Abstract
In order to identify the shape of underground small magnetic anomaly objects, we use Support Vector Machines (SVM) to identify the underground magnetic anomaly targets. Firstly, as the SVM needs a lot of training data, and we also need to make full use of the magnetic field signal, nine component signals including total magnetic intensity (TMI) and five independent components of tensor are calculated from the original detected magnetic signal. Secondly, the nine component signals are subjected respectively to two-dimensional adaptively variational mode decomposition (2D-AVMD), which is advanced based on the two indicators, namely Mutual information (MI) and empirical entropy (EE), and we can get the nine primary signals from the decomposition results of nine component signals called the Intrinsic Mode Function (IMF). Then, the Histogram of Oriented Gradients (HOG) of the nine primary signals is extracted, and the feature data would be constructed into feature vectors. In the end, Support Vector Machines (SVM) are adopted to process these feature vectors. The output of the SVM can indicate the result of small objects’ shape recognition under the ground. Experiments prove that the shape recognition accuracy of underground small magnetic anomaly object recognition reaches 90%.
Highlights
There is little research on detecting small ferromagnets underground for the specific and detailed information
Based on the above background, this paper describes the application of the Support Vector Machines (SVM) with the 2D geomagnetic data
The nine Intrinsic Mode Function 1 (IMF1) which are the most similar with nine component signals are selected, and the major information of the original signals is contained as the primary signals
Summary
There is little research on detecting small ferromagnets underground for the specific and detailed information (exact shape, posture etc.). There is much research on magnetic field gradient tensor data, including interpretation and applications, magnetic object localization and other magnetic anomaly researches.. There were predecessors, who apply the SVM or Pattern Recognition ideas in the field of geophysics.. There were predecessors, who apply the SVM or Pattern Recognition ideas in the field of geophysics.18–21 This solves the dependence of magnetic anomaly object recognition on signal accuracy, and gets rid of complicated theoretical derivation and algorithm design. Mode decomposition has been applied to various applications, because the detected original geomagnetic signal has a lot of mixed components and noise. 2D-AVMD is used to process nine component signals calculated from the original magnetic anomaly signals, for adaptively extracting the primary signals.. The purpose of identifying the shape of the underground magnetic body is achieved effectively
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