet time series analysis, we defined local cycles and developed a forecast for the Debrecen area.

Using DFT, we calculated the time-period distributions (spectra) of monthly and annual rainfall data. Spectra from the annual rainfall data showed 16 dominant periods in Debrecen and 17 in Pécs. At the two stations, the most dominant cycles were 3.6 and 5 years, respectively; there were several other cycles locally present in the data sets. From the monthly data sets, several other periodic components were calculated locally and countrywide as well.

Using wavelet analysis, the time dependence of the cycles was determined in the 110-year data set for two Hungarian cities, Debrecen and Pécs.

Open access

This paper deals with a question: how many stochastic realizations of sequential Gaussian and indicator simulations should be generated to obtain a fairly stable description of the studied spatial process? The grids of E-type estimations and conditional variances were calculated from pooled sets of 100 realizations (the cardinality of the subsets increases by one in the consecutive steps). At each pooling step, a grid average was derived from the corresponding E-type grid, and the variance (calculated for all the simulated values of the pooling set) was decomposed into within-group variance (WGV) and between-group variance (BGV). The former was used as a measurement of numerical uncertainty at grid points, while the between-group variance was regarded as a tool to characterize the geologic heterogeneity between grid nodes. By plotting these three values (grid average, WGV, and BGV) against the number of pooling steps, three equidistant series could be defined. The ergodic fluctuations of the stochastic realizations may result in some “outliers” in these series. From a particular lag, beyond which no “outlier” occurs, the series can be regarded as being fully controlled by a background statistical process. The number of pooled realizations belonging to this step/lag can be regarded as the sufficient number of realizations to generate. In this paper, autoregressive integrated moving average processes were used to describe the statistical process control. The paper also studies how the sufficient number of realizations depends on grid resolutions. The method is illustrated on a computed tomography slice of a sandstone core sample.

Open access

Upon completion, the National Radioactive Waste Repository in Bátaapáti will provide safe storage for low- and medium-level radioactive waste. The emplacement chambers were excavated in a fractured, blocky, granitic rock mass approximately 240 m below surface. One of the tasks related to the repository development is the feasibility demonstration of the permanent repository closure, including long-term rock mass associated issues. The required lifetime exceeds the usual one of an engineering structure. The long-term behavior of the repository needs to be extrapolated from observation over a shorter time period, or from analogous natural caverns. Numerical methods are the most promising techniques to carry out the extrapolation. It is commonly understood that there are significant uncertainties in long-term predictions. Uncertainties can be mitigated by utilizing independent methods to assess long-term behavior and by improving the prediction capability of the calculation model in the short term. The aim of the paper is to: (1) create a numerical model to effectively capture a wide range of the observed behavior of the rock mass, including tunnel-excavation-induced stress change and stress-dependent permeability and (2) identify the possible cause of long-term creep and show that the long-term creep can be captured by the selected calculation method. The moderately fractured rock mass is modeled using the Universal Distinct Element Code, released by Itasca. The joints in the rock mass are explicitly modeled; the blocky nature of the rock mass is captured. The model is verified with actual field observations and monitoring results. Based on the predicted stress state of the rock mass, the potential cause of long-term creep is identified. By fulfilling the two aims explained above, it is concluded that the model can be used to extrapolate in time and serve as a possible estimation method for the long-term behavior of the repository.

Open access
Central European Geology
Ana Brcković
Monika Kovačević
Marko Cvetković
Iva Kolenković Močilac
David Rukavina
, and
Bruno Saftić

Lithofacies definition in the subsurface is an important factor in modeling, regardless of the scale being at reservoir or basin level. In areas with low exploration level, modeling of lithofacies distribution presents a complicated task as very few inputs are available. For this purpose, a case study in the Požega Valley was selected with only one existing well and several seismic sections within an area covering roughly 850 km2. For the task of expanding the input data set for lithofacies modeling, neural network analysis was performed that incorporated interpreted lithofacies (sandstone, siltite, marl, and breccia-conglomerate) in a single well and attribute data gathered from a seismic section. Three types of different neural networks were used for the analysis: multilayer perceptron, radial-basis function, and probabilistic neural network. As a result, three lithofacies models were built alongside a seismic section based upon predictions acquired from the neural networks. Three lithofacies were successfully predicted on the section while the breccia-conglomerate was either missing or underpredicted and mostly positioned in a geologically invalid interval. Results obtained by single networks differed from one another, which indicated that a result from a single network should not be treated as representative; thus, the facies distribution for modeling should be acquired from either an ensemble of neural networks or several neural networks. Analysis showed the initial potential of the usability of neural networks and seismic attribute analysis on vintage seismic sections with possible drawbacks of the applications being pointed out.

Open access