Rough coal-rock boundary identification method for exposed coal walls based on multi-domain robust features of GPR images and improved FCM algorithm

In the confined space of mining faces, where sensor deployment is restricted, developing a coal-rock structural perception system based on a single ground-penetrating radar (GPR) device holds significant engineering value. The accurate identification of rough coal-rock boundaries on exposed coal walls represents a critical challenge in constructing such a system. To address the limitations of single-domain or homogeneous multi-feature representations in fully capturing the electrical differences between coal and rock, and to overcome the accuracy degradation of the conventional Fuzzy C-Means (FCM) algorithm caused by its equal-weighting strategy, a rough coal-rock boundary identification method is developed by integrating multi-domain robust features of radar images with an improved FCM algorithm. Twelve electromagnetic features capable of characterizing coal-rock electrical differences are first extracted from the time domain, frequency domain, time-frequency domain, and wavelet domain, and their effectiveness is validated through forward simulations. Subsequently, three rough-surface models with varying root-mean-square heights are constructed to compute the mean coefficient of variation and Pearson correlation coefficients among features. Seven features—envelope area, pulse width, spectral centroid, phase variation rate, mean instantaneous frequency, mean instantaneous phase, and scale energy ratio—are selected to establish a multi-domain robust feature space for identifying transition zones of rough coal-rock interfaces. Based on the FCM framework, position encoding, L1-norm distance metric, fuzziness criterion, label median filtering, and ground-truth judgment strategies are introduced to enhance the perception of spatial continuity in coal-rock distributions. Furthermore, an attention mechanism is incorporated to dynamically adjust feature weights, enabling adaptive clustering of coal-rock transition regions. Finally, the spatial distribution characteristics of the coal-rock interface within the transition zone are utilized to achieve precise boundary identification. Experimental results demonstrate that the proposed method effectively identifies rough coal-rock boundaries on exposed coal walls, achieving a recognition error of 2.6%.