3D Geostatistical Modeling and Metallurgical Investigation of Cu in Tailings Deposit: Characterization and Assessment of Potential Resources

Abstract

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.

1. Introduction

In addition to metal extraction, the mining industry generates a significant amount of solid waste, particularly waste rock and tailings. Annually, approximately 25 billion tonnes of solid waste are produced worldwide [1]. Waste rock consists of diverse granulometric particles produced during preparation operations to access the ore body. Tailings, generated during ore processing stages, exhibit a finer and more uniform particle size compared to waste rock [2,3,4,5]. Tailings are primarily transported as sludge via pipelines and stored in surface structures known as tailings storage facilities (TSFs) [6,7]. Data from 1743 TSFs show a consistent increase in their number from 1955 to 2020, with a total storage capacity reaching up to 44.5 billion m³ by 2024 [8,9], which is projected to exceed 55.8 billion m³ in 2025. Mine waste remains on-site indefinitely if there are no technical and/or economic means to reuse or recover it [10,11,12,13].

Depending on the geological context of the deposit, tailings can contain various types and quantities of sulfides/sulfosalts, which may be subject to oxidation when in contact with atmospheric air and water [14,15,16,17,18,19,20,21,22,23,24,25,26]. This oxidation can lead to the formation of acid mine drainage (AMD) if the neutralization potential (NP) of neutralizing minerals present in the tailings is insufficient to buffer the acidity [27,28,29]. Contaminated neutral drainage (CND) can also form when one or more heavy metals/metalloids are present in leachates in quantities exceeding regulatory standards [30,31,32,33,34]. Both AMD and CND damage surrounding ecosystems, biodiversity, and groundwater quality [35,36,37] and cause environmental problems that persist for decades or even millennia [3,38]. On the other hand, tailings can contain valuable elements that were not extracted in the past, either because they were not considered economically viable at the time or because the available processing technologies were not advanced enough to allow for their efficient recovery [39,40,41].

The reprocessing of tailings to recover valuable metals has become a topic of considerable interest in recent years. Conventional ore processing techniques can be used to reprocess tailings. The decision to use one technique over another and the efficiency of metal recovery depend on several factors, such as the physical, chemical, and mineralogical properties of the metal and metal-bearing minerals, the particle size distribution, and the age of the tailings [42,43]. To ensure the effective implementation of this reprocessing, it is essential to accurately estimate the residual resources by treating the tailings stored in TSFs as a primary deposit. This way, we can determine if there are valuable materials still worth extracting. To assess residual resources in tailings, techniques originally developed for primary ore body evaluation can be adapted and applied [44,45]. Previous research on tailings assessments used different estimation methods to determine residual metal content, such as combining geophysical surveys with average metal grades or estimating metal content through its relationship with resistivity values [46]. The difficulty in estimating residual resources arises from mechanisms such as the modification of the geological context of the ores and the process of filling and managing TSFs, resulting in regions with variable metal concentrations and creating heterogeneous layered structures [47,48,49]. Additionally, mechanisms such as weathering, metal migration, and metal precipitation contribute to the heterogeneity of the material in the TSF [1,6].

Several studies use spatial interpolation techniques to estimate recoverable resources in TSFs. The relative simplicity of the shape of the tailings makes regression models, Inverse Distance Weighting (IDW), and kriging popular choices [50,51,52,53,54,55,56,57,58]. The management and valorization of mining waste constitute a sustainable and economical approach to reducing environmental risks through reprocessing and reusing waste as valuable materials [59,60,61]. These practices support the circular economy by reusing mine wastes as potential secondary resources [62,63]. They generate additional income, promote local socio-economic development, and contribute to the conservation of natural resources by reducing overexploitation of raw materials. The geochemical, physical, and mineralogical characterization of mining waste is crucial in selecting the most appropriate reuse domains and determining the most effective methods for recovering targeted useful metals stored in TSFs.

The objective of this study is to examine the chemical, physical, and mineralogical characterizations of the Bleïda mine tailings (BMT) and to apply 3D geostatistical modeling to estimate the residual copper resources and their distribution in the Bleïda TSF. This study also proposes guidelines for selecting reprocessing methods based on chemical and mineralogical properties and estimated resources. This study provides unique information on the Bleïda mine tailings, which have never been sampled by drilling. Additionally, the estimation of the volume and tonnage of the resources is based on topographic images captured by drones.

