# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 2 Recent Advances in UXO Classification
The study underscores the importance of integrating dipole-based physical modeling with statistical classification methods to advance the automation and reliability of marine UXO rationalization. On the data analysis front, machine learning and deep learning have transformed the UXO classification process. Traditional manual interpretation of sensor data is labor-intensive and subjective, but automated classification using Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models such as Bi-LSTM-RBM has demonstrated superior accuracy and efficiency [9; 10]. Advanced deep learning models are capable of extracting subtle, high-dimensional features from raw sensor data, enabling the reliable differentiation of UXO from non-hazardous objects. Notably, transformer-based architectures and Deep Neural Networks have shown promise in handling the temporal and spatial complexities inherent in various types of underwater data used in UXO detection, for example, visual or sonar data [11; 12]. A persistent challenge in developing robust machine learning models is the scarcity of high-quality labeled training data. To address this, the use of synthetic datasets generated with digital twin frameworks and numerical modeling has become increasingly prevalent. Digital twins simulate realistic sensor responses to UXO and other non-dangerous objects under varying environmental and operational conditions, facilitating the creation of diverse and balanced datasets for model training and validation [3]. This approach not only accelerates the development of effective classifiers, but also allows for systematic evaluations of model performance across a range of scenarios. Recent advances in Unexploded Ordnance (UXO) classification have increasingly focused on solving inverse problems and applying machine learning techniques to improve detection accuracy and discrimination capabilities. Inversion approaches, such as cooperative and joint inversion, have been explored to simultaneously utilize multiple datasets for more robust parameter estimation, enhancing the physical characterization of targets [13]. Techniques leveraging the magnetic gradient tensor, including eigenvector decomposition and singular value decomposition (SVD), have demonstrated efficient and automatic localization of dipole-like magnetic sources without requiring prior knowledge of magnetic moment orientation [14; 15]. Complementing these physics-based methods, machine learning models have gained prominence, particularly when applied to electromagnetic induction (EMI) data. Early implementations employed probabilistic neural networks trained on physics-based model outputs to classify UXO [16]. Subsequent research expanded the use of classifiers such as support vector machines (SVMs), Random Forests, and neural networks, often combined with feature extraction techniques that capture intrinsic target properties from EMI decay curves [17; 18]. Hybrid approaches integrating supervised and unsupervised learning methods, including Gaussian mixture models, have shown promise in reducing training data requirements while providing probabilistic interpretations of classification outcomes [19]. Recent advances in UXO classification focus on enhancing sensing technologies, including the introduction of electromagnetic induction (EMI) and quantum magnetometry sensors, as well as developing measurement platforms such as Autonomous Underwater Vehicles equipped with gradiometers for conducting surveys. Another major area of re search involves modeling UXO objects and developing machine learning algorithms to improve classification accuracy. Overall, the integration of advanced sensors, deep learning techniques, and digital twin technologies is significantly advancing UXO classification accuracy and operational efficiency in complex marine environments.