Structural Controls and Predictive Mapping of Tin, Niobium and Tantalum Mineralization Associated with the Mayo Darlé Stanniferous Granitoids;Contributions of Geostatistics

The aim of this study of the spatial dispersion of tin, niobium and tantalum mineralization associated with the Mayo Darlé granitoids was to produce prospecting guides through predictive maps of Sn, Nb and Ta in the region. It was based on a database (in appendix) obtained after analysis of rock samples (greisens and quartz veins) collected in the field, using a portable X-ray fluorescence (XRF) spectrometer. Two approaches were used: 1) structural studies in the field using the directions of veins and fractures 2) the use of variographic maps, an essential element in geostatistics for determining directional anisotropies. A joint synthesis of the modelling results shows that tin, tantalum and niobium mineralization at Mayo Darlé is concentrated along strike intervals N315E to N320E, with mineralization also occurring along strike N35E for high-grade Sn, medium-grade Ta and low-grade Nb. In short, mineral concentrations disperse progressively in space: positively from east to west for tantalum and niobium, and inversely for tin.

An Assessment and Geostatistics of Land-Use and Selected Physico-Chemical Properties of Soils in the Mount Cameroon Area

This work investigated the land-use/land-cover and some physico-chemical properties of the soils of Mt Cameroon and presented same in maps. ArcGIS Pro mapping software, Landsat images, Global Positioning Systems (GPS) coordinates collected from the field combined with updated shape files from competent services were used to produce the location and land-use/land-cover maps. Sixteen topsoil samples (0 - 20 cm) were collected, 4 from each land use/cover category: farmland, forest, plantation and settlement, and analysed for soil pH, cation exchange capacity (CEC), bulk density, moisture content and soil texture, in the laboratory using standard analytical procedures. This data was used to produce spatial distribution maps using ordinary kriging, in ArcGIS Pro. The main terrestrial land use/cover categories comprised of the forest (mangrove, lowland, montane and sub-montane), agroforestry, plantations, grassland, settlement, cropland, shrubby savannah, and bare lava. Bulk density showed the highest values in settlement areas and least values under forest land-use categories. Soil moisture content exhibited a reverse trend compared to that of soil bulk density. Forest soils were the sandiest while soils in plantation agricultural land were the most clayey. The soils were slightly acidic to neutral with soils from agricultural land being more acidic (pH(water) = 5.43). It is discernible from the results that the conversion from forest to other land use/cover classes enhances soil degradation and that soil physico-chemical properties adequately serve as indicators of soil quality in the Mt Cameroon area.

Sediment distribution pattern mapped from the combination of objective analysis and geostatistics in the large shallow Taihu Lake, China

Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method was used to get all the values of recent sediment thickness at the 69×69 grids in the whole lake. The results showed that there is the significant difference in sediment depth between the eastern area and the western region, and most of the sediments are located in the western shore-line and northern regimes but just a little in the center and eastern parts. The notable exception is the patch between the center and Xishan Island where the maximum sediment depth is more than 4.0 m. This sediment distribution pattern is more than likely related to the current circulation pattern induced by the prevailing wind-forcing in Taihu Lake. The numerical simulation of hydrodynamics can strong support the conclusion. Sediment effects on water quality was also studied and the results showed that the concentrations of TP, TN and SS in the western part are obviously larger than those in the eastern regime, which suggested that more nutrients can be released from thicker sediment areas.

Spatial analysis of carbon storage density of mid-subtropical forests using geostatistics： a case study in Jiangle County, southeast China

The mid-subtropical forest is one of the biggest sections of subtropical forest in China and plays a vital role in mitigating climate change by sequestering carbon.Studies have examined carbon storage density(CSD) distribution in temperate forests. However, our knowledge of CSD in subtropical forests is limited. In this study, Jiangle County was selected as a study case to explore geographic variation in CSD. A spatial heterogeneity analysis by semivariogram revealed that CSD varied at less than the mesoscale(approximately 2000–3000 m). CSD distribution mapped using Kriging regression revealed an increasing trend in CSD from west to east of the study area.Global spatial autocorrelation analysis indicated that CSD was clustered at the village level(at 5% significance).Some areas with local spatial autocorrelation were detected by Anselin Local Moran's I and Getis-Ord G*. A geographically weighted regression model showed different impacts on the different areas for each determinant. Generally, diameter at breast height, tree height, and stand density had positive correlation with CSD in Jiangle County, but varied substantially in magnitude by location.In contrast, coefficients of elevation and slope ranged from negative to positive. Based on these results, we propose certain measures to increase forest carbon storage,including increasing forested area, improving the quality of the current forests, and promoting reasonable forest management decisions and harvesting strategies. The established CSD model emphasizes the important role of midsubtropical forest in carbon sequestration and provides useful information for quantifying mid-subtropical forest carbon storage.

