Predictive Mapping of Prospectivity for Gold in the Central Portion of the Tapajós Mineral Province, Brazil
Abstract
:1. Introduction
2. Geological Setting
Structures, Metallogeny, and Gold Mineralization
3. Materials and Methods
3.1. Dataset
3.2. Data Processing
3.3. Mineral Prospectivity Modelling
4. Results
4.1. Hydrothermal Alteration Mapping with Gamma-ray Spectrometry
4.2. Interpretation of Magnetic Lineaments
4.3. Semi-Automatic Surface Linear Features
4.4. Data Integration
4.4.1. Fuzzy Model
4.4.2. WofE Model
4.4.3. Support Vector Machine (SVM) Model
4.5. Validation
5. Discussions
6. Conclusions
- Radiometric data enhancement is effective for mapping K-enrichment and identifying gold-related hydrothermal alteration zones based on higher intensities of the parameters examined.
- Magnetic data processing with enhancement filters produced satisfactory results for interpreting the structural framework, despite challenges in calculating RTP at low magnetic latitudes.
- Semi-automated extraction of linear features from DEM provided valuable information on morphostructural lineaments in the study area, which is difficult to access in a tropical zone with high vegetation and cloud cover. The distribution of features is consistent with the regional tectonic framework of the area, and the density reflects the higher incidence of structuring at intermediate to shallow crustal levels.
- Three different methods for data integration were used for prospectivity modeling in the central portion of the TMP, resulting in prospectivity sites indicative of the main deposits in the region.
- The fuzzy model (i) effectively identified potential targets, especially in the eastern portion, reflecting the data availability panorama; and (ii) mapped known mineralization sites reasonably, although some deposits had low or zero prospectivity scores. The model validation with an ROC/AUC curve of 0.980 demonstrates high confidence in the degree of randomness explored to map the mineralizing event.
- The WofE method (i) indicated elongated potential zones aligned with prospective structural trends; and (ii) mapped most of the known deposits in areas of higher probability and performed well with points not used in the modeling, attested by an AUC of 0.948 and an ROC curve demonstrating excellent model efficiency in mapping known deposits and predicting new potential targets.
- The ML algorithm (SVM) (i) presented better-defined prospectivity sites and (ii) mapped nearly all known deposits in areas of higher scores and performed even better with omitted points, achieving an AUC of 0.969—the closest to the best classification value. Although the training data set is limited and not ideal, satisfactory results using a limited training data set (<20) can be achieved (e.g., [8,53,123]). The advancement in the use of more sophisticated machine learning techniques, as well as the use of classical methods guided by knowledge or data, can substantially contribute to the risk reduction in mineral exploration and enable decision making through indirect spatial information complementary to field data.
- An agreement map combining the top 5% scores from each model pinpointed the best prospective interest areas, offering valuable insights for future exploration.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Occurrence | Mineralization Style | Alteration | Structural Control | References |
---|---|---|---|---|
Abacate | qz veins | sil, tur | N80E/70SE | [32] |
Água Branca | qz veins | sil, carb, sulf, ser, cl | 88/125, 35/290, 65/315 | [58,59] |
Asa Branca | qz veins | sulf, epid | 88/175, 88/125 | [14,58] |
Batalha | stockwork, qz veins | ser, potas, alb, epid, sil, cl | 70/315, 90/300 | [32,60,61] |
Boa Esperança | qz veins | arg, ser, cl, sulf | 220/75SW, 80/80SE | [14] |
Cantagalo | qz veins, stockwork, disseminations | sulf, lim, sil, kaol, arg, ser | 85/060, 90/055, 80/350 | [32,34,62] |
Cuiú–Cuiú/Central | qz veins, hydrothermal breccia | ser, cl, carb | 75/075 | [58,63] |
Cuiú–Cuiú/Moreira Gomes | stockwork, veins | ser, sulf, carb, cl, sil, epid | 31/305, 10/341 | [64,65] |
Carneirinho | stockwork | sulf, sil, ser, epid, potas | N80W/85NE, N15/85SE | [14,62] |
Davi | qz veins | ser, carb, sulf, epid, cl, potas | 90/135, 90/305 | [32,62] |
Mamoal | mafic dykes disseminations | sulf, potas | 88/020 | [58,62] |
