# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
###### Abstract  
The classification of Unexploded Ordnance (UXO) from magnetometer data is a critical but challenging task, frequently hindered by the data scarcity required for training robust machine learning models. To address this, we leverage a high-fidelity digital twin to generate a comprehensive dataset of magnetometer signals from both UXO and non-UXO objects, incorporating complex remanent magnetization effects. In this study, we design and evaluate a custom Convolutional Neural Network (CNN) for UXO classification and compare it against classical machine learning baseline, including Random Forest and kNN. Our CNN model achieves a balanced accuracy of 84.65%, significantly outperforming traditional models that exhibit performance collapse under slight distortions such as additive noise, drift, and time-wrapping. Additionally, we present a compact two-block CNN variant that retains competitive accuracy while reducing the number of learnable parameters by approximately 33%, making it suitable for real-time onboard classification in underwater vehicle missions. Through extensive ablation studies, we confirm that architectural components, such as residual skip connections and element-wise batch normalization, are crucial for achieving model stability and performance. The results also highlight the practical implications of underwater vehicles for survey design, emphasizing the need to mitigate signal drift and maintain constant survey speeds. This work not only provides a robust deep learning model for UXO classification, but also offers actionable suggestions for improving both model deployment and data acquisition protocols in the field. for effective methods to identify and classify hazardous objects is becoming increasingly urgent. Traditional UXO detection methods rely on sensors such as magnetometers, sonars, or video cameras, but in underwater environments, magnetometers are particularly important, as they record disturbances in the Earth's magnetic field caused by the presence of ferromagnetic objects, even those that are buried. However, the analysis of data obtained in this way is time-consuming, costly, and requires the involvement of highly qualified specialists. Automating the object classification process using machine learning methods can significantly increase the efficiency and accuracy of UXO detection, and may even surpass the effectiveness of human experts in distinguishing dangerous objects from harmless ones [2]. One of the key challenges in applying machine learning methods to underwater UXO classification is the limited availability of representative and high-quality training datasets. Acquiring empirical data in marine environments is associated with high costs, considerable labor, and technical limitations, such as changes in magnetic inclination depending on geographic latitude or differences resulting from the orientation of the object relative to the measurement trajectory. The literature [3] highlights that the lack of appropriate datasets is a significant barrier to the development of effective AI-based systems. In response to these challenges, the concept of digital twins is gaining increasing popularity. Digital twins enable the simulation of real-world conditions and the generation of synthetic data for training machine learning models. This approach allows for flexible modeling of both UXO and non-UXO objects under controlled conditions of the Earth's magnetic field, which significantly accelerates and facilitates the process of building datasets and, consequently, machine learning tools for automating field studies. Previous studies have shown that the use of digital twins makes it possible to achieve a high degree of consistency between simulation results and real measurements, with agreement levels reaching approximately 95% [3], and allows for testing various neural network architectures in terms of their effectiveness in classifying UXO based on raw magnetometer data.