After the catastrophic failures at Brumadinho and Mount Polley, tailings dam monitoring has become a board-level concern across the mining industry. AI systems promise continuous surveillance and early warning capabilities that traditional inspection regimes can’t match.

But promising and delivering are different things. Here’s what’s actually working in tailings AI—and what still needs development.

The Monitoring Challenge

Tailings storage facilities present unique monitoring difficulties:

Scale: Major facilities can span hundreds of hectares with kilometres of embankment.

Slow failure modes: Many failures develop over weeks or months through gradual internal erosion, settlement, or groundwater changes.

Complex physics: Interaction between fluid dynamics, geotechnical stability, and weather creates multivariable systems.

High stakes: Failure consequences include environmental devastation, potential fatalities, and company-ending liability.

Traditional monitoring relies on periodic inspections, piezometer readings, and survey benchmarks. These catch obvious problems but can miss gradual deterioration between inspection cycles.

Where AI Adds Value

Current AI applications focus on three areas:

Anomaly Detection

Machine learning models establish baseline behaviour patterns from sensor data, then flag deviations. Applications include:

Piezometric pressure monitoring: AI identifies unusual pressure patterns that might indicate seepage changes or internal movement.

InSAR displacement analysis: Satellite radar interferometry data processed by AI can detect millimetre-scale surface movements across entire facilities.

Seepage flow rates: Automated tracking of weir measurements with pattern recognition for trend changes.

The value here isn’t analysing any single reading—humans can do that. It’s maintaining continuous vigilance across thousands of data points simultaneously.

Predictive Modelling

More advanced applications attempt to forecast future conditions:

Weather impact prediction: Integrating forecast data with historical response patterns to anticipate pore pressure changes from rainfall events.

Consolidation modelling: Predicting settlement behaviour based on tailings deposition patterns and material properties.

Operational optimisation: Suggesting deposition locations and water management adjustments to maintain stability.

These models remain less mature than anomaly detection, with accuracy highly dependent on site-specific calibration.

Image Analysis

Computer vision applications process visual data at scale:

Drone imagery: Automated detection of surface cracking, vegetation stress, seepage wet spots, and erosion features.

Monitoring camera feeds: Continuous analysis of visual surveillance footage for changes.

Lidar processing: Change detection in facility geometry from regular survey flights.

Implementation Realities

Deploying these systems involves challenges rarely mentioned in vendor presentations:

Sensor Reliability

AI systems are only as good as their data. Tailings environments are hostile to instrumentation:

• Corrosive process water
• Extreme temperature ranges
• Lightning exposure
• Physical damage from operations

Maintaining sufficient sensor coverage for reliable AI operation requires ongoing investment in instrumentation maintenance. Budget for 15-20% annual replacement of field sensors.

Data Integration

Many sites run disparate monitoring systems—different vendors, different data formats, different collection frequencies. Integrating these into unified AI platforms requires significant middleware development.

The Global Industry Standard on Tailings Management (GISTM) emphasises integrated monitoring, but practical implementation often requires custom engineering.

False Positive Management

Early AI systems generated too many alerts. Operating teams learned to ignore them—defeating the purpose entirely.

Modern implementations include:

• Alert prioritisation and confidence scoring
• Graduated escalation based on persistence and severity
• Integration with operational context (was there recent deposition? maintenance activity?)
• Human confirmation workflows before escalation

Validation Requirements

Regulators and independent reviewers increasingly question AI-based monitoring claims. Sites need:

• Transparent documentation of model logic and training data
• Regular validation against manual measurements
• Defined model performance metrics
• Clear escalation protocols when AI and human assessment conflict

What’s Not Ready Yet

Some marketed capabilities remain aspirational:

Autonomous response: No credible system operates without human oversight. The decision to trigger emergency protocols remains with qualified engineers.

Complete failure prediction: AI can identify concerning trends; it cannot reliably predict exactly when or if failures will occur. The physics remain partially unpredictable.

Replacement for inspections: AI monitoring supplements, not replaces, qualified engineer inspections. Regulatory requirements still mandate periodic physical assessment.

Technology Providers

The Australian mining sector works with several AI monitoring providers:

• CSIRO Data61 — Research partnerships on tailings monitoring algorithms
• Major mining technology vendors (Hexagon, Caterpillar, Komatsu subsidiaries)
• Specialised geotechnical monitoring firms expanding into AI
• Startup companies focused specifically on tailings AI

Due diligence on providers should include:

• Track record at operating sites (not just pilot projects)
• References from independent engineers who’ve validated systems
• Clear responsibility delineation in contracts
• Ongoing support and model maintenance commitments

Regulatory Direction

Australian state regulators are developing updated guidance on AI-assisted monitoring:

Western Australia: DMP is consulting on acceptable AI monitoring approaches for Category 1 facilities.

Queensland: Resources Safety & Health expects AI systems to meet documented validation standards before regulatory reliance.

NSW: Resources Regulator guidance remains principles-based, emphasising outcomes rather than specific technologies.

Industry bodies like the Minerals Council of Australia are developing voluntary standards for AI monitoring implementation.

Practical Recommendations

For mining companies evaluating AI tailings monitoring:

1. Start with data infrastructure. AI needs reliable, integrated sensor networks. Invest here first.

2. Set realistic expectations. AI enhances monitoring capability; it doesn’t eliminate risk or replace engineering judgment.

3. Budget for ongoing operation. These aren’t install-and-forget systems. Models need retraining, sensors need maintenance, staff need training.

4. Involve regulators early. Discuss planned AI monitoring with relevant inspectorates before implementation.

5. Maintain traditional monitoring. AI systems can fail. Parallel manual monitoring provides redundancy.

Tailings AI represents genuine progress in facility safety. But it’s one layer in a comprehensive risk management approach—not a solution by itself.