Research on the Design of Coal Mine Microseismic Monitoring Network Based on Improved Particle Swarm Optimization
The quality of a mine’s microseismic network layout directly affects the location accuracy of the microseismic network. Introducing the microseismic probability factor <i>F<sub>e</sub></i>, the microseismic importance factor <i>F<sub>Q</sub></i>, and t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/17/8439 |
| _version_ | 1827667227607826432 |
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| author | Kaikai Wang Chun’an Tang Ke Ma Tianhui Ma |
| author_facet | Kaikai Wang Chun’an Tang Ke Ma Tianhui Ma |
| author_sort | Kaikai Wang |
| collection | DOAJ |
| description | The quality of a mine’s microseismic network layout directly affects the location accuracy of the microseismic network. Introducing the microseismic probability factor <i>F<sub>e</sub></i>, the microseismic importance factor <i>F<sub>Q</sub></i>, and the effective range factor <i>F<sub>V</sub></i>, an improved particle swarm algorithm with bacterial foraging algorithm is proposed to optimize the mine’s microseismic network layout and evaluation system based on the <i>D</i>-value optimization design theory. Through numerical simulation experiments, it is found that the system has the advantages of fast optimization speed and good network layout effect. Combined with the system application at Xiashijie Coal Mine in Tongchuan City, Shaanxi Province, the method in this paper successfully optimizes the layout of the 20-channel network, ensuring that the positioning error of key monitoring areas is controlled within 20 m, and the minimum measurable magnitude can reach −3.26. Finally, it is verified by blasting tests that the maximum spatial positioning accuracy of the site is within 12.2 m, and the positioning capability of the site network is more accurately evaluated. The relevant research can provide a reference for the layout of the microseismic monitoring network for similar projects. |
| first_indexed | 2024-03-10T03:05:48Z |
| format | Article |
| id | doaj.art-8fe46212dc544123bac0b040814d4777 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:05:48Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8fe46212dc544123bac0b040814d47772023-11-23T12:39:31ZengMDPI AGApplied Sciences2076-34172022-08-011217843910.3390/app12178439Research on the Design of Coal Mine Microseismic Monitoring Network Based on Improved Particle Swarm OptimizationKaikai Wang0Chun’an Tang1Ke Ma2Tianhui Ma3State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaThe quality of a mine’s microseismic network layout directly affects the location accuracy of the microseismic network. Introducing the microseismic probability factor <i>F<sub>e</sub></i>, the microseismic importance factor <i>F<sub>Q</sub></i>, and the effective range factor <i>F<sub>V</sub></i>, an improved particle swarm algorithm with bacterial foraging algorithm is proposed to optimize the mine’s microseismic network layout and evaluation system based on the <i>D</i>-value optimization design theory. Through numerical simulation experiments, it is found that the system has the advantages of fast optimization speed and good network layout effect. Combined with the system application at Xiashijie Coal Mine in Tongchuan City, Shaanxi Province, the method in this paper successfully optimizes the layout of the 20-channel network, ensuring that the positioning error of key monitoring areas is controlled within 20 m, and the minimum measurable magnitude can reach −3.26. Finally, it is verified by blasting tests that the maximum spatial positioning accuracy of the site is within 12.2 m, and the positioning capability of the site network is more accurately evaluated. The relevant research can provide a reference for the layout of the microseismic monitoring network for similar projects.https://www.mdpi.com/2076-3417/12/17/8439microseismic network layoutthe <i>D</i>-value optimization design theoryimproved particle swarm algorithm (IPSO)numerical simulation experimentblasting test |
| spellingShingle | Kaikai Wang Chun’an Tang Ke Ma Tianhui Ma Research on the Design of Coal Mine Microseismic Monitoring Network Based on Improved Particle Swarm Optimization Applied Sciences microseismic network layout the <i>D</i>-value optimization design theory improved particle swarm algorithm (IPSO) numerical simulation experiment blasting test |
| title | Research on the Design of Coal Mine Microseismic Monitoring Network Based on Improved Particle Swarm Optimization |
| title_full | Research on the Design of Coal Mine Microseismic Monitoring Network Based on Improved Particle Swarm Optimization |
| title_fullStr | Research on the Design of Coal Mine Microseismic Monitoring Network Based on Improved Particle Swarm Optimization |
| title_full_unstemmed | Research on the Design of Coal Mine Microseismic Monitoring Network Based on Improved Particle Swarm Optimization |
| title_short | Research on the Design of Coal Mine Microseismic Monitoring Network Based on Improved Particle Swarm Optimization |
| title_sort | research on the design of coal mine microseismic monitoring network based on improved particle swarm optimization |
| topic | microseismic network layout the <i>D</i>-value optimization design theory improved particle swarm algorithm (IPSO) numerical simulation experiment blasting test |
| url | https://www.mdpi.com/2076-3417/12/17/8439 |
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