Geostatistical Methods Research Articles

Interpolation of non-stationary geo-data using Kriging with sparse representation of covariance function

Interpolation of non-stationary geo-data using Kriging with sparse representation of covariance function

Assessment of indoor radon distribution and seasonal variation within the Kpando Municipality of Volta Region, Ghana.

This study uses CR-39 radon detectors to examine radon distributions, seasonal indoor radon variations, correction factors, and the influence of building materials and characteristics on indoor radon concentration in 120 dwellings. The study also determines the spatial distribution of radon levels using the ArcGIS geostatistical method. Radon detectors were exposed in bedrooms from April to July (RS), August to November (DS); December to March (HS), and January-December (YS) from 2021 to 2022. The result for the radon levels during the weather seasons were; 32.3 to 190.1 Bqm-3 (80.9 ± 3.2 Bq/m3) for (RS), 30.8 to 151.4 Bqm-3 (68.5 ± 2.7 Bqm-3) for HS and 24.8 to 112.9 Bqm-3(61.7 ± 2.1 Bqm-3) for DS, and 25.2 to 145.2 Bq/m3 (69.4 ± 2.7 Bqm-3). The arithmetic mean for April to July season was greater than August to November. The correction factors associated with this study ranged from 0.9 to 1.2. The annual effective dose (AE) associated with radon data was varied from 0.6 to 4.04 mSv/y (1.8 ± 0.1 mSv/y). The April to July period which was characterized by rains recorded the highest correlation coefficient and indoor radon concentration. Distribution and radon mapping revealed radon that the exposure to the occupant is non-uniformly spread across the studied dwellings. 15.4% of the studied data exceeded WHO reference values of 100 Bq/m3. The seasonal variation, dwelling age, and building materials were observed to have a substantial impact on the levels of radon concentration within the buildings.

A facies‐constrained geostatistical seismic inversion method based on multi‐scale sparse representation

Abstract Geostatistical seismic inversion is an important method for establishing high‐resolution reservoir parameter models. There is no accurate representation method for reservoir structural features, and prior information about structural features cannot be incorporated into geostatistical inversion. Based on the assumption of the sparsity of stratigraphic sedimentary features, the same type of structural feature is used to represent the sedimentary pattern of reservoirs within the same facies. Different sparse representation patterns are used to represent the differences in sedimentary patterns between facies. Although changes in depositional environment might result in the multi‐scale characteristics of geological structures for varying sedimentary rhythms, this paper proposes a facies‐constrained geostatistical inversion method based on multi‐scale sparse representation to better accommodate such situation. Using the method of sparse representation combined with wavelet transform, the multi‐scale sedimentary structural features of reservoirs are learned from well‐logging data. Seismic facies and multi‐scale features are used as prior information for geostatistical inversion. Further, the likelihood function is constructed using seismic data to obtain the posterior probability distribution of reservoir parameters. Finally, the accurate inversion result is obtained by using multi‐scale sparse representation as a constraint in the posterior probability distribution of reservoir parameters. Compared with conventional geostatistical methods, this algorithm can better match the structural features of reservoir parameters with varying geological conditions. Field data tests have shown the effectiveness of this method in improving the accuracy and resolution of reservoir parameter structural features.

Modelling the coupled heterogeneities of the lacustrine microbialite-bearing carbonate reservoir of the Yacoraite Formation (Salta, Argentina)

The Yacoraite Formation is a complex lacustrine microbialite-bearing carbonate formation whose heterogeneity is due to different sedimentary facies associations and several microbialite geobodies. The bi-variate plurigaussian geostatistical method is applied to simulate in parallel these two types of heterogeneity in a 3D reservoir-scale geological model. Each variable is simulated by a complete plurigaussian method based on geological interpretation and field observations and quantifications. They are coupled through the occurrence of the microbialites within the sedimentary facies and associated into combined facies giving a one-glance representation of the reservoir heterogeneity, then used to constrain the porosity simulation.

Summarizing soil chemical variables into homogeneous management zones – case study in a specialty coffee crop

Summarizing soil chemical variables into homogeneous management zones – case study in a specialty coffee crop

Wormholing behavior in matrix acidizing in vuggy, naturally fractured carbonate reservoirs

Wormholing behavior in matrix acidizing in vuggy, naturally fractured carbonate reservoirs

Conditional generative adversarial networks for groundwater contamination characterization and source identification

Conditional generative adversarial networks for groundwater contamination characterization and source identification

Spatial and Temporal Variability of Soil Moisture and Its Driving Factors in the Northern Agricultural Regions of China

Soil moisture, as an important variable affecting water–heat exchange between land and atmosphere, is an important feedback to climate change. Soil moisture is of great concern in Northern China, where arable land is extensive, but water resources are distributed unevenly and extremely sensitive to climate change. Using measured soil moisture data collected by the China Meteorological Administration from 164 stations during 1980–2021, we explored the drivers of soil moisture variation by analyzing its spatiotemporal variability using linear regression, partial correlation analysis, and geostatistical methods. The results indicated that (1) soil moisture increased from northwest to southeast in Northern China, with the lowest soil moisture in the IM; (2) the overall trend of soil moisture in most regions decreased, especially in the arid northwest and northeast China. However, soil moisture in some regions began to increase gradually in recent years, such as in northwestern Xinjiang and the central-eastern part of IM; and (3) soil moisture in the whole region was negatively correlated with temperature and sunshine duration and positively correlated with precipitation and relative humidity. The results of the study can provide valuable guidance for timely agricultural irrigation and the adjustment of cropping structures, thereby ensuring agricultural production and food security.

