🎓 Lesson 4
D3
RTK vs PPK: When to Choose Which in GPS-Challenged Mines
RTK and PPK are two ways drones or survey equipment get super-accurate positions using GPS signals — RTK gives instant high accuracy in open areas, while PPK records raw data for later precise correction, especially useful where GPS signals are blocked or noisy.
🎯 Learning Objectives
- ✓ Explain the functional difference between RTK and PPK in terms of data flow, latency, and dependency on communication links
- ✓ Analyze GNSS signal conditions at a mine site (e.g., canyon effect, multipath, sky visibility) to select the optimal positioning method
- ✓ Apply error budget analysis to compare expected horizontal/vertical accuracies of RTK vs. PPK under specified baseline lengths and observation durations
- ✓ Design a field data collection workflow that integrates PPK processing steps (base setup, rover logging, time synchronization, post-processing software selection)
📖 Why This Matters
In deep open-pit mines—like Chuquicamata (Chile) or Bingham Canyon (USA)—GNSS signals suffer severe degradation due to steep walls, metallic equipment, and atmospheric turbulence. Choosing RTK over PPK—or vice versa—can mean the difference between ±2 cm survey-grade mapping and >30 cm errors that invalidate volume calculations, endanger blast design, or trigger regulatory non-compliance. This lesson equips you to make technically defensible, operationally practical GNSS positioning decisions—not based on vendor marketing, but on physics, infrastructure, and mine geometry.
📘 Core Principles
RTK requires three synchronized elements: a fixed base station with known coordinates, a rover (e.g., drone), and a real-time correction stream (typically RTCM 3.x). Its accuracy degrades with increasing baseline length (>10–15 km) due to spatially correlated atmospheric and orbital errors. PPK bypasses real-time constraints by logging raw L1/L2 carrier-phase and pseudorange measurements on both base and rover; position is solved later using precise point positioning (PPP) or double-difference algorithms. Because PPK doesn’t require live telemetry, it tolerates signal dropouts (e.g., flying behind pit walls) and avoids radio interference from haul trucks or blasting operations—critical for safety-critical inspections and pre-blast surveys.
📐 Baseline-Dependent Accuracy Degradation
RTK horizontal accuracy degrades approximately linearly with baseline length due to unmodeled atmospheric delays. PPK accuracy is less sensitive to baseline length but depends strongly on observation duration and satellite geometry (PDOP). This formula estimates RTK’s worst-case horizontal error growth.
💡 Worked Example
Problem: A mine uses an RTK-enabled drone with a base station 8.2 km away. Published system specs state base-only noise = ±8 mm, and baseline-dependent error coefficient = 1 mm/km. What is the estimated 95% horizontal error?
1.
Step 1: Identify baseline distance (L = 8.2 km) and coefficients (σ₀ = 8 mm, k = 1 mm/km)
2.
Step 2: Apply formula: σ_h = √(σ₀² + (k × L)²) = √(8² + (1 × 8.2)²) = √(64 + 67.24) = √131.24
3.
Step 3: Compute √131.24 ≈ 11.46 mm → multiply by 2 for 95% confidence interval: 2 × 11.46 ≈ 22.9 mm
Answer:
The estimated 95% horizontal error is 23 mm, well within typical RTK spec (±2 cm), confirming viability for this baseline.
🏗️ Real-World Application
At Newmont’s Ahafo Mine (Ghana), surveyors deployed DJI M300 RTK drones for stockpile volumetrics in a 400-m-deep pit. Initial RTK flights suffered >15 cm vertical drift near highwalls due to multipath and signal reflection. Switching to PPK (using Emlid Reach RS2 base + drone-integrated u-blox ZED-F9P loggers) reduced vertical uncertainty to ±1.8 cm (95%)—validated against 12 ground control points—and eliminated radio-link failures during blasting blackouts. Processing was done in GrafNav v9.0 with 30-minute static base sessions and 15-minute rover observation windows per flight line.
🔧 Interactive Calculator
🔧 Open Mine Drone-Based Surveying & Inspection Calculator📋 Case Connection
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