Mineral Mapping Research Articles

Intelligent framework for mineral segmentation and fluid-accessible surface area analysis in scanning electron microscopy

Imaging is powerful means of sample characterization where mineral abundances and surface areas can be quantified from mineral maps. Images are typically manually processed by domain experts, which is time-consuming, labor intensive, and subjective. Emerging techniques, such as machine learning based image processing, can potentially address these limitations and accelerate image processing but the performance of these models for accurate sample characterization and surface area analysis has not been completely evaluated. This study evaluates the potential of Random Forest and U-Net machine learning methods for mineral characterization and surface area analysis of six sandstone samples. Various input variable sets including filter extracted features, scanning electron microscopy (SEM) backscatter electron (BSE) images and SEM-energy dispersive x-ray spectroscopy images (EDS) images were considered. The evaluation was conducted by providing an intelligent framework that not only evaluates the accuracy of prediction for each pixel but also investigates the accuracy of predicted neighboring pixels. In addition, a new methodology is proposed to distinguish the more susceptible places to dissolution on the surface of a given mineral using a ranked mineral dissolution risk assessment map. The results showed both methods had an acceptable performance with the U-Net model outperforming Random Forest. Both methods showed an improved accuracy when filter extracted features were added to the dataset as input variables. The models’ performance predicting mineral abundances and accessibility agreed well with ground truth data for majority classes (e.g., quartz) compared to minority classes. Finally, the proposed methodology was shown to reliably identify the locations susceptible for dissolution indicated via proposed risk assessment maps. The intelligent segmentation and surface area analysis framework is a promising tool for accelerating the processing of SEM data and reactivity assessment of samples.

Utilizing Remote Sensing and Lithostratigraphy for Iron and Clay Minerals Mapping in the North of Aswan Area, Egypt

Utilizing Remote Sensing and Lithostratigraphy for Iron and Clay Minerals Mapping in the North of Aswan Area, Egypt

Potential of Multispectral Satellite Data for Superficial Iron Oxide Detection in Sulaimaniyah, Iraqi Kurdistan Region

This study primarily investigates the total (Fe) iron presence in Sulaimaniyah Governorate, the Iraqi Kurdistan Region (IKR), which has an abundance of iron mines. Spatial quantification and frequent monitoring of mineral existence in the soil are essential in the mining regions. To achieve this goal, a remote sensing technique was utilized to predict soil minerals, particularly iron, in the study area using a multispectral satellite image, Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A robust methodology was perceived and developed from image processing to estimate and map iron oxide-rich soils, and the soil’s spectral indices were obtained after algorithms were applied in processing the bands of the Landsat image. Soil samples were collected and analyzed in the laboratory to determine the chemical, physical, and mineralogical characteristics of soils. Correlation coefficients were carried out between soil properties and spectral band values retrieved from image analysis to examine the band potentials of Landsat. The statistical results showed that there was a significant relationship between the 3rd band of the ETM+ image and each of the total iron (R2 = 0.643), the free iron oxide (R2 = 0.659), and sand particles (R2 = 0.561). The predicted soil mineral maps were generated for the study area to visualize the study site's soil characterization and total iron spread. This study's results could help primarily identify the spatial distribution of some soil properties in Sulaimaniyah, Iraq.

Generating Soil Parent Material Environmental Covariates Using Sentinel – 2A Images for Delineating Soil Attributes

Soil mapping procedures typically involve the combination of possible soil-forming SCORPAN factors. Among the factors, parent materials/ mineralogy has been considered important for the soil classification besides the Organisms (O) and Relief (R). Inclusion of the parent material covariate for the Digital soil mapping involves implication through geological maps, spectral derivatives and predictive modelling. In this study, the most prominent parent materials identified were derived using the spectral indices formulated based on the Sentinel – 2A multispectral information. While considering the coarse spatial resolution constraints of the existing Landsat -8 bands that may limit certain applications, Sentinel-2 images were used for the indices derivation. The generated mineral maps can support the digital soil mapping of the soil attributes at different spatial scales.

