GPR-HIDiff: A diffusion-based model for horizontal interference suppression in urban underground detection radar profiles

Abstract

Automated subsurface utility detection systems in construction rely heavily on the quality of ground-penetrating radar (GPR) profiles, which are often degraded by high-amplitude horizontal interference. Existing low-rank decomposition methods lack the intelligence and flexibility required for multi-site data processing and involve labor-intensive parameter tuning, impeding their integration into intelligent construction workflows. To address these challenges, this paper proposes a horizontal interference suppression algorithm based on a diffusion model, termed GPR-HIDiff. The proposed model replaces conventional sequential convolutional operators with ResBlocks throughout the encoder, intermediate layer, and decoder of the UNet architecture, enhancing training stability. Lightweight agent attention modules are embedded between ResBlocks at each level to improve global information modeling capability. A spatial attention mechanism is deployed between the encoder and decoder to achieve adaptive spatial feature optimization. Furthermore, the forward diffusion phase adopts a cos$\theta $ schedule-based strategy to ensure a smooth temporal variation of noise variance. A standardized dataset comprising real-world measured samples and finite difference time domain simulation samples of urban road models has also been constructed. The effectiveness of the hybrid dataset, the introduced modules, the robustness analysis, and the cos$\theta $ schedule is validated through training with single/mixed datasets, ablation studies, evaluation of metric variations before and after the introduction of different noise levels, and comparative experiments with constant, linear, and cos$\theta $ schedules. Experimental results demonstrate that GPR-HIDiff significantly outperforms both traditional methods and state-of-the-art deep learning models on both simulated and real-world test samples. It effectively suppresses horizontal artifacts, preserves target hyperbolic contours, and avoids excessive reduction of target scattering, showcasing its exceptional performance. This method provides a powerful algorithmic foundation for high-resolution GPR imaging and target detection.

Keywords

Ground-penetrating radar / Horizontal interference / Diffusion model / Agent attention module / Spatial attention / Hybrid dataset

Data availability

The data that used in this study are available on https://github.com/txs129129/code--data--horizontal-iInterference.

CRediT authorship contribution statement

Xiaosong Tang: Writing - original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization. Feng Yang: Validation, Supervision. Xu Qiao: Visualization, Validation. Jialin Liu: Writing - original draft, Validation. Haitao Zuo: Visualization, Validation. Liang Gao: Writing - original draft. Jianshe Zhao: Visualization, Validation. Suping Peng: Validation, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 52427901) and the Fundamental Research Funds for the Central Universities (Ph.D. Top Innovative Talents Fund of CUMTB) (Grant No. BBJ2025073).

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