A Magnetic Anomaly Detection Method Based on Multi-Feature Classification-Fusion Neural Network in Colored Noise Background

Abstract

Magnetic anomaly detection (MAD) is a core technology for the detection of ferromagnetic targets, yet traditional methods such as the Orthogonal basis function (OBF) detector suffer from severe performance degradation in low signal-to-noise ratio (SNR) and Gaussian colored noise environments. To address this issue, this paper proposes a novel neural network architecture based on manual feature extraction, namely the Partitioned Classification Network with 42 features (PCN-42). First, a total of 42 magnetic anomaly features belonging to three categories, i.e., time–frequency features, statistical features, and magnetic moment features, are extracted from magnetic measurement data. Subsequently, each category of features is processed independently by parallel sub-networks followed by feature fusion. Finally, the performance of the proposed method for magnetic anomaly signal detection is verified under colored noise simulation conditions combined with measured geomagnetic noise. In the experiments, the target is modeled as a magnetic dipole with moment 200 A⋅m2, closest point of approach (CPA) ranges from 300 to 800 m, and noise conditions span Gaussian colored noise (α = 0.5, 0.8, 1.0) plus measured geomagnetic background. Simulation results demonstrate that compared with the OBF detector, the detection probability of the PCN-42 detector is improved by 45–75 percentage points, reaching over 80% in different magnetic moment directions and approximately 90% at CPA = 450 m. Experiments with measured noise further validate the effectiveness of the proposed method. This method significantly improves the detection probability of magnetic anomaly signals in colored noise environments. MAD serves a broad range of civilian purposes, including mineral exploration, buried pipeline monitoring, archeological prospection, and humanitarian unexploded ordnance (UXO) clearance.