Search Results (25)

Biobanks rely on ultra-low-temperature (ULT) storage for irreplaceable specimens, where precise 3D temperature field reconstruction is critical to preserve integrity. This is the first study to apply geostatistical methods to ULT field reconstruction in cryogenic biobanking systems. We address critical gaps in sparse-sensor environments where conventional interpolation fails due to vertical thermal stratification and non-stationary trends. Our physics-informed universal kriging framework introduces (1) the first domain-specific adaptation of universal kriging for 3D cryogenic temperature field reconstruction; (2) eight novel lag-binning methods explicitly designed for sparse, anisotropic sensor networks; and (3) a leave-one-out cross-validation-driven framework that automatically selects the optimal combination of trend model, binning strategy, logistic weighting, and variogram model fitting. Validated on real data collected from a 3000 L operating cryogenic chest freezer, the method achieves sub-degree accuracy by isolating physics-guided vertical trends (quadratic detrending dominant) and stabilising variogram estimation under sparsity. Unlike static approaches, our framework dynamically adapts to thermal regimes without manual tuning, enabling centimetre-scale virtual sensing. This work establishes geostatistics as a foundational tool for cryogenic thermal monitoring, with direct engineering applications in biobank quality control and predictive analytics. Full article

Accurate assessments are vital for the effective conservation of desert steppe ecosystems, which are essential for maintaining biodiversity and ecological balance. Although geostatistical methods are commonly used for spatial modeling, they have limitations in terms of feature extraction and capturing non-linear relationships. This study therefore proposes a novel remote sensing framework that integrates geostatistical methods and machine learning to predict the Shannon–Wiener index in desert steppe. Five models, Kriging interpolation, Random Forest, Support Vector Machine, 3D Convolutional Neural Network and Graph Attention Network, were employed for parameter inversion. The Helmert variance component estimation method was introduced to integrate the model outputs by iteratively evaluating residuals and assigning relative weights, enabling both optimal prediction and model contribution quantification. The ensemble model yielded a high prediction accuracy with an R² of 0.7609. This integration strategy improves the accuracy of index prediction, and enhances the interpretability of the model regarding weight contributions in space. The proposed framework provides a reliable, scalable solution for biodiversity monitoring and supports scientific decision-making for grassland conservation and ecological restoration. Full article

As shallow mineral resources become increasingly depleted, the search for deep-seated orebodies has emerged as a crucial focus in modern gold exploration. This study investigates Vein 171 in the Linglong gold field, Jiaodong Peninsula, using 3D attribute modeling for deep mineralization prediction and precise orebody delineation. The research integrates surface and block models through Vulcan 2021.5 3D mining software to reconstruct the spatial morphology and internal attribute distribution of the orebody. Geostatistical methods were applied to identify and process high-grade anomalies, with grade interpolation conducted using the inverse distance weighting (IDW) method. The results reveal that Vein 171 is predominantly controlled by NE-trending extensional structures, and grade enrichment occurs in zones where fault dips transition from steep to gentle. The grade distribution of the 171₁ and 171_sub-1 orebodies demonstrates heterogeneity, with high-grade clusters exhibiting periodic and discrete distributions along the dip and plunge directions. Key enrichment zones were identified at elevations of –1800 m to –800 m near the bifurcation of the Zhaoping Fault, where stress concentration and rock fracturing have created complex fracture networks conducive to hydrothermal fluid migration and gold precipitation. Nine verification drillholes in key target areas revealed 21 new mineralized bodies, resulting in an estimated additional 2.308 t of gold resources and validating the predictive accuracy of the 3D model. This study not only provides a reliable framework for deep prospecting and mineral resource expansion in the Linglong Goldfield but also serves as a reference for exploration in similar structurally controlled gold deposits globally. Full article

