Cluster- and PCA-based gamma-ray spectrometry data processing algorithm for hydrothermal alteration haloes mapping

• Published in: Journal of applied geophysics Vol. 210; p. 104935
• Main Authors: Sobolev, Igor S., Orekhov, Kirill A., Orekhov, Alexander N., Charkin, Alexander N., Soboleva, Nadezhda P.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2023
• Subjects: Cluster analysis; Gamma-ray spectrometry; Hydrothermal alteration; Improving data; Principal components analysis; Radioactive background fluctuation; Cluster analysis; Principal components analysis; Gamma-ray spectrometry; Hydrothermal alteration; Improving data; Radioactive background fluctuation
• ISSN: 0926-9851; 1879-1859
• DOI: 10.1016/j.jappgeo.2023.104935

In the present paper a high-resolution gamma-spectrometry data processing algorithm aimed at locating hydrothermal alteration haloes is discussed. On the first stage a cluster model of spatial variation of radioactive background is created using information on natural radioactive elements (NRE) concentrations and numerical geomorphological characteristics. This model allows for good terrain differentiation in terms of its lithological and landscape variability. Mathematical procedure for removal of NRE distribution variance, related to variability of lithology and elementary landscapes, improved radiometry data for mapping zones of hydrothermal alterations. On the second stage we integrated the “corrected” gamma-ray spectrometry data using principal component analysis (PCA). Comparing the acquired factor model with a priori radioactive models of hydrothermal alterations allowed us to differentiate quartz-hydromica and quartz-pyrite-sericite alteration haloes. •Cluster analysis is an effective tool for creating radioactive background model.•Differentiated standardization of radioactivity data reduces influence of background fluctuation.•Factor models of radioactive field are convenient for identifying facies type of hydrothermal alterations.•Gamma-ray spectrometry data processing algorithm increases the reliability of locating ore-related alterations.