Ordinary Kriging Method Research Articles

Machining error compensation of free-form surfaces by combination of ordinary kriging method and mirror compensation method

ABSTRACT Improving the accuracy and efficiency of free-form surface machining is a common goal of modern manufacturing. In this study, a compensation method combining the ordinary kriging method based on sequential quadratic programming method optimization (SQP-OK) and the mirror compensation method for machining errors of free-form workpieces was applied, with the aim of significantly improving the workpiece machining accuracy. Machining errors on the surface of the workpiece were predicted utilizing the SQP-OK after the workpieces were completed with semi-finishing machining based on the on-machine measurement (OMM) results of the workpiece. Based on the prediction result, the mirror compensation method was applied to calculate the compensation points. Free-form workpieces were compensated for by modifying the NC code for semi-finishing. To verify the effectiveness of the proposed method, two free-form workpieces were machined with computer numerical control (CNC) compensation, applying the proposed method and the method modifying the overall machining allowance. The experimental results revealed that the compensation method proposed in this paper reduced the average value of the machining errors decreased by 28.6% in absolute value, 31.1% in maximum undercut, and 28.8% in maximum overcut for the free-form workpiece, respectively, compared with the compensation method of modifying the overall machining allowance.

A novel on-line dual sensing system for soil property measurement and mapping

Real-time assessment of within-field soil fertility variation is crucial for making informed and sustainable decisions, like crop nitrogen (N) fertilization. Two commonly used soil sensors i.e., visible-near-infrared (vis-NIR) spectroscopy and ion-selective electrodes (ISE) have been reported to successfully estimate various soil macronutrients and nitrate-N, respectively, however, their integrated use for mapping a full-scale soil fertility variation has not been taken into consideration to date. The aim of this study is to use an integrated on-line dual-sensor system of vis-NIRS and ISE to estimate and map within-field variation in multiple soil fertility parameters including pH, organic carbon (OC), extractable- phosphorus (P), potassium (K), calcium (Ca) and moisture content (MC) and nitrate-N. The sensing system was mounted to the three-point linkage of a tractor and surveyed two arable fields in Belgium. Partial least squares regression models for vis-NIRS sensor were calibrated and validated, while a linear regression model was established for validation of the ISE sensor. Geostatistical surface maps for on-line measured soil attributes were generated using the inverse distance weighting and ordinary kriging methods for ISE data and vis NIR, respectively. The linear correlation confirms a very high similarity between ISE-measured nitrate-N and laboratory-analyzed nitrate-N (coefficient of determination (R2) = 0.93). Besides, the vis-NIRS demonstrates very good prediction accuracies for all the fertility attributes with R2 = 0.70-0.78, root mean square error (RMSE) =0.52-2.46 %, residual of prediction deviation (RPD) = 1.89 – 2.13 and the ratio of the performance to interquartile distance (RPIQ) = 1.72 – 6.56. Validation between laboratory and on-line measured soil maps also shows quite comparable spatial distribution patterns. Therefore, the proposed dual on-line sensor system has a high potential to estimate and map within-field spatial fertility distributions including nitrate-N, offering a basis for sustainable management decisions for precision soil and crop production.

A Novel Interpolation Method for Soil Parameters Combining RBF Neural Network and IDW in the Pearl River Delta

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions.

Estimasi Sumberdaya Bauksit Menggunakan Metode Ordinary Kriging Pada PT. Wahana Petra Sejahtera Kabupaten Bengkayang Provinsi Kalimantan Barat

PT. Wahana Petra Sejahtera, which operates in the bauxite mining sector, requires resource calculations as a basis for carrying out its mining activities. This research aims to determine the levels and distribution of Al2O3, SiO2, and Fe2O3 and estimate bauxite resources using the ordinary kriging method. This research uses secondary data in coordinates, elevation, test pit depth, and slope data. Data processing uses software in database creation, statistical analysis, block and composite modeling, variogram analysis, resource estimation, and model validation. The calculation results for the Jirak block show that the Al2O3 waste content is 1,488,000 tons, low-grade 4,532,000 tons, medium-grade 1,824,000 tons, and high-grade 32,000 tons. For SiO2 the average content is 13.54% and the average Fe2O3 content is 13.50%. Meanwhile, in the Sindu block the Al2O3 waste content is 64,000 tons, medium-grade 1,188,000 tons, and high-grade 3,056,000 tons. Meanwhile, the average SiO2 content is 9.13% and the average Fe2O3 content is 12.06%. The total resources in the Jirak block are 7,696,000 tons with the amount meeting the COG of 800 tons. Meanwhile, in the Sindu block, the total resources are 5,008,000 tons with an amount that meets the COG of 1,280,000 tons.

