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Harris, James B.; Wilkinson, L; Heather, K B; Fumerton, Stewart Lloyd; Bernier, Marc-André; Ayer, J. A.; Dahn, R.

doi:10.1023/a:1011548709573

Cited by 109 publications

(13 citation statements)

References 30 publications

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“…The models are suitable for well-explored areas in order to detail exploration surveys and c) Hybrid models which are considered based on both expert's opinions and locations of known mineralization occurrences in the area to assign weight to the evidence criteria. Some of the most important knowledge-driven methods are index overlay [5,20], fuzzy logic technique [26,29,31], wildcat method [7]and multi-criteria decision making methods [1,28]. In recent decades, various data-driven techniques developed and led to advances in the development of MPM such as neural networks(NN) [3,32,38,40], Bayesian classifiers [35], support vector machines (SVM) [12,19,54,58], and random forest method [8,39,44].…”

Section: Introductionmentioning

confidence: 99%

A New Frontier in Mineral Exploration: Hybrid Machine Learning and Bat Metaheuristic Algorithm for Cu-Au Mineral Prospecting in Sonajil area, E-Azerbaijan

Mohammadzadeh

Mahboubiaghdam

Nasseri

et al. 2023

Preprint

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Modeling mineral prospectivity is a challenging strategy in characterization of blind ore zones and mineral prospecting. Therefore, the application of advanced spatial modeling techniques and machine learning algorithms is important in exploration pattern recognition. In this study, a combination of a support vector machine (SVM) and the bat algorithm (BA) has been employed to improve the classification and produce an optimal prospectivity map of porphyry copper-gold deposit in the Sonajil area, NW of Iran. In the proposed model, BA was used to optimize the SVM parameters. In data preparation, evidence layers of 10 exploratory features (geological, geochemical, remote sensing and boreholes) were prepared. Then based on 100-meter buffering around boreholes, the data (training and testing) was selected. For mapping mineral prospectivity in the area, the results of two predictive single SVM and hybrid SVM-BA models were compared. Both models were trained by way of predictor maps and then the models performance were evaluated by confusion matrix and receiver operating characteristic (ROC) curve. The results showed that both predictive models had an obvious optimization effect on recognizing the Cu-Au mineralization pattern but the hybrid model had higher accuracy in anomalous zone enhancement, with ROC values more than 0.8, indicating this optimization was successful and the selected optimal model is the best predictor for mineral prospectivity in the area. The delineated targets are also in accordance with the characteristics of the area metallogenic system showing that the established hybrid model is an effective tool in mineral prospectivity mapping.

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Section: Introductionmentioning

confidence: 99%

A New Frontier in Mineral Exploration: Hybrid Machine Learning and Bat Metaheuristic Algorithm for Cu-Au Mineral Prospecting in Sonajil area, E-Azerbaijan

Mohammadzadeh

Mahboubiaghdam

Nasseri

et al. 2023

Preprint

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“…Generating a mineral potential map derived from remote-sensing data through a GISbased approach has therefore became a fast and accurate tool for identification of target areas for mineral exploration [7,8], particularly during the reconnaissance stage. Since the advent of GIS-based spatial analysis approaches, advances have been achieved in revealing prospective areas of hydrothermal mineral resources [24][25][26]. This is because integration of spatially distributed remote-sensing data using a GIS technique is a significant approach to mineral exploration, as it allows combining multiple datasets through digital overlay methods in order to optimize mineral prospection maps [27].…”

Section: Introductionmentioning

confidence: 99%

“…This is because integration of spatially distributed remote-sensing data using a GIS technique is a significant approach to mineral exploration, as it allows combining multiple datasets through digital overlay methods in order to optimize mineral prospection maps [27]. For example, the GISbased knowledge-driven method is efficient to produce predictive maps based on expert judgment [8] as each GIS predictive layer is assigned a weight reflecting importance in the modeling process [1,24]. Furthermore, each evidential map representing HAZs and/or fracture/fault zones was given a weight reflecting its significance in the prospective mode.…”

Section: Introductionmentioning

confidence: 99%

Synergy of Remote Sensing Data for Exploring Hydrothermal Mineral Resources Using GIS-Based Fuzzy Logic Approach

