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The Yushenfu mining area is characterized by shallow coal seams, thin overlying bedrock, and thick loose layers, and most mines in this mining area involve repeated mining of multiple coal seams. Affected by multiple factors such as coal seam mining height and spacing, the spatial interaction of surrounding rock in the upper and lower stopes makes it challenging to accurately predict fracture zone height. In this paper, the fracture zone height under multi-coal seam repeated mining in typical coal mines in the Yushenfu mining area was taken as the research object, and the research methods of physical similarity simulation, theoretical analysis, and deep learning were used. First, the fracture development law under multi-coal seam repeated mining was analyzed. Subsequently, a multi-factor coupling nonlinear regression model was established to describe the relationship between the fracture zone height and key parameters, including coal seam mining height, spacing, burial depth, dip angle, working face length, and interval rock strength. On this basis, the prediction method of fracture zone height under multi-coal seam repeated mining based on the SSA-BP neural network was established, and its accuracy was verified. The results indicate that the fracture development under repeated mining in Ciyaota Coal Mine exhibits a three-stage characteristic, i.e., localized slow growth, nonlinear rapid increase through interconnection, and dynamic stabilization. The ultimate height of the fracture zone reaches 139.0 m. The nonlinear regression model incorporating the coupled effects of coal seam mining height, interlayer spacing, strength of intervening rock strata, and working face length achieves an R2 value of 0.880, confirming these parameters as key influencing factors for the fracture zone height. Compared to predictions from traditional empirical formulas and the BP model, the SSA-BP model demonstrates reductions in MAPE values by 22.96% and 6.70%, respectively, and attains a low RMSE of 1.79, indicating superior stability. Validation at the 14205 working face of Zhonghui Funeng Coal Mine in the Yushenfu mining area shows a relative error of 1.3% between the predicted and measured heights, well below 5%. The study demonstrates strong generalizability for predicting the height of water-conducting fracture zones under multi-coal seam repeated mining in the Yushenfu mining area and provides valuable insights for water hazard prevention and control under such mining conditions.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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