Modeling and Spatial Variability of Soil Nutrients - A Case from Soil Health Card Project, India

Ascertaining and mapping soil nutrient data is crucial for governments to maintain soil health on farmlands. As part of the soil health card project, a total of 329 geo-referenced soil samples were collected from Thaticherla village, Anantapur mandal, Andhra Pradesh, India. These samples were analyzed for various soil properties such as soil pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), available phosphorus (P), available potassium (K), available sulphur (S), DTPA extractable micronutrients (Fe, Mn, Zn, Cu), and hot water-soluble boron (B) at a depth of 0 to 15 cm. The results showed high variability (>35%) in coefficients of variation in Cu, EC, Zn, and B. The findings indicated positive correlation between Zn and Mn; N and OC; and OC and Zn. The data underwent logarithmic and Box-Cox transformations to achieve normalization. The ordinary kriging method was employed to analyze the spatial variability. The findings revealed that exponential model was appropriate for B, Fe, Mn, Zn, and OC; Gaussian for K; J-Bessel for N; K-Bessel for Cu, P, and S; stable for EC and rational quadratic for pH, respectively. The analysis showed a strong to weak spatial dependency. In the study area, the spatial variability maps exhibited deficiencies of 97%, 96% and 40% for N, OC and Zn, respectively. Therefore, it is urgent to apply suitable manures and fertilizers in the study area to address these issues. The study area exhibited significant variation in spatial patterns, emphasizing the importance of implementing field-specific plans for soil health and environmental management.

Distribution, characteristics, and ecological risks of microplastics in the Hongyingzi sorghum production base in China

Microplastics (MPs), an emerging pollutant of global concern, have been studied in the Hongyingzi sorghum production base. In this study, we investigated MPs in the surface soil (0–10 cm) and deeper soil (10–20 cm) in the Hongyingzi sorghum production base. Pollution characterization and ecological risk evaluation were conducted. The results revealed that the MP abundance ranged from 1.31 × 102 to 4.27 × 103 particles/kg, with an average of 1.42 ± 1.22 × 103 particles/kg. There was no clear correlation between the MP abundance and soil depth, and the ordinary kriging method predicted a range of 1.26 × 103–1.28 × 103 particles/kg in most of the study area, indicating a relatively uniform distribution. Among the 12 types of MPs detected, acrylates copolymer (ACR), polypropylene (PP), polyurethane (PU), and polymethyl methacrylate (PMMA) were the most frequently detected. These MPs primarily originated from packaging and advertising materials made from polyurethane and polyester used by Sauce Wine enterprises, as well as plastic products made from polyolefin used in daily life and agricultural activities. The particle size of MPs was primarily 20–100 μm. Overall, the proportion of the 20–100 μm MP was 95.1% in the surface soil layer and 86.7% in the deeper soil layer. Based on the pollution load index, the MP pollution level in the study area was classified as class I. Polymer hazard index evaluation revealed that the risk levels at all of the sampling sites ranged from IV to V, and ACR, PU, and PMMA were identified as significant sources of polymer hazard. Potential ecological index evaluation revealed that most of the soil samples collected from the study area were dangerous or extremely dangerous, and the surface soil posed a greater ecological risk than the deeper soil. These findings provide a scientific foundation for the prevention, control, and management of MP pollution in the Hongyingzi sorghum production base.

Prediction of grain size distribution using ordinary kriging and compositional kriging methods

Grain size analysis plays a crucial role in understanding the geological characteristics of the coastal environments that influence the optimizations for oil and gas production operations. This paper aims to explore a sophisticated geostatistical approach using the ordinary kriging and compositional kriging techniques, to forecast the grain size fluctuations of sediments in the Long Island region located in the United States. In addition, utilizing a comprehensive dataset collected from the same region about an integrated seventeen compositional components for investigation using the spatial model of the grain size distribution. Moreover, a variogram and the scatter plot predicted a distinctive spatial dependency was achieved. The compositional kriging method used to predict the grain size distribution in the coastal areas presented an accurate result based on the shape of the histogram, Root Mean Square Error (RMSE), and the Mean Squared Error (MSE). In conclusion, the geo-statistics assisted in the integration of the sedimentological analysis in the coastal settings and showed an effective configuration for the decision-making in the oil and gas industry business.

