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Article

An Underground Goaf Locating Framework Based on D-InSAR with Three Different Prior Geological Information Conditions

1
Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources (MNR), China University of Mining and Technology (CUMT), Xuzhou 221116, China
2
School of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), Xuzhou 221116, China
3
State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, China University of Mining and Technology (CUMT), Xuzhou 221116, China
4
Guizhou First Institute of Surveyingand Mapping, Guizhou 550001, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(15), 2714; https://doi.org/10.3390/rs17152714
Submission received: 1 July 2025 / Revised: 1 August 2025 / Accepted: 2 August 2025 / Published: 5 August 2025

Abstract

Illegal mining operations induce cascading ecosystem degradation by causing extensive ground subsidence, necessitating accurate underground goaf localization for effectively induced-hazard mitigation. The conventional locating method applied the synthetic aperture radar interferometry (InSAR) technique to obtain ground deformation to estimate underground goaf parameters, and the locating accuracy was crucially contingent upon the appropriateness of nonlinear deformation function models selection and the precision of geological parameters acquisition. However, conventional model-driven underground goaf locating frameworks often fail to sufficiently integrate prior geological information during the model selection process, potentially leading to increased positioning errors. In order to enhance the operational efficiency and locating accuracy of underground goaf, deformation model selection must be aligned with site-specific geological conditions under varying cases of prior information. To address these challenges, this study categorizes prior geological information into three different hierarchical levels (detailed, moderate, and limited) to systematically investigate the correlations between model selection and prior information. Subsequently, field validation was carried out by applying two different non-linear deformation function models, Probability Integral Model (PIM) and Okada Dislocation Model (ODM), with three different prior geological information conditions. The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. Furthermore, this investigation discusses the influence of deformation spatial resolution, the impacts of azimuth determination methodologies, and performance comparisons between non-hybrid and hybrid optimization algorithms. This study demonstrates that aligning the selection of deformation models with different types of prior geological information significantly improves the accuracy of underground goaf detection. The findings offer practical guidelines for selecting optimal models based on varying information scenarios, thereby enhancing the reliability of disaster evaluation and mitigation strategies related to illegal mining.
Keywords: D-InSAR; underground goaf locating; PIM; ODM D-InSAR; underground goaf locating; PIM; ODM

Share and Cite

MDPI and ACS Style

Zhang, K.; Wang, Y.; Zhao, F.; Ma, Z.; Zou, G.; Wang, T.; Zhang, N.; Huo, W.; Diao, X.; Zhou, D.; et al. An Underground Goaf Locating Framework Based on D-InSAR with Three Different Prior Geological Information Conditions. Remote Sens. 2025, 17, 2714. https://doi.org/10.3390/rs17152714

AMA Style

Zhang K, Wang Y, Zhao F, Ma Z, Zou G, Wang T, Zhang N, Huo W, Diao X, Zhou D, et al. An Underground Goaf Locating Framework Based on D-InSAR with Three Different Prior Geological Information Conditions. Remote Sensing. 2025; 17(15):2714. https://doi.org/10.3390/rs17152714

Chicago/Turabian Style

Zhang, Kewei, Yunjia Wang, Feng Zhao, Zhanguo Ma, Guangqian Zou, Teng Wang, Nianbin Zhang, Wenqi Huo, Xinpeng Diao, Dawei Zhou, and et al. 2025. "An Underground Goaf Locating Framework Based on D-InSAR with Three Different Prior Geological Information Conditions" Remote Sensing 17, no. 15: 2714. https://doi.org/10.3390/rs17152714

APA Style

Zhang, K., Wang, Y., Zhao, F., Ma, Z., Zou, G., Wang, T., Zhang, N., Huo, W., Diao, X., Zhou, D., & Shen, Z. (2025). An Underground Goaf Locating Framework Based on D-InSAR with Three Different Prior Geological Information Conditions. Remote Sensing, 17(15), 2714. https://doi.org/10.3390/rs17152714

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