Mapping of hydrothermal alteration in the Cuprite mining district, Nevada, using aircraft scanner images for the spectral region 0.46 to 2.36µm

Abrams, Michael J.; Ashley, Roger P.; Rowan, Lawrence C.; Goetz, Alexander; Kahle, Anne B.

doi:10.1130/0091-7613(1977)5<713:mohait>2.0.co;2

Cited by 164 publications

(87 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The AVIRIS derived mineral distribution maps for the Cuprite mining district, Nevada, well known in the remote sensing community as a test site for many years because of its exposed altered bedrock, alluvial deposits and minimal vegetation cover (e.g. Abrams et al, 1977;Kruse et al, 1990;Rast et al, 1991), is given in Figure 14 (a, b). Figure 14 (a, b) utilizes the tricorder algorithm (Clark et al, 2003), that uses a digital spectral library of known reference minerals and a fast, modified least squares method of determining if a diagnostic spectral feature for a given mineral is present in the image.…”

Section: Imaging Spectrometrymentioning

confidence: 99%

Application of remote sensing and GIS in mineral resource mapping-An overview

Rajesh

2004

Journal of Mineralogical and Petrological Sciences

107

View full text Add to dashboard Cite

Remote sensing, as a direct adjunct to field, lithologic and structural mapping, and more recently, GIS have played an important role in the study of mineralized areas. A review on the application of remote sensing in mineral resource mapping is attempted here. It involves understanding the application of remote sensing in lithologic, structural and alteration mapping. Remote sensing becomes an important tool for locating mineral deposits, in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithologic mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. In addition to these, understanding the use of hyperspectral remote sensing is crucial as hyperspectral data can help identify and thematically map regions of exploration interest by using the distinct absorption features of most minerals. Finally coming to the exploration stage, GIS forms the perfect tool in integrating and analyzing various georeferenced geoscience data in selecting the best sites of mineral deposits or rather good candidates for further exploration.

show abstract

Section: Imaging Spectrometrymentioning

confidence: 99%

Application of remote sensing and GIS in mineral resource mapping-An overview

Rajesh

2004

Journal of Mineralogical and Petrological Sciences

107

View full text Add to dashboard Cite

show abstract

“…Pionecr work by a nwnbcr of authors, including Abrams et al (1977Abrams et al ( , 1983, Rowan & Kahle (1982) and Podwysocki et al (1983), demonstrated the potential of the 2.2 )lm region of the electromagnetic spectrum for detecting hydroxyl-bearing minerals and carbonates usually associated with hydrothermal alteration processes. This was added to previous work demonstrating the usefulness of the visible/near infrared region of thc spectrum for mapping Fe3~_bearing minerals (Rowan el al., 1974(Rowan el al., , 1977Goetz et aI., 1975;Goetz & Rowan, 1981).…”

Section: Txtroductionmentioning

confidence: 99%

Evaluating geoscan AMSS MK-II for gold exploration in the Fazenda Maria Preta District, Rio Itapicuru greenstone belt, Bahia State, Brazil

Prado

Crósta

1997

Bol. IG-USP, Sér. cient.

