Application of Airborne Geophysical and ASTER Data for Hydrothermal Alteration Mapping in the Sar-Kuh Porphyry Copper Area, Kerman Province, Iran

Honarmand, Mehdi

doi:10.4236/ojg.2016.610092

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…Lithological mapping of the exposed outcrops and their associated features are demarcated with the help of PCA, ICA, and MNF analyses to govern the mineral potentiality of the outcrops [9,17,40,42,44,62,63]. Different supervised, unsupervised, machine learning and prospectivity mapping algorithms were applied to the optical datasets to prepare the mineralogical and prospective zone maps of the region and these maps contain the information about the mineral potential zones which were utilized for the perspective of mineral explorations [17,20,21,31,42,47,48,[64][65][66][67][68][69][70][71].…”

Section: à2mentioning

confidence: 99%

“…Multispectral Remote Sensing is utilized in the domain of geosciences for lithological mapping [9][10][11][12][13][14][15][16], mineral mapping [17][18][19][20][21][22][23][24], identification of the alteration zones related to the base metal mineralization [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], structural features as a controlling factor for mineralization [26,28,[42][43][44][45][46] and mapping for demarcating favorable zones of mineralization [21,47,48]. Spectral characteristic absorption features of the rocks and minerals are utilized for the identification and mapping of lithologies and minerals like calcite, dolomite, clay, mica, and ultramafics, etc.…”

Section: Introductionmentioning

confidence: 99%

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Feature-Oriented Principal Component Selection (FPCS) for Delineation of the Geological Units Using the Integration of SWIR and TIR ASTER Data

Jain¹

2022

Recent Remote Sensing Sensor Applications - Satellites and Unmanned Aerial Vehicles (UAVs)

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Geological studies have been performed using the Band Ratios (BR), Relative Band Depth (RBD), Mineral Indices (MI), Principal Component Analysis (PCA), Independent Component Analysis (ICA), lithological and mineral classification techniques from Short-Wave Infrared (SWIR) and Thermal Infrared (TIR) data. The chapter aims to delineate various geological units present in the area using the combination of SWIR and TIR ASTER bands through the Feature-Oriented Principal Component Selection (FPCS) technique. Different BRs and RBDs were applied to map the minerals having Al-OH and Mg-OH compounds with the chemical composition of clay (kaolinite, smectite), mica (sericite, muscovite, illite), ultramafic (lizardite, antigorite, chrysotile), talc, and carbonate (dolomite) from SWIR bands. The MI was used to map quartz-rich, mafic/ultramafic, and carbonate rocks using TIR bands. The BRs, RBDs, and MIs mapped the geological units but every single greyscale image showed a variety of features. To compile these features False Color Composite (FCC) was prepared by the combination of RBDs and MIs in the R:G:B channels which demarked various geological units to a larger extent present in the region. To overcome the limitation, the FPCS technique was applied with the integration of all BRs, RBDs, and MIs. The FPCS technique extracts valuable information from different input bands and shifts the information in the first few bands. The generated eigenvalues and eigenvectors represented the retrieved information in the specific band. The loadings of the eigenvector were used for the selection of the different brands to create the FCC for the delineation of geological strata. The best discrimination was made by the selection of FPCS1, FPCS3, and FPCS6 which differentiated all the geological units like ultramafics, dolomites, thin bands of talc, and muscovite and illite (as phyllite and mica-schist), silica-rich rocks (as quartzite), and granite outcrops.

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Section: à2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature-Oriented Principal Component Selection (FPCS) for Delineation of the Geological Units Using the Integration of SWIR and TIR ASTER Data

Jain¹

2022

Recent Remote Sensing Sensor Applications - Satellites and Unmanned Aerial Vehicles (UAVs)

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“…Hydrothermal alteration and gossan zones are keys to porphyry copper deposit exploration (Lowell and Guilbert 1970;Mars and Rowan 2006;Honarmand et al 2011). Each zone has one or more specific minerals that could be mapped and explored via processing satellite images (Sabins 1999;Hosseinjani Zadeh et al 2014), creating different potassium (K), equilibrium thorium (eTh), equilibrium uranium (eU) radioelement ratios, and processing magnetometry data (Dickson and Scott 1997;Honarmand 2016). Weathering and destruction of copper minerals, transportation, and subsequent deposition within stream sediments could make a catchment basin anomalous in copper element concentration.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the modified AHP-VIKOR for mapping and ranking copper mineralized areas, a case study from the Kerman metallogenic belt, SE Iran

