MINING IN FOCUS
Poor quality and blurry core tray photos will be useless for any image
classification and consequently it would be a waste of time.
"The success rate of
predictions will increase as
increasing amounts of data are
collected.
type, representativeness or quality to be able to solve the problem. For
example, poor quality and blurry core tray photos will be useless for
any image classification and consequently it would be a waste of time.
The old saying, garbage in, garbage out holds true for ML.
Care should also be taken to select the correct method, as some
techniques are only suitable for particular types of data.
Tabled data is referred to as ‘structured data’ in this type of application.
The most common geoscience examples would be core logs,
multi element assays and other geochemistry results from assays,
grab samples and stockpile samples captured in the form of excel
spreadsheets or in SQL database platforms. Other types of applicable
geological data include LIDAR point clouds, hyperspectral images and
seismic sections, to name a few.
Along with predicting the presence of metals in rocks, physical properties
combined with machine learning have the potential to classify lithologies,
characterise hydrothermal alteration, and estimate exploration vectors and
geotechnical information in the drill core.
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Common challenges for ML database development are mainly related
to source and format. Data used tends to be at multiple resolutions of
space and time, with varying degrees of noise, incompleteness, and
uncertainties. The process is also based on a number of assumptions,
African Mining
African Mining September 2019
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