Application of Multivariate Geostatistics in Environmental Epidemiology: Case Study from Houston, Texas

This study represents an example of investigating the associations between the joint exposure to ozone (O3) and particulate matter of sizes less than or equal to 2.5 micrometers in aerodynamic diameter (PM2.5) and cardiovascular disease (CVD) emergency room (ER) visits and chronic obstructive pulmonary disease (COPD) ER visits using multivariate geostatistics in Houston, Texas, from 2004 to 2009. Analyses showed lack of strong pair-wise association among the predictors of O3, PM2.5, wind speed, relative humidity, and temperature. Whereas CVD and COPD ER visits exhibited a strong positive correlation. Both outcomes drastically increased from 2006 possibly due to immigration from neighboring locations. Parametric testing showed that the data differed significantly between the years. Multivariate multiple regression results on the 2009 data showed that PM2.5, relative humidity, and temperature were significant to both CVD and COPD ER visits. Codispersion coefficients were constant which justified the assumption of intrinsic correlation. That is, our predictors had strong influence on the spatial variability of CVD and COPD ER visits. This multivariate geostatistics approach predicted an increase of 34% in CVD ER visits and 24% increase in COPD ER visits, which calls for more attention from policy makers. The use of multivariate geostatistics analyses enabled us to successfully detect the effects of risk factors on both outcomes.

Application of Multivariate Geostatistics to Investigate the Surface Sediment Distribution of the High-Energy and Shallow Sandy Spiekeroog Shelf at the German Bight, Southern North Sea

Surface sediment data acquired by the grab sampling technique were used in the present study to produce a high-resolution and full coverage surface grain-size mapping. The objective is to test whether the hypothetically natural relationship between the surface sediment distribution and complex bathymetry could be used to improve the quality of surface sediment patches mapping. This is based on our hypothesis that grain-size characteristics of the ridge surface sediments must be intrinsically related to the hydrodynamic condition, i.e. storm-induced currents and the geometry of the seabed morphology. The median grain-size data were obtained from grab samples with inclusive bathymetric point recorded at 713 locations on the high-energy and shallow shelf of the Spiekeroog Barrier Island at the German Bight of the Southern North Sea. The area features two-parallel shoreface-connected ridges which is situated obliquely WNW-SSE oriented and mostly sandy in texture. We made use the median grain-size (d50) as the predictand and the bathymetry as the covariable to produce a high-resolution raster map of median grain-size distribution using the Cokriging interpolation. From the cross-validation of the estimated median grain-size data with the measured ones, it is clear that the gradient of the linear regression line for Cokriging is leaning closer towards the theoretical perfect-correlation line (45°) compared to that for Anisotropy Kriging. The interpolation result with Cokriging shows more realistic estimates on the unknown points of the median grain-size and gave detail to surface sediment patchiness, which spatial scale is more or less in agreement with previous studies. In addition to the moderate correlation obtained from the Pearson correlation (r = 0.44), the cross-variogram shows a more precise nature of their spatial correlation, which is physically meaningful for the interpolation process. The present study partially contributes to the framework of habitat mapping and nature protection that is to fill the gaps in physical information in a high-energetic and shallow coastal shelf.

Improving the Prediction Accuracy of Soil Mapping through Geostatistics

This research aimed to implement and compare the accuracy of different interpolation methods using cross validation errors for interpolating the spatial pattern of soil properties. This paper investigates whether the use of kriging, instead of traditional interpolation methods, improves the accuracy of prediction of soil properties. To this end, various interpolation (kriging) techniques that rely on the spatial correlation between observations to predict attribute values at ensampled locations are studied. Geostatistics provides descriptive tools such as semivariograms to characterize the spatial pattern of continuous and categorical soil attributes. The maps obtained from Ordinary Kriging, Inverse Distance Weighting and splines show clearly that the map from Universal Kriging (UK) is better than the other three interpolation methods. Therefore, UK can be considered as an accurate method for interpolating soil (EC, pH, CaCO3) properties.

