Magnetic Anomaly Detection Based on Energy-Concentrated Discrete Cosine Wavelet Transform

The magnetic anomaly signal tends to be contaminated by ambient environmental noise due to the complexity and diversity in the field of magnetic anomaly detection (MAD). The current denoising methods are effective in improving the signal-to-noise ratio (SNR). However, most of them are only applicable to the Gaussian noise and perform poorly for the practical geomagnetic noise, that is, the nonstationary noise with a power spectral density (PSD) of 1/f(a). To solve this issue, an MAD method based on energy-concentrated discrete cosine wavelet transform (WT) is proposed in this article. A novel framework through fusing WT and discrete cosine transform (WT-DCT), is constructed, which consists of abnormal signal acquisition, frequency-domain energy concentration, and discrete wavelet transformation. The SNR of the magnetic anomaly is improved by extracting the principal components of the scale signal in wavelet transformation through the DCT. Through comparing the WT-DCT to four commonly used and accepted methods with extensive simulations and field tests, the results demonstrate that with variations of the geomagnetic noise strengths in the range from 250 to 1000 nT, the proposed WT-DCT achieves the highest SNR improvement by about 44.81% and the best structural similarity (SSIM) improvement by about 84.75%.

Automated summary

The article introduces an MAD method based on energy-concentrated discrete cosine wavelet transform (WT), which improves the signal-to-noise ratio (SNR) of the magnetic anomaly by extracting the principal components of the scale signal in wavelet transformation. The experimental results demonstrate that the proposed WT-DCT achieves the highest SNR improvement and the best structural similarity (SSIM) improvement among four commonly used methods.