# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 3 Dataset Description
### Data Generation Methodology  
The synthetic dataset was produced by simulating the magnetic field disturbances caused by various underwater objects using a high-fidelity FEM model. The simulation environment accounted for the Earth's geomagnetic field parameters, including inclination, as well as the magnetic properties and geometric configurations of the objects. To enhance computational efficiency without compromising accuracy, model simplifications were applied based on sensitivity analyses, reducing simulation time by approximately threefold compared to the full-scale model.  
The dataset comprises time-series magnetometer readings generated by simulating a sensor trajectory over the objects at a fixed altitude and velocity, replicating realistic survey conditions. Multiple orientations and positions of each object relative to the sensor path were included to capture variability in the magnetic signatures. Noise consistent with typical underwater magnetometer measurements was incorporated to improve the dataset's representativeness of real-world conditions. For building the data generator, a computational model was developed using the Gmsh (version 4.8.4)-GetDP (version 3.3.0) software environment, a versatile and open numerical modeling platform that enables the creation of three-dimensional models of various physical fields. In this study, Gmsh-GetDP was employed specifically to model the static magnetic field around the objects (Figure 1) incorporating the effects of remanent magnetization.  
Figure 1: Sample Gmsh-GetDP software output. General view of the discretization mesh, with the object in the center of the calculation area in the red circle (almost invisible).  
The dataset comprises magnetometer signal samples acquired through simulated measurements replicating the operational parameters of real-world underwater platforms, including Remotely Operated Vehicles (ROVs), Autonomous Underwater Vehicles (AUVs), and tracked submersible platforms (bottom crawlers). A horizontally aligned sensor array configuration was implemented, featuring three magnetometers spaced 1 m apart to capture spatial magnetic field gradients. For each target object, data acquisition covered a 10 m trajectory, emulating a platform transiting over the object, with the central magnetometer passing directly above the target. The total magnetic field intensity was sampled at 10 cm intervals, yielding a three-dimensional data tensor (3 sensors \(\times\) 101 samples) per measurement instance. Trajectory parameters, including altitude and azimuthal direction relative to the target, were recorded as metadata.  
The virtual environment model used for synthetic data generation was rigorously verified against physical trials, demonstrating high compliance between simulated and experimental magnetometer signatures [3]. The comparison results previously presented in our aforementioned article indicate that the numerical model based on the finite element method achieved over 95% correct representation of real-world measurements, confirming the reliability of the digital twin approach for dataset generation (Table 1).  
The table shows that, in nearly all cases, the \(R^{2}\) values reach approximately 0.95-0.98, indicating an almost perfect fit, while the PSNR values generally exceed 20 dB. Additionally, a MAX-MIN column was included to represent the signal amplitude, revealing that error metrics such as RMSE, ME, and MAE are one to two orders of magnitude smaller than this amplitude, further confirming the high accuracy of the results.