Utilization and Valorization of Mining Tailings Through a Comprehensive Approach in Central Peru

Abstract

To determine the reprocessing potential of mining liabilities, a comprehensive evaluation of the tailings deposit at the former Halcón mining unit in central Peru was conducted. Direct and indirect methods were employed, including geophysical surveys, sampling through six drill holes reaching depths of up to 9.62 m, textural analysis, mineralogical characterization using optical microscopy, SEM, XRD, and reflectance spectroscopy, and ICP-MS chemical analysis, complemented by geostatistical modeling for resource estimation. Geochemical and mineralogical results reveal highly oxidizing and acidic conditions (pH 1.83–4.91) along with significant concentrations of Zn (5.46%), Pb (1.58%), Cu (1.29%), Ag (244 ppm), and Au (3.6 ppm). These economic minerals predominate in fine-grained fractions, which exhibit high degrees of liberation and therefore favor metallurgical recovery. Geostatistical modeling estimated a total tailings tonnage of 87,642 tons, highlighting zones with high localized economic potential, particularly for Zn and Ag. Considering specific recovery scenarios, penalties, operational costs, and metal prices, a preliminary economic benefit of approximately USD 11.36 million was estimated. This pioneering study demonstrates the feasibility of converting environmental liabilities into economic assets through strategic reprocessing initiatives, providing a robust model for the valorization of mine tailings within the framework of the South American circular economy.

1. Introduction

Peru, with its long and prominent mining history, currently faces the critical challenge of managing a substantial legacy of mining environmental liabilities (PAMs, by its acronym in Spanish) [1]. While these tailings deposits pose significant environmental risks from acid rock drainage and heavy metal leaching, they also represent a largely untapped secondary source of valuable commodities [2]. According to the official regulatory inventory updated by the Peruvian Ministry of Energy and Mines (MINEM), there are over 6026 registered PAMs nationwide, of which 295 are mining tailings subtypes, concentrated predominantly in historical mining regions such as Cajamarca, Pasco, Lima, Puno, and Ancash [1]. The former Halcón mining unit (EUM), located in the Cashapampa district of the Ancash region in central Peru, typifies this scenario. Its legacy tailings deposit, accumulated over past decades of intensive polymetallic operation (Figure 1), represents an ideal case study for a multidisciplinary assessment aimed at transforming an environmental liability into a quantified economic asset.

Figure 1. (a) Schematic map of the location of the Halcón EUM tailings. (b) Photograph of the tailings. (c) Layers of tailings material and (d) drill holes.

In the global scientific literature, researchers have increasingly turned to circular economy models to balance industrial waste exposure with environmental, social, and economic sustainability [3]. Recent advances explore approaches such as waste reuse, stabilization, and direct chemical reprocessing [3,4]. However, hypotheses diverge on the economic viability of old tailings. Traditional linear mining approaches often assume that legacy waste lacks sufficient grade or mineral liberation to justify the high operational costs of secondary metallurgical extraction [5]. Conversely, modern circular frameworks hypothesize that advanced fine-grained characterization coupled with precise spatial geostatistical modeling can isolate high-grade sweet spots, making selective reprocessing financially profitable while simultaneously reducing remediation costs [4,6]. This technological shift is particularly crucial today, as legacy polymetallic deposits frequently contain critical raw materials, base metals, and rare earth elements required for emerging green technologies. Growing demand and increasing economic value of these metals [7] further enhance the potential attractiveness of recovering them from secondary resources such as mine tailings.

Previous studies on mine tailings have demonstrated the value of integrating systematic sampling, mineralogical characterization, geochemical analysis, and geostatistical methods to evaluate the spatial distribution and recoverability of residual metals [2,3,4,5]. These investigations have applied drilling and/or systematic sampling to characterize tailings deposits, complemented by mineralogical techniques to identify the mineral phases hosting valuable elements and geostatistical approaches to estimate their spatial continuity and tonnage.

To address this gap, this research provides a comprehensive geochemical, mineralogical, and geostatistical characterization of the legacy tailings at the former Halcón mining unit. The main aim of this work is to establish a robust resource estimation framework to delineate the spatial distribution of valuable metals and estimate their net financial recovery potential using direct drillhole data and predictive geostatistical tools. Ultimately, this study demonstrates that the Halcón tailings deposit presents significant potential for metal recovery, with a preliminary estimated economic benefit of approximately USD 11.36 million. These findings provide a novel framework for converting environmental liabilities into viable business opportunities, promoting a sustainable secondary mining model within the South American circular economy framework.

2. Site Location and Environmental Setting

The tailings from the former Halcón mining unit (EUM) are located on the left bank of the Pasacancha stream (Figure 1a), in the district of Cashapampa, province of Sihuas, Ancash region, central Peru, 635 km north of Lima. The deposit is located at an altitude of 4355 m above sea level. It can be accessed by road via Huaraz or Chimbote, followed by a crossing of the Huarochiri bridge and travel through the towns of Yanac, Tarica, and finally Pasacancha.

The climate in the study area is semi-arid and temperate with marked seasonal rainfall, concentrated between October and April, with an average annual precipitation of 700 mm and an average temperature of 12 °C. The deposit is located in the Pasacancha River micro-basin, within the Alto Marañón V Interbasin, on the western flank of the Western Andes. The predominant landscape includes structural and erosional landforms, with mountains composed of sedimentary rocks and slopes ranging from 25° to 45°.

3. Regional and Local Geological Setting

The legacy tailings deposit is directly underlain by Quaternary alluvial and colluvial deposits. These surface deposits consist of poorly sorted, eroded, matrix-supported materials derived from the weathering of shales and sandstones belonging to the Tinajones Formation of the Upper Jurassic Chicama Group. In the immediate vicinity of the deposit, particularly around the localities of Pasacancha, Escalón, and El Águila, multiple stocks of monzonite and diorite are exposed. These calc-alkaline igneous bodies intruded the Mesozoic sedimentary sequence, with emplacement structurally controlled by the regional NW–SE trending fault systems that characterize this Andean sector.

3.1. Stratigraphic and Lithological Framework

The Tinajones Formation outcrops extensively along the flanks of the Pasacancha River valley, displaying a dominant NW–SE structural trend. Lithologically, the unit is characterized by a rhythmic alternation of dark gray shales and carbonaceous black mudstones, interbedded with fine-grained, well-sorted quartz sandstone strata. The sedimentary layers exhibit steep dips ranging from 20° to 56°, tilting towards both the northeast and southwest, which define tight, map-scale folds, such as anticlines and synclines, oriented parallel to the regional tectonic trend.

In areas near the environmental liabilities of the former Halcón mining unit, the shales and fine-grained sandstones of the Tinajones Formation exhibit pervasive signs of hydrothermal alteration. This alteration is macroscopically manifested by disseminated iron oxides and primary sulfides. Genetically, this hydrothermal overprint is linked to shallow magmatic activity associated with the stocks exposed approximately 500 m away from the tailings deposit. These stocks are composed of phaneritic, medium-grained monzonitic rocks that are highly resistant to mechanical erosion. Locally, these intrusive centers are genetically linked to major polymetallic mineralization systems in the region, such as the El Águila and Magistral projects.

