Proceedings

Underground Diagnosis in 3D GPR Data by Learning in CuCoRes Model Space

Xiren Zhou, Shikang Liu, Xinyu Yan, Xiangyu Wang, Huanhuan Chen

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 7200-7208.

https://doi.org/10.24963/ijcai.2025/801

Keywords

  • Machine Learning: ML: Applications
  • Machine Learning: ML: Recurrent networks
  • Machine Learning: ML: Clustering

Abstract

Ground Penetrating Radar (GPR) provides detailed subterranean insights. Nevertheless, underground diagnosis via GPR is hindered by the fact that training data typically contain only normal samples, along with the complexity of GPR data’s wave-collection characteristics. This paper proposes subsurface anomaly detection within the Cubic Correlation Reservoir Network (CuCoRes) model space. CuCoRes incorporates three reservoirs with spatial correlation adjustment in each direction to adequately and accurately capture multi-directional dynamics (i.e., changing information) within GPR data. Fitting GPR data with CuCoRes and representing data with fitted models, the original GPR data is mapped into a category-discriminative CuCoRes model space, where anomalies could be efficiently identified and categorized based on model dissimilarities. Our approach leverages only limited normal GPR data, easily accessible, to support subsequent anomaly detection and categorization, enhancing its applicability in practical scenarios. Experiments on real-world data demonstrate its effectiveness, outperforming state-of-the-art.

Xiren Zhou, Shikang Liu, Xinyu Yan, Xiangyu Wang, and Huanhuan Chen. 2025. Underground Diagnosis in 3D GPR Data by Learning in CuCoRes Model Space. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 7200–7208. https://doi.org/10.24963/ijcai.2025/801
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