Composition and discrimination of sandstones:: A statistical evaluation of different analytical methods

被引:111
|
作者
von Eynatten, H
Barceló-Vidal, C
Pawlowsky-Glahn, V
机构
[1] FSU Jena, Inst Geowissensch, D-07749 Jena, Germany
[2] Univ Girona, Dept Informat & matemat Aplicada, E-17071 Girona, Spain
关键词
D O I
10.1306/070102730047
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
The discriminative power of four analytical approaches to sandstone composition is evaluated with respect to the separation of different formations and source areas. The case study is Cretaceous synorogenic sandstones (litharenites) from the Eastern Alps of Europe, which belong to four different formations and are derived from two source areas. Methods evaluated are light-mineral analysis (petrographic framework composition), heavy-mineral analysis, major-element XRF analysis, and trace-element XRF analysis. The statistical parameters calculated (percentages of well-classified samples, Mahalanobis distance) applying the logratio approach suggest that light-mineral analysis has a significantly lower discriminative power than the other three methods. Taking into account the analytical expenditure for data acquisition, trace-element analysis appears to be the most efficient method for discrimination of at least the sandstone units examined. Although based on a single case study, these results are interpreted to have a more general meaning with respect to sandstone discrimination based on composition. Concerning sandstone provenance, trace-element analysis provides a quick tool to estimate the discriminative potential of a sample suite, i.e., the potential to discriminate between contrasting source areas. If a provenance model already exists and discriminate functions between contrasting source areas are calculated, trace-element analysis is considered to be most efficient in correctly assigning an unknown sample to its source area. These results cannot be extended to all kinds of sands and sandstones, but they cast serious doubt on the belief that petrographic point-count methods are the best approach to discriminate between sandstones.
引用
收藏
页码:47 / 57
页数:11
相关论文
共 50 条
  • [1] COMPOSITION OF ROUGHAGE IN DIFFERENT ANALYTICAL METHODS
    RABE, E
    SIEVERT, D
    DEUTSCHE LEBENSMITTEL-RUNDSCHAU, 1986, 82 (12) : 401 - 401
  • [2] EVALUATION OF STATISTICAL AND ANALYTICAL METHODS IN PSYCHIATRY AND PSYCHOLOGY
    Alexander, Franz
    AMERICAN JOURNAL OF ORTHOPSYCHIATRY, 1934, 4 (04) : 433 - 448
  • [3] Evaluation of graphical and statistical representation of analytical signals of spectrophotometric methods
    Lotfy, Hayam Mahmoud
    Fayez, Yasmin Mohammed
    Tawakkol, Shereen Mostafa
    Fahmy, Nesma Mahmoud
    Shehata, Mostafa Abd El-Atty
    SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2017, 184 : 61 - 70
  • [4] STATISTICAL METHODS IN MODEL DISCRIMINATION
    REILLY, PM
    CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 1970, 48 (02): : 168 - +
  • [5] STATISTICAL METHODS IN ANALYTICAL CHEMISTRY
    MANDEL, J
    ANALYTICAL CHEMISTRY, 1948, 20 (03) : 281 - 281
  • [6] The analytical methods of statistical mechanics
    Khintchine, A
    COMPTES RENDUS DE L ACADEMIE DES SCIENCES DE L URSS, 1941, 33 (7/8): : 438 - 441
  • [7] Statistical Methods in Signal Processing and Discrimination
    Farova, Zuzana
    SPSM 2010: STOCHASTIC AND PHYSICAL MONITORING SYSTEMS, 2010, : 35 - 44
  • [8] STATISTICAL METHODS IN SIGNAL PROCESSING AND DISCRIMINATION
    Farova, Zuzana
    Kus, Vaclav
    NDE FOR SAFETY: DEFEKTOSKOPIE 2010, 2010, : 39 - 48
  • [9] Microscopic Failure Characteristics of Sandstones with Different Composition and Microstructures
    Sun D.-Z.
    Zhao W.
    Xu X.-L.
    Wang X.
    Dongbei Daxue Xuebao/Journal of Northeastern University, 2024, 45 (04): : 584 - 591
  • [10] Evaluation of different analytical methods to determine grape organic acids
    Spinardi, A.
    Beghi, R.
    Sambo, F.
    Longoni, S.
    Valenti, L.
    VI INTERNATIONAL SYMPOSIUM ON APPLICATIONS OF MODELLING AS AN INNOVATIVE TECHNOLOGY IN THE HORTICULTURAL SUPPLY CHAIN MODEL-IT 2019, 2021, 1311 : 69 - 74