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t-SNE Based Visualisation and Clustering of Geological Domain

Identification of geological domains and their boundaries plays a vital role in the estimation of mineral resources. Geologists are often interested in exploratory data analysis and visualization of geological data in two or three dimensions in order to detect quality issues or to generate new hypotheses. We compare PCA and some other linear and non-linear methods with a newer method, t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of large geochemical assay datasets. The t-SNE based reduced dimensions can then be used with clustering algorithm to extract well clustered geological regions using exploration and production datasets. Significant differences between the nonlinear method t-SNE and the state of the art methods were observed in two dimensional target spaces.

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This work has been supported by the Australian Centre for Field Robotics and the Rio Tinto Centre for Mine Automation.

1. Australian Centre for Field Robotic, University of Sydney, Sydney, Australia

   Mehala Balamurali & Arman Melkumyan

1. Mehala Balamurali
2. Arman Melkumyan

Correspondence to Mehala Balamurali .

1. The University of Tokyo , Tokyo, Japan

   Akira Hirose

2. Kobe University , Kobe, Japan

   Seiichi Ozawa

3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

   Kenji Doya

4. Nara Institute of Science and Technology , Ikoma, Japan

   Kazushi Ikeda

5. Kyungpook National University , Daegu, Korea (Republic of)

   Minho Lee

6. Chinese Academy of Sciences , Beijing, China

   Derong Liu

© 2016 Springer International Publishing AG

Balamurali, M., Melkumyan, A. (2016). t-SNE Based Visualisation and Clustering of Geological Domain. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_67

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• DOI: https://doi.org/10.1007/978-3-319-46681-1_67

• Published: 30 September 2016

• Publisher Name: Springer, Cham

• Print ISBN: 978-3-319-46680-4

• Online ISBN: 978-3-319-46681-1

• eBook Packages: Computer ScienceComputer Science (R0)

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