Spectroscopic and Petrographic Investigations of Lunar Mg-Suite Meteorite Northwest Africa 8687

Magnesian suite (Mg-suite) rocks represent plutonic materials from the lunar crust, and their global distribution can provide critical information for the early magmatic differentiation and crustal asymmetries of the Moon. Visible and near-infrared (VNIR) spectrometers mounted on orbiters and rovers have been proven to be powerful approaches for planetary mineral mapping, which are instrumental in diagnosing Mg-suite rocks. However, due to the scarcity and diversity of Mg-suite samples, laboratory measurements with variable proportions of minerals are imperative for spectral characterization. In this study, spectroscopic investigation and petrographic study were conducted on lunar Mg-suite meteorite Northwest Africa 8687. We classify the sample as a pink spinel-bearing anorthositic norite through spectral and petrographic characteristics. The ground-truth information of the Mg-suite rock is provided for future exploration. Meanwhile, the results imply that the VNIR technique has the potential to identify highland rock types by mineral modal abundance and could further be applied in extraterrestrial samples for primary examination due to its advantage of being fast and non-destructive.

Evidential belief functions modeling of geophysical and multi-element geochemical data for Pb-Zn mineral potential targeting

The Evidential Belief Functions model was used for investigating the geochemical and geophysical anomalies and delineating the Pb–Zn mineralization zones in the Chichakloo area, east of Takab, Iran. In addition, we evaluated the uncertainty of geochemical and geophysical results in the detection of mineralization zones. For this purpose, the individual belief, disbelief, plausibility, and uncertainty functions of geophysical and geochemical predictor maps were assigned and then integrated using belief function equations. The obtained belief map reveals a remarkable potential for Pb–Zn mineralization in the east part of the study area with a conservative estimation of more than 50% and uncertainty of less than 10%. An increase in conservative estimate and a decrease in the uncertainty rate in this zone, compared to any of the other individual exploration predictor maps, indicate remarkable overlapping anomalies. The results of drilled boreholes proved the existence of the Pb–Zn mineralization and confirmed the obtained mineral potential map.

Identifying and Mapping Alteration Minerals Using HySpex Airborne Hyperspectral Data and Random Forest Algorithm

Airborne hyperspectral remote sensing data provide rapid, non-destructive, and near laboratory quality reflectance spectra for mineral mapping and lithological discrimination, thereby ushering an innovative era of remote sensing. In this study, NEO HySpex cameras, which comprise 504 spectral channels in the spectral ranges of 0.4–1.0 μm and 1.0–2.5 μm, were mounted on a delta wing XT-912 aircraft. The designed flexibility and modular nature of the HySpex aircraft hyperspectral imaging system made it relatively easy to test, transport, install, and remove the system multiple times before the acquisition flights. According to the design fight plan, including the route distance, length, height, and flight speed, we acquired high spectral and spatial resolutions airborne hyperspectral images of Yudai porphyry Cu (Au, Mo) mineralization in Kalatag District, Eastern Tianshan terrane, Northwest China. By comparing the features of the HySpex hyperspectral data and standard spectra data from the United States Geological Survey database, endmember pixels of spectral signatures for most alteration mineral assemblages (goethite, hematite, jarosite, kaolinite, calcite, epidote, and chlorite) were extracted. After a HySpex data processing workflow, the distribution of alteration mineral assemblages (iron oxide/hydroxide, clay, and propylitic alterations) was mapped using the random forest (RF) algorithm. The experiments demonstrated that the workflow for processing data and RF algorithm is feasible and active, and show a good performance in classification accuracy. The overall classification accuracy and Kappa classification of alteration mineral identification were 73.08 and 65.73%, respectively. The main alteration mineral assemblages were primarily distributed around pits and grooves, consistent with field-measured data. Our results confirm that HySpex airborne hyperspectral data have potential application in basic geology survey and mineral exploration, which provide a viable alternative for mineral mapping and identifying lithological units at a high spatial resolution for large areas and inaccessible terrains.