3.2. Structural Framework

The local geological structure is dominated by a complex system of low-angle reverse faults and tightly compressed, recumbent folds, both sharing a preferential NW–SE orientation. The regional Chonta fault system acts as the primary first-order structural control in the area. This regional architecture has generated tight, compressional folds (anticlines and synclines) with their axial planes oriented toward the northeast (Figure 1a). Additionally, this major fault system played a fundamental role in generating dilation zones that facilitated the structural emplacement of the Miocene stocks.

3.3. Economic Geology

The study area is located within the prominent Miocene Metallogenic Belt of central Peru, a domain highly prospective for Cu–Mo–Au porphyry systems, Pb–Zn–Cu–Ag skarns, and polymetallic vein deposits genetically linked to intermediate intrusive bodies. In the Cashapampa district, the El Águila and Pasacancha mining projects represent the main metallogenic landmarks. El Águila is classified as a Cu–Mo porphyry deposit hosted in quartz sandstones of the Lower Cretaceous Chimú Formation. The intrusion of porphyritic dioritic and monzonitic bodies generated localized secondary biotite (potassic) alteration and sulfide mineralization that overprinted the sedimentary host rocks.

Conversely, in the Pasacancha sector, the mineralization corresponds to classic Ag–Pb–Zn polymetallic hydrothermal vein systems, which have been exploited on a small scale since the 1960s (Figure 1a). These vein systems are hosted within the shales and fine sandstones of the Tinajones Formation and are genetically related to the cooling of the monzonitic and dioritic stocks [8]. It is interpreted that these high-grade polymetallic veins mined in Pasacancha were the primary ore source for the former Halcón mining unit. The extracted ore was reportedly subjected to manual sorting prior to sale, and the resulting mineral processing and handling generated waste that ultimately contributed to the legacy tailings deposit investigated in this study. This historical mining activity may explain the high residual concentrations of base and precious metals observed in the tailings.

4. Tailings Deposit Morphology and Textural Classification

The tailings deposit covers a surface area of approximately 8342 m², displaying an elongated morphology along the valley floor (Figure 1b) and reaching a maximum vertical embankment height of 10 m. To perform a systematic textural characterization of the stratified tailings material, the numerical coding system described by De La Cruz et al. [9] was implemented in the geochemical characterization study of soils in the Junín and Huancavelica regions. This methodology assigns a three-digit code where each digit sequentially represents the relative volumetric proportion of sand, silt, and clay. The scale ranges from 0 (absent), 1 (low: 0%–25%), 2 (intermediate: 25%–75%), to 3 (dominant: >75%). For instance, a numerical textural class defined as 211 corresponds to a matrix dominated by intermediate sand content alongside an intermediate proportion of silt, and a low clay content.

Applying these criteria, a strong predominance of sandy loam textures (coded as 310) was identified, accounting for 41% of the deposit, followed by sandy clay loam textures (coded as 211), which represent 28% of the total volume (Figure 1c and Figure 2). Minor facies consist of loam (121) and clay loam (112) textures, which together comprise 15% of the material. Spatially, the 310 textural facies are widely distributed across all vertical levels (surface, intermediate, and deep horizons) throughout the majority of the exploratory drill holes (Figure 1d). Conversely, the 121 and 112 textures are restricted to the upper surficial layers of drill holes S03 and S04, while the 211 class is highly representative of the deeper, unoxidized core levels of the deposit (Figure 2).

Figure 2. Schematic profile of the drillholes. Colors represent the variations observed during geological logging and were determined using the Munsell Soil Color Chart [10].

From a chromatic perspective, the core profiles reveal a well-defined vertical zoning (Figure 1c). The shallow surficial horizons exhibit lighter hues, including distinct yellows, dark browns, and light beige tones, which are directly associated with active supergene oxidation and environmental weathering. In contrast, the deeper, water-saturated strata display significantly darker colors, ranging from dark grays to greenish-brown tones, which are genetically related to pristine tailings higher proportions of primary sulfides (Figure 1d). Granulometric and sieve analyses classify the bulk of the processed material as fine sand, a physical characteristic that has critical implications for the internal shear strength, liquefaction potential, and overall physical stability of the tailings deposit.

5. Materials and Methods

5.1. Sample Collection

The characterization of the Halcón EUM tailings was based on a field campaign that included drilling, geological logging, and sampling. Six vertical drillholes were drilled using a WACKER BH 55 gasoline-powered percussion hammer and an LMSR-H Modular dynamic sampling rig (Nordmeyer GEOTOOL GmbH, Werneuchen, Germany), depending on the required drilling depth (Figure 2).

Geological logging allowed for the recording of color, texture, mineralogy, and evidence of alteration, as well as the presence of carbonates through reaction to hydrochloric acid (HCl). Horizons were defined based on changes in hue, mineralogy, alteration, and texture. Colors were determined using the Munsell Soil Color Chart [10].

For geochemical analysis, 37 representative samples were collected from the drilled drillholes at regular intervals of 1 m or according to individual horizons, depending on the observed homogeneity. For quality assurance and control, 10 control samples were included: 4 blanks, 3 medium grade standards, and 3 field duplicates. The samples were coded, packaged, and sent to the laboratory for analysis. In addition, samples were taken for mineralogical characterization. This methodology allowed for a detailed characterization of the deposit, both in terms of its physical and chemical properties and its vertical distribution, providing key information for its environmental assessment and potential recovery.

5.2. Methodology

The study was developed using a comprehensive approach that combined direct and indirect methods, including surface sampling and drilling. This approach addressed four key aspects of the tailings deposit: geophysical, physical (textural-granulometric), mineralogical, and geochemical characteristics. All of this information was subsequently integrated to construct the geological model, estimate resources, and evaluate their potential for recovery. The general workflow used in this research is summarized in Figure 3.

Figure 3. Workflow applied in the study of the Halcón EUM tailings.

5.2.1. Chemical Analysis

The tailings samples were sent to the accredited SGS laboratory (Lima, Peru), in accordance with the INACAL standard NTP-ISO/IEC 17025:2017 [11]. Multi-element quantification for 50 elements was conducted via multi-acid digestion followed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Gold concentrations were determined by fire assay followed by Atomic Absorption Spectroscopy (AAS). Rare earth elements (REEs) were solubilized using lithium metaborate fusion prior to ICP-MS analysis, whereas mercury (Hg) content was quantified via cold vapor atomic absorption.

5.2.2. Reflectance Spectrometry

For surface mineralogical identification, reflectance spectrometry was applied, a non-destructive technique that does not require prior sample preparation. Spectral measurements were performed on 16 samples using a TerraSpec 4 Hi-Res spectrometer (ASD Inc., Boulder, CO, USA), and the resulting data were processed with specialized TSG 8 software.