Moud

Shahri²,

Ruitenbeek

et al. 2022

Arab J Geosci

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Exploration and prioritization of potentially mineralized areas by integrating different geoscientific datasets help to manage the time and costs of an exploration project. In this study, copper exploration and prioritization of explored areas were done on the Kerman copper belt, which was formed due to the subduction of the Arabian plate under the Iranian plate. After processing geoscientific layers (geology, hydrothermal alteration zones, stream sediment geochemistry, and airborne geophysics), the analytic hierarchy process (AHP) method was used to compute the weights of geoscientific layers by a decision-making group consisting of ten local experts in copper exploration. The weight of evidence (WofE) method was used to compare and assess the fairness of the AHP weights. The AHP weights were used for the multi-criteria optimization and compromise solution (VIKOR) method to integrate the geoscientific layers and create an AHP-VIKOR copper mineralization potential map. The “v” parameter of the VIKOR method was modified by replacing the concept of mathematical expectation value with the manual assignment. The real value-area (RV-A) fractal method was used to select optimum threshold values for different AHP-VIKOR copper mineralization potential classes. The accuracy of the classified AHP-VIKOR copper mineralized potential map was assessed by creating a confusion matrix and using existing mineralization locations. The average pixel values of the copper mineralization class were extracted and sorted to rank the very high potential areas. The AHP-VIKOR ranking results were compared with recorded ore grade and ore tonnage information of each copper deposition. The results demonstrated that the AHP ranks are roughly similar to the WofE ranks, except for lithology. An accuracy of 84% for classified copper mineralization potential map with the RV-A fractal method was obtained. The used expectation value for the “v” parameter of the VIKOR method showed that the decision-making group’s opinion was more important than the individual opinions (v = 0.6). The AHP-VIKOR method identified mineralization areas, especially porphyry deposits. Also, the outcome of the AHP-VIKOR ranking showed that the currently active mines, especially porphyry deposits, have the highest priority for mining. This study showed that the AHP-VIKOR approach helps explore and rank favorable areas with high copper ore grade and tonnage characteristics.

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“…As a field of active research in airborne geophysics (e.g. Pitcher et al ., 1994; Honarmand et al ., 2002; Ranjbar et al ., 2004; Karimpour et al ., 2008; Hewson and Robson, 2014; Yazdi, et al ., 2015; Honarmand, 2016; Gemail et al ., 2016; Eldosouky et al ., 2017 and 2020), processing and integrating airborne data (geophysics, remote sensing satellite and other geological data) for prioritizing favourable zones in association with ore mineralization have been widely considered in several retrospective studies.…”

Section: Introductionmentioning

confidence: 99%

Integration of airborne geophysics and satellite imagery data for exploration targeting in porphyry Cu systems: Chahargonbad district, Iran

Riahi

Bahroudi

Abedi

et al. 2021

Geophysical Prospecting

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This study illustrates the application of a geometric average integration of aeromagnetic, radiometric and satellite imagery data over a region prone to Cu‐bearing mineralization at Chahargonbad area in Kerman province of Iran. Processing aeromagnetic, radiometric and Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data can provide exploratory insights about favourable zones in association with porphyry‐type ore occurrences, which can be synthesized through a combination of knowledge‐ and data‐driven approaches as a geometric average and be represented in a mineral prospectivity map. The existence of known deposits in a prospect region can facilitate the investigation of significant exploratory footprints extracted from airborne data by calculating each indicator layer's weight by plotting a prediction–area curve accompanied by a concentration–area fractal curve. Among various indicators, the most important ones are determined based on derived weights from the prediction–area plots to be synthesized in a single Cu favorability map. To fulfil this aim, indicator layers from airborne geophysics (magmatic bodies, magnetic lineaments and potassium radiometry) and remote‐sensing data (alterations such as argillic, phyllic, propylitic and iron oxide along with geological lineaments) were prepared and evaluated using the known porphyry Cu mineralization by the simultaneous plot of the concentration–area fractal model and the prediction–area curve to attain the ore prediction rate and the relevant occupied area of each map for weight assignment of indicators. The geometric average prospectivity model was applied to synthesize the leading indicators, and the result was compared with a multi‐class index overlay map. This study's significance lies in improvement of the performance of the mineral prospectivity/potential mapping after running a geometric average by a higher ore prediction rate of 79%, which has occupied 21% of the area as potential zones for further mining investigations.

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Application of Airborne Geophysical and ASTER Data for Hydrothermal Alteration Mapping in the Sar-Kuh Porphyry Copper Area, Kerman Province, Iran

Cited by 15 publications

References 18 publications

Feature-Oriented Principal Component Selection (FPCS) for Delineation of the Geological Units Using the Integration of SWIR and TIR ASTER Data

Feature-Oriented Principal Component Selection (FPCS) for Delineation of the Geological Units Using the Integration of SWIR and TIR ASTER Data

Evaluation of the modified AHP-VIKOR for mapping and ranking copper mineralized areas, a case study from the Kerman metallogenic belt, SE Iran

Integration of airborne geophysics and satellite imagery data for exploration targeting in porphyry Cu systems: Chahargonbad district, Iran

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