融合门控循环单元和自注意力机制的矿山微震P波到时拾取方法

焦明若, 董方杰, 罗浩, 于靖康, 马莉

焦明若,董方杰,罗浩,于靖康,马莉. 2023. 融合门控循环单元和自注意力机制的矿山微震P波到时拾取方法. 地震学报,45(2):234−245. DOI: 10.11939/jass.20220034
引用本文: 焦明若,董方杰,罗浩,于靖康,马莉. 2023. 融合门控循环单元和自注意力机制的矿山微震P波到时拾取方法. 地震学报,45(2):234−245. DOI: 10.11939/jass.20220034
Jiao M R,Dong F J,Luo H,Yu J K,Ma L. 2023. P-arrival picking method of mine microseisms by fusing of GRU and self-attention mechanism. Acta Seismologica Sinica45(2):234−245. DOI: 10.11939/jass.20220034
Citation: Jiao M R,Dong F J,Luo H,Yu J K,Ma L. 2023. P-arrival picking method of mine microseisms by fusing of GRU and self-attention mechanism. Acta Seismologica Sinica45(2):234−245. DOI: 10.11939/jass.20220034

融合门控循环单元和自注意力机制的矿山微震P波到时拾取方法

基金项目: 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)和国家自然科学基金(51704138)共同资助
详细信息
    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

    通讯作者:

    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

  • 中图分类号: P315.63;TD76

P-arrival picking method of mine microseisms by fusing of GRU and self-attention mechanism

  • 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。
    Abstract: 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 detected 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 microseisms monitoring and accurate identification of rock burst and other disasters.
  • 珊溪水库位于浙江省温州市文成县与泰顺县交界处,隶属于浙东南褶皱带次级构造单元温州—临海坳陷带南部的泰顺—青田断坳,其西侧以北东向的余姚—丽水深断层为界(马志江等,2016)。该水库于2000年5月开始蓄水,蓄水前地质构造相对稳定,无历史地震记录(赵冬等,2006)。蓄水两年后,库区附近浅源地震频发,且持续至今,震群在时空上呈明显的丛集分布特性,地震总体沿贯穿水库的双溪—焦溪垟断裂成组成段优势分布,先后发生多组显著的高密度震群活动,时序上沿断裂走向由断层库区淹没区向断层两端迁移(钟羽云等,2015)。该水库的地震活动与库区岩性、断层分布及水库蓄水等特征密切相关。双溪—焦溪垟断裂为高倾角(>70°)、右旋走滑兼逆冲型断层,总长度超过20 km,切割深度在5 km以上,切穿变质岩地层基底,为库区附近淹没段最长的断层分支,并发育次级断面,破碎带周围竖向节理发育,胶结程度差,断层两侧为隔水性较好的层状岩层,此断面结构易于库水下渗(周昕等,2006马志江等,2016)。珊溪水库孕震区是水库地震研究的热点(周昕等,2006朱新运等,2010钟羽云等,2015马志江等,2016马起杨,朱新运,2016侯林锋等,2018),通过库区地震震源深度的定位圈定孕震区范围,为水库地震孕震机理的分析提供重要信息。

    震源深度是确定孕震区范围的重要位置参数,能够反映地壳岩石流变特性和脆塑性特征(Scholz,2002),因此准确地确定震源深度一直是地震学研究的重要内容之一。然而震源深度的反演既依赖于地震波波速模型,又与发震时刻之间存在折衷,尤其是稀疏台网下,对震源深度的测定尤为困难。经典线性走时拟合方法是目前使用较为广泛的地震定位方法,通过拾取多个地震台的P波、S波震相到时,给定波速模型拟合各个台站的观测到时,使全部台站的走时误差达到最小,从而解算出发震时刻和震源位置。在此基础上发展的诸如联合定位法、相对定位法和双重残差法等方法,均是基于走时计算,需要计算观测值与理论值之间的残差函数,对台网的台站密度和波速模型的依赖较大(崇加军等,2010)。

    地震波深度震相蕴含丰富的震源深度信息,为震源深度定位提供了一种新的途径。深度震相是指地震波从震源上行出射经地表反射后传播至台站而形成的震相。震相经地表反射后的传播路径与其对应参考震相的传播路径相似,因此两者到时差主要与震源深度相关,利用深度震相进行震源深度定位可以规避传统走时定位发震时刻与定位深度的折衷性,且基本不依赖于震中距,降低了传统方法对传播路径的三维波速模型的依赖。不同深度震相有其相应的优势震中距范围。中强震在震中距远的波形记录上能够观测到远震深度震相pP和sP。对于中小地震可以采用近震深度震相(sPg,sPn,sPL)来约束震源深度(张瑞青,吴庆举,2008崇加军等,2010)。因首波的发育需要超过临界震中距,故以首波Pn为参考震相的深度震相sPn,观测台站的震中距通常大于100 km。震相sPg在地壳速度结构具有一定梯度时发育较为明显,其观测台站的震中距介于50—100 km之间。对于震中距50 km以内的台站,上行S波经地表反射转化为P波则会沿地表浅部直接传播到台站,传播过程同时耦合P波在地表浅部的多次波及散射波,其震相为sPL震相(崇加军等,2010Wang et al,2011)。由于sPL震相发育于较近震中距,可约束中小地震震源深度,适用于类似于珊溪水库这样震群震级普遍偏小的弱震区。

