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 of100 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 ofnumerical 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. Keywords: geostatistics, statistical process control, ARIMA chart, sequential Gaussian simulation, sequential indicator simulation
Widely distributed freshwater carbonate sediments, i.e., limestone, dolomitic limestone and dolomite, developed in inter-dune alkaline ponds of the Danube-Tisza Interfluve in the centre of the ...Carpathian Basin during the Holocene. The key parameters that determine the formation of any given type of carbonate mineral (calcite, dolomite) are temperature, evaporation rate, pH and ion concentrations, in addition to CO
absorption by aquatic plants. CT analysis is capable of recording small-scale density variations attributable to compositional differences of sedimentary rocks. As the type and proportion of rock-forming minerals and other components is an artifact of past environmental and climatic conditions, CT values may act as potential palaeoenvironmental proxies. The present study compares variations in rock-forming components obtained for freshwater carbonates utilizing the CT method with already available geochemical and palaeoecological proxy data. Variations in molluscan ecology and isotope geochemistry, sedimentation times and CT-based rock density values all indicate the relevance of millennial-scale, climate-driven changes in carbonate formation. As previously observed, the emergence of colder conditions in the North Atlantic, which resulted in increased cyclonic activity and heavier rainfall in western Europe and the Danube watershed area between 10.3 and 9.3 kyr cal BP, resulted in the emergence of humid conditions favouring a rise in the groundwater table at our site and precipitation of calcite from pore waters as opposed to high-magnesium calcite. This is clearly reflected in a negative shift in CT density values in our dated rock samples.
X-ray computed tomography (CT) can reveal internal, three-dimensional details of objects in a non-destructive way and provide high-resolution, quantitative data in the form of CT numbers. The ...sensitivity of the CT number to changes in material density means that it may be used to identify lithology changes within cores of sedimentary rocks. The present pilot study confirms the use of Representative Elementary Volume (REV) to quantify inhomogeneity of CT densities of rock constituents of the Boda Claystone Formation. Thirty-two layers, 2 m core length, of this formation were studied. Based on the dominant rock-forming constituent, two rock types could be defined, i.e., clayey siltstone (20 layers) and fine siltstone (12 layers). Eleven of these layers (clayey siltstone and fine siltstone) showed sedimentary features such as, convolute laminations, desiccation cracks, cross-laminations and cracks. The application of the Autoregressive Integrated Moving Averages, Statistical Process Control (ARIMA SPC) method to define Representative Elementary Volume (REV) of CT densities (Hounsfield unit values) affirmed the following results: i) the highest REV values corresponded to the presence of sedimentary structures or high ratios of siltstone constituents (> 60%). ii) the REV average of the clayey siltstone was (5.86 cm
) and (6.54 cm
) of the fine siltstone. iii) normalised REV percentages of the clayey siltstone and fine siltstone, on the scale of the core volume studied were 19.88% and 22.84%; respectively. iv) whenever the corresponding layer did not reveal any sedimentary structure, the normalised REV values would be below 10%. The internal void space in layers with sedimentary features might explain the marked textural heterogeneity and elevated REV values. The drying process of the core sample might also have played a significant role in increasing erroneous pore proportions by volume reducation of clay minerals, particularly within sedimentary structures, where authigenic clay and carbonate cement were presumed to be dominant.
