Full Tensor Eigenvector Analysis on Air-Borne Magnetic Gradiometer Data for the Detection of Dipole-Like Magnetic...

Area of Science:

• Geophysics
• Applied Magnetism

Background:

• Magnetic gradiometer systems are increasingly used for detecting dipole-like sources like unexploded ordnances (UXO).
• Accurate interpretation of magnetic gradient tensor data is crucial for source localization.

Purpose of the Study:

• To present a novel algorithm for detecting dipole-like magnetic sources using eigenvector analysis of magnetic gradient tensor data.
• To enable automatic detection and localization of magnetic dipole-like sources without prior knowledge of magnetic moment direction.

Main Methods:

• Theoretical analysis of eigenvector decomposition of the magnetic gradient tensor.
• Development of a detection algorithm based on tensor eigenvector properties.
• Quantitative interpretation of airborne magnetic data.

Main Results:

• The algorithm successfully locates dipole-like magnetic sources, including weak anomalies.
• Automatic detection is achieved without estimating the magnetic moment direction.
• Validation through synthetic experiments and real-world high-altitude airborne magnetic data.

Conclusions:

• The proposed algorithm is effective for detecting magnetic dipole-like sources.
• The method is practical for interpreting airborne magnetic gradiometer data.
• Demonstrated suitability for identifying targets like unexploded ordnances (UXO).