Machine learning classification of geochemical and geophysical data

Abstract

Accurate classifications in downhole exploration data are essential for exploration, geological modelling and prediction of mining outputs. These holes are typically interpreted manually, which is slow, subjective and prone to error. Machine learning techniques can potentially automate these classifications. Our test deposit contains the shale dominated West Angelas (WA) Member, and banded iron formation and iron ore in the Mount Newman (MN) Member. Deep learning using autoencoders, Support Vector Machine (SVM) and k-means were applied to classify stratigraphy and rock type using mineral groups or geochemical assays. Autoencoder produced accuracies of 83.2–95.1%, K-means accuracies of 30.4–58.8%, and SVM accuracies of 81.6–88.5%. Geochemical assays were a better indicator of stratigraphy. Autoencoder and SVM produced significantly better results than k-means. This was probably due to k-means not using training data. Although they gave similar results, autoencoder is preferable to SVM for this application as it can handle more than two categories.

Cite this paper

@incollection{huang2019machine,
  title = {{Machine learning classification of geochemical and geophysical data}},
  author = {Huang, Leijun and Balamurali, Mehala and Silversides, Katherine L.},
  pages = {101--105},
  year = {2019},
  doi = {10.1201/9780429320774-12}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

Related papers