Caracterización geoquímica de rocas sedimentarias formadas por silicificación como fuentes de suministro de utensilios líticos (Mioceno, cuenca de Madrid)

  • Ma Ángeles Bustillo
  • José Luis Pérez-Jiménez
  • Manuel Bustillo
Keywords: cherts, opals, geochemistry, statistical analysis, archaeology


The Miocene of the Madrid basin includes relatively abundant silica rocks (cherts, opals and opaline cherts), which were used during the Palaeolithic and Neolithic to make lithic tools. This work establishes the chemical characteristics that can be used to classify those silica rocks on the basis of their geographical location, explaining at the same time their mineralogical, petrological and geochemical features. The geographical classification of silica rocks is very useful to define the source areas of lithic tools made during the Prehistory.

The mineralogical and petrological classification was made using transmitted light optical microscopy, X-Ray diffraction and Raman spectrometry. Chemical analyses of major, minor and rare elements were performed using Inductively Coupled Plasma (ICP) mass and emission spectrometry.

Most of the silica rocks from the Miocene of the Madrid basin were formed by the replacement of sedimentary rocks that constitute the infill of the basin. This process implies the dissolution of the host rock (limestone, dolostone, gypsum, clays, etc.) and the precipitation of silica minerals (quartz, opaline phases and moganite). Both, the chemical composition of the host rock relicts and the elements incorporated by the opaline phases, constitutes a characteristic geochemical signature from each sampled zone (sample group). Therefore, this signature can be used to define and characterize silica rocks on the basis of their geographical location. The study of the data was carried out using bivariate analysis (i.e. correlation coefficients) and multivariate analysis (i.e. main components and the discriminant function). All the chemical elements analyzed are important in the discriminant analysis, because when fewer elements are taken into account (even when only those elements with a lower weight in the discriminant function are removed), the number of samples that can be included in the groups decreases importantly.


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