###### Abstract
[23] used continuous wavelet transform and Euler Deconvolution to analyze the magnetic field data of the Jharia Coalfield in India and calculated different lithospheric depths. [PERSON] et al. [24] used discrete wavelet to analyze the transient data of external magnetic field to achieve symmetry detection of the wound rotor. Although the above methods demonstrate that the wavelet transform is advanced enough to analyze very stationary signals, it is still limited by the Fourier Transform, i.e., the limited wavelet basis inevitably leads to energy leakage and ambiguity of the resolution ratio. To eliminate the extra simple harmonics without physical meanings, which are introduced by the Fourier Transform (FT) and its derived methods for reflecting nonlinear and non-stationary processes, [PERSON] proposed the Hilbert-Huang Transform (HHT) [25] in 1998. Compared with the wavelet analysis method, it not only retains the advantages of the wavelet transform method but also eliminates the ambiguity of the wavelet transform, having a more accurate spectral structure. However, the core algorithm of the Hilbert-Huang Transform--EMD lacks rigorous mathematical analysis. To address the aforementioned issues, [PERSON] et al. [26] suggested a new method that integrates the concept of EMD, called synchrosqueezed wavelet transform. The method uses the synchronous compression operator to reorganize the energy of the time-frequency plane based on the wavelet transform. Finally, a time-frequency image is obtained with a more focused frequency curve. The Synchrosqueezed Wavelet Transform (SSWT) provides a new idea for time-frequency analysis. The literature [27] proves that the synchrosqueezed wavelet transform is stable in the case of finite disturbance or white noise pollution and discusses a detailed Matlab implementation method. But the data image processed by the synchrosqueezed wavelet transform lacks a visual display of the target. This paper combines the power frequency magnetic field detection method with the traditional magnetic anomaly signal detection method and proposes a method to detect the target by using the power frequency magnetic field. The power frequency magnetic field anomaly signal is weak, the noise is complex, and there is no effective time-frequency analysis method to detect the power frequency magnetic anomaly signal. In this paper, the synchrosqueezed wavelet transform method introduces the time-frequency characteristics of the power frequency magnetic anomaly signals extraction to improve the magnetic anomaly detection method in the following aspects: 1. It proposes to use the power frequency magnetic signal generated by the worldwide distributed power grid as the signal transmission source, which reduces the requirements of establishing a transmission source as compared with the magnetic anomaly detection method of artificial field source; 2. The synchrosqueezed wavelet transform introduces the magnetic anomaly signal detection, which increases the signal time-frequency resolution as compared with conventional methods; 3. It proposes to use the Greedy Algorithm to extract time-frequency ridges to efficiently capture the magnetic anomaly disturbance of the measured data. The rest of this paper is organized as follows: In Section 2, we systematically review the previous magnetic detection and signal processing methods; in Section 3, we detail the processing method of the power frequency magnetic anomaly signal as proposed in this paper; in Section 4, we introduce the relevant simulations and experiments to prove the effectiveness of this method; Section 5 is a discussion; Section 6 is a summary of the work in this paper and discusses plans for future work.