Correlation Tracking and Multifeature Fusion Net for GPR 3-D Dense Array Construction

Ground-penetrating radar (GPR) 3-D modeling can intuitively reveal complex subsurface structures, facilitating a more comprehensive understanding and interpretation of the data. In practical engineering applications, the collected data often exhibit high sparsity and low consistency, posing challenges in directly constructing high-precision 3-D models. This article presents a method for generating dense arrays aimed at 3-D modeling. It employs a multifeature fusion net to extract temporal–space and temporal–frequency information from sparse GPR slices, aiming to achieve highly consistent coupled features. multivariate variational mode decomposition (MVMD) is utilized for multifrequency decomposition of input slice data. Additionally, a temporal–frequency feature encoder is designed, integrating continuous wavelet transforms (CWTs) to extract slice spectrograms. Temporal–space features are extracted by another encoder and weighted with temporal–frequency features to form a new multifeature representation. To establish strong correlations between sparse arrays, a correlation tracking network is proposed as the terminal model. Embedded within the network is the feature enhancement and alignment module (FEAM), which enhances bidirectional feature similarity and redistributes features to align feature maps. Contextual features and correlated volumes jointly update motion fields and intermediate features in a multiscale manner, synthesizing final intermediate data and enhancing slice array density. Experimental results demonstrate the network’s strong capabilities for continuous multilevel expansion across large interline spacings and robust performance on both simulated and real data.