## 1 Introduction
Although these traditional approaches have advanced UXO detection, they often rely on regularization or sequential constrained inversion, which can be limited when dealing with highly complex geological settings. In contrast, the framework proposed in this study integrates TEM and magnetic data through a deep learning paradigm, enabling simultaneous data fusion and leveraging the strong generalization capacity of convolutional neural networks (CNNs) to improve both accuracy and robustness in joint inversion. Recent advancements in deep learning (DL) have enabled further innovation in geophysical inversion. Deep neural networks (DNNs), particularly CNNs, offer powerful tools for analyzing complex data relationships in joint inversion tasks. Algorithms such as CNNs have demonstrated remarkable potential in solving complex geophysical problems, including one-dimensional inversion of electromagnetic data [21; 22], structural interpretation of seismic images [23], and magnetization direction prediction [24]. CNNs have also been employed for gravity inversion tasks, including depth-to-basement prediction [25] and three-dimensional gravity analysis [26]. [PERSON] et al. [7] showcased the potential of DL-based inversion techniques for subsurface EMI response, achieving faster and more accurate inversion results compared to traditional algorithms. However, ML-based methods are not without challenges; the scarcity of high-quality training datasets often restricts their generalizability, posing a significant obstacle to their widespread application [27]. Building on this foundation, we propose MagEMNet, a CNN-based joint inversion framework designed to integrate both electromagnetic induction (EMI) and magnetic field data. This approach leverages adaptive moment estimation (Adam) [28] and a learning rate decay strategy to optimize convergence and enhance the model's predictive accuracy. By simultaneously incorporating EMI and magnetic datasets, MagEMNet addresses the limitations of single-method inversions and captures the interrelationships between different geophysical properties, resulting in a more comprehensive understanding of subsurface targets. Simulated and field experiments demonstrate that our framework not only improves inversion accuracy but also significantly accelerates computational speed, providing a practical and efficient solution for applications such as UXO detection and subsurface resource exploration. Our key contributions can be succinctly summarized as follows:  
1. Proposing a Unified Deep Learning Framework for Joint Inversion: We developed MagEMNet, a CNN-based model tailored for the joint inversion of electromagnetic (EM) and magnetic data. By leveraging the complementary nature of these two modalities, MagEMNet achieves higher accuracy and robustness compared to traditional single-modality methods, addressing challenges such as non-uniqueness and noise susceptibility. 2. Innovative Integration of Physics-Constrained Learning and Advanced Deep Learning Architectures: The framework incorporates Squeeze-and-Excitation (SE) blocks [29] to enhance feature selection and a physics-constrained loss function to ensure physical consistency in inversion results. This integration significantly improves the reliability of parameter recovery while preserving interpretability and adherence to geophysical principles. 3. Comprehensive Validation Through Synthetic and Field Experiments: The study presents rigorous evaluations of MagEMNet using a large-scale synthetic dataset generated from a three-dimensional orthogonal magnetic dipole model. Field experiments further demonstrate the model's practical applicability, with results showing depth errors of less than 10 cm and efficient inversion of target parameters. This paper is organized as follows. Section 2 introduces the integrated detection framework, detailing the structures and functionalities of the unmanned aerial vehicle magnetic (UAVMAG) and the unmanned aerial vehicle time-domain electromagnetic (UAV-TDEM) systems.