.png){kind=icon}
Return
Geochemical pattern recognition through matrix decomposition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.oregeorev.2018.11.026.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This study compares the use of joint singular value decomposition and semi-discrete decomposition (JSS) and non-negative matrix factorisation with univariate analysis of raw data, to detect multi-element patterns in soils related to geochemical dispersion from Mississippi Valley-Type Pb-Zn deposits in the Irankuh area of central Iran. Joint singular value decomposition and semi-discrete decomposition clustering of the data identified a suite of mineralisation-related variables and corresponding sample clusters that are spatially associated with mineralisation or variations in parent lithology. Non-negative matrix factorisation generated three main factors and sample clusters that relate to the main zones of sulphide mineralisation, variations in clay mineralogy and the composition of unaltered host rocks. These two matrix decomposition techniques deliver similar results, though JSS delivers a significantly higher sample classification accuracy. Both methods result in delineation of a contiguous cluster of samples above an extensive zone of blind mineralisation at the Tappe Sorkh Pb-Zn deposit and define new potentially mineralised targets between this deposit and the Gushfil Pb-Zn deposit. More conventional univariate methods to detect anomalous populations of Pb and Zn in the soil geochemical data, such as number-size fractal analysis, were less effective at defining these targets. Joint application of practical matrix decomposition techniques, utilising noise removal and modelling of the underlying geochemical patterns associated with ore-forming processes, has led to more reliable prospectivity mapping of the Pb-Zn deposits in Irankuh.
Keywords:
Buried Pb-Zn mineralisation
Soil geochemistry
Singular value decomposition
Semi-discrete decomposition
Non-negative matrix factorisation
Irankuh
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
6.6K
Citations:
2.2W
Organization
Cited Papers
ShareSave
ShareSaveFuzzy k-means clustering of fields in an elementary catchment and extrapolation to a larger area.png)
GEODERMA
GEODERMAIF6.6
PREAI
ShareSaveIdentification and mapping of geochemical patterns and their significance for regional metallogeny in the southern Sanjiang, ChinaOre Geology Reviews
Ore Geology ReviewsIF3.6
PREAI
ShareSave
View more