Predictive Mapping of Soil Micronutrients Using Geographic Information System for Site Specific Management Interventions in a Semiarid Farm, India

Deficiency of soil micronutrients has an adverse impact on crop productivity in intensive agriculture. Plant availability, spatial pattern and distribution of soil micronutrients such as iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu) content in surface soils were evaluated for an agricultural farm in semiarid region of India. Other soil properties viz. soil pH, electrical conductivity (EC), soil organic carbon (SOC) content and equivalent calcium carbonate (CaCO3) content at the farm were also analysed to depict the soil chemical environment, controlling micronutrient availability. Plant available micronutrient contents within farm soils had very high data variability (coefficient of variation >30%). Soil available micronutrients content were negatively correlated with soil pH and positively correlated with SOC content. As per semivariogram analysis, plant available Fe, Mn, Zn and Cu content within farm soils had moderate spatial dependency as indicated by nugget to sill ratio between 0.30 and 0.50 and had spatial parameter ranges of 404, 801, 954 and 1529 m, respectively. Prediction map of plant available Fe content by inverse distance weightage (IDW) method showed a few patches of iron deficiency (< 4.50 mg kg-1) and a marginal level (4.50 - 9.00 mg kg-1). Spatial distribution map of plant available Zn content through lognormal ordinary kriging method indicated a patch of marginal Zn level (0.60 - 1.20 mg kg-1) within the farm soils. Farm scale spatial variability maps of plant available Mn and Cu content, generated by ordinary kriging method with good accuracy and effectiveness, indicated its adequate level with respect to crop nutrition. The spatial distribution maps of soil available micronutrients content for the farm could be served as reference for its precise and site specific management for intensive crop cultivation, higher productivity and profitability.

SALINE MAPS OF SOME SOILS OF MUTHANNA GOVERNORATE

performed based on data collected from 15 soil samples from 0 to 20 cm deep. We used salinity of the irrigated soils of Al-Muthana governorate in southern Iraq. These maps were 20% of the world's land. This study was made in order to map the spatial distribution of soil or anthropogenic processes, which is certainly an environmental problem that affects more than environment and agricultural production. The main causes of this salinization come from natural It is important to study the problem of salinization, soil salinity has adverse effects both on the ordinary kriging (OK) to analyze the spatial variability of soil salinity, The results confirmed that the use of saline maps was supportive in giving a clear vision of the of 12.11 dS m-1, for developing probability maps to determine the distribution of risk areas. showed the different classes of salinity with the highest value of 23.17 dS m-1 and lowest value map predicted by the electrical conductivity (EC) values using the ordinary kriging (OK) method while indicator kriging (IK) was used to analyze salinity versus threshold values . The salinity salinity of the soils for the study area

Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery

<p>The aim of this research is to compile a tsunami wave hazard scale based on built-up land density extracted and classified by machine learning from Sentinel 2A satellite and digital elevation model (DEM) imageries. This research was carried out in 5 stages, namely: (i) pre-processing of Sentinel 2A and DEM images, (ii) Classification of VI data using the machine learning algorithms, (iii) Spatial prediction using the ordinary kriging method, (iv) Field testing using the confusion matrix method, (v) Preparation of decision matrix for tsunami wave hazard. The results of the study show that the most accurate classification algorithm for classifying built-up indices data is the k-nearest neighbor (k-NN) algorithm. The results of the statistical accuracy test show that the most accurate is normalized difference built-up index (NDBI) with a mean of square error (MSE) value of 0.073 and a mean of absolute error (MAE) of 0.003. DEM analysis shows that the research area is at an altitude of 0–15 meters above sea level so it is in the high vulnerability to medium vulnerability category. Field testing showed user accuracy of 91.11%, manufacturer accuracy of 92.16%, and overall average accuracy of 91%.</p>