5.2.3. X-Ray Diffraction

Complementary mineralogical characterization was performed using X-ray diffraction (XRD) in the petromineralogy laboratory of the Geological, Mining and Metallurgical Institute (INGEMMET). This technique allowed the identification and quantification of crystalline phases present in the tailings samples obtained from the drill holes, although it does not detect amorphous components. The analysis was performed using an SHIMADZU XRD-7000 diffractometer (Shimadzu Corporation, Kyoto, Japan) with the powder method. Seven representative samples were selected for this study, considering their textural, physicochemical, and compositional variability, as previously evaluated by X-ray fluorescence.

5.2.4. Scanning Electron Microscopy

Additionally, mineralogical studies and scanning electron microscopy (SEM) analyses were performed on four samples using a ZEISS EVO MA15 scanning electron microscope (Carl Zeiss Microscopy GmbH, Jena, Germany). Mineralogical characterization included macroscopic observations (color, shape, texture, grain size, magnetism, and reactivity with hydrochloric acid), as well as microscopic observations using reflected-light polarization microscopy attachment coupled to the ZEISS EVO MA15. The latter allowed the identification of metallic minerals, their modes of occurrence, and particle sizes. The degree of liberation was based on the counting of individual grains, differentiating between free and mixed particles, which were subsequently used to calculate the actual percentages, weight-by-volume proportions, and degree of liberation of each identified mineral.

5.2.5. X-Ray Energy Dispersive Spectroscopy

For scanning electron microscopy (SEM) analysis, the same ZEISS EVO MA15 described above was operated in high vacuum mode was used on conductive samples, employing secondary electron (SE) and backscattered electron (BSE) detectors to obtain high-resolution images and compositional contrast. Qualitative and semi-quantitative chemical characterization was performed using energy dispersive X-ray spectroscopy (EDS) with an EDS system coupled to the ZEISS EVO MA15, both at specific points and through compositional mapping. This information complemented the mineralogical identification and provided further insight into the mineral associations present.

5.2.6. Geophysical Surveys

Electrical resistivity tomography (ERT) surveys were conducted to characterize subsurface conditions and support the integrated assessment of the study area. A total of six survey lines, covering 795 m, were acquired using a pole–dipole array, selected for its high sensitivity to lateral resistivity variations and its suitability for detecting elongated subsurface features. Field measurements included apparent resistivity, current intensity, electrode geometry, and voltage, which were stored in the instrument’s internal memory. Data quality control was performed in Oasis Montaj 2026.1 (20260610.24) (Geosoft) to verify electrode positions and remove noisy measurements. The filtered datasets were subsequently inverted in Res2DInv 2026.2.1.1655 (Geotomo) using a smoothness-constrained least-squares inversion algorithm, generating two-dimensional true resistivity models for subsurface characterization.

5.2.7. Geostatistical Modeling

The evaluation of the tailings from the former Halcón mining unit is a fundamental stage in this study, as it allows for the estimation of the economic potential associated with the metals contained in this mining environmental liability. In contexts where metal prices are favorable and viable reprocessing technologies exist, these deposits can become exploitable resources.

The resource estimate began with the compositing of geochemical data, which was performed in Leapfrog Geo 5.0. The samples were composited to a fixed length of 1 m using a regular-length compositing approach, in which the original assay intervals were combined to generate standardized 1 m composites. Residual intervals shorter than 1 m at the end of each composite sequence were incorporated into the preceding composite interval. This composite length was selected because it corresponded to the mode of the sampling intervals, thereby ensuring adequate statistical representativeness and improving the consistency of the subsequent geostatistical analysis [12].

Using topographic data from photogrammetry, interpretations of geophysical data, and survey results, a 3D geological model was created to define the deposit’s geometry, including its lateral extent and depth.

Subsequently, those elements whose average concentrations exceeded the cut-off grades reported in previous studies on mine tailings [3,4,5,6,13,14] were considered for resource estimation. These cut-off grades were adopted based on the economic and metallurgical assumptions reported in the respective studies, including metal prices, processing costs, and recovery efficiencies. In addition, the selected cut-off grades were considered representative reference values for processing plants that commonly treat mineralized material from artisanal and small-scale mining operations, under the assumption that the tailings evaluated in this study could potentially be directed to such facilities for secondary processing. Therefore, no independent cut-off grade calculation was performed in the present study.

For tonnage estimation, an average bulk density of 1.6 g/cm³ was adopted based on density values reported in national and international studies of mine tailings with comparable characteristics [3,13,14,15,16]. This value was not directly measured in the present study but was selected as a representative density for the tailings material. The adopted density was then used to convert the estimated volume into tonnage and subsequently to evaluate the preliminary economic potential of the deposit.

Geostatistical modeling was based on the development of variograms for the selected elements, identifying anisotropies with preferential directions. Spherical models were used to capture variability at small scales. Based on this information, a three-dimensional block model was constructed with dimensions of 10 × 10 × 1 m, subdivided into sub-blocks of 2.5 × 2.5 × 0.25 m, which allowed for greater spatial resolution.

The interpolation of grades was performed using the Inverse Distance Weighting (IDW) method, with a power parameter of 2. IDW was considered appropriate given the distribution characteristics and the size of the database (n = 37) [17]. This technique adequately reflected the spatial variability revealed by the variograms, especially in the case of elements such as silver (Ag), zinc (Zn), and gold (Au), which had relatively high nugget values. The model was validated both visually and using swath plots generated in Leapfrog Geo 5.0, which allowed the consistency of the interpolation to be verified.

Finally, the cut-off grades for the metallic elements of interest (Au, Ag, Zn, Cu, and Pb) were established considering their economic viability for recovery under current market conditions, as well as references to cut-off grades used in previous studies on tailings reprocessing [4,5,18]. The economic assessment was performed using a selective extraction approach based on metal-specific economic cut-off grades. Each metal (Au, Ag, Zn, Cu, and Pb) was evaluated independently, and only the portion of the tailings exceeding the corresponding cut-off grade was considered in the economic calculation. Operating costs and applicable penalties were calculated based on the tonnage associated with each metal-specific cut-off scenario rather than on the total tailings inventory. Therefore, the reported tonnages represent metal-specific economic scenarios and are not additive, as the corresponding volumes may spatially overlap. The economic evaluation of potential tailings reprocessing was performed using the estimated tonnages and grades, together with metal market prices recorded in 2026. Figure 4 schematically presents the methodological process applied for the economic evaluation of the tailings.

Figure 4. Workflow for tailings valuation.

6. Results

6.1. Geochemistry

Geochemical characterization was performed using 37 samples collected from six drilling stations at depths ranging from 0 to 9.62 m. Twelve physicochemical parameters of the tailings in situ were recorded using the HANNA HI98194 portable multiparameter device. The measurements taken show a range of hydrogen potential (pH) values from 1.83 to 4.91, which classifies the nature of the tailings as acidic. The oxidation–reduction potential (ORP) of the samples ranged from 169.2 to 508 mV; indicating that the tailings were potentially oxidizing.

The geochemical distributions of 16 elements were evaluated, in addition to rare earth oxides. The selection of these elements was based primarily on the main minerals previously mined at the site, due to their potential economic interest and environmental relevance. They were classified in descending order of average concentration: Zn > Pb > As > Cu > Sb > Cr > Mn > Sr > W > Ag > V > Sn > Co > Mo > Hg > Au and rare earth oxides.