2. Materials and Methods

2.1. Bleïda Mining District

The Bleïda copper mine is located in the southeastern Bou Azzer-El Graara inlier, central Anti-Atlas, Morocco, approximately 80 km from the city of Zagora (Figure 1). Mining activity began in the Middle Ages, with modern copper prospecting starting in 1962 and major activity occurring from 1971 to 1997. Following the development of new deposits and the implementation of novel processing methods, the mine was reopened in 2008 and has continued to operate to the present day.

The Bleïda geological series consists of four units: (i) stromatolitic limestone and quartzite unit, (ii) basalt unit, (iii) shale unit, and (iv) sandy shale unit [64]. Volcanogenic massive sulfide (VMS) mineralization in the Bleïda district is represented by three types of mineralization. Firstly, polymetallic massive copper sulfides are associated with the basic magmatic part of the volcanoclastic unit. Secondly, there are indications of pyritic polymetallic massive sulfides, which take the form of lenticular bodies. Thirdly, there are indications of ferromanganese closely linked to the volcanic facies [65]. Copper is mainly found in sulfide minerals (bornite, chalcopyrite, pyrite, and chalcocite), carbonate minerals (malachite, azurite, and cuprite), and silicate minerals such as chrysocolla. Currently, the mining district comprises several deposits, with the most important being Bleïda Centre, Jbel Laassel, Jbel N’zourk, Ait Abdellah, and Takroumt. The location, host rocks, and types of Cu-bearing sulfide phases for each deposit are detailed in

Table 1. Location, host rocks, and Cu-bearing minerals in the different deposits of the Bleïda district.

Deposit	Location		Host Rocks	Cu-Bearing
	X	Y
Bleïda centre	399,121.102	373,216.574	Sandstone, black shales, keratophyre tuffs, and gabbros	Bornite and chalcopyrite
Jbel Laassel	413,083.771	382,762.432	Massive limestone and dolomite with intercalations of siltstone and shale	Chalcocite, chrysocolla, and malachite.
Jbel N’zourk	352,574.600	372,753.384	Dolomite and siltstone [66]	Chalcocite, bornite, chalcopyrite, covellite, and malachite [66]
Ait Abdellah	379,269.669	389,580.902	Silicified sandstone	Copper sulfides

. Nowadays, ore is extracted from all these deposits using the open pit mining method.

Annually, more than 1.2 Mt of copper ore are extracted and transported to the processing plant. The ore undergoes several crushing stages, followed by a three-stage screening process (55 mm, 20 mm, and 8 mm). Material rejected at 20 mm and 8 mm is sent to the secondary crusher, while particles smaller than 8 mm are ground in a ball mill to a size of D80 = 125 µm. The ground product is classified by a hydrocyclone at 125 µm. The underflow (UF) is returned to the ball mill, while the overflow (OF) proceeds to the flotation unit. Copper recovery rates are 95% for sulfide minerals and 75% for oxidized minerals. The process yields 150 t/day of filtered and dried concentrate and 3500 m³/day of tailings slurry, which is pumped to the surrounding TSFs (Figure 2).

2.2. Tailings Sampling and Sample Preparation

Sampling involves selecting a subset of individuals from the total population, which is necessary as measuring the entire population is impractical. In soil and earth sciences, the most common sampling methods are random and systematic [67,68]. Sampling locations can be chosen through haphazard, judgment, or probability sampling [69]. This study used a sampling strategy based on an orthogonal grid-oriented NE-SW and NW-SE pattern, with 60 m NE-SW and 50 m NW-SE spacings, as shown in Figure 3. Sampling in the dry zone of the Bleïda TSF was conducted using a tubing method, focusing on the dry area for safety considerations and to avoid water in the western zone. The superficial weathered layer, likely oxidized, was removed to differentiate between weathered and unweathered layers, identified by the hardpan layer and color contrast.