A 3D model integrating mineralogical, petrological, and geostatistical resource estimation was developed for Zone C of the Kofi Birimian gold deposit in Western Mali. Petrographic analysis identified two forms of gold mineralization: (i) native gold or electrum inclusions within pyrite, and (ii) disseminated native gold along pyrite fractures. Four types of hydrothermal alteration–epidotization, chloritization, carbonatization, and albitization were observed microscopically. Statistical analysis of geochemical data classified five lithologies: mafic dyke, felsic dyke, diabase, faulted breccia, and intermediate quartz diorite. Minerals identified petrographically were corroborated by multivariate correlations among elements (Cr, Fe, Ni, Al, Ti, Na, and Ca), as revealed by Principal Component Analysis (PCA). A 3D borehole-based model revealed spatial correlations between hydrothermal alteration zones and associated geochemical anomalies, notably tourmalinization (B) and albitization (Na), with the latter serving as a key indicator for new exploration targets. The spatial associations of anomalous Ag, B, Hg, As, and Na commonly linked to tourmalinization suggest favorable zones for gold and silver mineralization. Geostatistical analysis identified isotropic continuous mineralized structures for most elements, including gold. Spherical isotropic variograms with ranges from 35 to 75 m were fitted for in situ resource estimation (e.g., silver ≈ 40 m; gold ≈ 60 m). The resulting estimated resources (indicated + inferred), based on a 1.0 g/t Au cut-off, are 2.476 Mt at 3.5 g/t Au indicated (0.278 Moz or 8.67 t), and 1.254 Mt at 2.78 g/t Au inferred (0.112 Moz or 3.49 t). This study provides a framework for identifying new mineralized zones, and the multidisciplinary approach demonstrates the connections between mineralogy and the information embedded in geochemical datasets, which are revealed through appropriate tools and an understanding of the underlying processes. Full article

Groundwater modeling in data-scarce regions faces significant challenges due to the lack of comprehensive, high-quality data, impacting model accuracy. This systematic review of Scopus-indexed papers identifies various approaches to address these challenges, including coupled hydrological-groundwater models, machine learning techniques, distributed hydrological models, water balance models, 3D groundwater flow modeling, geostatistical techniques, remote sensing-based approaches, isotope-based methods, global model downscaling, and integrated modeling approaches. Each methodology offers unique advantages for groundwater assessment and management in data-poor environments, often combining multiple data sources and modeling techniques to overcome limitations. However, these approaches face common challenges related to data quality, scale transferability, and the representation of complex hydrogeological processes. This review emphasizes the importance of adapting methodologies to specific regional contexts and data availability. It underscores the value of combining multiple data sources and modeling techniques to provide robust estimates for sustainable groundwater management. The choice of method ultimately depends on the specific objectives, scale of the study, and available data in the region of interest. Future research should focus on improving the integration of diverse data sources, enhancing the representation of complex hydrogeological processes in simplified models, and developing robust uncertainty quantification methods tailored for data-scarce conditions. Full article

The management of mine tailings presents a global challenge. Re-mining these tailings to recover remaining metals could play a crucial role in reducing the volume of stored tailings, as historical mining methods were less efficient than those used today. Consequently, mine wastes have the potential to become unconventional resources for critical minerals. To assess this potential, critical minerals and metals in the mine tailings were investigated through sampling, characterization, and 3D geostatistical modeling. The Bleïda copper mine tailings in Morocco were modeled, and residual copper resources were estimated using ordinary kriging (OK). Tailings were systematically sampled at a depth of 1.8 m using a triangular grid and tubing method. The metallic and mineralogical content of the samples was analyzed, and a numerical 3D model of the tailing’s facility was created using topographic drone surveys, geochemical data, and geostatistical modeling. The results from the 3D block model of the Bleïda tailings facility reveal that the volume of deposited tailings is 3.73 million cubic meters (mm³), equivalent to 4.85 million tonnes (Mt). Furthermore, based on the average copper grade (~0.3% by weight) in the studied part of the tailings pond, the copper resources are estimated at 2760 tonnes. Mineralogical characterization indicates that this metallic content is mainly associated with sulfide and carbonate minerals, which exhibit a low degree of liberation. This study aims to serve as a reference for assessing the reprocessing feasibility of tailings in both abandoned and active mines, thereby contributing to the sustainable management of mine tailings facilities. Geostatistical modeling has proven effective in producing tonnage estimates for tailings storage facilities and should be adopted by the industry to reduce the technical and financial uncertainties associated with re-mining. Full article