Integrating Remote Sensing and Geostatistics to Model Woody Density Distribution in a Tropical Savanna

remote sensing of woody vegetation in savannas has been inhibited by its complex stand structure and abundant vegetation species.An understanding of the distribution and spatial variation in savanna vegetation is critical for making timely assessments of the ecosystem conditions.This study investigated the possibility of improving the prediction of woody vegetation in tropical savannas using an approach that integrates spatial statistics and remote sensing.

Spatio-temporal Change Characteristics of Summer Mean Temperature in Northeast China during 1974-2004 Based on Geostatistics

[ Objective] The research aimed to study the spatio-temporal change characteristics of summer mean temperature in northeast China during 1974 -2004 based on geostatistics. [ Method ] By combining climate tendency rate with geostatistics, the spatio-temporal change characteris- tics of summer mean temperature in northeast China during 1974 -2004 were discussed. [ Result] Summer mean temperature distribution in north- east China for many years showed a trend of decreasing from south and west to north and east. Summer mean temperature in northeast China overall showed rise trend, and the biggest temperature rise magnitude was in Liaoning Province. Summer average temperature in 1994 was significantly higher than that in other years, and climate was abnormal. The rise speed of summer mean temperature in northeast China showed a trend of decreasing from southeast Jilin Province to other areas. [ Conclusion ] The research contributed to recognize spatio-temporal structure and change characteristics of the temperature in northeast China.

Geostatistical Analysis of Soil Nutrients Based on GIS and Geostatistics in the Typical Plain and Hilly-Ground Area of Zhongxiang, Hubei Province

Techniques of geostatistics are used to perform traditional statistical analysis and spatial structural analysis with ArcGIS, geostatistical software GS+ and statistical software SPSS in order to obtain the knowledge of characteristics of distribution and spatial variability of soil nutrients in different parts of Zhongxiang, Hubei Province. Some skewed values appeared during the analyses. To decrease the influence of those skewed values, domain processing and Box-Cox transformation were used. The results indicated spatial variability of Total N, Avail. P, rapidly-available potassium (R-Avail. K) and effective zinc (Effect. Zn) was strong, that of organic carbon (Org. C), effective molybdenum (Effect. Mo) and effective copper (Effect. Cu) was medium while that of others was weak. Fitted model of Total N, R-Avail. K and Effect. Mo was spherical model, that of Org. C and Effect. Zn was exponential model, while fitted model of Avail. P and Effect. Cu was Gaussian model. Ratio of variability caused by random factors to overall variability was large. What’s more, the ranges of spatial autocorrelation of soil nutrients had much difference. The smallest value was 3600 m in Effect. Zn while the largest was 77970 m in Org. C. Other characteristics were also included. The study is helpful to soil sampling design, to make people realize the influence of Han River to spatial variability of soil nutrients in this area, and to spatial interpolation and mapping.

A training image optimization method in multiple-point geostatistics and its application in geological modeling

Based on the analysis of the high-order compatibility optimization method proposed by predecessors, a new training image optimization method based on data event repetition probability is proposed. The basic idea is to extract the data event contained in the condition data and calculate the number of repetitions of the extracted data events and their repetition probability in the training image to obtain two statistical indicators, unmatched ratio and repeated probability variance of data events. The two statistical indicators are used to characterize the diversity and stability of the sedimentary model in the training image and evaluate the matching of the geological volume spatial structure contained in data of the well block to be modeled. The unmatched ratio reflects the completeness of geological model in training image, which is the first choice index. The repeated probability variance reflects the stationarity index of geological model of each training image, and is an auxiliary index. Then, we can integrate the above two indexes to achieve the optimization of training image. Multiple sets of theoretical model tests show that the training image with small variance and low no-matching ratio is the optimal training image. The method is used to optimize the training image of turbidite channel in Plutonio oilfield in Angola. The geological model established by this method is in good agreement with the seismic attributes and can better reproduce the morphological characteristics of the channels and distribution pattern of sands.

Analysis of Spatial Distribution Pattern of Tobacco Mosaic Virus (TMV) Based on Geostatistics

Using geostatistical method, the semi-variation function models of tobacco mosaic virus （TMV） in east-west and north-south directions were established, and the distribution pattern of TMV in large scale space was studied. The results showed that the distribution pattern of the disease in east-west and north-south directions belonged to linear model with abutment, and the spatial distribution pattern within the investigated areas was aggregated model. The spatial correlation distances in east-west and north-south directions were 29.953 4 and 47.813 8 km, and the spatial variabilities were 95.71% and 80.05%, respectively. This indicated that they had strong spatial correlation. Isoline map accessed by Kringing interpolation method could clearly reflect the spatial aggregated model.

