| 郭瑞,吴浩明,高兴运.基于多源遥感与 AI微震监测技术的矿山越界开采协同监管体系研究[J].矿产勘查,2025,16(S2):158-168 |
| 基于多源遥感与 AI微震监测技术的矿山越界开采协同监管体系研究 |
| Research on a synergistic supervision system for mining over-boundary extrac-tion based on multi-source remote sensing and AI microseismic monitoring |
| 投稿时间：2024-12-27 |
| DOI：10.20008/j.kckc.2025s2022 |
| 中文关键词: 矿山测绘 矿业权 InSAR技术 AI微震监测 |
| 英文关键词: mining surveying mineral rights InSAR technology AI microseismic monitoring |
| 基金项目:本文受中国煤炭地质总局科技创新项目(ZMKJ-2025-ZX01-3)资助。 |
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| 摘要点击次数: 30 |
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| 中文摘要: |
| 针对传统矿山测绘技术在矿业权监管中存在的数据获取滞后、监测精度不足、越界开采识别困难等关键问题，本研究构建了基于 InSAR与人工智能微震监测技术的协同监管体系，实现了对矿山开采活动的全天候、高精度、实时化监测。研究以大海则煤矿( 38. 422179°N，109. 277001°E)和魏墙煤矿( 38. 095060°N，109. 367276°E)为试验区，成功识别出越界开采区域，监测精度达到厘米级。同时，创新性地应用 AI微震监测技术，通过 U-Net深度学习模型自动识别 P波震相，实现了对开采活动震源位置的精确定位。研究结果表明:该协同监管体系相比传统监测方法，在监测效率提升的同时，显著增强了对非法越界开采行为的识别能力，为矿产资源监管提供了科学依据和技术支撑。 |
| 英文摘要: |
| To overcome the critical limitations of conventional mine-surveying techniques in mining-rights supervision，such as delayed data acquisition，insufficient accuracy，and the difficulty of detecting over-boundary extraction， this study establishes an integrated supervision system that combines InSAR with AI-based microseismic monitoring. The system enables all-weather，centimetre-level，real-time surveillance of mining activi. ties. Taking the Dahaize coal mine(38. 422179°N，109. 277001°E)and the Weiqiang coal mine(38. 095060°N， 109. 367276°E)as test sites，the study successfully delineated over-boundary extraction areas with centimetre-level precision. In addition，an AI microseismic technique was innovatively introduced:aU-Net deep-learning model automatically picks P-wave arrivals，allowing accurate location of mining-induced seismic sources. The results demonstrate that，compared with traditional methods，the proposed synergistic supervision system markedlyimproves monitoring efficiency and substantially enhances the capability to identify illegal over-boundary extrac. tion，thereby providing scientific evidence and technical support for mineral-resource regulation. |
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