Can radiometric data improve lithology mapping and geological understanding through unsupervised classification?

Zeinab Smilie*, Jennifer McKinley, Vasily Demyanov, Mark Cooper

*Corresponding author for this work

Research output: Contribution to conferenceAbstractpeer-review

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Pattern classification algorithms can enhance the pattern recognition and prediction of large multivariate data sets, that otherwise would be difficult to detect. These techniques can be used to visualise the contribution or role of various features in shaping the patterns of a large data set. Self-organising map (SOM) is an unsupervised classification tool that is trained by competitive learning. The
method is useful in analysing and visualising high-dimensional data, based on principles of vector quantification of similarities and clustering in a high-dimensional space. The method can be used to perform prediction, estimation, pattern recognition of large data sets. One main advantage of the SOM is that it can be applied to categorical and continuous variables making the
tool ideal for analysing a complex combination of geological feature such as rock classifications, ages, geochemical composition, terrain elevations, etc. We here employ the tool to predict geological features using geophysical data, mainly the airborne geophysical
data acquired through the Tellus project 2011/12. Tellus radiometric data present a high-resolution data set (Line spacing of 200 m and point spacing of 60 m). The data characterise the K, U and Th distribution associated with the natural geological features in Northern Ireland. The SOM of the radiometric data displayed patterns that are evidently associated with both bedrock and
superficial geology. However, the addition of other natural features, such as terrain elevations modifies the clarity of the clusters and contribute to the prediction of geological formations. The SOM enhances the visualisation and recognition of the signals of geochemical variations within the bedrocks, although now concealed with superficial deposits. These advantages of SOM, combined with the
high-resolution nature of the radiometric data input, presents an efficient tool to improve or complement conventional geological mapping techniques especially for “hard to recognise” stages of igneous rock emplacements, rock mass zonation and alteration/contact zones and also provides fundamental attempt toward understanding geological processes.
Original languageEnglish
Number of pages1
Publication statusPublished - 18 Jun 2021
EventgeoENV2020 - Parma (virtual), Parma, Italy
Duration: 18 Jun 2021 → …
Conference number: 13


Period18/06/2021 → …
Internet address


  • machine learning
  • Unsupervised classification
  • radiometric data
  • lithology
  • mapping

ASJC Scopus subject areas

  • Geology
  • Artificial Intelligence
  • Computers in Earth Sciences
  • Environmental Science (miscellaneous)


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