Full
Outline
During the mining process of underground ore bodies, the collapse of goaf can disrupt the stress balance of rock strata, leading to geological disasters and endangering surface facilities. In order to improve the accuracy of surface subsidence prediction in mines without actual monitoring data during the unmined stage, as well as to ensure personnel safety and mining production, a method combining neural networks and transfer learning for underground mining surface subsidence prediction was proposed. The method combines the advantages of long-short-term memory neural networks to accurately extract time series features and deep convolutional neural networks to extract local features of data, improving the accuracy of the prediction model. According to the mining design of Zhonggangou vanadium titanium magnetite, the prediction model was used to predict the surface subsidence under different mining schedules. The results show that with the increase of ore output, the mean square error of each segment is less than 0.4.The proposed model shows a high accuracy in surface subsidence prediction under different mining schedules. This method can provide a reference for surface subsidence prediction and safety monitoring of similar mines.
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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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