Evaluating the performance of Bayesian geostatistical prediction with physical barriers in the Chesapeake Bay.

The Chesapeake Bay is one of the most widely studied bodies of water in the United States and around the world. Routine monitoring of water quality indicators (e.g., salinity) relies on fixed sampling stations throughout the Bay. Utilizing this rich monitoring data, various methods produce surface predictions of water quality indicators to further characterize the health of the Bay as well as to support wildlife and human health research studies. Bayesian approaches for geostatistical modelling are becoming increasingly popular and can be preferred over frequentist approaches because full and exact inference can be computed, along with more accurate characterization of uncertainty. Traditional geostatistical prediction methods assume a Euclidean distance between two points when characterizing spatial dependence as a function of distance. However, Euclidean approaches may not be appropriate in estuarine environments when water-land boundaries are crossed during the modelling process. In this study, we compare stationary and barrier INLA geostatistical models with a classic kriging geostatistical model to predict salinity in the Chesapeake Bay during 4 months in 2019. Cross-validation is conducted for each approach to evaluate model performance based on prediction accuracy and precision. The results provide evidence that the two Bayesian-based models outperformed ordinary kriging, especially when examining prediction accuracy (most notably in the tributaries). We also suggest that the non-Euclidean model accounts for the appropriate water-based distances between sampling locations and is likely better at characterizing the uncertainty. However, more complex bodies of water may better showcase the capabilities and efficacy of the physical barrier INLA model.

Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects

Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with R2 values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness.

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

Contribution of Geostatistical Methods to Water Quality Assessment: a Case Study of the Tebessa Plain (Easternmost Algeria)

Contribution of Geostatistical Methods to Water Quality Assessment: a Case Study of the Tebessa Plain (Easternmost Algeria)

Spatial-temporal risk factors in the occurrence of rabies in Mexico.

Rabies is a zoonotic disease that affects livestock worldwide. The distribution of rabies is highly correlated with the distribution of the vampire bat Desmodus rotundus, the main vector of the disease. In this study, climatic, topographic, livestock population, vampire distribution and urban and rural zones were used to estimate the risk for presentation of cases of rabies in Mexico by co- Kriging interpolation. The highest risk for the presentation of cases is in the endemic areas of the disease, i.e. the States of Yucatán, Chiapas, Campeche, Quintana Roo, Tabasco, Veracruz, San Luis Potosí, Nayarit and Baja California Sur. A transition zone for cases was identified across northern Mexico, involving the States of Sonora, Sinaloa, Chihuahua, and Durango. The variables topography, vampire distribution, bovine population and rural zones are the most important to explain the risk of cases in livestock. This study provides robust estimates of risk and spread of rabies based on geostatistical methods. The information presented should be useful for authorities responsible of public and animal health when they plan and establish strategies preventing the spread of rabies into rabies-free regions of México.

Effective indicators and drivers of soil organic matter in intensive orchard production systems

Effective indicators and drivers of soil organic matter in intensive orchard production systems

Carbon storage in mollusk shells: An overlooked yet significant carbon sink in terrestrial ecosystems

Carbon storage in mollusk shells: An overlooked yet significant carbon sink in terrestrial ecosystems

Numerical-geostatistical-based approach to investigate the earth pressure evolution within the large grid wall foundation under adjacent surcharge loading

Numerical-geostatistical-based approach to investigate the earth pressure evolution within the large grid wall foundation under adjacent surcharge loading

Estimated Sulfur Dioxide Pollutant Concentrations In Medan City Using Ordinary Kriging and Inverse Distance Weighting Approaches

Sulfur dioxide (SO2) pollution is a serious problem that negatively affects air quality and human health. So2 is generated by various human activities, especially the combustion of fossil fuels, as well as from natural sources such as volcanic eruptions. The emission of SO2 in urban areas, including Medan, Indonesia, has raised concerns as it is associated with respiratory diseases and the formation of acid rain. This research aimed to estimate comparative SO2 concentrations in Medan City using geostatistical methods, specifically ordinary kriging and inverse distance weighted (IDW). Air quality monitoring data from the Dinas Lingkungan Hidup Kota Medan were collected during a certain period. The collected data were analyzed and then interpolated using ordinary kriging and IDW methods. Furthermore, the Ordinary Kriging method involves testing the OK assumption, calculation of the experimental semivariogram, calculation of the theoretical semivariogram, structural analysis, and calculation of Root Mean Square Error (RMSE). Meanwhile, the Inverse Distance Weighted method involves calculating the Euclidean distance, determining the weights based on the power parameter, calculating the RMSE value, and estimating the SO2 concentration. The comparison results show that the OK method is more accurate in determining SO2 concentration compared to the IDW method in Medan City. In the estimation of sulfur dioxide (SO2) concentration, the OK method used the best theoretical semivariogram model, namely the exponential model, and in the estimation process of the IDW method, the power parameter 1 was used.