The first step of sampling involved the removal of the superficial weathered layer, likely oxidized, using a trowel. Afterward, a hole 20 cm to 30 cm deep was dug, ensuring that the walls of the hole were stable (Figure 4a). This step was crucial to prevent surface materials from falling back into the hole and contaminating the collected sample. A total of 73 samples were collected from the Bleïda TFS. Each sample was placed in a plastic bag (Figure 4c) to avoid contact with air and to preserve the physico-chemical properties of the samples as much as possible. Sampling depths ranged from 1.5 m to 1.8 m (Figure 4b), resulting in 73 samples. Subsequently, the samples were transported to the mine laboratory to begin preparation.

All samples were initially weighed, homogenized, and then dried at 115 °C for 12 h. The dried samples were divided using a divider, resulting in small composite subsamples that retained the same properties as the initial sample. The composites were then crushed and reduced to less than 100 µm using specialized equipment before being transported to the Reminex laboratory for analysis. This meticulous process, underpinned by QAQC measures, ensures the reliability and validity of the analytical results, which are crucial for informed decision-making.

2.3. Bleïda Mine Tailings (BMT) Characterization

The total copper content in the 73 samples was measured using an inductively coupled plasma atomic emission spectrometer (ICP-AES) at the Reminex laboratory, employing peroxide fusion. A blank was analyzed every 20 samples. Standards were analyzed every 20 samples, duplicates were analyzed every 10 samples, and the instrument was recalibrated every 50 samples. Particle size distribution (PSD) of the BMT was determined using a laser grain size analyzer (Malvern Mastersizer 2000 Ver. 6.00). X-ray diffraction (XRD) characterization was performed to identify the well-crystallized major phases in the tailings using an Aeris Panalytical diffractometer (Co Kα radiation; λ = 1.724 Å), equipped with High Score Plus software version 4.9 for mineralogical identification and semi-quantitative analysis. This quantification method offers precision within a range of approximately ±0.5 to 1 wt%, with reduced accuracy for phases comprising less than 1%. The mineralogical composition and texture of the BMT were further characterized using automated quantitative mineralogy (AQM) and SEM-EDS analysis. AQM was performed using a ZEISS Sigma VP microscope in the Reminex laboratory, fitted with Bruker EDS X Flash 30/60 spectrometers. Mineralogical data were obtained using mineralogical software. SEM analyses provided fully quantified modal mineralogy, textural information, mineral size, elemental deportment, and liberation [5,70]. The specific density of the tailings was measured using an Anton Paar Ultrapyc 5000 gas pycnometer with helium gas at a pressure of 10 psi and a temperature of 20 °C. Specific density, or density, is defined as the mass of a substance per unit volume. To ensure the accuracy of the density measurements, the pycnometer was regularly calibrated with certified reference materials to verify its precision. Additionally, multiple measurements were obtained for each sample to ensure repeatability and to identify any potential discrepancies. The samples were also pre-treated to remove moisture and contaminants that could affect the density measurements. In this study, specific density was used to convert volume estimates into mass estimates for the tailings.

2.4. Bleïda TSF 3D Model Construction

The main purpose of building a 3D model of the TSF was to create a 3D grid that could be used to analyze and predict the properties of the tailings. The 3D model of the Bleïda TSF was meticulously crafted by integrating two comprehensive topographic surveys conducted before and after the TSF’s construction. These surveys employed advanced photogrammetric drone technology, which enabled precise data capture through a GPS-enabled camera, allowing the acquisition of geographic coordinates for each surveyed point. Subsequently, the drone-generated output files were formatted into DXF files and then converted into a multi-point shapefile. To ensure compatibility and accuracy, both topographic surfaces were aligned to a unified geographic reference system. The intersected topographies were seamlessly integrated to generate a comprehensive 3D model of the TSF. This advanced model not only provides a visually immersive representation but also serves as a powerful tool for calculating various morphological parameters (perimeter, area, and volume), which are essential for comprehensive analysis and evaluation. From the 3D shell of the TSF, a 3D grid with 2 m spacing was created for geostatistical modeling.

2.5. Geostatistical Modeling

2.5.1. 3D Geostatistical Modeling

Geostatistical estimation using kriging has become a standard technique in mining engineering and earth sciences. The geostatistical processing steps are summarized as follows: data were structured and extracted from sampling points, including copper grades, coordinates, and sampling depths, and formatted as CSV files named collar, assay, and survey. Each file contained specific information (see

Table 2. Different tables: input collar, survey, and assay. ID: sample identifier; sampling point coordinates: X, Y, and Z.

