Abstract: Effectively separating target anomalies while minimizing over- or under-separation remains challenging in gravity and magnetic field separation. In this study, the low-rank decomposition was employed to separate gravity and magnetic anomalies. Additionally, to determine the balance parameters that affect potential field separation, this study proposed an optimal estimation method based on the minimum correlation coefficient. Tests of various separation methods based on theoretical gravity and magnetic anomaly models demonstrate that the proposed method allows for effective separation, significantly reducing under- or over-separation caused by the sliding window average and wavelet analysis methods. The proposed method was applied to the Bouguer gravity anomaly data from a mining area in western China. The separated local anomalies clearly reflected the presence of rock and/or ore bodies with low magnetic susceptibility and high density. Model experiments and field applications demonstrate that the proposed method can enhance the accuracy and practicality of potential field separation.