Integrating Aster 07XT, Landsat 8, and aeromagnetic data for the delineation of potential mineralization sites in North Cameroon

In mineral exploration, detailed hydrothermally altered minerals mapping is an important approach prior to the field campaign. The study area contains several gold‐bearing quartz veins associated with hydrothermally altered zones rich in sulphides, muscovite/sericite, iron oxides, and hydrous silica. In this study, Aster 07XT, Landsat 8, and aeromagnetic data were integrated to detect potential zones associated with gold mineralization in Northern Cameroon. For this purpose, the relative band absorption depth, band ratioing, and analytic signal techniques were applied to enhance, identify and map hydrothermal alteration minerals, that is, hydroxyl‐bearing, iron oxide/hydroxide. In addition, a semi‐automatic approach was applied to extract remotely sensed and aeromagnetic lineaments from PCI Geomatica and Centre for Exploration Targeting Grid Analysis respectively. Hence, several potential mining targets were delineated from the processing of both remote sensing and aeromagnetic data. The major directions of the lineaments affecting the area include NE–SW/ENE–WSW, E–W, and N–S. Detailed field campaigns, petrographic analyses, and spectral measurements of rock samples were carried out in Tchollire and environs to verify the results from remotely sensed data. The analytical observations indicated the presence of quartz, epidote, chlorite, magnetite, haematite, sericite, galena, sulphide, telluride minerals, etc. Moreover, the target hydrothermal alteration zones were proved to be spatially associated with known mining areas and gold occurrences in the study area. Consequently, new prospective mining areas have been designated. This study confirmed the reliability and applicability of the processing of satellite imagery in mineral exploration in Northern Cameroon. It is suggested that the dataset used in this study provides a suitable tool for mapping hydrothermal alteration minerals linked to gold deposits and can be applied in other provinces with similar geological settings.

Improving the accuracy of hydrothermal alteration mapping based on image fusion of ASTER and Sentinel-2A data: a case study of Pulang Cu deposit, Southwest China

Mapping hydrothermal alteration is an important means of mineral exploration, therefore, effective improvement of the accuracy of identification of hydrothermal alteration minerals is a hot topic. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2A data were selected for the Pulang copper deposit, to improve the accuracy of hydrothermal alteration mineral mapping. Three data fusion methods of Principal Component Analysis (PCA), Gram–Schmidt (GS) and High-Pass Filtering (HPF) were employed, and method of spectral matching was introduced to obtain high spatial resolution and high spectral fidelity fused data. The comparison showed that the HPF fusion method had the best effect. The fused data by the HPF fusion method were then implemented to map hydrothermal alteration minerals by using a multifractal-based method of PCA + Spectrum–Area. Through the verification of a field investigation, the results exhibited a similar hydrothermal alteration distribution, compared with the results obtained from the original ASTER data, but with higher identification accuracy. Therefore, the case study strongly suggests that the image fusion method with spectral matching is an effective tool to increase spatial resolution, while maintaining high spectral fidelity. Thus, it is a valuable and economic method for improving the identification accuracy of hydrothermal alteration minerals. HIGHLIGHTS The spectral matching method was introduced to unify the surface reflectance of ASTER and Sentinel-2A data. Principal Component Analysis (PCA), Gram-Schmidt (GS) and High-Pass Filtering (HPF) methods were employed to fuse AST ER and Sentinel-2A data. The results show that the extraction accuracy of hydrothermal alteration minerals based on AST ER - Sentinel-2A fused data has 6.98% higher than that of original ASTER data.

