摘要
基于深度学习方法提出了一种矿山微震P波到时拾取方法。首先构建CNNDet模型进行事件监测和到时预拾取;其次引入自注意力机制,融合门控循环单元(GRU)构建CGANet模型,对检测到的事件进行P波到时精确拾取;最后将该方法与长短时窗能量比法、DPick和PpkNet方法进行对比,结果显示测试集的事件检测精确率和召回率都达到98%以上,P波到时估计的误差均值和标准差分别为0.014 s和0.051 s,说明本文方法在精确率、召回率及标准差等方面均明显优于上述三种方法。此外,对不同信噪比样本进行测试的结果也证明,本文方法在低信噪比下依然能保持较高的精度。在实际震源定位中,该方法也展现出了更优异的性能。
Seismic phase picking is the first key step of mine microseisms detection,and its accuracy often directly affects the quality of subsequent event processing,so we proposed a method for P-arrival picking of mine microseisms which is based on deep learning method.Firstly the CNNDet model is constructed for events detection and P-arrival pre-picking,and then the CGANet model was constructed to accurately pick up the P-arrival time for the detec-ted events by introducing the self-attention mechanism and the gated recurrent unit.Comparison with STA/LTA,DPick and PpkNet shows that the precision and the recall ratio of seismic event detection by our method are more than 98%for the test sets,and the mean error and the standard deviation of P-arrival are 0.014 s and 0.051 s,respectively. Our method is superior to the above three methods in terms of precision,the recall ratio and the standard deviation. In addition,the experimental tests on samples with different SNRs prove that our method can still maintain high precision on the condition of low SNR. In the source location,our method also shows more excellent performance. The P-arrival picking method proposed in this paper which is based on gated recurrent unit and self-attention mechanism provides a new idea for micro- seisms monitoring and accurate identification of rock burst and other disasters.
作者
焦明若
董方杰
罗浩
于靖康
马莉
Jiao Mingruo;Dong Fangjie;Luo Hao;Yu Jingkang;Ma Li(School of Information,Liaoning University,Shenyang 110036,China;Liaoning Earthquake Agency,Shenyang 110034,China)
出处
《地震学报》
CSCD
北大核心
2023年第2期234-245,共12页
Acta Seismologica Sinica
基金
辽宁省科技厅科学技术计划项目(2019010223-JH8/103)
辽宁省教育厅科学技术研究项目(LJKMZ20220450)
国家自然科学基金(51704138)共同资助。
关键词
矿山微震
P波到时拾取
门控循环单元
自注意力机制
mine microseisms
P-arrival picking
gated recurrent unit
self-attention mechanism
作者简介
焦明若,博士,研究员,主要从事地震预报及前兆机理的研究,e-mail:mjrou1963@yahoo.com;罗浩,博士,副教授,研究方向为矿山安全大数据和人工智能,e-mail:luohao8711@163.com。