In this study, a novel chromotropic acid-based color development method was proposed for quick estimation of soil nitrate (NO₃⁻). The method utilized a 3D printed device integrated with the rear-end camera of a smartphone and a stand-alone application called SMART NP. By analyzing the mean Value (V) component of the sample’s image, the SMART NP provides instant predictions of soil NO₃⁻ levels. The limit of detection was calculated as 0.1 mg L⁻¹ with a sensitivity of 0.26 mg L⁻¹. The device showed a % bias of 0.9% and a precision of 1.95%, indicating its reliability. Additionally, the device-predicted soil NO₃⁻ data, combined with kriging interpolation, showcased spatial variability in soil NO₃⁻ levels at the regional level. The study employed a Gaussian model of variogram for kriging, and the high Nugget/Sill ratio indicated low spatial autocorrelation, emphasizing the impact of management factors on the spatial distribution of soil NO₃⁻ content in the study area. Overall, the imaging device, along with geostatistical interpolation, provided a comprehensive solution for the rapid assessment of spatial variability in soil NO₃⁻content. Full article

Spatial prediction of orebody characteristics can often be challenging given the commonly complex geological structure of mineral deposits. For example, a high nugget effect can strongly impact variogram modelling. Geological complexity can be caused by the presence of structural geological discontinuities combined with numerous lithotypes, which may lead to underperformance of grade estimation with traditional kriging. Deep learning algorithms can be a practical alternative in addressing these issues since, in the neural network, calculation of experimental variograms is not necessary and nonlinearity can be captured globally by learning the underlying interrelationships present in the dataset. Five different methods are used to estimate an unsampled 2D dataset. The methods include the machine learning techniques Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network; the conventional geostatistical methods Simple Kriging (SK) and Nearest Neighbourhood (NN); and a deep learning technique, Convolutional Neural Network (CNN). A comparison of geologic features such as discontinuities, faults, and domain boundaries present in the results from the different methods shows that the CNN technique leads in terms of capturing the inherent geological characteristics of given data and possesses high potential to outperform other techniques for various datasets. The CNN model learns from training images and captures important features of each training image based on thousands of calculations and analyses and has good ability to define the borders of domains and to construct its discontinuities. Full article

Olive pruning residues are a by-product that can be applied to soil or used for energy production in a circular economy model. Its benefits depend on the amount of pruning, which varies greatly within farms. This study aimed to investigate the spatial variability of shreddable olive pruning in a traditional olive grove in Córdoba (Spain) with an area of 15 ha and trees distanced 12.5 m from each other. To model the spatial variability of shreddable olive pruning, geostatistical methods of stochastic simulation were applied to three correlated variables measured on sampled trees: the crown projected area (n = 928 trees), the crown volume (n = 167) and the amount of shreddable pruning (n = 59). Pearson’s correlation between pairs of variables varied from 0.71 to 0.76. The amount of pruning showed great variability, ranging from 7.6 to 76 kg tree⁻¹, with a mean value of 37 kg tree⁻¹. Using exponential and spherical variogram models, the spatial continuity of the variables under study was established. Shreddable dry pruning weight values showed spatial autocorrelation up to 180 m. The spatial uncertainty of the estimation was obtained using sequential simulation algorithms. Stochastic simulation algorithms provided 150 possible images of the amount of shreddable pruning on the farm, using tree projected area and crown volume as secondary information. The interquartile range and 90% prediction interval were used as indicators of the uncertainty around the mean value. Uncertainty validation was performed using accuracy plots and the associated G-statistic. Results indicate with high confidence (i.e., low uncertainty) that shreddable dry pruning weight in the mid-western area of the farm will be much lower than the rest of the farm. In the same way, results show with high confidence that dry pruning weight will be much higher in a small area in the middle east of the farm. The values of the G-statistic ranged between 0.89 and 0.90 in the tests performed. The joint use of crown volume and projected areas is valuable in estimating the spatial variability of the amount of pruning. The study shows that the use of prediction intervals enables the evaluation of farm areas and informed management decisions with a low level of risk. The methodology proposed in this work can be extrapolated to other 3D crops without requiring modifications. On a larger scale, it can be useful for predicting optimal locations for biomass plants, areas with high potential as carbon sinks or areas requiring special soil protection measures. Full article