Squirrel Search Optimization with Deep Transfer Learning-Enabled Crop Classification Model on Hyperspectral Remote Sensing Imagery

With recent advances in remote sensing image acquisition and the increasing availability of fine spectral and spatial information, hyperspectral remote sensing images (HSI) have received considerable attention in several application areas such as agriculture, environment, forestry, and mineral mapping, etc. HSIs have become an essential method for distinguishing crop classes and accomplishing growth information monitoring for precision agriculture, depending upon the fine spectral response to the crop attributes. The recent advances in computer vision (CV) and deep learning (DL) models allow for the effective identification and classification of different crop types on HSIs. This article introduces a novel squirrel search optimization with a deep transfer learning-enabled crop classification (SSODTL-CC) model on HSIs. The proposed SSODTL-CC model intends to identify the crop type in HSIs properly. To accomplish this, the proposed SSODTL-CC model initially derives a MobileNet with an Adam optimizer for the feature extraction process. In addition, an SSO algorithm with a bidirectional long-short term memory (BiLSTM) model is employed for crop type classification. To demonstrate the better performance of the SSODTL-CC model, a wide-ranging experimental analysis is performed on two benchmark datasets, namely dataset-1 (WHU-Hi-LongKou) and dataset-2 (WHU-Hi-HanChuan). The comparative analysis pointed out the better outcomes of the SSODTL-CC model over other models with a maximum of 99.23% and 97.15% on test datasets 1 and 2, respectively.

Implications and interrelations of litho-boundaries and vicinity of lineaments for hydrothermal alteration zones under remote sensing and GIS environment

The applicability and significance of remote sensing images varies according to capability of synoptic view. These synoptic views of satellite images have tremendous utility to solve many geological enigmas due to constraints of limitations of investigations (manual/ mapping error) in the field of geology. To prepare more accurate remote locations, structural maps such as lineament, fold, faults, shear zone, geomorphology, lithology and mineralogy of remote sensing image helps to solve these problems. In the research work of the hydrothermal alteration minerals zoning and identification completed through spectroscopic and advanced visible-infrared imaging spectroscopy new generation (AVIRIS-NG) hyperspectral remote sensing data. To establish intercorrelation and implications of lineaments with hydrothermal alteration zone of the study area prepared lineament map through manual and auto extraction using Landsat 7 ETM+ and ASTER digital elevation model (DEM) remote sensing data with the applications of algorithms such as Sobels filtering and “LINE” tool of PCI Geomatica. The networks of lineaments have a significant role in demarcation of potential mineral zones. To get vicinity of lineaments, prepared a lineament density map through extracted lineaments. In the final output the vicinity of lineaments density, mineral sample locations, mineral map and regolith zones have shown strong correlation among each other. So, the conclusion is that there is a strong significance in intercorrelation of hydrothermal alteration zones, lineaments and litho-boundaries.

Mineralogical mapping using EO-1 Hyperion data for iron mine identification

The Tamera open pit mine is located in the Nefza mining district (north-west of Tunisia) where the reddish soil reveals its strong potential for heavy Fe mineralization, along with other metals (Pb, Zn, and Mn). This study explored Iron oxide mineralization in a particular nappe zone using multiscale field visible and near infrared/short waves infrared and EO-1 Hyperion hyperspectral data. Thus, spectral features fittings were conducted on Hyperion data for mineral mapping using scene-extracted endmembers. A first minimum noise fraction algorithm was used to reduce the hyperspectral data’s dimensionality, followed by the Pixel Purity Index and n-dimensional visualization to extract the pure pixels (endmembers) from the cluster of pixels in the data cube. Furthermore, 56 soil samples were collected from the test site and spectrally measured using Analytical Spectral Devices FieldSpecHiRes spectrometer, which covers the spectral range between 350 and 2500 nm. All samples were analyzed in the laboratory with x-ray diffraction. Endmember extracted spectra were compared with field measurements before EO-1 Hyperion processing. Mapping results revealed the occurrence of a mineralogy composition consisting particularly of iron oxide minerals (hematite and goethite) and bearing minerals, such as clays (kaolinite), within all samples.

