Advanced sensing
Grade control sensors such as XRF, XRD, LIBS and hyperspectral scanners rely on AI calibration software to turn raw spectra into grade and mineralogy.
Grade control sensors such as XRF, XRD, LIBS and hyperspectral scanners rely on AI calibration software to turn raw spectra into grade and mineralogy.
Ore texture analysis with AI: run samples or core through TIMA, Mineralogic or Corescan for ML-classified grain size and mineral maps that size your grind.
Model alteration proxies in 3D: Corescan hyperspectral maps feed Leapfrog Geo implicit surfaces, replacing a geologist's logged intensity scores alone.
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.
Automated core photography from Corescan, Datarock or Minalyze gives AI fracture, lithology and mineral tools the consistent images they depend on.
Core scanning with HyLogger 4, Corescan HCI-3 or Minalyze MCore maps mineralogy and alteration along the full drill core, ready for AI models and logging.
Rock density prediction with AI: train a regression model on XRF, hyperspectral and hardness proxy data instead of running a physical test on every sample.
Hyperspectral scanning, point or imaging: use a handheld spectrometer for spot mineral ID or a core imager for full coverage, then classify spectra with ML.
Point a portable spectrometer like the ASD TerraSpec at rock or core, and onboard spectral-library matching identifies alteration minerals in seconds.
Hyperspectral clay models start with Corescan HCI or CSIRO HyLogger scans, about 800,000 spectral samples per metre, then automated mineral interpretation.