The samples were chemically analyzed using the ICP-MS method at the SGS (Société Générale de Surveillance SA) laboratory, which reported Zn, Pb, As, and Ag concentrations exceeding the upper detection limits of the analytes. These samples were reanalyzed using four-acid digestion and atomic absorption spectrometry (AAS) analysis. The results obtained showed concentrations of 5.46% zinc, 1.58% lead, 1.29% copper, 244 ppm silver, 3.6 ppm gold, and 1.8% arsenic. The concentrations of the other elements is summarized in Table 1.

Table 1. Statistical summary of the main elements studied in the tailings of the Halcón EUM.

Element	Unit	Average	SD	Min	P25	P50	P75	P95	Max
Zn	ppm	19,721.0	16,755.0	108.0	1624.0	17,100.0	33,300.0	50,520.0	54,600.0
Pb	ppm	6530.20	3315.30	465.7	4663.00	5823.00	6836.70	14,060.00	15,800.00
As	ppm	4846.80	4028.50	532	2775.00	3588.00	4558.00	13,600.00	18,000.00
Cu	ppm	2460.20	3014.20	81.1	367.6	818.9	4347.80	7764.00	12,940.00
Sb	ppm	615.1	822.3	90.8	201.6	263.1	480.1	2517.60	3100.00
Cr	ppm	311.3	95.4	51	252	294	391	450.4	567
Mn	ppm	129.4	66.5	16	88	124	165	248.6	292
Sr	ppm	41.7	34.9	7.1	22.6	29.4	52	115.6	167.6
W	ppm	97.3	159.1	11.7	24.6	34	66	414.6	814.4
Sn	ppm	45.2	24	11	27.3	43.4	61	91.6	112
Ag	ppm	84.3	58.4	11	57	70	88	223.4	244
Co	ppm	25.3	18.8	1.3	8.9	21	36.7	58.8	69.5
Mo	ppm	17.2	6.6	9	12.6	16.9	19	27.4	42.2
V	ppm	46.9	19.2	6	41	48	59	73.6	78
Hg	ppm	2.5	0.8	1.3	2.2	2.5	2.5	4.2	5.1
Au	ppm	0.8	1	0.1	0.2	0.3	0.7	3.4	3.6
TREO	ppm	42.5	27.1	9.2	24.4	31.1	58.5	71	148.9

Note: TSF Area: 8342 m²; Depth profile evaluated: 0–9.62 m; Total exploratory drill holes: 6 surveys. SD: Standard Deviation; P: Percentile; TREO: Total Rare Earth Oxides.

Based on descriptive statistics, thirteen elements were identified as having a positively skewed distribution, while Zn, Mo, and Ag had a negatively skewed distribution. Zinc concentrations were highest in samples obtained from drillholes S03 (5.46%) and S06 (5.14%), at depths between 2 and 5 m, in a highly acidic pH environment associated with a sandy texture, a minor proportion of silt and clay, and the presence of sphalerite. Meanwhile, at depths of less than one meter, its concentration decreases sharply due to strong oxidation, as shown in Figure 5.

Figure 5. Zinc distribution in drill hole S03 and details of sample HAL8381-RE24-S03-006. Colors represent the variations observed during geological logging and were determined using the Munsell Soil Color Chart [10].

The highest concentrations of lead (Pb) are associated with samples with a sandy-silty-clay texture, located at depths starting from the first meter in drillhole S01, under ultra- to extremely acidic pH conditions. As for copper (Cu), significantly high concentrations were observed between 1 and 3 m in deep, particularly in drillholes S02 and S04, with maximum values of 0.79% and 1.29%, respectively. These concentrations occurred in sandy-silty-clayey (S02) and sandy (S04) textured materials.

Gold (Au) and silver (Ag) showed a clear enrichment trend in the surface horizons, specifically between 0 and 1 m in depth. The presence of high Ag concentrations in drillholes S01, S02, S03, S04, and S05 is noteworthy. Likewise, the highest Au concentrations were recorded in drillhole S05, characterized by a silty sandy texture and an ultra-acidic pH. This distribution suggests a vertical and lateral differentiation of the elements according to the physicochemical conditions of the environment and the texture of the material, aspects that are decisive in the mobility and retention of metals in residual matrices [19,20].

Figure 6 shows a positive correlation between the concentrations of gold (Au) and silver (Ag) in the samples analyzed. This association may be related to the mineral paragenesis characteristic of the deposit, in which both precious metals coexist in the same mineral phases, such as electrum, base metal sulfides, or silver minerals [21]. Furthermore, this correlation could reflect similar geochemical conditions during the evolution of the hydrothermal system, such as temperature, pH, and mineralizing fluid composition, which favor the co-precipitation of Au and Ag [22]. On the other hand, the joint presence of these elements in residual fractions of tailings could indicate limitations in metallurgical recovery processes, especially in contexts where both metals are associated with refractory matrices or finely disseminated in incompletely oxidized sulfides [23,24].

Figure 6. Distribution of Ag and Au in relation to pH in drill hole S02. The gray, red, and blue lines represent Ag, Au, and pH, respectively. Colors in the drill hole profile represent the variations observed during geological logging and were determined using the Munsell Soil Color Chart [10].

Regarding the geochemical distribution of rare earth elements, these elements were grouped and expressed as total rare earth oxides (TREO), which corresponds to the sum of the oxides of heavy rare earth elements and light rare earth elements. TREO concentrations ranged from 9.2 ppm to 148.9 ppm, decreasing irregularly with depth. However, based on analogous frameworks, where the cut-off grade for rare earth oxides in tailings in northern Chile is 200 ppm, the rare earth oxides present in the tailings from the Halcón EUM are considered of limited relevance [4].

Bivariate analysis using Pearson’s coefficient identified a linear correlation between the elements under study, including tellurium (Te), given its relationship with precious elements such as Au, Ag, Cu, and Pb. Table 2 shows the most notable correlations. Zn exhibits a correlation with siderophile and chalcophile elements, with a particularly strong correlation with Cd and Co, and a moderate correlation with Ni. Silver shows a predominant association with chalcophile elements, with a very strong correlation with Pb and Sb, a strong correlation with gold, and a weak correlation with mercury and sulfur. On the other hand, Cu shows a weak correlation with Co and Te. Au correlates with Ag, Sb, Pb, and Hg.

Table 2. Main Pearson correlations of elements in the tailings of the Halcón EUM.

Element	Very Strong	Strong	Moderate	Weak
Zn	-	Cd, Co	Ni	S
Ag	Pb, Sb	Au	-	Hg, S
Cu	-	-	-	Co, Te
Au	-	Ag, Sb	Pb	Hg
Pb	Sb	-	Au	Hg
Mo	-	W	Co	Cr

Note: Very strong: 0.85–1.00; Strong: 0.73–0.84; Moderate: 0.58–0.72; Weak: 0.41–0.57.