Hydrosystems in the Saskatchewan River Basin of the Canadian Prairies are subject to natural and socioeconomic pressures. Increasingly, these strong pressures are exacerbating problems of water resource accessibility and depletion. Unfortunately, the geometric heterogeneity of the aquifers and the presence of lithologically varied layers complicate groundwater flow studies, hydrodynamic characterization, and aquifer storativity calculations. Moreover, in recent hydrogeological studies, hydraulic conductivity has been the subject of much more research than storativity. It is in this context that the present research was conducted, to establish a 3D hydrostratigraphic model that highlights the geological (lithology, thickness, and depth) and hydrodynamic characteristics of the aquifer formations and proposes a new uncertainty framework for groundwater storage estimation. The general methodology is based on collecting and processing a very fragmentary and diverse multi-source database to develop the conceptual model. Data were harmonized and entered into a common database management system. A large quantity of geological information has been implemented in a 3D hydrostratigraphic model to establish the finest geometry of the SRB aquifers. Then, the different sources of uncertainty were controlled and considered in the modeling process by developing a randomized modeling system based on spatial random bagging simulation (SRBS). The results of the research show the following: Firstly, the distribution of aquifer levels is controlled by tectonic activity and erosion, which further suggests that most buried valleys on the Prairies have filled over time, likely during multiple glaciations in several depositional environments. Secondly, the geostatistical study allowed us to choose optimal interpolation variographic parameters. Finally, the final storativity maps of the different aquifer formations showed a huge potential of groundwater in SRB. The SRBS method allowed us to calculate the optimal storativity values for each mesh and to obtain a final storativity map for each formation. For example, for the Paskapoo Formation, the distribution grid of groundwater storage shows that the east part of the aquifer can store up to 5920 × 10³ m³/voxel, whereas most areas of the west aquifer part can only store less than 750 × 10³ m³/voxel. The maximum storativity was attributed to the Horseshoe Canyon Formation, which contains maximal geological reserves ranging from 107 to 111 × 10⁹ m³. The main contribution of this research is the proposed 3D geological model with hydrogeological insights into the study area, as well as the use of a new statistical method to propagate the uncertainty over the modeling domain. The next step will focus on the hydrodynamic modeling of groundwater flow to better manage water resources in the Saskatchewan River Basin. Full article

It has been more than a decade since sequential indicator simulation was proposed to model geological features. Due to its simplicity and easiness of implementation, the algorithm attracts the practitioner’s attention and is rapidly becoming available through commercial software programs for modeling mineral deposits, oil reservoirs, and groundwater resources. However, when the algorithm only uses hard conditioning data, its inadequacy to model the long-range geological features has always been a research debate in geostatistical contexts. To circumvent this difficulty, one or several pieces of soft information can be introduced into the simulation process to assist in reproducing such large-scale settings. An alternative format of Bayesian sequential indicator simulation is developed in this work that integrates a log-linear pooling approach by using the aggregation of probabilities that are reported by two sources of information, hard and soft data. The novelty of this revisited Bayesian technique is that it allows the incorporation of several influences of hard and soft data in the simulation process by assigning the weights to their probabilities. In this procedure, the conditional probability of soft data can be directly estimated from hard conditioning data and then be employed with its corresponding weight of influence to update the weighted conditional portability that is simulated from the same hard conditioning and previously simulated data in a sequential manner. To test the algorithm, a 2D synthetic case study is presented. The findings showed that the resulting maps obtained from the proposed revisited Bayesian sequential indicator simulation approach outperform other techniques in terms of reproduction of long-range geological features while keeping its consistency with other expected local and global statistical measures. Full article

Groundwater is the main source of freshwater available for human beings and is generally extracted through wells. The objective of this work was to schematize the groundwater flow systems within the Calera Aquifer through 3D geostatistical estimations of hydraulic head and physico-chemical parameters and the integration of hydrogeological features. The evolution of groundwater during its circulation in the subsoil can be done by identifying different types of flow (local, intermediate, regional, or mixed). Two main approaches have been proposed for the identification of flow systems: explaining the evolution of physico-chemical parameters of water through its interaction with the geologic medium, and using cluster analysis; however, these approaches usually do not consider simultaneously the 3D distribution of hydraulic head, water quality parameters, and the geological media that can be useful to delineate converging flow systems with a differentiated origin. In this paper, the determination of groundwater flow systems within the Calera aquifer in Mexico is supported with 3D representations of these hydrogeological variables besides constructive data of the sampled well. For the case study, the convergence of different flow systems that are not identified through a single cluster analysis was actually noticed by the proposal done in this paper. Full article

