Combined analysis of helicopter-borne magnetic and stream sediment geochemical data around an ancient Tiouit gold mine (Eastern Anti-Atlas, Morocco): Geological and mining interpretations

Miftah, Abdelhalim; Azzab, Driss El; Attou, Ahmed; Rachid, A.; Ouchchen, Mohammed; Soulaimani, Ayoub; Soulaimani, Saâd; Manar, Ahmed

doi:10.1016/j.jafrearsci.2020.104093

Cited by 6 publications

(4 citation statements)

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“…From a mining point of view, several showings, including Au, Cu, Pb and Mn, have been reported along and near this fault. This type of fault may favor the circulation of mineralizing hydrothermal fluids from a deep source that may enrich the host rocks and tectonic structures [22,23]. -Area 5 coincides with hydrothermal alteration associated with the Wawitcht granite and volcanic rocks of the Lower Ouarzazate Group.…”

Section: Discussionmentioning

confidence: 97%

“…The method of processing multispectral satellite images, especially those of the ASTER sensor, is qualified among the most used approaches [4,[6][7][8]10,11,[13][14][15][16][17][18]. Apart from remote sensing data, processing of geophysical data contributes assuredly to minerals exploration through their different geophysical components (gravimetric, magnetometric, electrical, electromagnetic and natural gamma spectrometric methods) [19][20][21][22][23][24]. The natural gamma-ray spectrometric method has been widely and successfully used in mineral exploration and identification of alteration zones [20,21,[25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Mapping of Hydrothermal Alteration Zones in the Kelâat M’Gouna Region Using Airborne Gamma-Ray Spectrometry and Remote Sensing Data: Mining Implications (Eastern Anti-Atlas, Morocco)

et al. 2022

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The mapping of hydrothermal alteration zones associated with mineralization is of paramount importance in searching for metal deposits. For this purpose, targeting alteration zones by analyzing airborne geophysical and satellite imagery provides accurate and reliable results. In the Kelâat M’Gouna inlier, located in the Saghro Massif of the Moroccan Anti Atlas, natural gamma-ray spectrometry and ASTER satellite data were used to map hydrothermal alteration zones. Natural gamma-ray spectrometry data were processed to produce maps of Potassium (K in %), Uranium (eU in ppm), Thorium (eTh in ppm) and ratios of K/eTh and K/eU. In addition, four-band ratios were computed, on ASTER data, to map the distribution of clay minerals, phyllitic minerals, propylitic minerals, and iron oxides. The combined results obtained from geophysical and satellite data were further exploited by fuzzy logic modelling using the Geographic Information System (GIS) to generate a mineral prospectivity map. Seven hydrothermal alteration zones likely to be favorable for mineralization have been identified. They show a spatial correlation with (i) known surface prospects and mineral occurrences, (ii) the granite-encasing contact zone, and (iii) the fault zones (Sidi Flah and Tagmout faults). This research therefore provides important information on the prospecting of mineral potential in the study area.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Mapping of Hydrothermal Alteration Zones in the Kelâat M’Gouna Region Using Airborne Gamma-Ray Spectrometry and Remote Sensing Data: Mining Implications (Eastern Anti-Atlas, Morocco)

et al. 2022

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“…Separation of these communities can affect the results of classifying elements by means of the methods used. Integration of different methods (e.g., machine learning and geostatistical techniques) can reduce the methods' weaknesses and intensify their advantages for an accurate geochemical modeling of ore mineralization [44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran

Shirazi,

Hezarkhani,

Shirazy

et al. 2023

Minerals

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Analyzing geochemical data from stream sediment samples is one of the most proactive tools in the geochemical modeling of ore mineralization and mineral exploration. The main purpose of this study is to develop a geochemical model for prospecting copper mineralization anomalies in the Sahlabad area, South Khorasan province, East Iran. In this investigation, 709 stream sediment samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), and geostatistical and machine learning techniques. Subsequently, hierarchical analysis (HA), Spearman’s rank correlation coefficient, concentration–area (C–A) fractal analysis, Kriging interpolation, and descriptive statistics studies were performed on the geochemical dataset. Machine learning algorithms, namely K-means clustering, factor analysis (FA), and linear discriminant analysis (LDA) were employed to deliver a comprehensive geochemical model of copper mineralization in the study area. The identification of trace elements and the predictor composition of copper mineralization, the separation of copper geochemical communities, and the investigation of the geochemical behavior of copper vs. its trace elements were targeted and accomplished. As a result, the elements Ag, Mo, Pb, Zn, and Sn were distinguished as trace elements and predictors of copper geochemical modeling in the study area. Additionally, geochemical anomalies of copper mineralization were identified based on trace elements. Conclusively, the nonlinear behavior of the copper element versus its trace elements was modeled. This study demonstrates that the integration and synchronous use of geostatistical and machine learning methods can specifically deliver a comprehensive geochemical modeling of ore mineralization for prospecting mineral anomalies in metallogenic provinces around the globe.

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“…Variations in measurable magnetic intensity at the surface result from lithological changes are associated with the subsurface or igneous intrusions. Thus, aeromagnetic surveys provide unique geological information and serve as an indirect guide to stress-related epigenetic mineralization in surrounding rocks and direct detection of certain iron minerals [3].…”

Section: Introductionmentioning

confidence: 99%

Geostatistical Study of Airborne Geophysical Data of the Tiouit Mining Area (Eastern Anti-Atlas, Morocco)

Miftah

Azzab

Attou

et al. 2021

Proceedings of the 2nd International Electronic Conference on Mineral Science

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The main objective of the present work is to use principal component analysis (PCA) on the various airborne geophysical data, including gamma-ray spectrometry (concentrations of radioactive elements 40 K and 232 Th), magnetism (magnetization contrast due to magnetic susceptibility), and electromagnetism (apparent electrical resistivity) to describe the physicochemical behavior of the mining zones in operation as well as to prospect other zones favorable for exploration. This data's geostatistical study highlights the adequate principal component (PC) to visualize these geophysical variables. We chose PC4 to visualize the zones favorable for mining concentration because it describes the same physical and chemical characteristics represented by the Tiouit alteration zone. The collection and X-ray fluorescence analysis of 32 samples in the field yielded high grades of Arsenic (24 g/t), Copper (5.87%), Iron (19.30 %), Lead (5.70%), and Zinc (8.85%).

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Combined analysis of helicopter-borne magnetic and stream sediment geochemical data around an ancient Tiouit gold mine (Eastern Anti-Atlas, Morocco): Geological and mining interpretations

Cited by 6 publications

References 20 publications

Mapping of Hydrothermal Alteration Zones in the Kelâat M’Gouna Region Using Airborne Gamma-Ray Spectrometry and Remote Sensing Data: Mining Implications (Eastern Anti-Atlas, Morocco)

Mapping of Hydrothermal Alteration Zones in the Kelâat M’Gouna Region Using Airborne Gamma-Ray Spectrometry and Remote Sensing Data: Mining Implications (Eastern Anti-Atlas, Morocco)

Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran

Geostatistical Study of Airborne Geophysical Data of the Tiouit Mining Area (Eastern Anti-Atlas, Morocco)

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