Collar
ID	X	Y	Z	Dip
Survey
ID	AT	Azimuth	Dip	Depth
Assay
ID	From	To	Cu %

). The experimental data were merged and composited to create a statistical database. Variability in both horizontal and vertical planes was then estimated and analyzed. A numerical model was created using ordinary kriging (OK). The results from these processes were combined to estimate the residual copper resources.

2.5.2. Variography

The variogram function reflects the spatial structure of a regional variable, accounting for both spatial variation and randomness. To investigate the anisotropy of the ore deposit, a variogram was constructed for various directions and at different dip orientations of the deposit [71]. The graph of the sample or experimental variogram γ(h) displays the mean of increments divided by two as the distance increases. It is calculated using distance classes, with the center of gravity of the sampling points in each class of the variogram cloud being determined. A variogram shows how the difference in dissimilarity between two sampling points, $Z\left(x\right)$ and $Z\left(x+h\right)$, varies depending on the distance (h) [72], as shown in Equation (1).

$$\gamma \left(h\right)={\displaystyle \frac{1}{2}}\mathrm{V}\mathrm{a}\mathrm{r}\left[Z\left(x+h\right)-Z\left(x\right)\right]$$

(1)

Variograms can be estimated along different directions using a complete dataset, resulting in directional experimental variograms, which are then fitted with a theoretical model.

$$\gamma \left(h\right)=\left\{\begin{array}{c}{c}_0+c\left({\displaystyle \frac{3}{2}}{\displaystyle \frac{‖h‖}{\mathrm{a}}}-{\displaystyle \frac{1}{2}}{\displaystyle \frac{{‖h‖}^3}{{\mathrm{a}}^3}}\right), \mathrm{if} 0\le ‖h‖\le a\\ c_0+c , \mathrm{if} ‖h‖>a\end{array}\right.$$

(2)

For all sample pairs N located at a vector distance h, denoted $N\left(h\right)$ ≡ {($xi$,$xj$): ($xi$ − $xj$) = $h$; $i$, $j$ = 1, 2..., n}, the variogram algebraic expression can be formulated as Equation (3), where |N(h)| represents the number of different pairs.

$$\gamma \left(h\right)\equiv {\displaystyle \frac{1}{2\left|N\left(h\right)\right|}}{\sum }_{N\left(h\right)}{\left[z\left(xi\right)-z\left(xj\right)\right]}^2$$

(3)

2.5.3. Kriging

Generally, resource estimation requires the use of various statistical methods, with kriging being used to perform spatial interpolation and block modeling. Furthermore, the most common type of kriging is ordinary kriging (OK), developed in [73,74]. By definition, ordinary kriging is a spatial interpolation estimator used to predict the value at a specific point in a region by leveraging survey data collected nearby. It is also employed to estimate block values and to determine the most accurate linear unbiased estimate of a second-order stationary random field with an unknown constant mean, as demonstrated by Equation (4) [71,74,75]. Equation (5) is used to calculate the error of the ordinary kriging method.

$$\hat{\mathrm{Z}}\left(x_0\right)={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\lambda }_{i}Z\left(x_i\right)$$

(4)

$\hat{\mathrm{Z}}\left(x_0\right)$: kriging estimates at location $x_0$; $Z\left(x_i\right)$: the sampled value at location $x_i$; ${\lambda }_i$: the weighting factor for $Z\left(x_i\right).$

$$R\left(x_0\right)=\hat{\mathrm{Z}}\left(x_0\right)-Z\left(x_0\right)={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\lambda }_{i}Z\left(x_i\right)-Z\left(x_0\right)$$

(5)

$R\left(x_0\right)$: the estimation errors; $Z\left(x_0\right)$: the unknown true value at $x_0$.

For an unbiased estimator, the mean of the estimation error should be zero, and the sum of the weighting factors ${\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\lambda }_i=1$ with $E\left\{R\left(x_0\right)\right\}$ = 0. To minimize the variance of the estimation error while maintaining fairness, a set of linear algebraic equations for the weighting factors (${\lambda }_i$) needs to be solved.