Spatial Distribution of Soil Carbon and Nitrogen Content in the Danjiangkou Reservoir Area and Their Responses to Land-Use Types

Understanding the spatial distribution of soil properties is essential for comprehending soil fertility, predicting ecosystem productivity, enhancing environmental quality, promoting sustainable agriculture, and addressing global climate change. This study focuses on investigating the spatial distribution and influencing factors of soil carbon (C) and nitrogen (N) in the Danjiangkou Reservoir area, a vital water source for the South-to-North Water Transfer Project. Utilizing both geostatistical and traditional statistical methods, this research explores the impact of various land-use types—such as orchards, drylands, paddy fields, and Hydro-Fluctuation Belts (HF belts)—on soil C and N content. The findings reveal predominantly low levels of soil organic carbon (SOC) (ranging from 2.95 to 21.50 g·kg−1), total nitrogen (TN) (ranging from 0.27 to 2.44 g·kg−1), and available nitrogen (AN) (ranging from 18.20 to 170.45 mg·kg−1), mostly falling into deficient categories. Notably, spatial variability is observed, especially in agriculturally developed regions, leading to areas of enrichment. Paddy fields and HF belts are identified as influential contributors to increased SOC and nitrogen content compared to orchards and drylands. Correlation and stepwise regression analyses unveil intricate interactions among SOC, TN, AN, and environmental factors, underscoring the necessity for a holistic approach to soil management. This study emphasizes the critical role of adopting rational land-use types and sustainable agricultural practices for effective soil management in the Danjiangkou Reservoir area.

Применение геостатистических методов интерполяции коэффициентов фильтрации на участке месторождения Нурказган Восточный с использованием языка программирования Python

This article presents the results of applying geostatistical interpolation methods to filtration coefficients obtained from interval hydrogeological studies in six geotechnical wells, with depths up to 1500 meters, in the Nurkazgan East field. Packers were used to isolate the required intervals, and pressure changes during tests were recorded using autonomous sensors. Subsequent processing of the results was carried out using the Python programming language. Geostatistical interpolation methods (including simple kriging and stochastic methods) for geological features were examined. For this purpose, several Python programming language libraries were utilized for data preparation, interpolation, visualization, and export to the required data format, such as Pandas, Numpy, PyGSLIB, GeONE, and others. As a result, the distribution of filtration coefficients within the boundaries of the modeled block was obtained. The applied stochastic methods allowed obtaining the necessary number of equiprobable realizations of the filtration coefficient distribution, which on average converged to the solution of simple kriging. Using these equiprobable realizations in further solving the problem of determining the forecast volume of drainage water, using geofiltration modeling, will provide a probabilistic distribution of these volumes. The forecast volume of drainage water significantly impacts the economy of the entire mining enterprise, as it affects the chosen mine dewatering scheme, selection of infrastructure for drainage water pumping, the magnitude of unbalanced volumes of pumped water discharged into the evaporator pond, and much more. Accordingly, the described method will allow determining both the most probable volumes of drainage water and their upper and lower bounds. Conducting geostatistical calculations using programming languages, particularly Python, enables hydrogeologists to fully utilize the theoretical foundations of any direction, whereas ready-made software products to some extent impose limitations.

Geoestadística para integrar mediciones de campo con estimaciones satelitales adecuados para escala local

In countries such as Mexico, there is a lack of rain measurement stations. Additionally, in the Bajo Grijalva Basin, data of only three or fewer stations are integrated into satellite products of missions such as Tropical Rainfall Monitoring Mission (TRMM) and Global Precipitation Mission (GPM). Although Satellite missions enable obtaining rainfall at constant spacing (e.g., 11 km for GPM), this resolution is not suitable for local management. Integrating a larger quantity of gauge data with downscaled satellite values allows for obtaining local-scale precipitation data. In this work, Ordinary kriging (OK) was applied to downscale yearly aggregated precipitation satellite data (GPM-IMERG and TRMM: TMPA/3B43) and regression kriging (RK) to integrate them with the gauge measurements available in the basin of study. The resulting data were compared with the interpolation results of gauge measurements using OK and universal kriging (UK). Leave-one-out cross-validation (Lou-CV), principal components analysis, a correlation matrix, and a heat map with cluster analysis helped to evaluate the performance and to define similarity. An Inverse Distance Weighting (IDW) interpolation was included as a low-performance criterion in the comparison. OK performed well to downscale GPM satellite estimates. The RK integration of gauge data with downscaled GPM data got the best validation values compared to the interpolation of gauge measurements. Geostatistical methods are promising for downscaling satellite estimates and integrating them with all the available gauge data. The results indicate that the evaluation using performance metrics should be complemented with methods to define similarity among the values of the obtained spatial layers. This approach allows obtaining precipitation data useful for modeling and water management at the local level.