The gas concentration monitoring data contains a large amount of potentially effective early-warning information. However, the effective information that can be extracted from the raw data and its statistical features is limited, which restricts its application in the early warning of coal and gas outburst disasters. In this study, gas concentration monitoring data from different tunneling faces under various operational conditions were collected, along with gas outburst prediction indicators drilling cuttings weight from the tunneling faces. Based on the EN-TSFRESH method, a total of 784 features were extracted from 8-h (one work shift) gas concentration monitoring data, covering seven categories including statistical, time-frequency, trend, and stability features. The results indicate that the high-dimensional gas concentration features within each time window contain potential early-warning information and show correlation with static indicators. A weakly supervised feature selection method was employed to extract low-dimensional features such as Benford law correlation, peak value, cumulative value, absolute values of Fourier transform coefficients, and energy distribution across different time segments. By combining t-SNE and PCA for dimensionality reduction and visualization, unsupervised clustering methods were applied to classify various gas concentration monitoring datasets. Compared with k-means clustering, hierarchical clustering achieved the highest accuracy, approximately 97.2 %. The high-risk events identified through unsupervised clustering showed a strong correlation with on-site measured drilling cuttings weight, demonstrating the method's potential for early warning of gas outbursts. This study provides theoretical support for dynamic early warning of gas outbursts in tunneling faces and holds significant implications for safe and efficient coal mine production.
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