SPATIAL INSIGHTS INTO EARTHQUAKE STRENGTH: A SULAWESI CASE STUDY USING ORDINARY AND ROBUST KRIGING METHODS

The data from the Meteorology, Climatology and Geophysics Agency (BMKG) in the last 22 years shows that there have been 230 destructive earthquakes in Indonesia with the highest incidence in 2021. One of the islands frequently hit by earthquakes is Sulawesi Island. According to the 2020 Disaster Risk Index Book (IRBI), 63 of the 81 regencies/cities on Sulawesi Island have a high category earthquake risk index. Based on this, information is needed as a first step in disaster mitigation so that the government can take preventive and anticipatory actions to reduce risks associated with earthquakes and ensure the safety of people on the island of Sulawesi, one of which is obtained through spatial interpolation. In this study, the Kriging methods of interpolation, Ordinary Kriging (OK) and Robust Kriging (RK) were used. From the analysis with OK and RK, the best theoretical semivariogram model is the Exponential model with nugget, sill and range values of ​​respectively 0.40, 0.70, and 6.50 for OK and 0.35, 0.90 and 9.50 for RK. Both methods produced the results that most areas of Sulawesi Island have the potential for shallow earthquakes with a magnitude of around 3.2 to 4.0 on the Richter scale. The potential for earthquakes with high strength is more common around the seas to the east and north of Central Sulawesi Province. The highest estimation results are at the coordinates of 120,029° East Longitude, 1.159° North Latitude, namely in the sea north of South Dampal. According to the results of K-Fold Cross Validation and Leave One Out Cross Validation, the more accurate method for estimating earthquake strength on Sulawesi Island is the RK method because the RMSE and MAPE values ​​in the RK method are smaller than the OK method.

Characterizing, Predicting, and Mapping Soil Available Phosphor, pH, and Electrical Conductivity in Rubber Plantation Based on Three Different Methods of Ordinary Kriging in GIS Environment

Soils in rubber plantations have unique characteristics due to their prolonged and excessive uses. These practices have changed soil qualities, production, and the geographical distribution of soil conditions. For this reason, research on the spatial analysis of soil attributes in rubber plantations is essential. Although it is acknowledged that there are proven methods for accounting for geographical variability, their use in rubber plantations is still somewhat restricted. Evidence also demonstrates ongoing debate and variation in the findings on the capacity of methods to predict spatial variability. Therefore, the primary goal of this study is to examine how well various approaches perform when using ordinary kriging to analyze spatial variability of three soil properties: Soil Available Potassium (SAP), pH, and Electrical Conductivity. The methodology employed in this work includes (a) grid sampling for data collection, (b) interpolation using Ordinary kriging with three methods (exponential, spherical, and Gaussian), (c) mapping, and (d) evaluation. The findings of this study demonstrate that semi-variogram analysis using three distinct methods yields somewhat varied outcomes with accuracy in higher order from SAP, soil EC, and soil pH. The results of this study also show that the different methods have unique characteristics when representing spatial structure. These findings suggested that the number of samples and the selection of interpolation techniques are essential factors in studying these three soil properties and determining the accuracy of the results.

Risk assessment of radon exposure inside residential dwellings in Safi city, Morocco

ABSTRACT A radon study was conducted in 50 residences in Safi city, Morocco, using LR-115 detectors. The annual effective dose and Excess Lifetime Cancer Risk were also estimated. The measured radon concentrations ranged between the Lower Limit of Detection (LLD) and 132 Bq.m−3 with an arithmetic mean of 53 Bq.m−3 and a geometric mean of 41 Bq.m−3 (2.32). The results indicated that 58% of the obtained values exceeded the worldwide average value (39 Bq.m−3). In addition, the results revealed significant impacts of floor level and region on indoor radon levels. Inhabitants of the surveyed houses were found to receive a mean annual effective dose of 1.5 ± 0.1 mSv.y−1, corresponding to an Excess Lifetime Cancer Risk of 0.6%. Importantly, all annual effective dose values were below the recommended action level by the ICRP (3–10 mSv.y−1). Using the Ordinary Kriging method implemented in the QGIS software, an indoor radon map was created based on the collected data. The findings in this study serve as baseline values for future studies concerning radon concentrations and their fluctuations.