This study was undertaken to quantify and evaluate the density and porosity characteristics of a Boda Claystone Formation (BCF) core sample using medical CT. Each voxel of the 3D CT volume was ...described with three variables: dry CT number, saturated CT number, and effective porosity. Disparity pore voxels were revealed using the genetic groups' algorithm of data-mining techniques. The K-fold cross-validation algorithm has been applied to determine the number of the most stable cluster. The 3D spatial distributions of voxel-porosity by rock constituents, as well as the 3D distribution of porosity clusters by rock components, were found by Boolean function implementation. The terrigenous detrital fragments had the lowest porosity mean (0.16%) and highest coefficient variation value (1039.39%). While the Fine siltstone component had the highest porosity mean (3.39%) and lower coefficient of variation (134.99%). The difference in the variation of coefficient proportions is related to the outlier ratios in each rock component. Independently of both the rock types and the sedimentary structures, two clusters could be defined: one for the micro-porosity and one for the macro-porosity regimes. The former showed a continuous 3D spatial appearance, while the latter appeared in patches. These patches may also be connected, at least partly, to some local smectite aggregates. These clay minerals could lose their structured water content during vacuuming and swell when adsorbing water during sample saturation. In each rock type, the micro-porosity regime could be related to low-density rock fragments. The mean effective porosity of the micro-pore regime was about 0.02, which corresponds to the petrophysical core measurements. For the macro regimes, the average was 0.1
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Continuous Wavelet Transformation (CWT) was applied to study the small-scale repetitive oscillations of porosity distribution patterns in a 5 m silty-claystone core sample of the Boda Claystone ...Formation. We handled the fluctuations in voxel porosity averages over unequal depth distributions as signals over uneven time intervals. The strength of wavelet analysis lies in the ability to study the fluctuation of a signal in detail, i.e., the wavelet transforms permit automatic localization of the cyclic attributes' sequences both in time (the depth domain) and according to their frequency (the frequency domain). Thereupon, three main frequency branches (cycles) were discerned: small scale (5, 6.67, and 11 cm), intermediate scale (20, 30 cm), and large scale (66.67 cm). Depending on the CWT coefficients magnitude plot, we were able to detect the developments of porosity oscillation according to the depth variable. Thus, small-scale cycles were seen throughout the core sample., the intermediate-scale cycles were strong in the upper parts of the core sample and dwindled toward greater depths, and the large cycle was predominant in the lower part of the core sample. The cross-correlation of the wavelet coefficients of porosity and rock-forming components allows a detailed study of the inter-dependence of such parameters as their relationship changes over time. The distinct peaks at zero lag indicates that the measured wavelet coefficient series were contemporaneously correlated; their strong positive correlations suggest that both examined series respond similarly and simultaneously to other exogenous factors. The results emphasize that cyclical porosity fluctuations at all scales would concern three main factors; sediment deposition, diagenetic processes, and structural deformation (i.e., convolute laminations).
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Grain size distribution is one of the paleoenvironmental proxies that provide insight statistical distribution of size fractions within the sediments. Multivariate statistics have been used to ...investigate the depositional process from the grain size distribution. Still, the direct application of the standard multivariate methods is not straightforward and can yield misleading interpretations due to the compositional nature of the raw grain size data. This paper is a methodological framework for grain size data characterization through the centered log ratio transformation and euclidean data, coupled with principal component analysis, cluster analysis, and linear discriminant analysis to examine Quaternary sediments from Tövises bed in the southeast Great Hungarian Plain. These approaches provide statistically significant and sedimentologically interpretable results for both datasets. However, the details by which they supplemented the conceptual model were significantly different, and this discrepancy resulted in a different temporal model of the depositional history.
To obtain the multiproxy paleoenvironmental dataset from southeast Great Hungarian Plain (GHP), 345 sediment samples were collected at one cm intervals from the cores retrieved from Tovisies bed ...paleochannel, and six samples were analyzed for 14C dates. The obtained radiocarbon dates were calibrated to calendar ages using the IntCal20 calibration curve. Bayesian statistics within the R bacon 2.5.8 age-depth modeling package were used to establish the age-depth model and it represented the time frame for the entire dataset. The obtained polymodal grain size distribution (GSD) data was unmixed into four EMs using the AnalySize v.1.2.0 algorithm, employing the built-in General Weibull function which helped explain the dynamicity of the endmembers' sedimentation process. To understand the alluviation history, the endmember abundances were correlated with LOI55, LOI950, and magnetic susceptibility. The dataset presented in this article could be of potential reuse for studying the spatial-temporal environmental changes and in geoarchaeological research, providing insights into how human societies adapted to environmental shifts across the southeast GHP.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This work addresses three topics: (1) the study of the joint areal distribution of the Rn and hydrocarbon components of soil gases over a large region overlying some known hydrocarbon reservoirs in ...the southern part of Hungary; (2) the relationships between the positive anomalies of Rn and hydrocarbon components of soil gases to the existing reservoirs; (3) suggestions for new targets for surface hydrocarbon exploration based on the results. Given the very low correlation coefficients between the Rn and hydrocarbon components of the soil gases, factor analysis was used to reveal a background process controlling the common migration of hydrocarbon and Rn components. The lateral distribution of the factor scores were studied using sequential Gaussian distribution. The E-type grid generated from 100 realizations indicated several positive anomalies at the surface. Indications with a larger than 0.7 probability were kept for further analysis. Seismic sections of a 3D survey support the comparison of the surface locations of these anomalies and the surface projections of the known reservoirs. The results proved the connection between the known reservoirs and the Rn and HC components of soil gases. From the positive verification, regions with a high probability positive anomaly of factor scores, but without any reservoir counterparts may be suggested as targets for further surface hydrocarbon exploration.
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