How consultants at AMC turned old, handwritten logs with incomplete data into a functional geotechnical model

Creating a Functional Model with Substandard Data
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Recently, AMC were able were able to help improve the design and scheduling of a large underground mine by using sparse historical data to construct a reliable geotechnical model containing RQD and Q prime derived values. The historical data was in the form of a database of hand-written logs and contained no direct RQD measurements.

Typically, a variety of Kriging is used on the recorded values to estimate the RQD values throughout the deposit and use these to influence the safe design of stopes and determine the difficulty and timing of their extraction. Because the historical data did not contain direct RQD measurements the standard estimation methodology was not automatically applicable. Qualitative descriptions were used as a proxy to derive discrete indicator values to represent rock quality.

A multiple indicator Kriging methodology was used with the discrete values to estimate a model. Further post processing based on a probabilistic approach, including Monte Carlo simulations,  was used to refine the MIK output resulting in a defensible and practical model that represented the deposit’s rock quality.

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Through imaginative use of a dozen or so Studio RM processes, whilst working within the limitations of a discrete dataset, AMC were able to convert previously unusable qualitative data into a defensible and practical model that reasonably represented the deposit’s rock quality.

By incorporating the poor-quality secondary data into a RQD model, the need gather data through mapping and drilling was completely eliminated.

The model AMC generated gave the engineers a stable, robust platform from which to design the stopes and support requirements appropriately.

Without this model, engineers would be forced to work with low resolution assumptions - normally worst-case assumptions. This would have changed the economics of the stopes, the planned extraction rates and extraction sequence. By working with the tools in Studio RM, AMC was able to generate a time and cost saving product that will benefit the client in the short and long term.

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Why do AMC Use RM?

AMC consultants have been tackling some of the most challenging modelling and engineering problems facing the mining industry for over thirty years. As a long-term customer of Datamine, AMC have used their software on hundreds of consulting projects.

One reason for AMC’s continued use of Datamine software is because it provides the ability to implement data modelling and engineering methodologies that are highly specific to individual situations.