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 hypot...

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Bibliographic Details
Published inNeural Information Processing pp. 565 - 572
Main Authors Balamurali, Mehala, Melkumyan, Arman
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Clustering
Dimensionality reduction
Geochemical data
Geological domain
Visual analytics
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Summary: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.
ISBN:9783319466804
3319466801
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-46681-1_67