Assessment of new spectral indices and multi-seasonal ASTER data for gypsum mapping

Advanced spaceborne thermal emission and reflection radiometer (ASTER) data and different spectral indices were employed to map gypsum minerals. However, most proposed gypsum indices are designed based on the 2.21 μm gypsum absorption, overlapping with most hydroxy-bearing minerals. Moreover, the seasonal mutual transformation between gypsum, bassanite, and anhydrite may lead to seasonal reflectance variability of gypsum formation pixels, affecting the classification accuracy of gypsum indices. In this research, the feasibility of 2.26 μm (ASTER band7) reflectance absorption for gypsum mapping was assessed, using lab and ASTER reflectance. On the basis of this, two new ASTER gypsum spectral indices (GI1: B4*B8/B6*B7; GI2: B4*B8/B7*B7) were proposed and applied to exclude the interference of hydroxyl-bearing minerals effectively. Seasonal reflectance variability of gypsum formation pixels was confirmed, and it causes the accuracy difference of gypsum indices for multi-seasonal ASTER data. The GI1 achieves the most robust accuracy for multi-seasonal ASTER data with average areas under receiver operator characteristic (ROC) curve of 98.5% and 98.7% for summer and winter ASTER data. Therefore, the GI1 can be used for gypsum mineral mapping, especially in the areas where clay minerals and other hydroxyl-containing minerals are widely distributed.

Toward a mineral potential map of central Aceh, North Sumatra: contributions from spectrum-area and local singularity fractal analysis of stream sediment geochemistry and its relationships to magmatic-hydrothermal Cu and Au mineralisation

ABSTRACT A reassessment of the geochemistry of stream sediments collected by the British-led North Sumatra Project from 1975 to 1980 in central Aceh with spectrum–area and local singularity fractal techniques refines previously determined areas with anomalous Cu and delineates As anomalies (proxy for Au) that correspond to porphyry Cu and epithermal Au–Ag deposits and prospects discovered in the 1980s and 1990s. Additionally, the application of these methods to stream sediment samples collected by an Australian company in the mid-1990s from part of central Aceh identifies Au, As, and Cu anomalies that are aligned with regional geochemical trends. Integrating singularity metal-enriched areas and the distribution of mineral occurrences and structures reveals northerly and easterly tectono-magmatic corridors controlling epithermal Au–Ag and porphyry Cu mineralisation. The above results attest to the usefulness of legacy data for first-pass mineral assessments and for providing evidence layers for a future mineral prospectivity map.

Evolution and genesis of hydrothermal fluids for the Cretaceous Dongnan Cu deposit, Zijinshan ore district (SE China)