Spatial coal seam modeling and reserve estimation using geostatistical methodologies by GIS technique in a selected coalfield in Ermenek Basin, Turkey

The accurate and reliable calculation of tonnage and quality of coal is vital for the success of mining projects. The Ermenek coal basin is located in the Western Taurus Mountains in the southern part of Turkey, which covers an area of approximately 620 km2 and is a Neogene coal basin. This paper generates spatial modeling of Pamuklu-Tepebaşı coal seams in the Ermenek Basin and the prediction of coal reserve by using geostatistical methods with Ordinary Kriging (OK) based on the GIS technique. The data used in the study were obtained from 82 boreholes belonging to a private company that has actively produced coal in the Pamuklu-Tepebaşı coalfield. In this study, the OK method was applied to spatial coal seam modeling for comparison of the performance of 11 distinct models. Using the rational quadratic models better explained the spatial structure of the coal seam depth data. The nugget-sill ratio indicated high spatial dependency with 0.01 and 0.02 for coal seam upper-lower surfaces, respectively. The kriged coal depth maps for upper and lower coal seam surfaces have been created using ArcGIS 10.2 software. As a result of these analyses, it was calculated that there is a coal reserve of 3.734.017 m3 using the determined surfaces with OK in the study area. Furthermore, developed spatial distribution maps in the study area could be helpful to plan further exploration activities.

A machine learning-based computer model for the assessment of tsunami impact on built-up indices using 2A Sentinel imageries

This study aims to build a computer model to detect built-up land in the identified tsunami hazard zone based on Sentinel 2A imagery using the normalized built up area index (NBI), urban index (UI), normalize difference build-up index (NDBI), a modified built-up index (MBI), index-based builtup index (IBI) algorithms, optimized with machine learning Random Forest (RF) and extreme gradient boosting (XGboost) algorithms and the spatial patterns are predicted using the ordinary kriging (OK) method. Testing of the accuracy of the classification and optimization results was performed using the Kohen Kappa and overall accuracy functions. The results of the study show that a built-up land consisting of open land and water, settlements, industry areas, and agriculture and tourism areas can be identified using the parameters of built-up indices. The accuracy testings that were performed using overall accuracy and Kohen Kappa methods show that classification and prediction are highly accurate using XGboost machine learning, namely 91%. This study produces a novelty of finding, namely a computer model to detect and predict the spatial distribution of built-up land in 4 scales, i.e., very low, low, high, and very high based on NBI, UI, NDBI, MBI, IBI data extracted from Sentinel 2A imagery.

Implementation of Proximal and Remote Soil Sensing, Data Fusion and Machine Learning to Improve Phosphorus Spatial Prediction for Farms in Ontario, Canada

One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to accurately reveal the spatial heterogeneity of P. Seven spatially variable fields located in Ontario, Canada are clustered into two zones; four fields are located in eastern Ontario and three others are located in western Ontario. This study compares Bayesian Additive Regression Trees (BART), Support Vector Machine regressor (SVM), and Ordinary Kriging (OK), along with novel data fusion concepts, to analyze integrated high-density spatial data layers related to spatial variability in soil available P. Feature selection and interaction detection using BART variable selection and Recursive Feature Elimination (RFE) for SVM were applied to 42 predictors, including soil-vegetation indices derived from PlanetScope multispectral imagery, high-density apparent soil electrical conductivity (ECa), and high-resolution topographic attributes derived from DUALEM-21S and a Real-Time Kinematic (RTK) global navigation satellite systems (GNSS) receiver, respectively. Modeling spatial heterogeneity of soil available P with BART showed higher accuracy than SVM and OK in both zones of this study when trained and tested on ground truth data from clusters of farms. A BART variable selection approach resulted in six auxiliary predictors of soil available P in the eastern zone, while only four predictors were selected to predict P in the western zone. RFE for SVM resulted in models with 15 and 12 auxiliary predictors in the eastern and western Ontario zones. Topographic elevation was the most influential predictor of soil available P in both zones. Compared with the SVM and OK methods, BART exhibited lower average RMSE values for individual fields of 1.86 ppm and 3.58 ppm across the eastern and western Ontario zones, respectively, along with higher R2 values of 0.85 and 0.83, respectively. In contrast, SVM had RMSE values for individual fields in the eastern and western Ontario zones, respectively, averaging 5.04 ppm and 7.51 ppm and R2 values of 0.27 and 0.43. RMSE values for soil available P in individual fields across the eastern and western Ontario zones averaged 4.77 ppm and 7.81 ppm, respectively, with the OK method, while R2 values averaged 0.19 and 0.44. The selection of suitable auxiliary predictors and data fusion, combined with BART spatial machine learning algorithms, have potential to be a useful tool to accurately estimate spatial patterns in soil available P for agricultural fields in Ontario, Canada.

