Open AccessMagnetic Anomaly Feature Extraction Using the Tunable Q-factor Wavelet Transform Based on Non-convex Overlapping Group Shrinkage
Author(s) -
Jie Hu,
Baolong Guo,
Nannan Liao,
Yi Zhong,
Shaohua Zhang,
Wangpeng He
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1646/1/012040
Subject(s) - pattern recognition (psychology) , shrinkage , wavelet transform , anomaly (physics) , wavelet , feature extraction , feature (linguistics) , noise (video) , anomaly detection , artificial intelligence , regular polygon , computer science , signal (programming language) , mathematics , physics , image (mathematics) , machine learning , geometry , linguistics , philosophy , programming language , condensed matter physics
Extraction of the magnetic anomaly signal is one of the difficulties in the magnetic anomaly detection as the weak features extracted are easily disturbed by strong background noise. To address this problem, a sparse feature extraction method based on the tunable Q-factor wavelet transform and overlapping group shrinkage is developed in this paper. Compared with the traditional wavelet transform, the proposed method has excellent characteristics, which can flexibly tune the Q-factor according to the oscillation characteristics of the useful features. In this way, the sparsity of extracted features can be induced more effectively. In addition, the non-convex overlapping group shrinkage can effectively extract weak features from signals with group property, enhancing the extraction accuracy of features. The practical experiment verifies the effectiveness of the proposed method in the magnetic anomaly detection.