The mineralogical relationships observed indicate a common genetic origin for the mineral associations, where chalcopyrite (CuFeS₂) is the main copper-bearing mineral. Sphalerite (ZnS), on the other hand, acts as a host phase for valuable trace elements, particularly silver (Ag), which is often associated with other minor elements such as cadmium (Cd) and cobalt (Co) through substitution in crystal structures or as submicroscopic inclusions [25,26]. These mineralogical associations are characteristic of hydrothermal environments, in which the simultaneous precipitation of sulfides allows the incorporation of minor metals into the crystal lattice of the main minerals [27].

Principal component analysis (PCA) is a statistical technique widely used in geochemistry, as it reduces the complexity of large datasets while retaining essential information. This methodology facilitates the identification of underlying patterns and relationships between geochemical variables by converting a set of correlated variables into a small number of independent components, highlighting the main sources of variation present in the data [28].

In the present study, the application of PCA was considered appropriate, as the variables showed a sufficient level of correlation to meet the criteria established by the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. The first two principal components were selected, which together explained 95% of the cumulative variance, allowing for an effective representation of the multivariate structure of the dataset.

The first elemental association, composed of zinc, cadmium, cobalt, nickel, and sulfur, was predominantly observed in samples collected from drillholes S01, S02, and S03, specifically at depths ranging from 1 to 4 m (Figure 7). This grouping suggests the presence of a characteristic geochemical ensemble that may be related to specific mineralogical processes at these levels [28,29].

Figure 7. Graph of the two main components. The blue, green, and red ellipses represent the As–Ag–Pb–Sn–Hg–Au, Bi–Te–Mo–W–Cr–Cu, and Cd–Ni–Co–Zn geochemical groups, respectively.

In contrast, the second association, consisting of arsenic, silver, lead, tin, mercury, and gold, is mainly found in drillholes S04, S01, and S03, concentrated in the first 3 m of depth. This configuration could be linked to low-temperature hydrothermal processes or epithermal mineralization, in which these elements often coexist and are indicative of areas of significant mineral alteration [30,31].

A sub-fraction chemical analysis was carried out on a representative sample of tailings to identify the granulometric fraction in which the chemical elements present were preferentially concentrated. To this end, the fractions corresponding to the following mesh sizes were considered: +80, +140, +200, +400, and −400.

Previous studies have shown that the distribution of elements in tailings can vary significantly depending on the grain size fraction. For example, research on tailings deposits has shown that elements such as lead and zinc are predominantly found in finer fractions, suggesting greater mobility and contamination potential in these fractions [18,19]. In addition, the granulometric characterization of the La Ciénaga tailings in La Libertad revealed that fractions smaller than 10 μm (−400 mesh) comprise between 37% and 67% of the material, with gold concentrations approximately 1.5 times higher than those in the coarsest fraction (140–400 mesh) [32].

The highest concentrations of gold, silver, cobalt, copper, nickel, lead, and zinc were recorded in the finest subfraction of the analyzed sample, corresponding to the −400 mesh (Table 3, Figure 8). In contrast, chromium and vanadium showed their highest concentrations in the coarsest fraction, corresponding to the +80 mesh (Table 3). Among the elements analyzed, lead and zinc stood out for having the highest concentrations.

Table 3. Economic metal contents in five sub-fractions (Mesh) for the representative composite tailings sample HAL-RE24-S03-006_009.

Element	Unit	+80 Mesh	+140 Mesh	+200 Mesh	+400 Mesh	−400 Mesh
Au	ppm	0.261	0.141	0.243	0.384	0.49
Ag	ppm	78	47.7	53	67	94
Co	ppm	23.6	12.8	15.5	20.7	27.4
Cr	ppm	135	81	43	20	30
Cu	ppm	822.9	511.2	697	799.6	1067.00
Ni	ppm	16.6	10.5	11.9	14.6	20.8
Pb	ppm	6582	4003	4241	4712	7466
V	ppm	47	44	36	33	37
Zn	ppm	35,900	28,900	37,300	43,400	52,200

Figure 8. Au, Ag, Cu, Pb, and Zn contents in subfractions.

6.2. Mineralogy

Mineralogical studies of tailings from the Halcón EUM were conducted to determine the mineral composition of the waste generated by mineral extraction and processing and to understand the nature and behavior of its physical and chemical properties. These studies are crucial for understanding potential for metal concentration and the possibilities for tailings reuse under safe environmental management.

6.2.1. Reflectance Spectrometry

Analysis using reflectance spectrometry allowed the identification of ten minerals, which were grouped into geological associations, alteration products, sulfates, and oxides/hydroxides. Among them, the most abundant minerals were quartz and muscovite. Jarosite, a sulfate characteristic of acidic and oxidizing environments (Figure 9), was also identified, and is associated with the oxidation of sulfides such as pyrite. Illite stood out as the most representative alteration mineral, resulting from the transformation of feldspars.

Figure 9. Minerals (jarosite-goethite) are at the surface level of the tailings.

Minerals such as goethite, paragonite, alunite, kaolinite, smectite, and chlorite were identified in minor proportions. This surface mineralogical association, dominated by quartz, muscovite, illite, jarosite, and goethite, clearly reflects the acidic and oxidizing geochemical conditions characterizing the exposed portion of the tailings deposit.

6.2.2. X-Ray Diffraction

A total of eleven main mineral phases were identified by X-ray diffraction, which were classified according to their origin as minerals from the geological environment, alteration products, sulfides, and sulfates (Table 4). The predominant minerals were quartz (51%) and muscovite (19%), typical of the geological environment, as well as albite and sanidine in minor proportions (Table 4). Among the secondary minerals, pyrophyllite and scorodite stood out in drillhole S05, as well as diaspore in S03, all of which are associated with advanced argillic alteration processes. Scorodite, an arsenic-bearing mineral, forms in oxidizing environments during the oxidation of sulfides.

Table 4. Average content of minerals identified by XRD.

Element	Mineral Name	Chemical Formula	Average Content (%)
Associated with the geological environment	Quartz	SiO2	51.1
	Clinochlore	(Mg,Fe2+)5Al(Si3Al)O10(OH)8	0.3
	Muscovite	KAl2(AlSi3O10)(OH)2	19.1
	Sanidine		1.2
	Albite	Na(AlSi3O8)	0.5
Secondary or alteration	Pyrophyllite	Al2Si4O10(OH)2	0.6
Sulfides	Sphalerite	ZnS	6.8
	Pyrite	FeS2	7.2
	Pyrrhotite	Fe7S8	2.3
Sulfate	Jarosite	KFe3(OH)6(SO4)2	6.3
	Anglesite	PbSO4	1.2
	Gypsum	CaSO4·2H2O	1.1
Arsenate	Scorodite	FeAsO4·2H2O	0.5
Oxides	Diaspore	AlO(OH)	0.3
Native element	Sulfur	S	1.4

6.2.3. Microscopy

Mineralogical analyses using microscopy were performed on samples HAL-RE24-S01-009_011 and HAL-RE24-S05-006, specifically on the subfractions corresponding to particle sizes of +140 and +200 mesh. A slight color change was observed between the fractions, with shades varying from greenish gray to brown and brownish gray, depending on the sample and particle size. In all subfractions, non-metallic gangue minerals were predominant, representing more than 73% of the total, with a slightly lower content in the finer fractions (+200).