Evaluating spatially enabled machine learning approaches to depth to bedrock mapping, Alberta, Canada.

Maps showing the thickness of sediments above the bedrock (depth to bedrock, or DTB) are important for many geoscience studies and are necessary for many hydrogeological, engineering, mining, and forestry applications. However, it can be difficult to accurately estimate DTB in areas with varied topography, like lowland and mountainous terrain, because traditional methods of predicting bedrock elevation often underestimate or overestimate the elevation in rugged or incised terrain. Here, we describe a machine learning spatial prediction approach that uses information from traditional digital elevation model derived estimates of terrain morphometry and satellite imagery, augmented with spatial feature engineering techniques to predict DTB across Alberta, Canada. First, compiled measurements of DTB from borehole lithologs were used to train a natural language model to predict bedrock depth across all available lithologs, significantly increasing the dataset size. The combined data were then used for DTB modelling employing several algorithms (XGBoost, Random forests, and Cubist) and spatial feature engineering techniques, using a combination of geographic coordinates, proximity measures, neighbouring points, and spatially lagged DTB estimates. Finally, the results were contrasted with DTB predictions based on modelled relationships with the auxiliary variables, as well as conventional spatial interpolations using inverse-distance weighting and ordinary kriging methods. The results show that the use of spatially lagged variables to incorporate information from the spatial structure of the training data significantly improves predictive performance compared to using auxiliary predictors and/or geographic coordinates alone. Furthermore, unlike some of the other tested methods such as using neighbouring point locations directly as features, spatially lagged variables did not generate spurious spatial artifacts in the predicted raster maps. The proposed method is demonstrated to produce reliable results in several distinct physiographic sub-regions with contrasting terrain types, as well as at the provincial scale, indicating its broad suitability for DTB mapping in general.

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

AbstractThree‐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 ore‐forming elements underpin deep geochemical predictions. The methodology includes the following steps: (1) Three‐dimensional 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.

Analysis of the seasonality of rain in Brazil from 1990 to 2022

Water resources are subject to hydroclimatic and spatio-temporal variations. This article aimed to analyze the seasonality of rainfall in Brazil. Precipitation data from 1306 rain gauge stations with data from 1990 to 2022. The precipitation seasonality index (PSI) was determined to evaluate the spatial and temporal variation in seasonality. The spatialization of ISP values ​​was carried out using the geostatistical method of ordinary kriging. The ISP values ​​found throughout the Brazilian territory vary from 0.273 to 1.176. This indicates that Brazil has humid regions with precipitation well distributed throughout the months throughout the year, to arid regions, with few months with rain. The lowest seasonal variability was found in the three states in the South region, on the coast of the states in the Southeast region and in the North region near the Amazon forest. The greatest seasonal variability occurs markedly in the Northeast region, close to the semi-arid Northeast, with long periods of drought

Spatial and temporal groundwater dynamics in extreme arid basins

AbstractTo accurately obtain the spatial distribution characteristics of groundwater level in an extremely arid zone and its dynamic change patterns under the influence of human activities, based on the data of 55 groundwater observation wells in the middle and lower reaches of the Kriya River, spatial interpolation of regional groundwater level data were performed using the inverse distance weight, spline function, trend surface, and the ordinary kriging methods. The optimal interpolation method was selected by its accuracy to spatially interpolate the groundwater level data in the study area from 2019 to 2021. The results show that: (1) the ordinary kriging method has the highest interpolation accuracy (MAE = 7.1393, MRE = 0.0058, RMSE = 9.4314) and reflects the spatial and temporal variability and distribution characteristics of groundwater levels with great accuracy. (2)The relationship between surface water–groundwater recharge and discharge in different areas of the river channel in the desert section varies depending on geological structure, surface water seepage, and other elements. (3) Groundwater in the Taklamakan Desert has little effect on groundwater recharge in the Dariyabui Oasis, and changes in groundwater dynamics in the oasis are predominantly influenced by surface runoff. (4) Monthly changes in groundwater levels in the Yutian Oasis are continuous, with ‘V’ shaped fluctuations, a declining trend in the southern part, no significant change in the central part, and a slight increase in the northern part. These results contribute to the sustainable management of water resources in the Kriya River Basin, provide a basis for groundwater prediction, and offer a reference for studies of other, similar extreme desert area basins.