Robotic rock classification and autonomous exploration
Robotic rock classification and autonomous exploration
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Machine Learning: An Algorithmic Perspective
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
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We present a robust and autonomous mineral classifier for analyzing igneous rocks. Our study shows that machine learning methods, specifically artificial neural networks, can be trained using spectral data acquired by in situ Raman spectroscopy in order to accurately distinguish among key minerals for characterizing the composition of igneous rocks. These minerals include olivine, quartz, plagioclase, potassium feldspar, mica, and several pyroxenes. On average, our classifier performed with 83 percent accuracy. Quartz and olivine, as well as the pyroxenes, were classified with 100 percent accuracy. In addition to using traditional features such as the location of spectral bands and their shapes, our automated mineral classifier was able to incorporate fluorescence patterns, which are not as easily perceived by humans, into its classification scheme. The latter was able to improve the classification accuracy and is an example of the robustness of our classifier.