The Dongnan Cu(-Mo) deposit is a newly discovered porphyry-epithermal system located between the Zijinshan high-sulfidation (HS) epithermal deposit and Luoboling porphyry deposit, within the Zijinshan ore district, a large porphyry-epithermal Cu-Au-Mo-Ag ore system in southeast China. The deposit offers a window to investigate the origins of hydrothermal fluids in a coupled porphyry-HS epithermal magmatic-hydrothermal system.Drill hole logging and alteration mineral mapping has identified four hydrothermal alteration and mineralization stages at the Dongnan deposit, namely: stage I porphyry-type alteration, which can be subdivided into stage IA potassic alteration and stage IB phyllic alteration, stage II HS mineralization and associated alteration which can be subdivided into stage IIA-1 pyrophyllite-dominated, stage IIA-2 illite and illite-montmorillonite and stage IIB dickite-dominated alteration, stage III late veins (i.e., calcite and fluorite) and stage IV supergene alteration. Chalcopyrite and molybdenite mineralization is associated with Stage I, whereas covellite and digenite mineralization formed during Stage II. Stage IB minerals (i.e., muscovite) were largely superimposed by minerals from stage II (i.e., dickite, kaolinite, pyrophyllite and alunite). Stage II epithermal alteration is located mainly in the western part of Zijinshan ore district, whereas the eastern part is dominated by porphyry-type mineralization and alteration. Microthermometry results show the average Th and salinity of each sample suite from stage IB ranging from 341 ± 22 to 442 ± 2 °C and 1.6 ± 1.6 to 19.2 ± 2.6 wt% NaCl equiv. By contrast, stage III fluid inclusions are liquid-dominated, with low temperature of homogenization and low salinity (156 ± 9 to 165 ± 4 °C and < 0.5 wt% NaCl equiv.). Thermodynamic modelling of stage II suggest that the fluid responsible for epithermal mineralization was characterized by high temperature (270 to 320 °C), high water to rock (w/r > 1) and high SO2 to H2S ratios (SO2/H2S > 1). Because stage IIA pyrophyllite alteration is overprinted by late stage IIB dickite alteration and relatively different forming condition for pyrophyllite (>270 °C) and kaolinite (<270 °C and w/r > 1), stage IIA and stage IIB may have resulted from two different fluids. The Dongnan porphyry-type mineralization formed mainly in response to the cooling and dilution, led by the mixing of acidic magmatic water with meteoric water, whereas stage II alteration was more likely related to volatile gas escaping, resulting in pH decrease and contributing to the formation of pyrophyllite, dickite and alunite.The distribution of alteration minerals and evolution of hydrothermal fluids all indicate that the hydrothermal fluids for the porphyry-type and HS epithermal-type alteration at Dongnan came separately from nearby Luoboling granodiorite porphyry and the Zijinshan HS epithermal Cu-Au deposit, which implies that Luoboling porphyry deposit and Zijinshan HS deposit formed from separate magmatic-hydrothermal events. It suggests that multiple magmatic centers exist in the Zijinshan ore district, a finding which will contribute to both understanding of ore genesis of this overprinted porphyry-epithermal deposit and further mineral exploration in the Zijinshan Ore district.

Two hydrothermal events associated with Au mineralization in the Youjiang Basin, southwestern China

The Youjiang Basin in southwestern China hosts remarkably large amounts of Carlin-type Au deposits. In such deposits, Au is mainly ionically bound to arsenian pyrite characterized by core and rim textures. Understanding the formation mechanism of zoned pyrite may improve our understanding of the genesis of massive Carlin-type Au deposits. This study examined the elemental content, mineral composition, and pyrite texture and chemistry of wall rock and ore from the Lannigou Au deposit, one of the largest Carlin-type Au deposits in the Youjiang Basin, to identify the genetic mechanisms of zoned pyrite.Five distinct types of pyrites, i.e., pyrite 1 (Py1), 2 (Py2), 3 (Py3), 4 (Py4), and ore pyrite (OPy), were identified in the Lannigou Au deposit. Py1, Py2, and Py3 were mainly observed in the wall rock, whereas Py4 and OPy, constituting the core and rim of zoned pyrite, respectively, were only discovered in the ore. Mineral mapping of the wall rock and ore indicated that pyrite is scarce in the wall rock (0.021 vol%). However, the amount of zoned pyrite significantly increased in the ore (1.275 vol%). Py1, Py2, and Py3, showing framboidal, nodular, and fine euhedral microcrystal textures, had high Pb, Co, Ni, Mn, Zn, and Mo contents (medians ranging from 10 to 1,000 ppm) and variable δ34S compositions ranging from −5.1 to 35.4‰, implying a sedimentary (syngenetic or diagenetic) origin. Py4 was homogeneous and contained numerous illite and quartz inclusions and fractures. In contrast, OPy showed multiple As-rich sub-bands and a lack of illite or quartz inclusions and fractures. Py4 had significantly lower Au, As, Tl, and Cu contents than OPy; however, Py4 had Pb, Co, Ni, Mn, Zn, and Mo contents similar to OPy. The range of δ34S values for Py4 and OPy were both narrow, but Py4 (7.6 to 13.3‰, with an average of 9.1‰) had lower δ34S values than OPy (9.4–14.1‰, with an average of 12.4‰). These texture and chemical features indicate that Py4 likely sedimented from a pre-ore hydrothermal fluid, but not at the early stage of the ore-forming fluid.Hydrothermal pyrite cores were also widespread and exhibited textural and chemical differences compared to Au-rich pyrite rims from other Au deposits in the Youjiang Basin. Based on the two geochronological clusters of Au deposits in the Youjiang Basin, we inferred the occurrence of at least two episodes of large-scale hydrothermal events. The two hydrothermal fluid episodes, which contributed to the zoned pyrite formation, likely originated from the mixing of initial magmatic fluid with δ34S values of ∼0 ± 5‰ and basin brine with elevated δ34S values (δ34S > 18‰). Pre-ore hydrothermal pyrite likely resulted in the rapid non-equilibrium crystallization of ore pyrite; thus, more Carlin-suite elements were captured by ore pyrite. Additionally, low-grade Au orebodies may have locally formed during the pre-ore hydrothermal event. These multiple hydrothermal events may have been crucial for the formation of massive Carlin-type Au deposits in the Youjiang Basin.