3. Results

3.1. Chemical, Physical, and Mineralogical Characterization

3.1.1. Chemical Analysis

The chemical properties of all samples were determined by ICP-AES. Due to the large amount of data, the results are presented as an average in Figure 4. The results revealed that the main chemical constituents are silica (Si), aluminum (Al), iron (Fe), calcium (Ca), magnesium (Mg), and potassium (K). The average contents of these elements are 19.89%, 4.72%, 1.835%, 8.19%, 5.27%, and 1%, respectively (

Table 3. Chemical and physical characteristics of the tailings composite.

Characterization	Parameter	Unit	Average
Chemical characteristics	Si	%	19.89
	Al		4.72
	Fe		1.35
	Ca		8.19
	Mg		5.27
	K		K 1
	C		3.52
	Cu		0.23
	S		0.14
Physical characteristics	D10	µm	4.57
	D30		11.83
	D50		27.12
	D80		92.87
	Specific surface area (SSA)	m2/g	0.481
	Density	g/cm3	1.3

). The results revealed that the copper content ranged between 0.15% and 0.53% (Figure 5). The copper concentration was the main input data used to estimate residual copper resources. The physical characterization of the BMT samples is shown in Table 3. The D80 of the sample was approximately 93 µm. This particle size is expected due to the grinding stage, which generates fine particles in the Bleïda flotation plant. The specific surface area and apparent density values are 0.48 m²/g and 1.3 g/cm³, respectively.

3.1.2. Bulk Mineralogy

Figure 6 illustrates the bulk mineralogical composition of the BMT analyzed by X-ray diffraction. The graph primarily highlights the gangue minerals, as no copper minerals were detected, indicating that their concentration was below the XRD detection limit. The results revealed that the Bleïda tailings are predominantly composed of quartz, with significant amounts of carbonates, specifically dolomite and calcite, and various silicates. The mineral percentages are as follows: quartz (46%), dolomite (31%), and calcite (5%). The identified silicates in the sample include clinochlore (10%), phlogopite (3%), albite (3%), and microcline (2%).

Automated quantitative mineralogy (AQM) and scanning electron microscope (SEM) analyses were carried out to identify both copper-bearing and gangue minerals. AQM revealed that the copper-bearing minerals are categorized into two types, copper sulfides and copper carbonates, with different contents, as shown in

Table 4. Modal mineralogical composition of the Bleïda tailings composite.

	Minerals	Chemical Formula	% Weight
Mineralogical composition of Bleïda tailings using AQM	Chalcopyrite	CuFeS2	0.1
	Bornite	Cu5FeS4	0.1
	Chalcocite	Cu2S	0.2
	Covellite	CuS	<0.1
	Calcite	CaCO3	2.4
	Dolomite	CaMg(CO3)2	27.4
	Malachite	Cu2CO3(OH)2	0.3
	Orthoclase	KAlSi3O8	3.6
	Kaolinite	Al2Si2O5(OH)4	2.1
	Hematite	Fe2O3	0.2
	Chlorite	(Mg,Fe)3(Si,Al)4O10(OH)2	12.4
	Titanite	CaTiSiO5	0.5
	Quartz	SiO2	28.4
	Biotite	K(Fe,Mg)3AlSi3O10(OH)2	9.5
	Muscovite	KAl2(AlSi3O10)(OH)2	11.5

. SEM analysis revealed a gangue mineral assemblage consisting of silicates such as quartz, orthoclase, biotite, chlorite, and muscovite, and calcium and magnesium carbonates, mainly represented by dolomite. Iron oxide grains are mixed with the silicates, while barium sulfates, in the form of barite, are also present. The identified copper carriers include primarily chalcocite, which is disseminated in iron-copper oxides and muscovite (Figure 7a). Bornite and covellite are less abundant, appearing as fine inclusions in carbonates and silicates (Figure 7b,c). Malachite is the main copper carbonate, found mixed with dolomites and silicates (Figure 7d).

