Image of Statistical levelling of multi-element geochemical data

Text

Statistical levelling of multi-element geochemical data

Regional compilations of multi-element geochemistry can show shifts of level and dynamic range between component surveys, for example when analyses have been made using different laboratories or procedures or at different times. To create a unified composite picture of the geochemistry over a region, some form of relevelling of individual surveys may be needed. Existing treatments have focused on individual elements, independently, across individual pairs of map sheets. Such approaches, however, may fail to preserve the important covariance structure between elements of interest, and may risk dependency of the final blend on the order of levelling of sheets. The paper proposes a method for levelling all elements, and their covariances, simultaneously across all component sheets, taking full account of the compositional nature of the data. The method is shown to be computationally feasible, requiring manageable execution time even for large numbers of elements and large numbers of component surveys.

Ketersediaan
116551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
14 hlm PDF, 2.313 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.10, June 2021
Subjek
Geochemical surveys
Data levelling
Compositional analysis
Yukon geochemistry
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Statistical levelling of multi-element geochemical data
    Regional compilations of multi-element geochemistry can show shifts of level and dynamic range between component surveys, for example when analyses have been made using different laboratories or procedures or at different times. To create a unified composite picture of the geochemistry over a region, some form of relevelling of individual surveys may be needed. Existing treatments have focused on individual elements, independently, across individual pairs of map sheets. Such approaches, however, may fail to preserve the important covariance structure between elements of interest, and may risk dependency of the final blend on the order of levelling of sheets. The paper proposes a method for levelling all elements, and their covariances, simultaneously across all component sheets, taking full account of the compositional nature of the data. The method is shown to be computationally feasible, requiring manageable execution time even for large numbers of elements and large numbers of component surveys.
Komentar
Anda harus masuk sebelum memberikan komentar