Detection of mineralization stages using zonality and multifractal modeling based on geological and geochemical data in the Au-(Cu) intrusion-related Gouzal-Bolagh deposit, NW Iran

Heidari, Seyed Mehran; Afzal, Peyman; Ghaderi, Majid; Sadeghi, Behnam

doi:10.1016/j.oregeorev.2021.104561

Cited by 17 publications

(3 citation statements)

References 81 publications

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“…For this reason, the fractal methods were applied by numerous geoscientific to define the threshold value for the classification of the models [75][76][77]. Different types of fractal models such as spectrum-area (S-A) [78], number-size (N-S) [79], densityarea (D-A) [80], and concentration-volume (C-V) [81] were successfully used by several researchers. In this study, the concentration area (C-A) proposed by Cheng et al [82] was applied (Figures 5b and 6b).…”

Section: Elaborated Model Assessmentmentioning

confidence: 99%

Application of Fuzzy Logic and Fractal Modeling Approach for Groundwater Potential Mapping in Semi-Arid Akka Basin, Southeast Morocco

Echogdali

Boutaleb

Kpan³

et al. 2022

Sustainability

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Groundwater potential delineation in the Akka basin, southwest Morocco, has been determined through the combination of geospatial techniques and geological data. The geometric average and expected value are two multi-criteria approaches used to integrate a set of factors–data for which the weights of each factor are assigned using the fuzzy logic function, which transforms values of factors influencing groundwater presence in a range of [0, 1]. The efficiency factors used in this study are the lineament density, node density, drainage density, distance from rivers, distance from lineament, permeability, slope, topographic witness index, plan curvature, and profile curvature. Thereafter, the groundwater potential map was generated in a GIS environment. To assess and compare the efficiency of the two models, the well data existing in the basin were used to choose the most efficient model. For that reason, the prediction area (P–A) graph, the normalized density (Nd), and its weight (We) were applied to estimate the capacity of each model to predict the target area. The analysis shows that the expected value model (Nd = 1.86 and We = 0.62) is more efficient than the geometric average model (Nd = 0.96 and We = −0.04). The results of the expected value model can be used in the future planning and management of water resources in the Akka basin.

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Section: Elaborated Model Assessmentmentioning

confidence: 99%

Application of Fuzzy Logic and Fractal Modeling Approach for Groundwater Potential Mapping in Semi-Arid Akka Basin, Southeast Morocco

Echogdali

Boutaleb

Kpan³

et al. 2022

Sustainability

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“…The existing studies focus on the geological analysis using existing GIS approaches, plotting stratigraphic columns and statistical graphs for spatial data visualization (Koshnaw et al, 2020;Mouthereau et al, 2007), geologic modelling data using in-situ experiments Heidari et al (2021); Hosseini et al (2021); Lindh and Lemenkova (2022c); Soleimani and Jodeiri Shokri (2016), geochemical data analysis Afzal et al (2017); Lindh and Lemenkova (2022a); Mokhtari and Sadeghi (2021) and do not deal with a detailed explanation of scripting techniques of the geological mapping. As a consequence, they either lack detailed explanation of the techniques of cartographic data visualization (Tavani et al, 2018) or have limited use of scripting in geologic modelling (Lemenkov and Lemenkova, 2021).…”

Section: Related Workmentioning

confidence: 99%

A Script-Driven Approach to Mapping Satellite-Derived Topography and Gravity Data Over the Zagros Fold-and-Thrust Belt, Iran

Lemenkova

2022

Artificial Satellites

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Integrated geophysical mapping benefits from visualizing multi-source datasets including gravity and satellite altimetry data using 2D and 3D techniques. Applying scripting cartographic approach by R language and GMT supported by traditional mapping in QGIS is presented in this paper with a case study of Iranian geomorphology and a special focus on Zagros Fold-and-Thrust Belt, a unique landform of the country affected by complex geodynamic structure. Several modules of GMT and ’tmap’ and ’raster’ packages of R language were shown to illustrate the efficiency of the console-based mapping by scripts. Data sources included high-resolution raster grids of GEBCO/SRTM, EGM-2008, SRTM DEM and vector geologic layers of USGS. The cartographic objective was to visualize thematic maps of Iran: topography, geology, satellite-derived gravity anomalies, geoid undulations and geomorphology. Various cartographic techniques were applied to plot the geophysical and topographic field gradients and categorical variations in geological structures and relief along the Zagros Fold-and-Thrust Belt. The structures of Elburz, Zagros, Kopet Dag and Makran slopes, Dasht-e Kavir, Dasht-e Lut and Great Salt Desert were visualized using 3D-and 2D techniques. The geomorphometric properties (slope, aspect, hillshade, elevations) were modelled by R. The study presented a series of 11 new maps made using a combination of scripting techniques and GIS for comparative geological-geophysical analysis. Listings of R and GMT scripting are provided for repeatability.

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“…There are numerous classical models for geochemical anomaly detection such as probability plots, spatial U statistics, and summation of mean and standard deviation [2][3][4][5][6]. Many mathematical processing techniques have been used for the detection of geochemical anomalies since the 1990s, especially concentration-area fractal/multifractal modeling [7][8][9][10][11][12][13][14][15][16], spatial analysis/geoinformatics [17], machine learning (ML) techniques such as neural networks [18][19][20] and deep learning algorithms [21]. On the other hand, two branches exist for geochemical mapping techniques, including structural (e.g., fractal and ML methods) and non-structural methods, especially classical statistics techniques.…”

Section: Introductionmentioning

confidence: 99%

Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Farhadi

Afzal

Konari³

et al. 2022

Minerals

Self Cite

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Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements' main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution.

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Detection of mineralization stages using zonality and multifractal modeling based on geological and geochemical data in the Au-(Cu) intrusion-related Gouzal-Bolagh deposit, NW Iran

Cited by 17 publications

References 81 publications

Application of Fuzzy Logic and Fractal Modeling Approach for Groundwater Potential Mapping in Semi-Arid Akka Basin, Southeast Morocco

Application of Fuzzy Logic and Fractal Modeling Approach for Groundwater Potential Mapping in Semi-Arid Akka Basin, Southeast Morocco

A Script-Driven Approach to Mapping Satellite-Derived Topography and Gravity Data Over the Zagros Fold-and-Thrust Belt, Iran

Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

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