Opaque minerals, such as pyrite, sphalerite, pyrrhotite, and arsenopyrite, were present in all fractions, with percentages equal to or greater than 0.5%, mostly as free particles (Figure 10). Pyrite was the most abundant metallic mineral, especially in the +200 fraction, where its content ranged from 8% to 15%, while in the +140 fraction it ranged between 7% and 8.5%. A similar range was observed for sphalerite (2.5%–4%), arsenopyrite (0.1%–2%), and pyrrhotite (0.5%–5%). In addition, trace minerals such as chalcopyrite, gray copper minerals, galena, marcasite, sulfosalts, stannite, covellite, and bornite were identified.

Figure 10. Photomicrographs of the subfractions. (A,B). Free crystals and intergrowths of pyrite (py), sphalerite (ef), galena (gn), sulfides (SFSs), pyrrhotite (po), chalcopyrite (cp), and gangue (GGs).

The mineralogical distribution as a function of particle size was also characterized by analyzing the degree of mineral liberation using scanning electron microscopy of the aforementioned subfractions (Table 5).

Table 5. Mineralogical composition and degree of liberation by grain-size fractions for representative tailings samples HAL-RE24-S01-009_011 and HAL-RE24-S05-006.

Sample Code	HAL-RE24-S01-009_011				HAL-RE24-S05-006
Mesh Fraction	+140 Mesh		+200 Mesh		+140 Mesh		+200 Mesh
Minerals	C (%)	GL (%)	C (%)	GL (%)	C (%)	GL (%)	C (%)	GL (%)
Pyrite	6.55	69.39	14.56	73.49	20.93	72.99	31.91	90.17
Pyrrhotite	2.86	92.44	4.25	90.91	7.36	84.96	9.38	94.31
Sphalerite	6.55	59.41	4.23	76.19	10.92	66.46	7.86	91.14
Chalcopyrite	0.28	76.92	0.08	0	0.24	0	0.14	100
Galena	0.2	0	0.51	50	0.58	0	0.44	57.14
Boulangerite	0.04	0	0.04	0	0.19	0	0.03	0
Hematite	0.08	0	—	—	—	—	—	—
Arsenopyrite	0.15	0	0.18	0	0.38	90.91	0.82	98.77
Gangue	83.23	81.07	76.11	93.5	59.11	66.81	48.81	89.05
Goethite	—	—	0.01	0	0.02	0	—	—
Argentotetrahedrite	—	—	0.03	0	—	—	0.14	100
Covellite	—	—	—	—	0.27	100	0.47	100

Note: C: Mineral composition in weight percent (%); GL: Degree of ore mineral liberation in percent (%). Gangue includes quartz, muscovite, albite, clinochlore, and alteration silicates/sulfates. “—” indicates concentrations below the analytical detection limit (trazas).

In general terms, the samples are mainly composed of gangue minerals, with weight proportions ranging from 59.1% to 93.5%. The non-metallic gangue content decreases in the +200 subfraction compared to the +140 subfraction. These minerals are intergrown with metallic minerals, forming different types of mineralogical associations. Among the metallic minerals of economic interest are pyrite, sphalerite, pyrrhotite, arsenopyrite, galena, chalcopyrite, argentotetrahedrite, covellite, and boulangerite. These minerals vary in weight percentage and degree of liberation, which is usually higher in the finer subfractions (+200), as shown in Table 5.

Pyrite is the most abundant metallic mineral in all subfractions, occurring predominantly as a free particle (Figure 11), especially in the +200 fraction, where it reaches up to 31.91% by weight and a liberation degree of up to 90.17%. Sphalerite, although less abundant, has a high degree of liberation in the finer fractions (+200), exceeding 90% in some samples. On the other hand, galena exhibits low concentration and poor liberation, especially in the +140 fraction, where it is present as occluded particles. Arsenopyrite and chalcopyrite show variable behavior, with degrees of liberation fluctuating between 0% and 100%, and occur both as free particles and in association with pyrite or gangue minerals.

Figure 11. (A,B) Photomicrographs of the +140 subfractions, showing free grains of minerals such as pyrite (py), galena (gn), sphalerite (ef), pyrrhotite (po), arsenopyrite (apy), and gangue, together with mixed grains in various mineralogical combinations.

6.3. Geophysical Surveys

The electrical resistivity tomography (ERT) survey successfully characterized the subsurface conditions of the Halcón tailings deposit. Apparent resistivity values ranged from 2.6 to 15,581 Ω·m, with an average of approximately 355 Ω·m, indicating a strong geoelectrical contrast between the tailings material and the surrounding natural deposits. Low-resistivity values (2.6–60 Ω·m) were associated with water-saturated tailings, whereas intermediate values (60–700 Ω·m) corresponded to soils and highly weathered rock, and resistivities exceeding 700 Ω·m were interpreted as less weathered bedrock. This resistivity distribution enabled the clear delineation of the tailings body and its contact with the underlying geological units.

The interpreted resistivity models indicate that the tailings thickness generally ranges from 1 to 9 m (Figure 12), although locally conductive zones extend to greater depths, suggesting preferential infiltration pathways that may facilitate downward migration of water and dissolved constituents. The widespread occurrence of low-resistivity anomalies in the shallow subsurface reflects elevated moisture content within the tailings, highlighting areas that may be more susceptible to reduced geotechnical stability during periods of intense rainfall or seismic loading. Integration of the six ERT profiles into a quasi-three-dimensional model yielded a preliminary tailings volume estimate of approximately 54,450 m³. This value should be regarded as a reference estimate derived from the geophysical data, whereas the final tailings volume reported in this study was obtained through photogrammetric modeling constrained by drillhole information and supported by the geophysical interpretation.

Figure 12. Geophysical profile—Line 05. Showing the Halcón tailings deposit with low electrical resistivity.

6.4. Modeling, Geostatistics, and Estimation

Geostatistical analysis was performed using Leapfrog 5.0 software, which allowed the identification of areas with greater tailings thickness (9.62 m), located mainly in the center and northwest of the tailings deposit (Figure 13). Among the elements analyzed, gold, silver, zinc, copper, and lead were considered economically viable, while other elements, such as molybdenum, cobalt, and rare earths, did not exceed the minimum thresholds for reprocessing.

Figure 13. Three-dimensional model and profiles of the Halcón EUM tailings. A–A′, B–B′, C–C′, and D–D′ indicate the profile lines across the tailings deposit.

The preparation of variograms (Figure 14) for these elements of economic interest showed anisotropy with a preferential direction of N 133° and a dip of 8° SW, which is consistent with the layered arrangement of the tailings material. The variographic models fitted using spherical models presented moderate to high nugget values, indicating considerable small-scale spatial variability. Based on these results, a three-dimensional block model was developed with dimensions of 10 × 10 × 1 m and sub-blocks of 2.5 × 2.5 × 0.25 m, comprising a total of 8610 blocks. This model allowed for the estimation of an approximate volume of 54,776 m³, equivalent to 87,642 tons, considering an average dry density of 1.6 t/m³.

