The seismogenic environment and focal mechanisms of moderate-strong earthquakes in Hubei Province
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摘要:
通过CAP技术反演了湖北省2018年秭归MS4.5和2006年随州ML4.7地震的震源参数,并采用接收函数与面波相速度频散曲线联合方法揭示了湖北省有地震记录以来的三次M6以上强震和1958年地震仪观测以来的六次M4.5—6.0地震震中处的地壳剪切波速度。结果显示:随州地震走向呈NW向,震源深度为8 km,发震断层与襄樊—广济断裂带以及皂市断裂或潜北断裂有关;秭归地震走向为NNE和NE向,震源深度为5 km,发震断层与新华—龙王冲断裂带和高桥断裂带有关。基于前人采用CAP等方法得到的2013年巴东MS5.1、2014年秭归MS4.6和2019年应城MS4.9地震的震源机制解以及本文接收函数与面波联合反演所得的地壳S波速度对孕震环境和震源机制进行研究,结果显示:湖北地区中等地震的发震断层都以走滑为主,与断裂构造分布状况相对应;获得震源机制解的五次中强地震分别发生在不同速度特征的垂向高低速转换区域,四次震源深度未知的中强震在传统发震层深度范围内也呈现明显的垂向高低速互层变化特征;2006年随州ML4.7、2014年秭归MS4.6和2019年应城MS4.9地震可能为构造型地震,2013年巴东MS5.1和2018年秭归MS4.5地震可能为水库触发型地震。
Abstract:Research on earthquake trends, investigations of seismo-geological characteristics, and studies of seismic activity in Hubei Province shows that Hubei and its neighboring regions exhibit a background conducive of moderate to strong earthquakes. In the Yangtze craton of Hubei Province, which is structurally stable and characterized by low heat flow and strong rigidity, moderate to strong earthquakes occurred one after another in recent years. The seismogenic background and seismogenic structure have drawn considerable attention, but systematic research in this regard remains relatively scarce.
In this paper we uses CAP (cut and paste) technology to invert the source parameters of the 2018 Zigui MS4.5 earthquake and the 2006 Suizhou ML4.7 earthquake. And then we have employed a joint method of receiver function and surface wave phase velocity dispersion curve to reveal the shear wave velocity of the crust at the epicenter of three earthquakes with M≥6 (documented since the start of earthquake records) and six earthquakes with M4.5−6.0 (recorded following the implementation of seismometer observations in 1958) in Hubei Province. The results indicate that for the 2006 Suizhou ML4.7 earthquake, the strike, dip angle, and rake were 126°, 78° and −30°, respectively, the strike direction was NW, and the focal depth was 8 km. The seismogenic fault was related to the northwest trending Xiangfan-Guangji fault zone and its subfaults (Zaoshi fault or Qianbei fault). For the 2018 Zigui MS4.5 earthquake, the strike, dip angle, and rake were 61°, 58° and 173°, the strike was NNE and NE, and the focal depth is ML4.75 km. The seismogenic fault was related to the Xinhua-Longwangchong fault zone and Gaoqiao fault zone. Based on the source mechanism solutions of the 2013 Badong MS5.1, 2014 Zigui MS4.6, and 2019 Yingcheng MS4.9 earthquakes obtained by previous researches using CAP and other methods, as well as the crustal S-wave velocity obtained by the joint inversion of receiver function and surface wave in this paper, it was found that the seismogenic faults of medium and strong earthquakes are mainly of strike-slip, which is corresponding to the distribution of fault structures. For the 2013 Badong MS5.1 earthquake and the 2014 Zigui MS4.6 earthquake, their S-wave velocities vary from low to high, with velocity percentage changes of 4% and 7%, respectively. In contrast, for the 2018 Zigui MS4.5, 2006 Suizhou ML4.7, and 2019 Yingcheng MS4.9 earthquakes, the S-wave velocities vary from high to low, with velocity variations percentage of −4%, −1%, and −2%, respectively. The five moderate-strong earthquakes, for which the focal mechanism solutions were obtained, occurred in vertical high-low velocity transition zones with different velocity characteristics. Additionally the four moderate to strong earthquakes with unknown focal depths also exhibited significant vertical high-low velocity interlayer variations within the traditional depth range of the seismogenic layer. The risk of moderate to strong earthquakes in Hubei Province has increased. Seismic activity is significantly higher in the western part of the province compared to the eastern part, with a concentration in Zigui and its adjacent areas. Small and medium-sized earthquakes are also clustered in the source area and its adjacent areas of Zigui, which needs to be monitored specially. The paper suggests that the 2006 Suizhou ML4.7, the 2014 Zigui MS4.6, and the 2019 Yingcheng MS4.9 earthquakes may be structural earthquakes, which are speculated to be related to the reverse compression of the northwest Yangtze Plate, the relative compression and impact of the southwest Indian Plate, and the activation of preexisting faults under the dual effects of subduction of the Pacific Plate and rock asthenosphere system. The 2013 Badong MS5.1 earthquake and the 2018 Zigui MS4.5 earthquake occurred in the vicinity of the Three Gorges Reservoir, with shallow epicentral depth. It is speculated that the reservoir’s water impoundment and subsequent downward infiltration altered the local seismic environment, potentially rendering these events reservoir-triggered earthquakes.
