Full
LIANG Xiaonan, postgraduate, majors in seismology, E-mail: liangxiaonan22@mails.ucas.ac.cn
Outline
This study focuses on the classification problem of mining seismic and blasting signals in a mine in Gansu province. Common unsupervised classification methods such as support vector machine, K-means clustering, and principal component analysis were analyzed. These methods face significant challenges in terms of insufficient feature selection, low classification accuracy, and imbalanced samples. To attempt to address these issues, this paper employs a lightweight collaborative learning method(LCL-SSS) that combines MiniRocket feature extraction with improved K-means clustering, aiming to enhance the accuracy and efficiency of classification. Through time-frequency analysis, it was found that the initial P-wave frequency of blasting events is mainly concentrated between 80 to 200 Hz, while the P-wave frequency of mining seismic events is distributed between 20 to 120 Hz. After 30 training experiments, the classification accuracy of the LCL-SSS method reached 95.07%, effectively capturing key features in mining seismic waveforms.
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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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