Geochemical Anomaly Detection: z-score, MAD and Background Ratio
Geochemical anomaly detection methods compared: z-score, median absolute deviation and background ratio, with when to use each and how to check results.

Geochemical anomaly detection methods compared: z-score, median absolute deviation and background ratio, with when to use each and how to check results.
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.
A conveyor belt analyzer like MineSense BeltSense reads ore grade on the belt in real time, so you can divert dilution or misrouted ore before the mill.
Design hydrogeologic monitoring around a wireless telemetry platform like Worldsensing or RST Instruments so AI anomaly detection can run on the data later.
AI-assisted environmental impact assessment for tailings feeds radar, sensor and InSAR deformation data into continuous, GISTM-aligned compliance reporting.
Use geochemical proxies with AI: train a model on known-deposit multi-element signatures, then run it on your data to map prospectivity, as VRIFY DORA does.
Geotechnical monitoring with AI runs slope radar (IDS GeoRadar) and satellite InSAR (SkyGeo) through ML layers so wall-failure warnings surface early.
Automate geotechnical reports by connecting Power BI or Tableau to your live geotechnical block model and monitoring feeds, so they regenerate on their own.
Make hydrological modeling AI-ready: script MODFLOW 6 in FloPy instead of a GUI, so the same pipeline can feed ML calibration and prediction workflows.
Slope stability monitoring during mining: link piezometers, inclinometers and crackmeters to a platform like Worldsensing so AI flags abnormal trends.