Figure 14. Major-axis variogram for Au, Ag, Cu, and Zn. Red dots represent experimental variogram values, solid red lines represent the fitted variogram models, dashed red lines indicate the total sill, and black triangular symbols indicate the nugget effect.

The model was validated both visually and using Swath graphs, which showed good overall agreement between the composite and estimated grades (Figure 15). More pronounced differences were observed at greater depths, attributable to a typical smoothing effect in scenarios with limited sampling density.

Figure 15. Swathplot analysis for Pb in the north and east directions.

Geostatistical modeling allowed the identification of variable concentrations of economically relevant metals in the deposit. In this regard, zinc had an average grade of 2.52%, with higher values at depths greater than 2 m. Copper had an average grade of 0.2%, reaching its maximum values at depths ranging from 1 to 5 m. Gold registered an average grade of 0.58 g/t, with higher grades in the surface layers of the southeast sector. Silver reached an average grade of 80.2 g/t, with concentrations between 50 and 80 g/t predominating at intermediate levels. Finally, lead had an average grade of 0.64%, with concentrations above 1% in the southeast sector, at depths of less than 4 m (Figure 16).

Figure 16. Spatial distribution of Zn, Cu, Au, Ag, and Pb according to the EUM Halcón tailings block model.

The economic assessment was performed using a selective extraction approach based on metal-specific economic cut-off grades. Each metal (Au, Ag, Zn, Cu, and Pb) was evaluated independently, and only the portion of the tailings exceeding the corresponding cut-off grade was considered for the economic calculation. Operating costs and applicable penalties were calculated based on the tonnage associated with each metal-specific cut-off scenario rather than on the total tailings inventory. Therefore, the reported tonnages represent metal-specific economic scenarios and are not additive, as the corresponding volumes may spatially overlap. The evaluation of the Halcón EUM tailings indicates significant potential for processing (Table 6), with zinc standing out, as 68% of the tonnage exceeds a cut-off grade of 2%, equivalent to 56,988 tons with an average grade of 3.14%. For copper, 9% of the material exceeds a cut-off grade of 0.5% Cu, representing 6641 tons with an average grade of 0.63%. In the case of gold, 14% of the tonnage exceeds at a cut-off grade of 1 g/t, corresponding to 10,751 tons and 16.25 kg of metal content. Silver shows the highest proportion of economically recoverable material, with 86% of the tonnage exceeding at a cut-off grade of 60 g/t, equivalent to 72,558 tons and 6081 kg of metal content. Finally, only 1% of the tonnage exceeds the 1% Pb cut-off grade, totaling 1893 tons with an average grade of 1.04%.

Table 6. Estimated potential mineral resources of the Halcón EUM tailings.

Element	Tonnage (t)	Cut-Off Grade	Average Grade	Contained Metal (t)	Contained Metal (kg)	Contained Metal (oz t)
Zn	56,988.37	2.00%	3.14%	1788.42	—	—
Cu	6640.85	0.50%	0.63%	41.78	—	—
Pb	1893.10	1.00%	1.04%	19.76	—	—
Au	10,751.38	1.00 g/t	1.51 g/t	—	16.25	522.6
Ag	72,558.96	60.00 g/t	83.80 g/t	—	6081.33	195,519.37

Note: Tonnage represents the geostatistically modeled volume per metal facies. Cut-off and average grades for Zn, Cu, and Pb are expressed in percent (%), while Au and Ag are in grams per ton (g/t). Contained metal resources are converted to metric tons (t) for base metals, and kilograms (kg) or troy ounces (oz t) for precious metals. “—” indicates not applicable.

Figure 17 shows the spatial distribution of metals with economically relevant grades in the tailings of the Halcón EUM. Zinc and silver have economically relevant concentrations at intermediate and deep levels, suggesting that they could potentially be processed together. In contrast, gold and lead are concentrated in the surface and intermediate layers and intermediate zones in the southeast sector. Copper is distributed mainly in intermediate layers, with additional occurrences in surface and deep levels. The spatial similarity in the distributions of high concentrations of Au, Pb, and Cu suggests the potential feasibility of their joint recovery.

Figure 17. Areas with economic grades in the tailings of the Halcón EUM.

To perform a realistic economic assessment, average recovery representative of Peruvian mining operations was used, with an average recovery of 85% for the metals analyzed. In addition, penalties for arsenic (USD 4/t) were considered, as it is the only element that exceeds the specified limits for the concentrates. Transportation costs (USD 35/t) and processing costs (US$55/t) were also included, resulting in a total OPEX of US$90/t.

Finally, the economic potential was calculated from the potential resources, considering the recovery factor for each metal analyzed in the tailings and multiplying the recoverable metal content by the corresponding metal price (Table 7) [7,33]. Ag has the highest economic potential at USD11.68 million, followed by Zn at USD 5.46 million. Pb has the lowest economic potential at USD 32,675. The economic potential, considering the five metals of economic interest, amounts to USD19.59 million in the Halcón EUM tailings (Table 7). Considering the estimated operating costs and penalties, a preliminary estimated economic benefit of USD 11.36 million is obtained. These results represent a preliminary operational-level economic assessment; capital expenditures (CAPEX), including processing infrastructure, mobilization, permitting, site preparation, and closure and reclamation costs, were not included. Therefore, the estimated economic benefit should not be interpreted as a definitive net profit.

Table 7. Economic valuation, projected revenue, operational expenditure, and net financial benefit for the potential reprocessing scenario of the Halcón EUM tailings.

Element	Potential Resources	Projected Metal Price	Estimated Value (US$)	Operating Expenses (US$)	Penalties (US$)	Estimated Economic Benefit (US$)
Zn	1520.16 t	3591.99 US$/t	5,460,399	—	—	—
Cu	35.51 t	13,735.89 US$/t	487,761	—	—	—
Au	444.21 oz t	4339.96 US$/oz t	1,927,853	—	—	—
Ag	166,191.46 oz t	70.32 US$/oz t	11,686,583	—	—	—
Pb	16.80 t	1944.92 US$/t	32,675	—	—	—
Total/Global Metrics	—	—	19,595,271	7,887,745	350,566	11,356,960

Note: Potential resources represent the total metal content calculated from metallurgical recovery factors. Base metal units are expressed in metric tons (t), and precious metals in troy ounces (oz t). Metal prices are based on global market dynamics projected for 2026. Operating expenses encompass reprocessing, transport, and environmental management costs. “—” indicates not applicable for individual rows.

7. Discussion

The in situ physicochemical results confirm that the tailings deposit has an acidic and strongly oxidizing environment, characterized by a hydrogen potential (pH) ranging from 1.83 to 4.91 and a positive oxidation–reduction potential. These conditions favor the dissolution and mobility of metals, which may explain the heterogeneous distribution and speciation observed in the geochemical analyses. The acidic pH, together with an oxidizing environment, promotes the release of potentially toxic metals, increasing their bioavailability and the associated environmental risk.