    珊溪水库蓄水后表现出间歇性的震群活动,最大余震与主震的震级差较小,持续时间长。浙江省地震监测台网记录到的最近一次高密度震群活动从2014年9月持续至2015年3月底,此次震群中地震的最大震级为ML4.2。自2002年起,浙江省地震局在珊溪水库的库区逐步建成了子台密度较大的水库地震监测台网,覆盖整个库区,库区周边台站及地震分布见图1。台网记录到的震群的最小完备震级Mc由2002年初始的ML1.5降至2014年的ML0.3(于俊谊,马起杨,2017),库区监测能力不断提高,台网的定位能力得到了改善。2014年之前监测到的水库地震,由于受台网台站密度条件限制,定位精度有限,台网定位地震的空间分布较为离散,未能显示出明显的优势分布方向;而台网定位的2014年震群的地震空间展布与贯穿库区的双溪—焦溪垟断裂一致,定位精度显著提高。

    图  1  珊溪水库区域断裂、地震和台站分布图
    灰色填充区为珊溪水库,三角形为台站,圆圈代表2014年震群,加号为2014年之前地震,黑线为断层
    Figure  1.  Fault structure and distribution of earthquakes and stations in Shanxi reservoir region
    Gray region indicates Shanxi reservoir,and its surrounding fault system is depicted by line segments,open circles denote the swarm epicenters of the year 2014 while plus signs denote the swarm before the year 2014,seismic stations are indicated by triangles

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    图  2  sPL射线路径示意图(a)和万阜台记录的三分向位移波形(b)
    图(a)中黑色为sPL及其参考震相Pg,灰色射线示意相对sPL震相其它深度震相则需更远的震中距才发育
    Figure  2.  Schematic illustration of sPL ray path (a) and three-component displacement waveforms recorded at Wanfu station to view sPL phase (b)
    Black ray for sPL-Pg pair and gray for other depth phases which appear in farther epicentral distance in Fig.(a)

    近震深度震相和其参考震相的走时差,与震源深度近于线性相关,可以用于约束地震震源深度。本文使用频率−波数域方法(Zhu,Rivera,2002)计算不同深度上走滑型双力偶源的三分量格林函数,来合成万阜台的理论地震图,以观测sPL震相随震源深度变化的敏感特性。速度模型采用从全球地壳模型CRUST1.0 (Laske et al,2013)提取的一维分层模型。如图3a所示,sPL与P波的到时差随着深度增加而明显增大,近乎呈线性关系。敏感性测试中波形合成采用的双力偶源震源机制是基于库区发震构造由CAP (cut and paste)波形拟合方法(Zhao,Helmberger,1994Zhu,Helmberger,1996)反演所得,符合库区实际情况。近震CAP方法是一种全波形拟合反演方法,由于反演结果对地壳速度模型和结构横向变化的依赖性相对较小,因此被广泛应用于震源机制和震源深度反演,其原理是在给定的参数空间中网格搜索使波形拟合误差达到最小的最佳解。2014年珊溪水库震群震级最大的主震震源机制和震源深度的CAP反演结果(图3b)显示,断层走向127°,倾角83°,滑动角−177°,为高倾角右旋走滑型震源机制,且走向与余震展布一致,表明其发震构造为双溪—焦溪垟断裂。

    图  3  基于sPL和CAP方法确定2014年珊溪水库震群主震震源深度
    (a) sPL震相敏感性测试,灰色波形表示不同深度三分向理论波形,红色波形表示万阜台显示的主震最佳拟合深度;(b) CAP反演所得的主震最优震源机制(左)和震源深度(右)
    Figure  3.  Focal depth determined by sPL and CAP method for the mainshock of the Shanxi reservoir swarm in 2014
    (a) sPL phase depth sensitivity test,where gray waveforms represent three-component synthetics at different focal depths and red ones represent data recorded by Wanfu staion at best fitting;(b) Focal mechanism (left) and depth (right) determined by CAP method

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    表  1  珊溪水库震群七次地震事件震源深度的测定结果
    Table  1.  Focal depths of seven events from Shanxi reservoir seismic swarm
    序号 发震时刻 震中位置 ML 震源深度/km
    年−月−日 时:分:秒 东经 北纬 sPL结果 台网结果* CAP结果
    0 2014−10−14 04:14:57 119.94 27.71 4.2 5 4 4
    1 2014−09−17 20:47:31 119.95 27.71 3.5 4—5 4 4
    2 2014−09−23 17:40:25 119.94 27.71 3.7 4—5 4 4
    3 2014−10−15 15:49:27 119.95 27.71 4.0 5—6 5 5
    4 2014−10−15 16:37:24 119.96 27.70 4.0 6 5 5
    5 2014−10−23 08:35:02 119.93 27.72 3.7 5 4 4
    6 2014−10−26 07:03:41 119.97 27.69 3.4 4 4 /
     *引自浙江省数字地震台网中心地震目录(内部资料).
    下载: 导出CSV 
    | 显示表格
    图  4  表1中万阜台水库震群事件1—6的位移波形图
    Figure  4.  Displacement records of six events labeled with one to six in Table 1 at Wanfu station with vertical (red),radial (blue) and tangential (green) components

