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Image of Unsupervised clustering of LA-ICP-MS raster map data for geological interpretation: A case study using epidote from the Yerington district, Nevada

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Unsupervised clustering of LA-ICP-MS raster map data for geological interpretation: A case study using epidote from the Yerington district, Nevada

Ayesha D. Ahmed - Nama Orang; Shawn B. Hood - Nama Orang; David R. Cooke - Nama Orang; Ivan Belousov - Nama Orang;

Raster element concentration maps created using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) can be used to interpret microscale compositional and textural domains within mineral grains. Raster maps are typically evaluated element by element; however, application of statistical techniques (such as cluster analysis) can enhance the generation of geochemical domains to support interpretation of growth zones, core-rim relationships, sector zones, and compositional-textural associations. Clustered LA-ICP-MS map data can be assessed within individual samples and between multiple samples, and can extend insight from the microscopic scale to the regional scale to better understand geological paragenesis of an area.
Our workflow (1) applies a centred log transformation to selected elements in a raster map dataset; (2) uses principal component analysis (PCA) applied to the multi-sample, mono-mineralic dataset to group similar elements in epidote based on geochemical character; (3) applies unsupervised clustering to separate different types and generations of epidote in chemical feature space; (4) presents clustered LA-ICP-MS raster map results for interpretation of inter- and intra-mineral chemical zones; and (5) plots results spatially, across a regional map area, to investigate geological paragenesis.
The workflow is illustrated using samples of epidote from the Yerington porphyry-skarn Cu (Mo–Au) district. In the case study area, six clusters are defined by unique mineral compositions: (1) low U; (2) elevated Pb, Mn and low Fe and Sr; (3) elevated Ce, U and low Mn and Pb; (4) elevated U, Ce; low Mn, Pb; (5) elevated Sr, Fe and low Mn, Pb; and (6) elevated Mn, Sr, and Fe and low Ce and U. The regional distribution of these groups is presented as indicating proximity to the porphyry environment (lower concentrations of Ce, U, As and Sb and higher concentrations of Mn, Sr and Fe) versus retrograde skarn (elevated Ce, U, As and Sb).


Ketersediaan
104551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier., 2020
Deskripsi Fisik
16 hlm PDF, 8.463 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.8, December 2020
Subjek
Geochemistry
PCA
Unsupervised learning
LA-ICP-MS
k-means clustering
Epidote
Ann mason
Nevada
Info Detail Spesifik
-
Pernyataan Tanggungjawab
-
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Unsupervised clustering of LA-ICP-MS raster map data for geological interpretation: A case study using epidote from the Yerington district, Nevada
    Raster element concentration maps created using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) can be used to interpret microscale compositional and textural domains within mineral grains. Raster maps are typically evaluated element by element; however, application of statistical techniques (such as cluster analysis) can enhance the generation of geochemical domains to support interpretation of growth zones, core-rim relationships, sector zones, and compositional-textural associations. Clustered LA-ICP-MS map data can be assessed within individual samples and between multiple samples, and can extend insight from the microscopic scale to the regional scale to better understand geological paragenesis of an area. Our workflow (1) applies a centred log transformation to selected elements in a raster map dataset; (2) uses principal component analysis (PCA) applied to the multi-sample, mono-mineralic dataset to group similar elements in epidote based on geochemical character; (3) applies unsupervised clustering to separate different types and generations of epidote in chemical feature space; (4) presents clustered LA-ICP-MS raster map results for interpretation of inter- and intra-mineral chemical zones; and (5) plots results spatially, across a regional map area, to investigate geological paragenesis. The workflow is illustrated using samples of epidote from the Yerington porphyry-skarn Cu (Mo–Au) district. In the case study area, six clusters are defined by unique mineral compositions: (1) low U; (2) elevated Pb, Mn and low Fe and Sr; (3) elevated Ce, U and low Mn and Pb; (4) elevated U, Ce; low Mn, Pb; (5) elevated Sr, Fe and low Mn, Pb; and (6) elevated Mn, Sr, and Fe and low Ce and U. The regional distribution of these groups is presented as indicating proximity to the porphyry environment (lower concentrations of Ce, U, As and Sb and higher concentrations of Mn, Sr and Fe) versus retrograde skarn (elevated Ce, U, As and Sb).
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