Author: Geovariances

With the “Sampling Density Variance” tool, Isatis.neo offers an innovative methodology allowing robust resource classification, independent from sample number, block size, and domain extension.

Auditing and reviewing a Mineral Resource Estimate either internally or publicly is a multifaceted process for which no software can pretend it will provide all the required tools to tick all the boxes. But some solution will provide better adapted platforms than others. Isatis.neo Resource Workflow falls in that category.

Isatis.neo quickly groups borehole samples into homogeneous classes (e.g., facies, geological or mining domains) in an automatic way. Those who have seen the tool run qualifies it as impressive.

Anyone involved with today’s mining industry understands that the sector is facing very tough challenges. And it is under enormous pressure from due cost control and budgetary management constraints that innovation and ingenuity must still find their way to propose new ways of tackling traditional issues. Mineral Resource Estimation (MRE) is no stranger to that conundrum and their practitioners all know the multi facets of the game: integrate more and more data, boost productivity, and come up with an answer fast and at the same time enriched with an assessment of the uncertainty that can be attached to that answer. All of that in a fraction of the time they used to have at their disposal and less resources to double check…

Isatis offers a Streamlines Calculator that allows computing flow path lines according to a 2D surface.

Geological modeling in Isatis can be achieved using MPS methodology or Flumy, our applications for meandering system modeling. Do you know that you can combine both of them to ensure model accuracy and realism? See the tests done for a NOC.

Everyone who uses Isatis scripting knows how powerful the tool is. It benefits our principal environmental consultant Claire Faucheux’s work every day. Discover why…

Through the years, Geovariances has always endeavored to improve Isatis performances to answer their client constraints related to the always increasing size of their data sets. Most of the algorithms in Isatis are thus optimized and parallelized to run on multi-threaded computers and decrease computation time in a significant manner.