3.2. Bleïda TSF 3D Modeling

The 3D model of the TSF was created by intersecting two different topographic surveys. The complete 3D model of the TSF is shown in Figure 8a. This study focuses on the dry western section of the TSF, with a maximum depth of 1.8 m, as depicted in Figure 8b. The perimeter, area, and volume of the entire TSF are 4314 m, 54.5 ha, and 3,727,700 m³, respectively. The geometric parameters of the sampled section are as follows: perimeter of 3078.06 m, area of 39.47 ha, and volume of 779,550 m³. Detailed geometric parameters are presented in

Table 5. Geometric parameters of the Bleïda TSF.

Geometric Parameters	TSF Total	Sampled Section	Emergent Area
Perimeter (m)	4313.66	3078.04	1235.62
Area (ha)	54.5	39.47	15.03
Volume (m3)	3,727,700	779,550	2,948,150

.

3.3. 3D Geostatistical Modeling

Data pre-processing is a crucial first step in geostatistical modeling, involving both data cleaning and transformation. Summary statistics were generated after analyzing the 73 composite samples, as illustrated in

Table 6. Summary statistics of composite values.

Mean (%)	Variance (%)	Minimum (%)	Maximum (%)	Variation Coefficient	Standard Deviation
0.274	0.004	0.15	0.53	0.230	0.063

.

3.4. Variography

After estimating the basic statistical parameters, the next step involved determining the variogram model. In this study, the variogram was calculated directly from the values attributed to each sample. The variogram for the Bleïda TSF was modeled as a spherical variogram, as shown in Figure 9, with an azimuth of 135°. The major axis variogram (Figure 9b) represents the variogram calculated along the direction of maximum variability or anisotropy in the dataset. Conversely, the minor axis variogram (Figure 9d) was computed along the direction of minimum variability or anisotropy. The semi-major axis variogram (Figure 9c) was calculated along an axis reflecting intermediate variability, at a lag distance of 60 m. Radial plots, automatically generated, illustrate the results of combining the semi-variograms along different axes, as shown in Figure 9a.

3.5. Quick Interpolation

An anisotropic interpolation was employed to determine the three-dimensional spatial distribution of copper.

Table 7. Anisotropic interpolation parameters.

	Variance	Ellipsoid Interpolant	Base Range	Direction			Ellipsoid Ratios
				Dip	Azimuth	Pitch	Max	Inter	Min
Anisotropic Variogram	0.0038	Spheroidal	60	0	135	90	10	5	1

shows the interpolation parameters used for this process. This technique creates a detailed 3D spatial distribution of copper and presents the results as iso-surfaces, with each surface representing a range of copper grades (Figure 10). Each iso-surface zone interval is represented by a volume displayed in cubic meters, as shown in

Table 8. The volumes represented by each isosurface using anisotropic interpolation.

	Cu < 2%	0.2 < Cu < 0.25%	0.25 < Cu < 0.3%	0.3 < Cu < 0.35%	0.35 < Cu < 0.4%	0.4 < Cu < 0.5%
Volume (m3)	25,456.4	182,173	483,074.72	96,542.84	34,490.64	6724.63

. The high-grade area, shown in light and dark red, is located to the east and has a grade range of 0.3% to 0.5%. Areas shown in green have grades ranging from 0.2% to 0.25%, while the majority of the area shown in yellow has copper grades ranging from 0.25% to 0.3%.

3.6. Block Model and Ressorces Estimation

A block model is a three-dimensional mathematical representation of a mining area or geological structure, segmented into small cubic or rectangular blocks of specified dimensions. The primary goal of creating a block model is to estimate the volumes and grades of ores or metals, such as copper, within each block, thereby facilitating the estimation of resources in the area.

In this study, ordinary kriging was used to calculate the copper resources based on the geostatistical parameters previously discussed. The resulting 3D block model, created after processing the input data, consists of 23,963 blocks, each with dimensions of 10 m × 10 m × 1.5 m. Figure 11 illustrates the 3D numerical block model generated. The residual resource tonnage in the Bleïda TSF can be estimated using this block model. Detailed spatial analysis, supported by sophisticated 3D modeling techniques, allowed for the examination of copper grade variability and tonnage. This detailed approach provides insights into the spatial variability of the tailings that are not commonly available, enhancing the understanding of the distribution and concentration of copper in the TSF.

The summary statistics of the results obtained from applying OK are presented in

Table 9. Summary statistics of the estimates obtained using ordinary kriging (OK).

