## 2 Related Works
### Magnetic Anomaly Signal Processing Methods  
Several common magnetic anomaly signal processing methods will be introduced below:  
1. The high-order crossing (HOC) technique is a discrete signal detection methodology based on statistical signal characteristics and time series signal analysis theory. It is also a zero-crossing number-based spectrum analysis approach. It can discriminate between signals and is frequently used in non-destructive testing, facial recognition, and seismic signal detection. The ferromagnetic target signal's HOC value differs from the noise signal's HOC statistical features. As a result, the HOC approach may be utilized to discriminate between the target signal and the noise signal, making it an efficient magnetic anomaly signal identification method [46]. 2. Stochastic Resonance (SR) is a new method type that uses noise to enhance weak signal information. In 2022, this method was adopted by [PERSON] to detect magnetic anomaly signals, and it produced great results [47]. The Stochastic Resonance Method distinguishes itself from other methods with two advantages: (a) ease of realization; and (b) satisfactory detection performance, that is, the output state can be switched when the magnetic anomaly signal is embedded in the noise detected. 3. Independent fractions can be suppressed by using the sparsity of the wavelet transform matrix, thus achieving an effective signal extraction and demagnetizing method. Current wavelet transform approaches for magnetic anomaly detection mostly combine wavelet transform with neural networks [48], autoencoders [49], and autoregressive-integrated moving average models [50]. The power frequency magnetic field disturbance signal is characterized by a low signal-to-noise ratio, difficult identification, and unclear signal features; therefore, the signal extraction method based on deep learning is of great practical significance for the extraction and identification of power frequency disturbance signals. Deep learning recognition methods based on neural networks can extract the features of nonlinear or high-dimensional spatial data through nonlinear activation functions in neural networks without previous information. These methods can roughly be divided into two categories:  
1. Deep learning recognition based on feature parameters. Identification is made by using self-coding networks, the PCA algorithm, the Fisherface algorithm, wavelet transform, and other methods, and combining the BP neural network, Softmax, K-Nearest Neighbor, the deep self-coding network, and other classifiers [51]. 2. Manual feature extraction is not required by deep learning recognition based on signal data. The preprocessed data are directly sent to the neural network [52], and the classification and recognition of modulating signals are achieved through a self-organizing and self-learning mechanism of the network. When dealing with a magnetic signal that is being interfered with by white noise, the orthogonal basis decomposition has greater advantages. This approach is less sensitive to the target magnetic moment and makes obtaining the target signal easier. The drawback is that the calculation is enormous and difficult to do in real time. However, because the noise in the magnetic signal is primarily random noise, it is frequently essential to whiten the noise first, such as differential elimination of common-mode noise of two magnetometers or the development of high-order whitening filters. Since the Orthonormal Basis Function (OBF) is more suitable for white noise, and noise in a power frequency environment is mostly random, noise whitening is necessary before the application of the OBF method, such as differential elimination of common mode noise for two magnetometers [53]. The HOC approach for magnetic anomaly identification has the advantage of requiring only over-zero counts without the requirement for spectral analysis, making it computationally simple and arithmetically efficient. However, HOC is only useful for signal processing with a low signal-to-noise ratio.