🎓 Lesson 8
D5
Extensometer Arrays vs LiDAR: Selection Criteria
Extensometer arrays measure tiny movements inside rock using wired sensors, while LiDAR scans the surface from a distance using laser light — choosing between them depends on what kind of movement you need to see and where.
🎯 Learning Objectives
- ✓ Analyze trade-offs between spatial coverage, measurement resolution, and installation constraints to select the appropriate monitoring technology for a given mine slope or stope scenario
- ✓ Explain how data latency, sampling frequency, and uncertainty propagation differ between extensometer arrays and terrestrial LiDAR systems
- ✓ Design a hybrid monitoring strategy that integrates extensometer arrays and LiDAR to validate surface-to-depth deformation correlation
- ✓ Apply ISO 18620-2:2017 criteria to evaluate data quality requirements for ground control decision support
📖 Why This Matters
In underground and open-pit mines, undetected ground movement can trigger catastrophic failures—like the 2019 Brumadinho tailings dam collapse, where insufficient subsurface deformation monitoring contributed to delayed response. Choosing the wrong tool—e.g., relying solely on surface LiDAR when deep-seated slip surfaces are active—can create dangerous blind spots. This lesson equips you to match monitoring technology to geomechanical risk, ensuring your data informs actionable ground control decisions—not just generates pretty point clouds.
📘 Core Principles
Deformation monitoring rests on three interdependent dimensions: depth sensitivity (subsurface vs. surface), spatial density (point vs. areal), and temporal fidelity (continuous vs. episodic). Extensometer arrays excel in the first: multi-point borehole extensometers (e.g., VW or servo-accelerometer types) resolve shear or dilation along predefined depth intervals (e.g., 1–5 m spacing), capturing creep, wedge movement, or fault reactivation below the weathered zone. LiDAR excels in the second and third dimensions when deployed terrestrially (TLS) or from drones (UAS-LiDAR): it captures >10,000 points/m² per scan, enabling cm-level detection of surface cracking, bulging, or bench-scale slumping—but only where line-of-sight exists and cannot detect movement behind cover or below the surface. Critically, LiDAR’s accuracy degrades with range, incidence angle, and surface reflectivity (e.g., wet clay vs. dry rock), whereas extensometers suffer from installation damage, grout bond failure, or cable breakage—both require rigorous uncertainty budgeting per ISO 18620-2.
📐 Data Confidence Index (DCI)
The Data Confidence Index quantifies suitability of a monitoring method for a specific engineering question by weighting resolution, coverage, and reliability against project requirements. It enables objective comparison before procurement or design.
Data Confidence Index (DCI)
DCI = Σ(w_i × s_i)Weighted composite score evaluating suitability of a monitoring method against project-specific performance criteria.
Variables:
| Symbol | Name | Unit | Description |
|---|---|---|---|
| w_i | Weight for criterion i | dimensionless | Relative importance of criterion i (e.g., depth relevance, resolution, uptime), normalized to sum = 1.0 |
| s_i | Score for criterion i | dimensionless | Normalized performance score (0–1) of monitoring method against criterion i |
Typical Ranges:
High-risk stope convergence: 0.85 – 1.0
Low-risk stockpile settlement: 0.3 – 0.6
💡 Worked Example
Problem: A highwall stability assessment requires detecting ≥2 mm displacement at 10 m depth within a 50 m × 30 m zone, with weekly updates. Compare DCI for a 5-point extensometer array (±0.2 mm resolution, 100% depth coverage, 100% uptime) vs. TLS-LiDAR (±3 mm surface-only resolution, 92% coverage due to shadowing, 85% operational uptime). Requirement weights: depth relevance = 0.5, resolution = 0.3, uptime = 0.2.
1.
Step 1: Assign scores (0–1) per criterion: Extensometer — depth=1.0, resolution=1.0, uptime=1.0 → weighted sum = (1×0.5)+(1×0.3)+(1×0.2)=1.0
2.
Step 2: LiDAR — depth=0.0 (no subsurface), resolution=0.4 (2 mm req. vs. 3 mm capability), uptime=0.85 → weighted sum = (0×0.5)+(0.4×0.3)+(0.85×0.2)=0.29
3.
Step 3: Normalize to 0–1 scale: Extensometer DCI = 1.0; LiDAR DCI = 0.29. Threshold for acceptance = 0.7 → only extensometer meets requirement.
Answer:
The extensometer array achieves DCI = 1.0, satisfying the highwall monitoring requirement; TLS-LiDAR scores DCI = 0.29 and is unsuitable as a standalone solution for this subsurface-critical application.
🏗️ Real-World Application
At Rio Tinto’s Yandicoogina iron ore mine (Pilbara, WA), a creeping basal shear zone was detected at 15–25 m depth beneath a 60° highwall. Initial TLS-LiDAR scans showed <5 mm surface movement over 3 months—within noise—while a 7-point inclinometer-extensometer array installed in a 30 m cored borehole revealed 12 mm cumulative displacement at 20 m depth, confirming incipient failure. This triggered immediate bench redesign and reinforcement. Post-event analysis showed LiDAR could not resolve the subsurface signal due to low surface expression—a classic case where extensometer data provided the critical early warning LiDAR missed.
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