Guided wave signal‐based sensing and classification for small geological structure

• Published in: IET signal processing Vol. 17; no. 7
• Main Authors: Sun, Hongyu, Song, Jiao, Zhou, Shanshan, Liu, Qiang, Lu, Xiang, Qi, Mingming
• Format: Journal Article
• Language: English
• Published: John Wiley & Sons, Inc 01.07.2023; Wiley
• Subjects: Algorithms; Analysis; Coal industry; feature extraction; Mineral industry; Mining industry; object detection; seismic waves; signal classification
• ISSN: 1751-9675; 1751-9683
• DOI: 10.1049/sil2.12223

Sensing, Computing and Communication Integration (SC2) is widely believed as a new enabling technology. A non‐negative tensor sparse factorisation (NTSF) algorithm based on tensor analysis is proposed for sensing and classification of Small Geological Structure in coal mines. Utilising this method, advanced detection of geological anomalies hidden in coal seams was achieved. The morphological properties of geological anomalies in coal seams and the propagation characteristics of guided waves were first thoroughly studied. A three‐dimensional (3D) medium geometry model was developed for a complicated coal seam with Goaf, collapse column, scouring zone, and tiny fault based on COMSOL Multiphysics. On this model, the third‐order tensors data was constructed. Then, the TUCKER‐based NTSF algorithm was employed for feature extraction and classification. To achieve multi‐dimensional feature, the two‐dimensional data in the form of a matrix is collected, and a multiplicative update method is introduced to update the algorithm iteratively. Finally, the Support Vector Machine (SVM) multi‐classifier with Gaussian radial basis kernel function is selected for classification of Small Geological Structure. The experimental results show that the classification accuracy based on the NTSF and SVM is as high as 97.33%, which demonstrates that the proposed algorithm is suitable for Sensing and Classification of Small Geological Structure in coal mines.