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The challenge

Getting a grip on overburdem

The predicted strength of a rock can usually be determined by the rock type: for example, well-cemented sandstones are stronger than mudstones, claystone and siltstone.

Understanding the characteristics of rocks (lithology) and their spatial distribution by interpreting geophysical logging greatly improves the understanding of the effects of overburden sediments.

For example, at a coal mine in Central Queensland, rock-type mapping is very important before blast planning, especially when poorly cemented sandstone is present.

This is a rock layer locally referred to as ‘sugar sands’, which are largely quartz sand and are very strong when dry, but behave like sand when wet.

Conventional predictive lithology using geophysical logging data is manual, time-consuming and can be subjective.

Our response

A tool to automatically characterise rocks

In collaboration with our partners, we developed automatic geophysical logging interpretation programs, including LogTrans. Developed in collaboration with Fullagar Geophysics, LogTrans has proved to be an effective tool for automatic classification of rock types.

Geophysical borehole logs provide a rich source of information on rock properties and can be used for qualitative interpretation, such as picking coal seams from density log and strata correlation, in addition to litho-stratigraphic interpretation, orebody delineation and grade estimation, and geotechnical and rock-mass characterisation.

Using limited data from two boreholes collected via our SIROLOG sonde device, LogTrans was able to perform automated lithology interpretation from geophysical logs to map the sugar sands at the Queensland mine.

Compared with the original manually interpreted geological logs, the automated interpretation was well-matched.

The results

Algorithms for classification

The results were very encouraging and the SIROLOG data interpreted rock classes including the sugar sands were well matched with the original geological logs as shown in the image below.

It clearly demonstrates that SIROLOG data can assist with recognition of sugar sands at this mine.

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