## 5 Discussion  
Magnetic dipole source parameter estimation is critical in magnetic anomaly detection and identification. However, traditional methods for estimating magnetic source parameters do not achieve stable and accurate estimations under low SNR conditions, which significantly hampers the practical application of these methods. This study introduces a novel method for magnetic source parameter estimation based on the LM-OBF algorithm, which can accurately estimate the parameters of magnetic dipole sources under low SNR conditions (down to \(-10\) dB), providing an innovative strategy for magnetic source parameter estimation in such scenarios. Although the LM-OBF algorithm enhances the accuracy and stability of traditional methods in the presence of strong noise interference, it has certain limitations. First, the LM-OBF algorithm depends on the results of two-dimensional OBF decomposition. The OBF method exhibits weaker noise suppression capability for mixed noise of granular and Gaussian noise and for colored noise, which are almost inevitable in complex real-world measurement environments. This reduction undoubtedly decreases the parameter estimation accuracy of the LM-OBF algorithm. Therefore, in practical measurement environments, it is essential to avoid such noise sources as much as possible. Simultaneously, future research should focus on noise reduction techniques such as autoregressive techniques and Kalman filtering to further filter out or whiten the noise, enhancing the practical performance of the LM-OBF algorithm. The tests were conducted on a computer equipped with an Intel Xeon Bronze 3204 CPU @ 1.90 GHz and 128 GB RAM. For a 50 \(\times\) 50 grid (2500 data points), the parameter inversion process required approximately 5.5 min to complete. The computation time increases significantly with larger datasets, and under high-noise conditions, the inversion algorithm may require extended iterations to adequately fit the observed data. To enhance computational efficiency, future work could explore GPU parallelization strategies or distributed computing frameworks to expand the applicability of the LM-OBF method in large-scale detection tasks. The practical deployment of UAV-based magnetic inversion inherently involves trade-offs between theoretical precision and real-world operational constraints. A critical factor is the positioning accuracy of the Real-Time Kinematic (RTK) system, which provides centimeter-level horizontal precision (\(\pm 1\) cm + 1 ppm) and sub-decimeter vertical accuracy (\(\pm 0.1\) m under stable conditions). While this meets the requirements for most near-surface magnetic surveys, residual errors arising from UAV attitude fluctuations or transient signal loss can introduce minor but non-negligible offsets in sensor positioning. These deviations were mitigated through post-processing gridding. Height-dependent anomalies further complicate inversion accuracy. Variations in UAV altitude--even within the RTK's specified tolerance--alter the magnitude and spatial distribution of the target's magnetic signature. For instance, a vertical drift of 10 cm at a flight height of 1 m can induce a \(\sim\)5-10% change in anomaly amplitude for a shallow dipole source. To address this, our workflow assumes a constant elevation plane during gridding, effectively averaging altitude-related variations. However, this approximation becomes less reliable in highly undulating terrains or for deeply buried targets, where altitude errors disproportionately amplify depth estimation uncertainties. In addition, the LM-OBF algorithm utilizes the traditional LM method, which encounters specific issues concerning iteration speed and global convergence properties. When processing small datasets, the LM-OBF algorithm can reach the optimal solution within a relatively short number of iterations and time. However, with the increase in data volume, such as in the case of large-area data or under dense sampling conditions, the LM-OBF algorithm faces prolonged iteration times.