| Autor: |
Fu Xiaoqiang, Ma Yan, Yu Jin, Dai Liangyu, Huang Lingjun |
| Jazyk: |
English<br />Chinese |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
矿业科学学报, Vol 8, Iss 3, Pp 348-356 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2096-2193 |
| DOI: |
10.19606/j.cnki.jmst.2023.03.008 |
| Popis: |
Aiming at the problem of insufficient time-frequency resolution of tunnel blasting vibration signal, a time-frequency image enhancement algorithm based on convolutional neural network is applied, through the time-frequency image enhancement of the measured tunnel blasting signal, the aggregation range of the blasting signal energy in the time-frequency domain is captured, and the real signal reflecting the blasting characteristics is reconstructed; according to the real signal, the initiation time of detonator in blasting network is accurately distinguished, and the characteristics of tunnel blasting detonator disaster source are identified.The analysis shows that the time-frequency image enhancement algorithm based on convolutional neural network can effectively suppress the cross-terms in the signal, retain the auto-terms of the signal to the greatest extent, and improve the energy aggregation and time-frequency resolution of the blasting signal; The mixed use of different batches of detonators is the main disaster causing factor of tunnel safety.Supervision should be strengthened to realize safe and efficient tunnel construction. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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