Magnetic-aided navigation using unmanned aerial vehicles (UAVs) is a promising method in case traditional navigation methods fail, but aeromagnetic platform noise from electric motors, electronics, and actuators can mask subtle geological signals. Traditional compensation methods, such as the Tolles-Lawson (TL) model, assume linear relationships between platform orientation and platform noise, failing to capture complex, time-varying disturbances from dynamic onboard systems. Existing machine learning approaches typically require noise-free reference measurements or known anomaly maps, resources often unavailable in practical surveying scenarios.

This thesis develops data-driven, reference-free methods for compensating platform noise in aeromagnetic measurements. The research addresses two key questions: whether deep learning methods can effectively predict and compensate platform noise without ground-truth references, and which drone subsystems contribute most significantly to platform noise.

A validation apporoach was implemented using flight data where crustal anomalies are naturally attenuated, enabling reference-free performance assessment. Comprehensive data from a fixed-wing UAV equipped with scalar and vector magnetometers, logging 273 platform-related input signals during figure-of-merit manoeuvres over an 800~m $\times$ 800~m survey area was used.

The compensation approach employed hierarchical modelling: Extended Tolles-Lawson (ETL) compensation incorporating drone inputs projected onto the magnetic field direction, followed by multilayer perceptron (MLP) neural networks trained to predict residuals. SHAP (SHapley Additive exPlanations) analysis provided model-agnostic feature importance assessment to identify the most influential platform inputs.

Results demonstrate substantial improvements over traditional methods. ETL compensation achieved improvement ratios averaging 7.31 for scalar magnetometers and 18.76 for vector magnetometers, compared to 4.90 and 10.19 respectively for standard TL compensation. The combined ETLNN approach (ETL + neural network) further enhanced performance to average improvement ratios of 8.87 on total field magnetometer and 22.22 on vector magnetometer data, a significant improvement over traditional TL methods.

SHAP analysis revealed that engine-related parameters (battery current, throttle commands), inertial measurement data (accelerations, gyroscopic rates, vibration), and attitude information (roll, pitch, yaw) are the primary contributors to platform noise. Features projected onto the magnetic field direction consistently outperformed raw inputs, validating the physical basis for this transformation, while derivative features contributed minimally whilst increasing overfitting.

The primary limitation is the inability to validate performance on data containing actual magnetic anomalies, as the high-altitude validation approach deliberately suppressed geological signals. Future work should prioritise validation using artificial magnetic sources or reference magnetometer configurations to assess preservation of genuine geological signals.