Detection and classification of internal defects in limestone blocks based on a deconvolution technique with SI-PLCA applied to GPR signals

In this work, a novel migration method is applied to Ground-Penetrating Radar (GPR) data to detect the internal flaws of ornamental stone blocks. To detect and classify fractures in accordance with their spatial orientation, a Shift-Invariant Probabilistic Latent Component Analysis (SI-PLCA) is proposed. GPR simulations are conducted using modeling software to test several types of fractures (with different positions, thicknesses, and lengths) in rock blocks and to train several patterns as inputs for the SI-PLCA method. An 800 MHz antenna is used to assess both simulated and real data. The accuracy rate of the proposed approach is evaluated and compared with that of classical migration methods for detection and is compared to a Template Matching approach for classification; promising results are obtained. In addition, GPR is applied to two blocks of a rock type known commercially as Crema Marfil. The 3D fracture maps obtained from the proposed approach are compared with the stone slabs from the cutting process. The results show that the proposed approach applied to GPR radargrams is an effective method for determining the internal structure of stone materials, particularly for detecting and classifying fractures.