View full text Add to dashboard Cite

The Rio ltapicuru Greenstone Belt (RI GB), located in north-eaSlem Brazil, is an importanl gold producing region. II hosts two important mining districts, the Faunda Brasileiro and the Fazenda Maria Prela, owned by Companhia Vale do Rio Once (CVRO), plus a number of gold OCCll!Tenccs. all associated with hydrothermal alteralion along shear zones. In 1992, Geoscan multispectral scanner data were acquired for mineral exploration purposes over more than 7,500 km' in this greenstone helt. Geoscan AMSS is a 24-band airborne scanner, with 5 meter spatial resolution Standard image processing (e.;hniques, applied immediately after data acquisition, defmed some spectral anomalies, particularly due to silicification and oxidation, which could be due either to hydrothermal alteration or to weathering processes. We have reprocessed the Geoscan data over a portion of the RIGB in order to define possible anomalies due to hydrothermal alteration minerals, using the feature-oriented principal component selection (FPCS) technique, modified for use with Geos.can data. Laboratory reflectance spectra of residual soils were acquired to establish superficial expression of hydroth ennal minera ls that OCCur in ~ub-surfac e and to guide image processing and interpretation. This Jed 10 the production of "mineral component" images, in which the spalial distribution of some minerals are shown. Results of the spectral analysis of soil samples showed that goethite may be used as a surface indicator of hydrothennal alteration and that it is not possible to differentiate the alteration zones from surrounding rocks based on the hydroxyl or carbonate spectral features RESUMO o Greenstone Belt do Rio Jtapicuru (RIGB). localizado no nordeste do Brasil, e uma importante regiilo produtora de ouro no pais. Ele comern dois importantes distritos mineiros, 0 da Fazenda Brasileiro e Fazenda Maria Prela, pertenccntes a Companhia Vale do Rio Doce (CVRD), alem de diversas outras ocorrencias auriferas. todas elas associadas a processo de a1tera~~o hidrotermal ao longo de zonas de cisalhamento. Em 1992. imagens do sensor multiespectral Geoscan foram obtidas ern mais de 7.500 kml do greenstone. 0 Geos.call AMSS Mk-Il t urn sensor aeroportado que cobre 24 banda~ espectrais, com S metros de resolu~a.o espaciaJ no caso deste levantamemo. Tecn icas padrao de proccssamento de imagens, aplicadas imediatamente ap6s 0 levantamento. definiram uma serie de anomalias cspcctrais, re lacionadas a processos de si1icifica~ilo e oxidao;.1o. que podem tanto ser devidos a processes de altera~a.o 'Companhia de RecUT"" Minerais (CPRM) -Servi~o Geologico do Brasil. Manaus. AM. Brasil. llrtstituto de Oeocienei"siUNTCAMP. Campinas. SP_ Brasil

show abstract

“…The value of element (i, j) indicates the J-M distance between two classes. For example, element (3,4) indicates the vector combination in Series 3 and row 4 in Table 2, namely SF + TF126; the value of element (3,4) indicates the J-M distance between two classes calculated using the vector combination of SF + TF126. The matrices in Figure 5 were quantified using shades of color, which will help to visually distinguish the different performances of vector combinations in identifying pairwise rocks.…”

Section: Feature Vector Selectionmentioning

confidence: 99%

“…Based on the spectral features, from visible to near-infrared (VNIR) wavelengths, some iron-bearing minerals, such as goethite and hematite, have been found in prior research to be relatively easy to distinguish [1,2]. Because of the fundamental vibrations of Al-OH, Mg-OH, and CO 3 2− bonds in shortwave infrared (SWIR) wavelengths, many alteration minerals such as carbonates, sulfates, hydroxides, and oxides have been successfully identified and mapped using remote sensing methods [1][2][3][4]. In thermal infrared (TIR) wavelengths, igneous rocks with relatively high SiO 2 contents have diagnostic emission spectral features due to the vibration of the Si-O bond, based on which, some rock indices have been constructed, such as the sulfuric acid rock index and the carbonate rock index [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

2016

View full text Add to dashboard Cite

Abstract:Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension method. Different combinations of texture images, fused with all seven spectral bands, were examined using the Jeffries-Matusita (J-M) distance to select the optimal input feature vectors for image classification. Then, a support vector machine (SVM) fusing textural and spectral features was applied for image classification. The results showed that the fused SVM classifier achieved an overall classification accuracy of 83.73%. Compared to the conventional classification method, which is based only on spectral features, the accuracy achieved by the fused SVM classifier is noticeably improved, especially for granite and quartzose rock, which shows an increase of 38.84% and 7.03%, respectively. We conclude that the integration of textural and spectral features is promising for lithological classification when an appropriate method is selected to derive texture images and an effective technique is applied to select the optimal feature vectors for image classification.

show abstract

Mapping of hydrothermal alteration in the Cuprite mining district, Nevada, using aircraft scanner images for the spectral region 0.46 to 2.36µm

Cited by 164 publications

References 0 publications

Application of remote sensing and GIS in mineral resource mapping-An overview

Application of remote sensing and GIS in mineral resource mapping-An overview

Evaluating geoscan AMSS MK-II for gold exploration in the Fazenda Maria Preta District, Rio Itapicuru greenstone belt, Bahia State, Brazil

Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

Contact Info

Product

Resources

About