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Gravity and Magnetic Data Extraction Based on Multispatial Sparsity Optimization
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AI
DOI：10.1109/TGRS.2025.3560979.png)
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Abstract
En 中文
Gravity and magnetic anomalies contain abundant geological information. However, redundant information complicates the study of exploration targets. Existing methods primarily rely on exploiting spectral differences between shallow and deep sources to separate anomalies of different depths. Nevertheless, spectral overlap limits these conventional methods to separating anomalies caused by significantly different depth sources. To reduce effects due to spectral overlap, we propose a novel method for potential field separation. This method capitalizes on the sparsity of gravity and magnetic data in both singular spectrum and model spaces and employs a single-layer equivalent source to represent anomalies induced by target sources. The anomalies caused by sources with different depths can be separated. After sparsely approximating single-layer equivalent sources, we obtain the local anomalies caused by sources within the same layer. Synthetic model experiments demonstrate that the proposed method achieves high separation accuracy, particularly with respect to effectively separating anomalies induced by models with small depth differences. In addition, when comparing the noise resistance of low-rank methods with existing potential field separation methods using synthetic data, the results show that low-rank methods can extract effective signals from signals contaminated by sparse noise and periodic noise. We then apply this method to extract local gravity anomalies caused by intrusive rocks in the Nanling region and effectively identify gravity anomalies associated with various intrusive rocks. This method facilitates the separation of gravity and magnetic anomalies originating from sources at both different and similar depths, thereby expanding the applicability of separation techniques and enhancing the resolution of gravity and magnetic detection.
Keywords:
Gravity and magnetic anomalies
information extraction
low-rank method
sparse optimization
Gravity and magnetic anomalies
information extraction
low-rank method
sparse optimization
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W