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    通过实际观测和正演分析可知,在震中距小于50 km的范围内,在近震深度发育sPL震相,sPL震相及其参考震相Pg之间的走时差与震中距无关,与深度存在线性关系,可用于约束震源深度。sPL震相具有低频特性,优势震中距30—50 km上的震相清晰,只在径向和垂向分量上出现,一般径向幅度大于垂向分量。

    采用万阜台观测到的sPL震相测定的珊溪水库地震序列中7次地震事件(包括主震)的震源深度介于4—6 km,与地下活动断层探测结果相一致。跨库区的花状构造的三条平行分支断层在地下深约6 km处交会(侯林锋等,2018),地下4—6 km处岩石相对更为破碎,库水沿断裂面向深部渗透易汇集于此深度,孔隙压升高,正应力降低,打破应力平衡诱发地震(马志江等,2016)。断层浅部有断层泥的充填难以积累足够应变产生地震,太深岩石强度又超过周围偏应力水平,地震很难往深部发展迁移。与高密度台站下台网采用Hyposat方法和波形CAP反演的深度结果进行对比,表明sPL震相测定的震源深度是非常可靠的。

    在地壳结构相对简单的情况下,sPL震相一般易于识别,可较好地应用于地震深度的测定,但不排除某些特殊情况,例如震源持续时间较长或震中距太近,sPL震相会受到扩展P波(Pnl)的干扰,震相拾取会相对困难。尽管精确拾取到时有时存在一定困难,但深度误差范围一般可控制在1—2 km之内。sPL震相的优势震中距为30—50 km,鉴于区域台网的台间距较小,通常不会大于50 km,sPL震相有很强的适用性。本文利用单台即可获取可信的震源深度,可运用于稀疏台网(如2014年之前珊溪水库台网),并适用于中小地震,扩展了其应用性。

    本研究虽然通过波形拟合,但sPL震相是利用到时差与深度的近乎线性关系来约束地震,对于区域台网,尤其震群来说,其线性关系是相对固定的,且一般从未滤波的原始波形能直接识别sPL震相,以理论到时差为量板,无需震中水平位置信息,sPL震相可快速地测定出可靠的震源深度。

  • 图  1   矿山微震P波到时拾取总体架构(CNNDet用于事件检测,CGANet进行P波到时拾取)

    Figure  1.   Overall architecture for P-arrival picking method of mine microseisms (CNNDet is used for event detection,and CGANet is used for P-arrival picking)

    图  2   CNNDet和CGANet神经网络模型

    Figure  2.   CNNDet and CGANet neural network model

    图  3   数据预处理及数据增强

    Figure  3.   Data preprocessing and data enhancement

    图  4   CNNDet模型的界限标签选取

    Figure  4.   Boundary label selection of CNNDet

    图  5   CNNDet (上)和CGANet (下)的训练迭代过程

    Figure  5.   Training iterative process of CNNDet (top) and CGANet (bottom)

    图  6   各种震相拾取算法在验证集上的误差直方图

    Figure  6.   Error histogram of various seismic phase picking algorithms on the verification set

    图  7   CNNDet+CGANet和传统方法STA/LTA在不同信噪比下的表现对比

    Figure  7.   Performance comparison of CNNDet+CGANet and STA/LTA under different SNRs

    图  8   DPick,DetNet+PpkNet与CNNDet+CGANet在不同信噪比下的拾取对比

    Figure  8.   Comparison of DPick,DetNet+PpkNet and CNNDet+CGANet picking at different SNRs

    图  9   2020年7月11日8:58—9:17事件检测和P波拾取处理情况

    Figure  9.   Event detection and P-wave pickup processing in 8:58−9:17 on July 11,2020

    表  1   本文方法与STA/LTA,DPick和PpkNet的性能比较

    Table  1   The performance of the proposed method in this study compared with STA/LTA,DPick and PpkNet

    方法精确率召回率平均绝对误差/s标准差/s误差>0.2 s占比
    CNNDet+STA/LTA0.724 00.724 00.028 1150.086 9682.28%
    DPick0.854 60.847 00.027 9570.060 3222.74%
    DetNet+PpkNet0.878 00.871 00.019 1240.051 4181.36%
    CNNDet+CGANet0.948 00.948 00.014 1580.051 0521.14%
    下载: 导出CSV
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  • 收稿日期:  2022-03-20
  • 修回日期:  2022-05-16
  • 网络出版日期:  2023-03-19
  • 发布日期:  2023-03-14

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