ABA Models
Acid rock drainage prediction with AI: run ABA and humidity cell tests per the GARD Guide, model results in PHREEQC, and see where machine learning fits.
Acid rock drainage prediction with AI: run ABA and humidity cell tests per the GARD Guide, model results in PHREEQC, and see where machine learning fits.
Grade control sensors such as XRF, XRD, LIBS and hyperspectral scanners rely on AI calibration software to turn raw spectra into grade and mineralogy.
Automated mineralogy on TIMA, MLA, QEMSCAN or Mineralogic uses ML classifiers to turn SEM X-ray spectra into modal mineralogy and liberation data in hours.
Implicit geological modelling of lithology, structure and alteration in Leapfrog or Vulcan, with Seequent's Driver adding ML clustering and classification.
Ore blending optimization with AI: layer MineMax or Deswik Blend, BOLT and GO on your systems to set stockpile, plant feed and blend targets continuously.
Airborne geophysics with AI: process TMI, gravity and magnetics surveys in Oasis montaj, then feed the grids to a platform like VRIFY DORA to rank targets.
Geochemical data analysis with AI: explore multi-element assays in ioGAS, then add Python and ML scripts that cluster samples and flag anomalies for you.
AI-assisted bench slope design uses ML models to generate rock-mass inputs like dip and dip-direction, then runs factor-of-safety analysis in RocScience.
Build a flotation soft sensor: keep JKSimFloat or METSIM for design, then train an ML model on live plant data to predict recovery, grade and throughput.
Clay mineral prediction with AI: scan core with Corescan or HyLogger, automate first-pass logging with CSIRO's MyLogger and feed the geomet block model.