Complex Magnetic Anomaly Detection Using Structured Low-Rank Approximation With Total Variation Regularization

In the field of magnetic anomaly detection (MAD), the anomaly signal is easily submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low rank (SLR) and total variation (TV) regularization constraints is proposed in this letter. The noise suppression performance is improved by leveraging the structured low rankness of the signal. To preserve clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV method with four state-of-the-art methods with extensive field tests, the results demonstrate that the proposed SLR-TV method achieves the greatest SNR improvement by about 63.24% and the best structural similarity (SSIM) improvement by about 53.02% over other methods in the range from -40 to 0 dB, showing the utility and high fidelity of the proposed framework in low SNR.

Automated summary

A novel magnetic anomaly detection (MAD) method based on structured low rank (SLR) and total variation (TV) regularization constraints is proposed to improve noise suppression performance and preserve clean boundaries. Field tests show that the proposed SLR-TV method achieves the greatest SNR improvement and the best structural similarity (SSIM) improvement compared to other methods.