AMC Studio RM Case Study
Earlier this year we had the opportunity to sit down with Justin Glanvill and Nick Szebor from AMC Consultants to discuss how Datamine Studio RM assisted in overcoming their modelling and engineering challenges on-site. As a long-standing customer of Datamine, AMC trust in our software’s ability to implement data modelling and engineering methodologies that are highly specific and customised to individual situations.
“Few other packages out there give you the level of fundamental control over your source data that Datamine does, which is a very valuable tool within the consultancy space because we deal with such disparate datasets of such different quality.” Justin Glanvill.
“What people call the basic functions but are very powerful so the extra commands, the ability to tailor macros and scripts for the software. I think that’s where I find the best benefits of the software packages. That raw adjustability that we can get to the nuts and bolts of the data and really pour more out of it than possibly with other software.” Nick Szebor.
What challenges did they face?
One the greatest challenges that AMC face are having older datasets that aren’t quite as good as the newer data. However, in order to incorporate this information into their projects, there just isn’t sufficient budget to go back and re-drill or re-capture data. One example explained by Justin was “a client that had a large legacy dataset of geotechnical information, and they needed this information incorporated into a three-dimensional model to enhance their support planning and support requirements within the mine plan.” This is a very common problem in mines that have operated for a long period of time, often only having incomplete or sparse historical data available.
AMC were able to help improve the design and scheduling of a large underground mine data, that was in the form of hand-written logs within a vast database and contained no direct RQD measurements, to construct a reliable geotechnical model that contained RQD and Q prime derived values using Studio RM.
How was this done?
Because the historical data did not contain direct RQD measurements, qualitative descriptions were instead 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, were used to refine the MIK output resulting in a defensible and practical model that represented the deposit’s rock quality, as accurately as possible.
What were the final benefits?
Using this method in Studio RM and incorporating poor quality data into an RQD model, the need to re-gather new data through mapping and drilling was eliminated, not only saving their clients a lot of time, but also a lot of otherwise additional expenses. The model that AMC generated gave the engineers on-site a stable, robust platform to design the stopes and support requirements appropriately. Without these tools in Studio RM, the engineers would have had to work with low resolution assumptions that had high potentials for inaccuracy and changed the economics of the stopes, the planned extraction rates and extraction sequence. The greatest benefit was in the time and cost-saving efficiencies in the short term and long-term planning.
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