Research on Grading Evaluation of Coal and Gas Dynamic Disasters Based on Fuzzy Mathematics

As mining depths increase, the highly metamorphosed anthracite in Southwest China progressively develops into a complex dynamic disaster influenced by both in situ stress and gas pressure. By utilizing characteristic indicators of mining-induced stress and gas dynamic emissions, a grading evaluation method for coal and gas dynamic disasters (CGDDs) based on fuzzy mathematics l theory is proposed and validated at the No. 1 Well of the Yuwang Coal Mine. The results indicate that the acceleration of microseismic wave velocity and the increase in the wave velocity anomaly coefficient are indicative of a more pronounced stress concentration. The working face exhibits distinct gradations of stress concentrations, categorized as weak, moderate, and strong. Moreover, the increase in microseismic wave velocity and the anomaly coefficient further confirm the intensity of the stress concentrations. Gas dynamic emissions show a clear correlation with the drill cuttings gas desorption indicator (K1 value) and drill cuttings volume (S value). Characteristic indicators A, B, and D are suitable for assessing the risk of CGDDs in the working face. For the application of individual indicators for classifying the CGDD risk at different distances from the crosscut (128 m, 247.5 m, 299.4 m, and 435 m) in the 1010201-working face, contradictory classification results were observed. However, the classification results derived from the fuzzy mathematics method were consistent with the findings of field investigations. As the working face advanced through the pre-concentrated stress zone, significant changes were observed in both the source wave velocity and wave velocity anomaly coefficient. Concurrently, gas emissions displayed a distinct pattern of fluctuation characterized by increases and decreases. The consistency between the periodic weighting of the working face, the gas emission, the drill cuttings gas desorption indicator, and the stress field inversion result further validates the classification outcomes. These research results can provide theoretical support for the monitoring of CGDDs.