A View on Stochastic Finite Element and Geostatistics for Resource Parameters Estimation

The resource parameter estimation using stochastic finite element, geostatistics etc. is a key point on uncertainty, risk analysis, optimization [1-5] etc. In this view, the paper presents some consideration on: 1) Stochastic finite element estimation. The concept of random element is simplified as a stochastic finite element (SFE) taking into account a parallelepiped element with eight nodes in which are given the probability density functions (pdf) on its point supports. In this context it is shown: a—the stochastic finite element is a linear interpolator, related to the distributions given at each nodes;b—the distribution pdf in whatever point x ∈ V;c—the estimation of the mean value of Z(x);2) Volume integrals calculus;3) SFE in geostatistics approaches;4) SFE in PDE solution. Finally, some conclusions are presented underlying the importance of SFE

The application of geostatistics in grain size trend analysis： A case study of eastern Beibu Gulf

There are 71 surface sediment samples collected from the eastern Beibu Gulf. The moment parameters （i.e. mean size, sorting and skewness） were obtained after applying grain size analysis. The geostatistical analysis was then applied to study the spatial autocorrelation for these parameters; while range, a parameter in the semivariogram that meters the scale of spatial autocorrelation, was estimated. The results indicated that the range for sorting coefficient was physically meaningful. The trend vectors calculated from grain size trend analysis model were consistent with the annual ocean circulation patterns and sediment transport rates according to previous studies. Therefore the range derived from the semivariogram of mean size can be used as the characteristic distance in the grain size trend analysis, which may remove the bias caused by the traditional way of basing on experiences or testing methods to get the characteristic distance. Hence the results from geostatistical analysis can also offer useful information for the determination of sediment sampling density in the future field work.

Three-dimensional Modeling of Ore-forming Elements and Mineralization Prognosis for the Yechangping Mo Deposit,Henan Province,China

Three-dimensional geochemical modeling of ore-forming elements is crucial for predicting deep mineralization.This approach provides key information for the quantitative prediction of deep mineral localization,three-dimensional fine interpolation,analysis of spatial distribution patterns,and extraction of quantitative mineral-seeking markers.The Yechangping molybdenum(Mo)deposit is a significant and extensive porphyry-skarn deposit in the East Qinling-Dabie Mo polymetallic metallogenic belt at the southern margin of the North China Block.Abundant borehole data on oreforming elements underpin deep geochemical predictions.The methodology includes the following steps:(1)Threedimensional geological modeling of the deposit was established.(2)Correlation,cluster,and factor analyses post delineation of mineralization stages and determination of mineral generation sequence to identify(Cu,Pb,Zn,Ag)and(Mo,W,mfe)assemblages.(3)A three-dimensional geochemical block model was constructed for Mo,W,mfe,Cu,Zn,Pb,and Ag using the ordinary kriging method,and the variational function was developed.(4)Spatial distribution and enrichment characteristics analysis of ore-forming elements are performed to extract geological information,employing the variogram and w(Cu+Pb+Zn+Ag)/w(Mo+W)as predictive indicators.(5)Identifying the western,northwestern,and southwestern areas of the mine with limited mineralization potential,contrasted by the northeastern and southeastern areas favorable for mineral exploration.

Graded and Quantitative Technology and Application of Coal-Bearing Reservoir Based on Seismic Reflection Characteristics

Taiyuan formation is the main exploration strata in Ordos Basin, and coals are widely developed. Due to the interference of strong reflection of coals, we cannot completely identify the effective reservoir information of coal-bearing reservoir on seismic data. Previous researchers have studied the reservoir by stripping or weakening the strong reflection, but it is difficult to determine the effectiveness of the remaining reflection seismic data. In this paper, through the establishment of 2D forward model of coal-bearing strata, the corresponding geophysical characteristics of different reflection types of coal-bearing strata are analyzed, and then the favorable sedimentary facies zones for reservoir development are predicted. On this basis, combined with seismic properties, the coal-bearing reservoir is quantitatively characterized by seismic inversion. The above research shows that the Taiyuan formation in LS block of Ordos Basin is affected by coals and forms three or two peaks in different locations. The reservoir plane sedimentary facies zone is effectively characterized by seismic reflection structure. Based on the characteristics of sedimentary facies belt and petrophysical analysis, the reservoir is semi quantitatively characterized by attribute analysis and waveform indication, and quantitatively characterized by pre stack geostatistical inversion. Based on the forward analysis of coal measure strata, this technology characterizes the reservoir facies belt through seismic reflection characteristics, and describes coal measure reservoirs step by step. It effectively guides the exploration of LS block in Ordos Basin, and has achieved good practical application effect.