Ouro Roxo | disseminations, py and qz-py veins | ser, cl, carb | 65/090, 35/095 | [32,34,66] |
Palito | veins | ser, sulf, epid, cl, carb, potas | 88/045 | [58] |
Pimenteiras | veins | sulf | 90/095 | [34] |
São Chico | veins | ser, cl, sulf, epid, musc, kaol, sil | 50/170, 88/170 | [58] |
São Domingos/Fofoca | qz-sulphide veins, stockwork | ser, sil, sulf, epid | 88/165, 72/320 | [58,67] |
São Domingos/Tucano | stockwork, qz-sulphide veins | ser, sil, sulf, epid | 60/75NW | [58,68] |
São João | qz veins | epid, arg, sulf | N45–65E, N30E/75SE | [37] |
São Jorge | qz veins, stockwork disseminations | sulf, musc, cl, carb | 80/160 | [58,69] |
Sucuba | qz veins | sil, cl | EW | [37] |
Tocantinzinho | qz veins, stockwork | mic, cl, ser, sil, carb | 80/125, 80/345 | [58,70] |
Parameters | K (%) | eTh (ppm) | eU (ppm) | |
---|---|---|---|---|
Mean (M) | 0.324491 | 19.620647 | 2.358534 | |
Standard Deviation (σ) | 0.166002 | 10.636025 | 0.932671 | |
Class | Interval | |||
Low | <M − ½σ | <0.24 | <14.30 | <1.89 |
Intermediate | >M − ½σ and <M + ½σ | 0.24–0.41 | 14.30–24.94 | 1.89–2.83 |
High | >M + ½σ | >0.41 | >24.94 | >2.83 |
Data Source | Evidence Map | Classifying Process | Prospective Thresholds | Fuzzy Operators | Integrative Operator |
---|---|---|---|---|---|
Geologic map, scale 1:100,000 | Favorable host rocks | Data classified according to the frequency of occurrences and geochemical signature. | - | - | GAMMA, index 0.75 |
Geologic map, scale 1:100,000 | Intrusive contacts | Proximity analysis of deposits with the contacts of intrusive bodies. | 5000 m | - | |
Airborne magnetometry | Magnetic lineaments | Proximity to lineaments interpreted from magnetometry, which represent the magnetic signature of mineralized shear zones, secondary deposit controls, and mineralized late veins. Euclidean distance. | 2000 m (NW) 1000 m (NE and EW) 2500 m (NS) | OR | |
Radar image | Density of surface traces | Density of lineaments extracted from DEM. | Densities > 1 | ||
Airborne radiometry | F parameter | Statistical classification of the values of parameters F, Kd, and Ud and the eTh/K ratio, which represent the signature of The hydrothermal alteration with positive a correlation with mineralization. | Values > M + 1σ | GAMMA, index 0.75 | |
Kd parameter | Values > M + 1σ | ||||
Ud parameter | Values < M − ½σ | ||||
eTh/K ratio | Values < 0.7 |
PC1 | PC2 | PC3 | PC4 | |
---|---|---|---|---|
F | 0.34639 | −0.89043 | 0.29401 | 0.02670 |
Kd | 0.20787 | −0.17201 | −0.70639 | −0.65438 |
Ud | 0.17715 | −0.11325 | −0.62031 | 0.75565 |
eTh/K | 0.89745 | 0.40587 | 0.17258 | −0.00790 |
Fuzzy | WofE | SVM | |
---|---|---|---|
Advantages | (1) Easy implementation and operator knowledge-based control. | (1) Allows analysis of the weight of each evidence map and selection of the best ones for integration. | (1) Greater robustness, and handles both linear and non-linear data; (2) generalization ability, even with few training and testing points; (3) effective separation of data with a maximum margin; (4) increases decision-making resolution. |
Disadvantages | (1) Less precise propositions and data inputs being more susceptible to bias; (2) Many steps and parameters to define (e.g., membership functions, data classification rules, integration operators). | (1) Need to reduce the number of classes (reclassification of evidential maps); (2) the assumption of conditional independence burdened and invalidated the modeling numerous times, a problem which can be minimized with the use of PCA. | (1) Requires extensive knowledge of ML architectures and programming languages. |
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Souza Gaia, S.M.d.; Souza Filho, C.R.d. Predictive Mapping of Prospectivity for Gold in the Central Portion of the Tapajós Mineral Province, Brazil. Minerals 2023, 13, 1432. https://doi.org/10.3390/min13111432
Souza Gaia SMd, Souza Filho CRd. Predictive Mapping of Prospectivity for Gold in the Central Portion of the Tapajós Mineral Province, Brazil. Minerals. 2023; 13(11):1432. https://doi.org/10.3390/min13111432
Chicago/Turabian StyleSouza Gaia, Sulsiene Machado de, and Carlos Roberto de Souza Filho. 2023. "Predictive Mapping of Prospectivity for Gold in the Central Portion of the Tapajós Mineral Province, Brazil" Minerals 13, no. 11: 1432. https://doi.org/10.3390/min13111432