Abdelkareem

Al‐Arifi

2021

Remote Sensing

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The Arabian Nubian Shield (ANS) contains a variety of gold deposits in the form of veins and veinlets formed by hydrothermal fluids. Characterizing potential areas of hydrothermal alteration zones therefore provides a significant tool for prospecting for hydrothermal gold deposits. In this study, we develop a model of exploration for hydrothermal mineral resources in an area located in the ANS, Egypt, using multiple criteria derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat-Operational Land Imager (OLI), and Sentinel-2 data and field work through GIS-based fuzzy logic approach. The hydrothermal alteration zones (HAZs) map extracted from combining mineral indices, spectral bands, and ratios is consistent with observed argillic alteration zones around the mineralized veins. Combining HAZs and lineament density led to identification of six zones based on their mineralization potential, and provides a tool for successful reconnaissance prospecting for future hydrothermal mineral deposits. The detected zones are labeled as excellent, very high, high, moderate, low, and very low, based on their potential for Au production, and the predictive excellent and very high zones cover about 1.6% of the study area. This model also shows that target prospective zones are quartz veins controlled by NNW-SSE trending fracture/fault zones all crosscutting Precambrian rocks of the ANS. Field observations and petrographic and X-ray diffraction analyses were performed to validate the mineral prospective map and revealed that quartz veins consist of gold–sulfide mineralization (e.g., gold, pyrite, chalcopyrite, and sphalerite). Consistency between the high potential hydrothermal alterations zones (HAZs) and the location of gold mineralization is achieved.

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“…Prediction of ore body location is critical for mineral exploration [1][2][3][4], which is generally achieved by analysing the associations of geological factors, geophysical and geochemical fields with mineralization. Such associations are commonly represented as 2D maps, and quantitatively analysed in the platform of 2D Geographic Information System (GIS), which have definitely facilitated the understanding of mineralization systems and predictive exploration of mineral resources [5][6][7]. However, such 2D studies are insufficient to present and analyse complicated mineralization systems.…”

Section: Introductionmentioning

confidence: 99%

“…These methods are divided into two classes, knowledge-driven methods and data-driven methods [41,42]. The common knowledge-driven methods include Fuzzy logic [43], Analytical hierarchy process [6], Index Overlay [44] and Inference Networks and Decision tress in expert systems [45]. The common data-driven methods include Weights of Evidence (WofE) [46,47], Logic Regression [48] and Neural Networks [49].…”

Section: Introductionmentioning

confidence: 99%

Quantitative 3D Association of Geological Factors and Geophysical Fields with Mineralization and Its Significance for Ore Prediction: An Example from Anqing Orefield, China

Qin

Liu

2018

Minerals

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Quantitative 3D spatial association of geological factors and geophysical fields with orebodies is critical for ore prediction. The Anqing orefield, a principal Cu-Fe orefield in China, is closely associated with the Yueshan intrusion. By compiling the data from drilling and tunnelling exploration, Controlled Source Audiofrequency Magnetotelluric (CSAMT) surveying and the computational modelling of magmatic intrusion's cooling process, we constructed models of the Yueshan intrusion, ore-favourable carbonate formation, orebodies, resistivity field and volume strain field. These models are used as evidential factors to analyse their spatial association with mineralization by the weights-of-evidence (WofE) method. The location of orebodies is closely related to the shape of the contact zone of the Yueshan intrusion. The spaces with the distance ≤200 m to the concaves that were selected by minimum principal curvature (|K min | ≥ 0.0025) from contact zones, are very favourable for localization of orebodies. Most orebodies are not located in the spaces of the lowest resistivity, suggesting that the lowest resistivity cannot be used as an indicator for mineralization. The spaces with higher positive volumetric strain have higher positive weights with orebodies, implying that the mineralization is positively related to the positive volumetric strain. The spaces of all evidential factors that had positive correlation with mineralization were integrated to create a 3D prospectivity map by calculating posterior probability. Five areas with higher posterior probability, indicating higher prospectivity potential, are selected as targets for future exploration.

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Untitled

Cited by 109 publications

References 30 publications

A New Frontier in Mineral Exploration: Hybrid Machine Learning and Bat Metaheuristic Algorithm for Cu-Au Mineral Prospecting in Sonajil area, E-Azerbaijan

A New Frontier in Mineral Exploration: Hybrid Machine Learning and Bat Metaheuristic Algorithm for Cu-Au Mineral Prospecting in Sonajil area, E-Azerbaijan

Synergy of Remote Sensing Data for Exploring Hydrothermal Mineral Resources Using GIS-Based Fuzzy Logic Approach

Quantitative 3D Association of Geological Factors and Geophysical Fields with Mineralization and Its Significance for Ore Prediction: An Example from Anqing Orefield, China

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