Research on the reconstruction method of porous media using multiple-point geostatistics

The pore structural characteristics have been the key to the studies on the mechanisms of fluids flow in porous media. With the development of experimental technology, the modern high-resolution equipments are capable of capturing pore structure images with a resolution of microns. But so far only 3D volume data of millimeter-scale rock samples can be obtained losslessly. It is necessary to explore the way of virtually reconstructing larger volume digital samples of porous media with the representative structural characteristics of the pore space. This paper proposes a reconstruction method of porous media using the structural characteristics captured by the data templates of multiple-point geostatistics. In this method, the probability of each structural characteristic of a pore space is acquired first, and then these characteristics are reproduced according to the probabilities to present the real structural characteristics in the reconstructed images. Our experimental results have shown that: (i) the deviation of LBM computed permeability respectively on the virtually reconstructed sandstone and the original sample is less than 1.2%; (ii) the reconstructed sandstone and the original sample have similar structural characteristics demonstrated by the variogram curves.

A reservoir skeleton-based multiple point geostatistics method

Traditional stochastic reservoir modeling,including object-based and pixel-based methods,cannot solve the problem of reproducing continuous and curvilinear reservoir objects. The paper first dives into the various stochastic modeling methods and extracts their merits,then proposes the skeleton-based multiple point geostatistics(SMPS) for the fluvial reservoir. The core idea is using the skeletons of reservoir objects to restrict the selection of data patterns. The skeleton-based multiple point geostatistics consists of two steps. First,predicting the channel skeleton(namely,channel centerline) by using the method in object-based modeling. The paper proposes a new method of search window to predict the skeleton. Then forecasting the distributions of reservoir objects using multiple point geostatistics with the restriction of channel skeleton. By the restriction of channel centerline,the selection of data events will be more reasonable and the realization will be achieved more really. The checks by the conceptual model and the real reservoir show that SMPS is much better than Sisim(sequential indicator simulation) ,Snesim(Single Normal Equation Simulation) and Simpat(simulation with patterns) in building the fluvial reservoir model. This new method will contribute to both the theoretical research of stochastic modeling and the oilfield developments of constructing highly precise reservoir geological models.

Global land mapping of satellite-observed CO_(2)total columns using spatio-temporal geostatistics

This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO_(2)total column(XCO_(2))using spatio-temporal geostatistics,which makes full use of the joint spatial and temporal dependencies between observations.The mapping approach considers the latitude-zonal seasonal cycles and spatio-temporal correlation structure of XCO_(2),and obtains global land maps of XCO_(2),with a spatial grid resolution of 1°latitude by 1°longitude and temporal resolution of 3 days.We evaluate the accuracy and uncertainty of the mapping dataset in the following three ways:(1)in cross-validation,the mapping approach results in a high correlation coefficient of 0.94 between the predictions and observations,(2)in comparison with ground truth provided by the Total Carbon Column Observing Network(TCCON),the predicted XCO_(2)time series and those from TCCON sites are in good agreement,with an overall bias of 0.01 ppm and a standard deviation of the difference of 1.22 ppm and(3)in comparison with model simulations,the spatio-temporal variability of XCO_(2)between the mapping dataset and simulations from the CT2013 and GEOS-Chem are generally consistent.The generated mapping XCO_(2)data in this study provides a new global geospatial dataset in global understanding of greenhouse gases dynamics and global warming.

Analysis of the Lost Circulation Problem

The well-known“lost circulation”problem refers to the uncontrolled flow of whole mud into a formation.In order to address the problem related to the paucity of available data,in the present study,a model is introduced for the lost-circulation risk sample profile of a drilled well.The model is built taking into account effective data(the Block L).Then,using a three-dimensional geological modeling software,relying on the variation function and sequential Gaussian simulation method,a three-dimensional block lost-circulation risk model is introduced able to provide relevant information for regional analyses.