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A Subsurface Targets' Classification Method Utilizing Gradient Learning Technique
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DOI：10.1109/LGRS.2020.3018324.png)
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Abstract
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Recent advances in the time-domain electromagnetic (TDEM) method have dramatically improved detection and discrimination of subsurface targets. Inversion of observed response using a 3-D orthogonal magnetic dipolar model provides location, orientation, and intrinsic responses of the target based on deterministic optimization methods, which is dependent on the initial values and could be trapped in local minimum solutions. In this letter, we applied a supervised descent method (SDM) to the inversion of electromagnetic induction (EMI) data accurately by individually and simultaneously training every single sample in the training set to avoid the direct use of the SDM that causes inaccurate classification results. This method provides a new way to incorporate prior information using gradient learning and reduce the computational complexity as it does not compute partial derivatives in the nonlinear least-squares problem or groups difference in the heuristic random search algorithm. Then the simulation and field experiments are performed to verify the feasibility of this method. Both the simulation and experimental results demonstrate that the SDM shows good performance and robustness in the classification of subsurface anomalous targets.
Keywords:
Training
Magnetic resonance imaging
Magnetic moments
Electromagnetic interference
Data models
Magnetic domains
Computational modeling
3-D orthogonal magnetic dipole
subsurface target classification
supervised descent method (SDM)
time-domain electromagnetic (TDEM) method
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