Kriging	Mean	Variance	L. Quartile	U. Quartile	Median	Minimum	Maximum
OK	0.276	0.004	0.240	0.298	0.269	0.182	0.513

. To validate the efficiency of ordinary kriging (OK), the numerical model produced by simple kriging and ordinary kriging was evaluated. The results indicate that ordinary kriging provides the most accurate estimates (Figure 12).

Table 10. Report of the Cu residual resource estimation of the sampled section of the Bleïda TSF.

Volume (m3)	Density (g/cm3)	Tonnage (t)	Cu Grade %	Cu Content (t)
779,550	1.3	1,013,415	0.276	2760

summarizes the residual resource estimates derived from ordinary kriging, incorporating the various analyzed parameters. The total volume of residues stored in the sampled section of the Bleïda TSF was calculated to be 0.78 million m³. The tonnage of the tailings was determined using the average apparent density of approximately 1.3 g/cm³. The tonnage (T) of copper (Cu) was then calculated using the following Equation (6) [55]:

$$\mathrm{T}=\mathrm{V}\times \sum _{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{g}}_{{\mathrm{x}}_{\mathrm{i}}}\times {\rho }_i\right)$$

(6)

where V represents the volume of the tailings, (g_xi) is the concentration of the copper, and ${\rho }_i$ is the apparent density of the tailings. This means that there are around 1.014 Mts of material stored in the sampled section of the Bleïda TSF, with an average copper grade of 0.276%, representing a total of 2760 tonnes of copper metal. Future research could focus on conducting economic studies to evaluate the viability of reprocessing and recovering copper from the tailings.

3.7. State of Copper Minerals in Bleïda Mine Tailings

3.7.1. Copper Deportment

Copper deportment was determined by analyzing the bulk chemical and mineralogical composition of the composite samples, along with the elemental composition of the identified copper-bearing minerals. Copper in the composite sample was found to be present in chalcocite, malachite, chalcopyrite, tenorite, covellite, bornite, and cuprite. Figure 13 illustrates the copper deportment within the studied sample. The results indicate the following distribution of copper: 49% in chalcocite, 28% in malachite, 10.8% in chalcopyrite, 7.2% in tenorite, 1.8% in covellite, 1.7% in bornite, and 1.4% in cuprite.

3.7.2. Textural Analysis and Mineral Liberation Quantification

Figure 14 displays the liberation degrees of copper-bearing minerals, their mineralogical associations, and the relationships between these minerals and their surrounding matrix. In this study, a mineral is considered “free” when its liberation degree exceeds 80%, “totally locked” when its liberation is below 20%, and “mid-liberated” when its free surface falls between 20% and 80%. The liberation rate results indicate that 96% of the malachite has a free surface of less than 20% (locked), and 4% is in a mixed state. For chalcopyrite, 68% is mid-liberated and 22% is free. Covellite, bornite, and chalcocite are totally locked, with most chalcocite grains having less than 20% free surface area. The majority of malachite grains have less than 10% free surface area. These findings will guide the selection of appropriate methods for recovering and enriching residual copper-bearing minerals.

3.7.3. Distribution and Accumulation of Copper Minerals by Particle Size

The distribution of copper-bearing minerals across different particle-size fractions was analyzed using AQM, with the results illustrated in Figure 15. This analysis elucidates how mineral concentrations vary with particle size. The findings indicate that 90% of chalcocite is concentrated in particles larger than 120 µm, while chalcopyrite predominantly resides in the 40 to 80 µm fraction, with smaller amounts in particles below 40 µm. Covellite and bornite are entirely concentrated in particles smaller than 20 µm. These insights are crucial for optimizing reprocessing methods. By pinpointing the particle-size fractions with the highest concentrations of copper-bearing minerals, we can focus on these fractions for more efficient recovery and enrichment. This targeted strategy enables a more effective and cost-efficient reprocessing approach, maximizing copper extraction from the most valuable mineral concentrates.