In this paper, an assessment of the uncertainty affecting a hybrid procedure (experimental/numerical) is carried out to validate it for industrial applications, at the least. The procedure in question serves to depict 3D incompressible flow fields by using 2D measurements of it and computing the third velocity component by means of the continuity equation. A quasi-3D test case of an incompressible flow has been inspected in the wake of a NACA 0012 airfoil immersed in a forced flow of water running in a rectangular open channel. Specifically, starting from a 2D measurement data in planes orthogonal to the stream-wise direction, the computational approach can predict the third flow velocity component. A 3D ADV instrument has been utilized to measure the flow field, but only two velocity components have been considered as measured quantities, while the third one has been considered as reference with which to compare the computed component from the continuity equation to check the accuracy and validity of the hybrid procedure. At this aim, the uncertainties of the quantities have been evaluated, according to the GUM, to assess the agreement between experiments and predictions, in addition to other metrics. This aspect of uncertainty is not a technical sophistication but a substantial way to bring to the use of a 1D and 2D measurement system in lieu of a 3D one, which is costly in terms of maintenance, calibration, and economic issues. Moreover, the magnitude of the most relevant flow indicators by means of experimental data and predictions have been estimated and compared, for further confirmation by means of a supervised learning classification. Further, the sensed data have been processed, by means of a machine learning algorithm, to express them in a 3D way along with accuracy and epoch metrics. Two additional metrics have been included in the effort to show paramount interest, which are a geostatistical estimator and Sobol sensitivity. The statements of this paper can be used to design and test several devices for industrial purposes more easily. Full article

Geological models are commonly used to represent geological structures in 3D space. A wide range of methods exists to create these models, with much scientific work focusing recently on implicit representation methods, which perform an interpolation of a three-dimensional field where the relevant boundaries are then isosurfaces in this field. However, this method has well-known problems with inhomogeneous data distributions: if regions with densely sampled data points exist, modeling artifacts are common. We present here an approach to overcome this deficiency through a combination of an implicit interpolation algorithm with a local smoothing approach. The approach is based on the concepts of nugget effect and filtered kriging known from conventional geostatistics. It reduces the impact of regularly occurring modeling artifacts that result from data uncertainty and data configuration and additionally aims to improve model robustness for scale-dependent fit-for-purpose modeling. Local smoothing can either be manually adjusted, inferred from quantified uncertainties associated with input data or derived automatically from data configuration. The application for different datasets with varying configuration and noise is presented for a low complexity geologic model. The results show that the approach enables a reduction of artifacts, but may require a careful choice of parameter settings for very inhomogeneous data sets. Full article

Optimal upscaling of a high-resolution static geologic model that reflects the flow performance of the reservoir is important for reasons such as time and calculation efficiency. In this study, we demonstrate that honoring reservoir heterogeneity is critical in predicting accurate production and reducing the time and cost of running reservoir flow simulations for the Hunton Group carbonate. We integrated three-dimensional (3D) seismic data, well logs, thin sections, outcrops, multiscale fracture studies, discrete fracture networks, and geostatistical methods to create a 100 × 150 × 1 ft gridded representative geologic model. We calibrated our gridded porosity and permeability parameters, including the evaluation of fractures, by history matching the simulated production rate and cumulative production volumes from a baseline fine-scale model generated from petrophysical and production data obtained from five wells. We subsequently reperformed the simulations using a suite of coarser grids to validate our property upscaling workflow. Compared to our baseline history matching, increasing the horizontal grid cell sizes (i.e., horizontal upscaling) by factors of 2, 4, 8, and 16 results in cumulative production errors ranging from +0.5% for two time (2×) coarser to +74.22% for 16× coarser. The errors associated with vertical upscaling were significantly less, i.e., ranging from +0.5% for 2× coarser to +10.8% for 16× coarser. We observed higher production history matching errors associated with natural fracture size. Results indicate that greater connectivity provided by natural fracture length has a larger effect on production compared to the higher permeability provided by larger apertures. We also estimated the trade-off between accuracy and run times using two methods: (a) using progressively larger grid cell sizes; (b) applying 1, 5, 10, and 20 parallel processes. Computation time reduction in both scenarios may be described by simple power law equations. Observations made from our case study and upscaling workflow may be applicable to other carbonate reservoirs. Full article