Drilling & Blasting Operations / Blasthole Analysis on Eigenform AI Geo Tooltips

Advanced Drilling

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

Autonomous and semi-autonomous blast-hole drill rigs — Epiroc Pit Viper and Sandvik AutoMine — are the current commercial reality of “advanced drilling.”
Autonomous drilling achieves roughly ±5 cm positioning accuracy at planned collar locations, versus roughly ±30 cm for conventional manned drilling.
Utilization rates for autonomous rigs run 85–90%, compared with 55–65% for manned rigs — the productivity case is well-documented, not speculative.
Onboard measurement-while-drilling (MWD) sensing on these rigs overlaps with — and can feed into — probe-based systems like BLASTDOG.
This category is still Emerging in the sense that full-fleet autonomy is a recent, actively-expanding capability rather than a decade-old standard.

TL;DR

“Advanced drilling” today means autonomous blast-hole rigs (Epiroc Pit Viper, Sandvik AutoMine) running onboard navigation and MWD sensing to hit collar positions far more precisely and consistently than manned drilling — this is real, deployed, and expanding, not a future promise.

Blastdog (IMDEX): AI-Powered Blast-Hole Logging

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

BLASTDOG is IMDEX’s multi-sensor, semi-autonomous downhole probe that logs blast-hole rock properties at high spatial density.
It’s deployed via a robotic logging system, combining automated data acquisition with machine-learning-based interpretation of the sensor stream.
QA/QC’d borehole data is available within minutes of logging, with full-shift data compiled and visualized in IMDEXHUB-IQ or 3D in MINEPORTAL — no manual export/import step.
This is a confirmed, commercially deployed product — not a research prototype.

TL;DR

BLASTDOG is a real IMDEX product: an autonomous multi-sensor probe you run down production blast holes to get near-real-time, ML-interpreted rock-property data feeding straight into your bench characterization and blast design.

Design of Hydrogeologic Monitoring System

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

Wireless sensor-network platforms — Worldsensing, RST Instruments, and Beyond Monitoring — are what most mines now use to plan and deploy piezometer/monitoring-well networks with telemetry.
These platforms are compatible with a wide range of vibrating-wire and digital sensor brands, so you’re not locked into a single sensor vendor when you adopt the network layer.
The “design” step is about telemetry and network topology as much as instrument placement — Worldsensing supports 70+ countries’ worth of deployments and handles setup, configuration, and ongoing technical support.
This is still an Emerging category for AI specifically: the platforms themselves are mature IoT/telemetry products, and AI-based anomaly detection on top of the data stream is the newer, less-standardized layer.

TL;DR

Design your hydrogeologic monitoring network around a wireless telemetry platform like Worldsensing or RST Instruments rather than a fixed set of standalone loggers — that gets you real-time data delivery and positions you to add AI-based anomaly detection on the stream later, even if that layer isn’t standard yet.

Frag TRACK

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

Orica FRAGTrack uses AI plus stereoscopic 2D/3D imaging for automated post-blast fragmentation analysis — no manual sieving or scaled photos required.
It ships in multiple deployment forms: FRAGTrack Conveyor, FRAGTrack Crusher, FRAGTrack GeoSpatial, and a newer excavator-mounted variant — covering everything from the dig face to the crusher.
WipWare WipFrag and Split-Desktop are the long-standing image-analysis alternatives if you want a lower-cost or desktop-based option.
Binocular/stereoscopic camera capture is what lets these systems handle variable lighting and material color/texture without needing a fixed reference scale in every shot.

TL;DR

Fragmentation analysis is done today with AI-driven stereoscopic image analysis — Orica’s FRAGTrack is the current market-leading deployed system, with WipFrag and Split-Desktop as established, lower-friction alternatives for teams not ready for a full sensor-network rollout.

Ground Water Model

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

Ground water models still rest on MODFLOW 6 or FEFLOW as the actual flow-simulation engine.
PyHydroGeophysX, published in 2026, is a new open-source Python platform that bridges hydrological models (MODFLOW, ParFlow) with geophysical inversion tools like pyGIMLi and SimPEG.
That bridge is the notable emerging integration point: it lets you constrain or validate your groundwater model directly against geophysical survey data (resistivity, EM) rather than treating them as separate workflows.
This is genuinely new (published in 2026), so treat it as a promising direction to evaluate, not an established standard yet.

TL;DR

Your groundwater model is still built in MODFLOW 6 or FEFLOW, but the new move worth watching is using PyHydroGeophysX to connect that model directly to geophysical inversion data (pyGIMLi/SimPEG) — turning two previously separate workflows into one data-fusion pipeline.