ASTER and GF-5 Satellite Data for Mapping Hydrothermal Alteration Minerals in the Longtoushan Pb-Zn Deposit, SW China

Hydrothermal alteration minerals are an effective prospecting indicator. Advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite data are some of the most commonly adopted multispectral data for the mapping of hydrothermal alteration minerals. Compared to multispectral data, hyperspectral data have stronger ground object recognition ability. Chinese Gaofen-5 (GF-5) is the first hyperspectral satellite independently developed by China that has the advantages of both wide-width and high-spectral-resolution technology. However, the mapping ability of GF5 data for hydrothermal alteration minerals requires further study. In this study, ASTER and GF-5 satellite data were implemented to map hydrothermal alteration minerals in the Longtoushan Pb-Zn deposit, SW China. Selective principal component analysis (SPCA) technology was employed to map iron oxide/hydroxides, argillic, quartz, and carbonate minerals at the pixel level using ASTER data, and the mixture tuned matched filtering (MTMF) method was implemented for the extracted hematite, kaolinite, calcite, and dolomite at the sub-pixel level using GF-5 data. When mapping the hydrothermal alteration minerals, the distribution features of the hydrothermal alteration minerals from the Longtoushan Pb-Zn deposit were systematically revealed. A comprehensive field investigation and petrographic study were conducted to verify the extraction accuracy of the hydrothermal alteration minerals. The results showed that the overall accuracies for the ASTER and GF-5 data were 82.6 and 92.9 and that the kappa coefficients were 0.78 and 0.90, respectively. This indicates that the GF-5 data are able to map hydrothermal alteration minerals well and that they can be promoted as a hyperspectral data source for mapping systematic hydrothermal alteration minerals in the future.

MAPPING THE MINERAL ZONATION AT THE ERNEST HENRY IRON OXIDE COPPER-GOLD DEPOSIT: VECTORING TO Cu-Au MINERALIZATION USING MODAL MINERALOGY

Abstract Iron oxide copper-gold (IOCG) deposits form in spatial and genetic relation to hydrothermal iron oxide-alkali-calcic-hydrolytic alteration and thus show a mappable zonation of mineral assemblages toward the orebody. The mineral zonation of a breccia matrix-hosted orebody is efficiently mapped by regularly spaced samples analyzed by the scanning electron microscopy-integrated mineral analyzer technique. The method results in quantitative estimates of the mineralogy and allows the reliable recognition of characteristic alteration as well as mineralization-related mineral assemblages from detailed mineral maps. The Ernest Henry deposit is located in the Cloncurry district of Queensland and is one of Australia’s significant IOCG deposits. It is known for its association of K-feldspar altered clasts with iron oxides and chalcopyrite in the breccia matrix. Our mineral mapping approach shows that the hydrothermal alteration resulted in a characteristic zonation of minerals radiating outward from the pipe-shaped orebody. The mineral zonation is the result of a sequence of sodic alteration followed by potassic alteration, brecciation, and, finally, by hydrolytic (acid) alteration. The hydrolytic alteration primarily affected the breccia matrix and was related to economic mineralization. Alteration halos of individual minerals such as pyrite and apatite extend dozens to hundreds of meters beyond the limits of the orebody into the host rocks. Likewise, the Fe-Mg ratio in hydrothermal chlorites changes systematically with respect to their distance from the orebody. Geochemical data obtained from portable X-ray fluorescence (p-XRF) and petrophysical data acquired from a magnetic susceptibility meter and a gamma-ray spectrometer support the mineralogical data and help to accurately identify mineral halos in rocks surrounding the ore zone. Specifically, the combination of mineralogical data with multielement data such as P, Mn, As, P, and U obtained from p-XRF and positive U anomalies from radiometric measurements has potential to direct an exploration program toward higher Cu-Au grades.

