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.

Geostatistical seismic inversion and 3D modelling of metric flow units,porosity and permeability in Brazilian presalt reservoir

Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow.

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.

Auto-parametric resonance of a continuous-beam-bridge model under two-point periodic excitation:an experimental investigation and stability analysis

The auto-parametric resonance of a continuous-beam bridge model subjected to a two-point periodic excitation is experimentally and numerically investigated in this study.An auto-parametric resonance experiment of the test model is conducted to observe and measure the auto-parametric resonance of a continuous beam under a two-point excitation on columns.The parametric vibration equation is established for the test model using the finite-element method.The auto-parametric resonance stability of the structure is analyzed by using Newmark's method and the energy-growth exponent method.The effects of the phase difference of the two-point excitation on the stability boundaries of auto-parametric resonance are studied for the test model.Compared with the experiment,the numerical instability predictions of auto-parametric resonance are consistent with the test phenomena,and the numerical stability boundaries of auto-parametric resonance agree with the experimental ones.For a continuous beam bridge,when the ratio of multipoint excitation frequency(applied to the columns)to natural frequency of the continuous girder is approximately equal to 2,the continuous beam may undergo a strong auto-parametric resonance.Combined with the present experiment and analysis,a hypothesis of Volgograd Bridge's serpentine vibration is discussed.

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.

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.

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.

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.

Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis

The study of temporal and spatial variations of nitrate in groundwater under different soil nitrogen environments is helpful to the security of groundwater resources in agricultural areas.In this paper,based on 320 groups of soil and groundwater samples collected at the same time,geostatistical analysis and multiple regression analysis were comprehensively used to conduct the evaluation of nitrogen contents in both groundwater and soil.From May to August,as the nitrification of groundwater is dominant,the average concentration of nitrate nitrogen is 34.80 mg/L;The variation of soil ammonia nitrogen and nitrate nitrogen is moderate from May to July,and the variation coefficient decreased sharply and then increased in August.There is a high correlation between the nitrate nitrogen in groundwater and soil in July,and there is a high correlation between the nitrate nitrogen in groundwater and ammonium nitrogen in soil in August and nitrate nitrogen in soil in July.From May to August,the area of low groundwater nitrate nitrogen in 0-5 mg/L and 5-10 mg/L decreased from 10.97%to 0,and the proportion of high-value area(greater than 70 mg/L)increased from 21.19%to 27.29%.Nitrate nitrogen is the main factor affecting the quality of groundwater.The correlation analysis of nitrate nitrogen in groundwater,nitrate nitrogen in soil and ammonium nitrogen shows that they have a certain period of delay.The areas with high concentration of nitrate in groundwater are mainly concentrated in the western part of the study area,which has a high consistency with the high value areas of soil nitrate distribution from July to August,and a high difference with the spatial position of soil ammonia nitrogen distribution in August.

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.

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 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.

Joint Application of Geostatistics and Multifractals for Decision-Making System of Ore Prospection in a Mineral Deposit

The strategy of ore prospection is made on the basis of raw exploratory data which are the products of coupled geological processes and random natural reformation. This decision-making system is extraordinary risky and needs to be supported by various statistical sciences. In this paper, geostatistics and multifractals are jointly employed to delineate the complexity of mineralization and to provide important guidelines for future ore prospecting. The geostatistical analysis indicates the mineralization in granite domain is more homogenous than that in wallrocks, and the exploratory spacing in these two lithological units should be different when taking into consideration the range of variogram. The multifractal analysis shows the spatial variation of mineralization homogeneity. The mineralization in the southwest of this region is much more heterogeneous than that in the granite. The schemes of borehole design are specified based on the combination of abovementioned analytical results. The joint application of geostatistics and multifractal proposed in this study can excavate the exploratory data and output mathematic models in an intuitive and quantitative way, assisting in decision-making and risk avoidance of mining industry.

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

Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried out.This has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the day-aheadmarket.The standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique.The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub limitations.Additionally,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.