Hydrological Modeling

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

MODFLOW 6 is the USGS-maintained, actively-updated standard groundwater flow engine (v6.7 shipped February 2026) and the default starting point for any new hydrological model.
FloPy wraps MODFLOW in Python, which is what actually opens the door to ML integration — scripted, reproducible model builds instead of manual GUI configuration.
FEFLOW (DHI) is the leading commercial alternative, commonly used where mining operations need vendor support or more built-in geotechnical/mining-specific modules.
ParFlow is the open-source option for coupled surface-subsurface flow when a simple saturated-flow model isn’t enough.
This is Emerging for AI specifically: the modelling engines themselves are classical numerical solvers, and the “AI” opportunity is in the Python-scriptable layer around them, not inside the solver.

TL;DR

Build your hydrological model in MODFLOW 6, scripted through FloPy rather than a GUI, so the same pipeline that builds your model can also feed ML-based calibration and prediction workflows — use FEFLOW instead if you need commercial support, or ParFlow if you need coupled surface-subsurface flow.

Monitoring during mining

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

Continuous, wireless geotechnical and hydrological monitoring has replaced periodic manual readings at most modern mines.
Platforms like Worldsensing, RST Instruments, and Beyond Monitoring stream piezometer, crackmeter, and inclinometer data in near real time.
The AI layer sits on top of the sensor network: anomaly-detection models flag abnormal readings before they’d trip a simple threshold alarm.
This is a Cloud/SaaS category, not a library you’d install — you’re buying (or renting) a monitoring platform, not building a model from scratch.

TL;DR

Wire your geotechnical and hydrological sensors into a cloud monitoring platform (Worldsensing, RST Instruments, or similar) and let its built-in anomaly-detection layer watch for abnormal trends across piezometers, crackmeters, and movement sensors — instead of a technician manually checking a spreadsheet.

Real-Time Ore Grade Sensing at the Shovel

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

MineSense ShovelSense mounts high-speed XRF sensors on shovel/excavator buckets to scan material for grade in real time, bucket by bucket.
The system’s ML models are trained per ore body, so grade estimates adapt to your specific deposit’s mineralogy rather than using a generic calibration.
MineSense BeltSense is the conveyor-belt equivalent — same sensing concept applied downstream of the shovel.
Real-time grade data feeds directly into automated truck-diversion decisions, routing ore to mill, stockpile, or waste without waiting for lab assays.
This is a mature, commercially deployed technology (ai_relevant: Yes) — not experimental.

TL;DR

Fit shovel buckets with high-speed XRF sensors (MineSense ShovelSense) that scan every dig pass and feed ore-body-specific ML grade models directly into your dispatch system, so trucks get routed by measured grade instead of block-model estimate alone.

Refined Hydrological Response Model

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

Model refinement — recalibrating a groundwater model against real monitoring data — runs on PEST++, the open-source parameter-estimation and uncertainty-analysis suite that works with MODFLOW 6 or FEFLOW.
PEST++ handles model-independent (non-intrusive) calibration, meaning it doesn’t require modifying your underlying hydrological model code.
Calibration workflows are increasingly ML-assisted — using techniques like ensemble methods and surrogate modeling to speed up what used to be a slow, manual trial-and-error process.
This is Emerging: PEST++ itself is mature and widely used, but the ML-assisted acceleration on top of it is a newer development.

TL;DR

Refine your hydrological response model by running PEST++ against your MODFLOW 6 or FEFLOW model and your monitoring-well data — it’s the established, open-source route to non-intrusive calibration, with ML-assisted variants emerging to speed up the process.

Tailings Management

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

Tailings dam monitoring has become its own specialized AI category following several high-profile dam failures.
GroundProbe (Orica) combines radar (including its SSR-SARx synthetic-aperture radar built specifically for tailings) with piezometers and drone imagery in one dashboard.
Insight Terra and similar platforms add satellite InSAR deformation data, extending coverage to areas without ground sensors.
The genuinely AI part is using ML to distinguish benign, expected consolidation settlement from the kind of shear deformation that precedes a failure.

TL;DR

Combine ground-based radar/piezometer monitoring (GroundProbe) with satellite InSAR deformation data (Insight Terra, Synspective) into one dashboard, and let the platform’s ML models separate normal settlement from failure-precursor deformation — rather than relying on a human eyeballing a deformation plot.

Water Quality Monitoring

Mon, 21 Sep 2026 00:00:00 +0800

Key Takeaways

The sensors themselves (multiparameter sondes measuring pH, conductivity, turbidity, dissolved oxygen, etc.) are mature, off-the-shelf hardware.
YSI EXO sondes and In-Situ multiparameter probes are the two most common choices for continuous mine-site water monitoring.
The “AI” part — on-device machine learning that flags contamination or anomalies automatically — is still an emerging research area, not a mature commercial standard.
Don’t expect a plug-and-play “AI water quality” product yet; expect to build the anomaly-detection layer yourself on top of mature sensor data.

TL;DR

Real-time water-quality sensing is solved with commercial sondes (YSI, In-Situ); AI-based anomaly detection on top of that stream is genuinely emerging — you’ll likely be building or adapting a research-stage model rather than buying a finished product.