4. Discussion and Future Work

This study has developed a framework for estimating residual resources in tailings storage facilities, offering a practical approach that can be applied to other similar sites. This framework enhances the management of residual resource and improves the accuracy of resource estimation using advanced 3D geostatistical modeling. Detailed mineralogical analysis is pivotal for evaluating the potential for reprocessing the copper tailings from the Bleïda TSF. By applying 3D geostatistical modeling and incorporating copper content data from various samples, we estimated the tailings volume in the TSF to be approximately 4.85 Mt. The 3D block model, developed using ordinary kriging, shows that the sampled area of the Bleïda TSF has a volume of 1.014 Mt and contains around 2.8 kt of copper metal with an average grade of 0.27%. This significant residual copper content, primarily in fine particles, suggests the potential for economically feasible reprocessing by reducing the need for expensive mechanical treatments such as crushing.

The results reveal that copper is predominantly found in particles smaller than 100 µm and is present in minerals such as covellite, malachite, chalcopyrite, bornite, and chalcocite. However, the relatively low degree of mineral liberation poses a metallurgical challenge, potentially necessitating additional regrinding to improve liberation and increase the specific surface area of the copper for more efficient processing.

From a metallurgical perspective, re-mining the TSF offers several advantages. The fine-grained nature of these tailings makes them more accessible and less energy-intensive to process compared to primary ores, eliminating the need for blasting and crushing. This reduction in operational costs enhances the economic feasibility of re-mining, even at lower grades and tonnages compared to primary ore deposits. Moreover, multiple tailings and waste deposits in historical mining areas can be collectively re-mined, increasing the overall tonnage and further improving economic viability.

In terms of reprocessing, our findings align with previous studies [76] that highlight various methods for reprocessing mine wastes and the distinct challenges associated with recovering critical minerals. Froth flotation is a common technique for tailings reprocessing and has been used to recover copper and other metals [77,78]. However, our results indicate that the flotation process may face limitations due to the fine particle size and poor liberation of the copper-bearing minerals, as well as issues such as weathering and surface coatings. This necessitates additional processing steps to create fresh surfaces before flotation [79].

Given these challenges, alternative methods such as leaching or bioleaching may offer more effective solutions for extracting copper from the Bleïda tailings. Bioleaching, in particular, is a promising technique for low-grade and fine particles like those in the Bleïda tailings. It avoids the need for regrinding to expose fresh surfaces, operates at a lower cost, and enhances the economic viability of metal recovery from tailings [80,81]. For the finer copper-bearing minerals, particularly those smaller than 20 µm, employing separation techniques that use centrifugal gravity forces may improve recovery efficiency by separating them from gangue minerals [13,22]. Combining these methods with leaching or bioleaching could significantly enhance the overall recovery of copper from fine particles.

5. Conclusions

This study underscores the critical importance of 3D geostatistical modeling and geometallurgical investigation to evaluate the resource potential of copper (Cu) in tailings deposits. Through an extensive analysis of the Bleïda tailings, which included physical, chemical, and mineralogical characterizations, we identified the presence of copper-bearing minerals and assessed the opportunities and challenges associated with their recovery. The 3D geostatistical modeling revealed significant potential for copper recovery, with the estimation indicating approximately 1.014 million tonnes of material containing 2.8 kt of Cu metal and an average copper grade of 0.27%. Ordinary kriging was employed for this estimation and proved effective in accurately estimating residual resources in the TSF. The modeling showed that the copper was primarily located in minerals such as chalcocite, malachite, chalcopyrite, covellite, and bornite.

Characterization of the tailings revealed key components, including Si, Al, Fe, Ca, Mg, and K. The dominant minerals were quartz, dolomite, and calcite. The liberation rates indicated that while chalcopyrite was relatively free, most other copper-bearing minerals were locked, presenting challenges for extraction. The particle size distribution analysis showed that copper minerals were concentrated in finer particles, suggesting the need for targeted metallurgical approaches.

The study highlights that future metallurgical testing is essential to optimize copper-mineral concentration and extraction. The detailed chemical, physical, and mineralogical data provided will support the development of efficient recovery methods. The findings of this study will guide future efforts in valorizing mine tailings, contributing to more sustainable mining practices by transforming waste into valuable resources. In summary, this study advances the scientific understanding of copper distribution in tailings and offers practical methodologies for resource estimation and management. The novel application of advanced techniques and the development of a practical framework for residual reserve estimation provide significant contributions to the field.