Compositional Maps of the Lunar Polar Regions Derived from the Kaguya Spectral Profiler and the Lunar Orbiter Laser Altimeter Data

Abstract Due to the challenging illumination conditions of the lunar polar regions, mineralogic maps have generally been constrained to within 0°–70°N/S. Here we generate a gridded reflectance data cube from the Kaguya Spectral Profiler measurements for each polar region and calibrate it to absolute reflectance using data from the Lunar Orbiter Laser Altimeter. We use this data set to derive the first quantitative mineral maps of iron oxide (FeO), the optical maturity parameter (OMAT), and nanophase iron poleward of 50°N/S at a spatial resolution of 1 km pixel–1. We evaluate potential latitudinal trends in space weathering and optical maturity and characterize the polar geology, with an emphasis on the Artemis region (84°–90°S). The maps of FeO are in excellent agreement with the abundances measured by the Lunar Prospector and provide an increased level of detail, such as the excavation of high- and low-FeO material by De Forest crater. The OMAT maps offer a fantastic view of both polar regions. They highlight small fresh craters, walls, and central peaks, as well as rays extending through multiple degrees of latitude, such as those from Tycho and De Forest, which extend into the Artemis region. Both polar regions are characterized by the ubiquitous presence of noritic anorthosites and anorthositic norite. Low-calcium pyroxene is largely the dominant mafic mineral present. The Artemis region has relatively homogeneous FeO and plagioclase content at the observed spatial resolution. The lowest FeO values are found near Shackleton, between Shoemaker and Faustini, and on the central peak of Amundsen crater.

A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization

The emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a classical hyperspectral classification method, spectral matching (SM) can automatically map the spatial distribution of minerals without the need for selecting training samples. However, due to the influence of noise, the mapping accuracy of SM is usually poor, and its per-pixel matching method is inefficient to some extent. To solve these problems, we propose an unsupervised clustering-matching mapping method, using a combination of k-means and SM (KSM). First, nonnegative matrix factorization (NMF) is used and combined with a simple and effective NMF initialization method (SMNMF) for feature extraction. Then, k-means is implemented to get the cluster centers of the extracted features and band depth, which are used for clustering and matching, respectively. Finally, dimensionless matching methods, including spectral angle mapper (SAM), spectral correlation angle (SCA), spectral gradient angle (SGA), and a combined matching method (SCGA) are used to match the cluster centers of band depth with a spectral library to obtain the mineral mapping results. A case study on the airborne hyperspectral image of Cuprite, Nevada, USA, demonstrated that the average overall accuracies of KSM based on SAM, SCA, SGA, and SCGA are approximately 22%, 22%, 35%, and 33% higher than those of SM, respectively, and KSM can save more than 95% of the mapping time. Moreover, the mapping accuracy and efficiency of SMNMF are about 15% and 38% higher than those of the widely used NMF initialization method. In addition, the proposed SCGA could achieve promising mapping results at both high and low signal-to-noise ratios compared with other matching methods. The mapping method proposed in this study provides a new solution for the rapid and autonomous identification of minerals and other fine objects.