From a mineralogical perspective, the tailings reflect these conditions through the dominant presence of secondary minerals such as jarosite, goethite, and scorodite, formed by the oxidation of sulfides such as pyrite and galena. The mineralogy is dominated by non-metallic gangue minerals, particularly quartz (40%–65%) and muscovite (5%–10%), with the additional presence of albite, clinoclase, sanidine, and illite, reflecting the original geological environment. The abundance of jarosite and scorodite, especially in surface areas, is consistent with oxidation processes and indicates the potential for acid rock drainage generation.

Primary sulfides such as pyrite, sphalerite, pyrrhotite, and chalcopyrite represent up to 16% of the total mineral content and are distributed at different depths, indicating the presence of residual mineralization with economic potential. Microscopic studies and mineralogical liberation analysis reveal that these metallic minerals have high degrees of liberation, especially in the finer grain size fractions (+200 mesh), where they occur predominatly as free particles. This is particularly relevant for optimizing metallurgical processes, given that minerals such as sphalerite, pyrrhotite, and pyrite exhibit degrees of liberation exceeding 90% in these fractions.

The QA/QC assessment indicates that the geochemical dataset is generally suitable for interpretation. No significant analytical contamination was identified in the blank samples, while the reference standards showed acceptable accuracy for medium and high concentrations of Au, Ag, Cu, Pb, and Zn. Although one field duplicate pair exceeded the established tolerance, this result was observed for the same sample across all evaluated elements and may be related to sampling variability.

The identification of valuable metals such as zinc, lead, copper, silver, and gold, with significant concentrations, suggests that the tailings not only represent an environmental liability but also a potentially valuable secondary resource for metal recovery. Multivariate analysis (PCA) revealed two distinct elemental clusters, pointing to complex geochemical processes and the existence of different geochemical horizons within the deposit. The surface concentration of precious metals such as gold and silver, in contrast to the enrichment of zinc and copper at deeper levels, suggests a pattern of metal migration and precipitation dynamic that may be conditioned by variations in pH, redox potential, and texture.

Likewise, the concentration of metals in the finer fractions highlights the importance of particle size in the chemical reactivity and mobility of metals, which must be considered in any environmental management and recovery strategy. This characteristic increases the risk of leaching and dispersion of contaminants, but also offers an opportunity for more efficient recovery processes using techniques that target these finer, more reactive fractions.

The three-dimensional geochemical model is subject to limitations associated with the relatively low sampling density, as it was developed from 37 geochemical samples. This number of samples may not fully capture the spatial heterogeneity of the tailings deposit. Furthermore, the concentrations estimated between sampling locations are derived through spatial interpolation and therefore involve uncertainty, particularly in areas with greater distances between sampling points.

This contrasts with the findings of Parviainen et al. [5] in the Haveri tailings (Finland) and González-Díaz et al. [4] in the El Buitre deposit (Chile), where the focus was on geochemical and mineralogical characterization without detailed economic valuation. In contrast, the present study was able to quantify the economic potential of the tailings, despite their small surface area. From an economic perspective, the integrated analysis confirms that the grades of the main metallic elements exceed the reference cut-off thresholds for economic viability in the tailings studied. Silver and zinc are positioned as the metals with the greatest potential for economic recovery, as evidenced by the grade–tonnage curves that reflect a high proportion of tonnage with potentially recoverable concentrations. This indicates that the EUM Halcón tailings deposit could represent a secondary resource, the reprocessing of which could contribute to converting an economic asset, facilitating comprehensive and sustainable management of mining waste.

Despite the progress made, some limitations restrict the accuracy of the recovery and environmental risk estimates, such as those related to the number and representativeness of samples, the lack of density data for individual units, the absence of metallurgical tests, sequential geochemistry, and detailed mining planning.

The economic evaluation is also subject to uncertainty associated with the use of bulk density and cut-off grades obtained from the literature rather than determined specifically for the studied tailings. Bulk density directly affects the conversion of the estimated volume into tonnage and, consequently, the estimation of the contained metal. Similarly, the selected cut-off grades influence the amount of material considered potentially recoverable and therefore affect the estimated economic potential. Although the values adopted from previous studies provide a reasonable basis for the preliminary assessment, they may not fully represent the physical and economic conditions of the studied tailings. Therefore, the economic results should be considered as a preliminary estimate, and site-specific measurements of bulk density and further assessment of appropriate cut-off grades would reduce the uncertainty associated with the preliminary economic evaluation.

In summary, these results highlight the importance of detailed geochemical, physicochemical, and mineralogical characterization of tailings to understand the mechanisms of metal concentration, mobility, and release. This information is essential for the design of metal recovery strategies integrated with effective environmental mitigation measures, optimizing the balance between economic potential and sustainability in the management of tailings deposits.

8. Conclusions

Geochemical analysis revealed economically significant concentrations of Zn (5.46%), Pb (1.58%), Cu (1.29%), Ag (244 ppm), and Au (3.6 ppm), which were heterogeneously distributed both vertically and laterally, with a clear association with fine-grained textures and more acidic pH conditions. Gold and silver tended to be enriched in the surface horizons (0–1 m), whereas zinc and copper showed higher concentrations at depths between 1 and 5 m. These patterns occurred under strongly oxidizing conditions, as indicated by the low pH values (1.83–4.91) and positive redox potentials (169.2–508 mV), which may influence the mobility and speciation of metals.

From a mineralogical perspective, there was a predominance of non-metallic gangue minerals (59.1% to 93.5%), especially quartz and muscovite. The presence of jarosite, scorodite, alunite, and other secondary minerals indicates acidic and oxidizing geochemical conditions at the surface, with the potential for acid rock drainage generation. In terms of metallic minerals, pyrite, sphalerite, arsenopyrite, pyrrhotite, and chalcopyrite were predominant, with high degrees of mineral liberation in the finer fractions, which is favorable for their potential metallurgical recovery. In contrast, galena and argentotetrahedrite showed low concentration and low degrees of liberation. This distribution and degree of mineral liberation indicate that the fine fractions of the tailings concentrate a substantial proportion of the valuable minerals, which is critical for defining reprocessing and metal recovery strategies.

The economic analysis indicates a preliminary economic potential of approximately USD11.36 million for the evaluated tailings, with silver representing the largest estimated economic contribution (USD 11.68 million), followed by zinc (USD 5.46 million). These results suggest that the tailings may have potential for recovery though reprocessing, particularly in the fine fractions where valuable minerals are concentrated. However, this assessment should be considered preliminary, as it is subject to uncertainties associated with the available sampling density, spatial interpolation, the use of literature-based bulk density, and the selected cut-off grades. Further site-specific characterization and metallurgical and economic studies are required to confirm the recoverability and economic feasibility of the identified materials.

To our knowledge, this study represents the first comprehensive assessment of this type at the national level and one of the first in South America promoted by the State. The results provide a basis for further evaluation of mining-tailings reprocessing within a more sustainable mining framework and support the application of circular-economy approaches to the management of mining liabilities.