CWT-Based Magnetic Anomaly Data Denoising Method Combining Stochastic Resonance System and Pixel Connectivity Thresholding

Magnetic anomaly detection (MAD) serves as a method for detecting ferromagnetic objects via magnetic data. However, MAD typically confronts issues such as low signal-to-noise ratio (SNR) data and lack of prior information, resulting in the failure of conventional anomaly detection methods. Additionally, distinguishing the effective signal from the noise proves challenging, leading to difficulties in subsequent target location and further interpretation. Most of the current denoising algorithms are only applicable to the Gaussian noise, and their performance is poor for geomagnetic noise (generally colored noise). To solve this issue, we propose a new adaptive denoising method for magnetic anomaly data based on continuous wavelet transform (CWT), which elevates the SNR of magnetic anomaly data by integrating the stochastic resonance (SR) and pixel connectivity thresholding. For the problem that the noise wavelet coefficients are difficult to separate from the effective signal in the existing denoising methods in the wavelet domain, we present an adaptive pure background noise estimation method based on SR, which can adaptively remove the noise wavelet coefficients in the wavelet domain. After that, the residual noise wavelet coefficients that still exist are suppressed by using the method of pixel connectivity thresholding in view of the poor continuity of the residual noise wavelet coefficients. In experiments, we compare our proposed method with traditional denoising algorithms on simulated and real data. The results show that our method has better denoising performance with a high SNR, structural similarity (SSIM), and correlation coefficient (CC).