随采地震数据质量定量评价

覃思, 崔伟雄, 王伟

覃思, 崔伟雄, 王伟. 随采地震数据质量定量评价[J]. 煤田地质与勘探, 2019, 47(3): 20-24. DOI: 10.3969/j.issn.1001-1986.2019.03.004
引用本文: 覃思, 崔伟雄, 王伟. 随采地震数据质量定量评价[J]. 煤田地质与勘探, 2019, 47(3): 20-24. DOI: 10.3969/j.issn.1001-1986.2019.03.004
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QIN Si, CUI Weixiong, WANG Wei. Quantitative quality evaluation of seismic-while-mining data[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(3): 20-24. DOI: 10.3969/j.issn.1001-1986.2019.03.004
Citation: QIN Si, CUI Weixiong, WANG Wei. Quantitative quality evaluation of seismic-while-mining data[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(3): 20-24. DOI: 10.3969/j.issn.1001-1986.2019.03.004
shu

 

随采地震数据质量定量评价

  • 1. 中煤科工集团西安研究院有限公司，陕西 西安 710077;

  • 2. 资源与环境信息系统国家重点实验室，中国科学院地理科学与资源研究所，北京 100101

基金项目: 

国家重点研发计划课题（2018YFC0807804）；贵州省科技重大专项项目（[2018]3003-1）；中煤科工集团西安研究院有限公司科技创新基金项目（2018XAYZD02）；中国科学院科研仪器设备研制项目（YJKYYQ20170033）

详细信息
    作者简介:

    覃思,1981年生,男,湖南吉首人,副研究员,博士,研究方向为随采地震勘探.E-mail:qinsi@cctegxian.com

  • 中图分类号: P631.4

Quantitative quality evaluation of seismic-while-mining data

  • 1. Xi'an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi'an 710077, China;

  • 2. The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China

Funds: 

National Key R&D Program of China(2018YFC0807804)

摘要

    摘要
  • 摘要: 在采煤工作面布设了随采地震监测系统后，为了自动筛选实时不间断传输回地面的巨量地震数据，利用采煤机积极割煤时，各接收道收到的信号相关性强，反之则弱这一特点，提出了一种自动定量化评估随采地震数据质量的方法。对内蒙某矿随采地震数据进行了处理，结果证明该方法能有效识别出单道的相关能量峰，对数据质量定量评价的效果很好。利用此法筛选了贵州某矿随采地震监测数据，将优选数据叠加后，数据的信噪比得到了明显的改善，相关能量轴明显增强。该方法可从海量随采地震数据中快速筛选出高质量的数据，大幅缩减进一步处理的工作量，改善处理效果。
    Abstract: A seismic-while-mining(SWM) monitoring system laid in a working face of an underground coalmine will continuously generate tremendous amount of SWM data. When a coalmine cutting machine cuts coal seam actively, the SWM data will be of high correlation, and when it is not, the SWM data will be of low correlation or even no correlation at all. Based on this characteristic, a method for automatically and quantitatively evaluating the quality of SWM data was proposed. Using this method to process SWM data from a coal mine in Inner Mongolia, the result showed that this method can recognize the correlation peak very effectively, and the data quality evaluation result is very good. To apply this method to screening SWM monitoring data from a coal mine working face in Guizhou Province, the stack result of selected high quality data shows that the S/N ratio is obviously improved, and the correlation event is obviously enhanced. This method can be used to rapidly pick out high quality data from enormous amount of data, and reduce the workload of next step while improving the processing result.
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    • 参考文献(17)
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出版历程
  • 收稿日期:  2018-12-20
  • 发布日期:  2019-06-24

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