In summary, it is necessary to persistently focus on and strengthen the research on earthquake trends, geological characteristics of earthquakes, and monitoring of earthquake activities in Hubei Province. This is crucial for averting the earthquake-related disasters risk and reducing the huge losses caused by earthquakes. The study of the seismogenic environment and focal mechanism of moderate to strong earthquakes can provide reference for understanding earthquake characteristics and earthquake prevention and disaster reduction in Hubei Province.
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引言
人工神经网络(artificial neural networks,缩写为ANN)在过去的三十年中取得了长足的发展,从最开始模仿神经元而建立数学模型发展到如今已经成为广泛应用于众多领域的实用技术(Murphy,2012;Jordan,Mitchell,2015),特别是在地震学领域也有很多应用,例如地震识别和分类(Dysart,Pulli,1990;Ursino et al,2001;周本伟等,2020)、地震相位拾取(Tiira,1999;Wiszniowski et al,2014;李安等,2020)等。深度神经网络作为人工神经网络的一个分支,由于需要大量的训练数据和其它约束条件,并未得到广泛使用,但最近十多年里该技术在数据收集、存储、传输和分析等方面的应用得到了突破性的发展。数据的爆炸性增长迫切需要能够对其进行有效分析的方法,而深度神经网络正好可以满足这一迫切需求,而且由于该方法具有对事物或抽象概念建立更复杂模型的能力,因而在处理大样本和复杂函数关系时更为便捷(隗永刚等,2019)。对于运用深度学习方法的地震学研究,其核心是利用深度学习模型分析数据以获取、使用有效的信息。经过地震学研究人员近年来的努力,深度学习技术已成功用于许多挑战性的研究中,例如地震岩性预测(Zhang et al,2018)、地震事件检测与定位(Huang et al,2018)、地震相位检测与拾取(Zhu et al,2019)、相位关联(Ross et al,2019)等。而现阶段,地震的准确预测作为公认的世界性科学难题,还很难实现(张肇诚,张炜,2016)。为了预防地震带来较大的危害和损失,须根据当地的抗震设防标准进行抗震设计。在工程抗震设计、研究和分析中,往往需要选择实际的地震动记录来代表地震对结构的作用,亦或是代表施加于该结构的一种地震荷载(谢礼立,翟长海,2003)。对于工程而言,考虑到路径和场地的影响,实际的地震动应该是当地的大震记录,显然满足此要求的地震动记录很少,甚至很多地方都无大的地震动记录,这就需要对当地的地震动记录进行一定调整以使其满足作为地震动输入的要求。国外对这方面的研究开展较早,美国太平洋地震研究中心(Pacific Earthquake Engineering Research,缩写为PEER)将地震动记录的缩放方法分成五类:① 通过震级、断层距将记录在已知结构基本周期处的加速度谱值缩放至目标谱在该周期处的值;② 通过缩放记录幅值,使所选记录的反应谱与危险谱拟合一致;③ 选择的记录谱应很好地拟合条件均值谱(Baker,Cornell,2005;Baker,Cornell,2006);④ 所选记录的
$ \varepsilon $ 应与预测地震的$ \varepsilon $ 值相接近,$ \varepsilon $ 是给定周期点的记录谱值与地震动预测方程平均值的差值(Goulet et al,2004),$ \varepsilon $ 在预测结构反应上有显著表现,是一个谱形的指标(Goulet,2005;Goulet et al,2006);⑤ 所选记录的位移谱应较好地拟合非线性目标位移谱(Baker,Cornell,2006b)。而在国内,对于地震动输入多是选择比较常用的大震记录,对于地震动记录的处理往往是将加速度记录调整到目标场地、目标设防的规范加速度值(朱晓炜,2011),具有明显的局限性。为了选择一个合适的地震动记录调整方法,本文拟建立一个卷积神经网络(convolutional neural networks,缩写为CNN)来分析地震加速度时程记录的特征,并选择归一化的加速度记录作为样本输入以训练模型对大、小地震进行分类,并基于单方向地震加速度记录来判断地震的震级大小,由此判断小震记录经过调整是否具有一定的大震特性,以提高抗震分析的有效性。1. 数据预处理
在训练过程中,为了提高模型的有效性,确保每个样本数据的规模相当,需先对数据进行归一化处理,使每个地面运动记录输入具有相同的峰值加速度。将每个地震加速度记录定义为一组向量,即
$$ {\boldsymbol{x}} = [{x_1}, {x_2}, \cdots, {x_n}] , $$ (1) 取每个地震记录的绝对加速度最大值为
$$ {x_{\max}} = \max ( \left| {{x_1}} \right|, \left| {{x_2}} \right|, \cdots, \left| {{x_n}} \right| ) , $$ (2) 依次将每个加速度值除以加速度最大绝对值xmax,相应的归一化公式为
$$ x_i^* = \frac{{{x_i}}}{{x{}_{\max}}}{, } \qquad i=1{, }2{, }\cdots{, }n, $$ (3) 式中,
$ x_i^* $ 为地震记录的归一化值,xi为地震记录的加速度值。卷积神经网络要求每个地震记录样本的输入形状必须一致,但地震记录具有不同的持时和采样频率,因此,在数据预处理阶段,本文以20 s的采样时间和100 Hz的采样频率对每个地震记录进行均匀采样。在每个地震记录截取五段共获取20 s长的输入数据,前0.05%的阿里亚斯强度(Arias,1970)是采样的起点,后0.05%的阿里亚斯强度是采样的终点,起点与终点之间平均取五段,每段的采样时间为4 s,总计20 s,如图1所示。
2. 卷积神经网络模型
近年来,深度学习的飞速发展使其成为智能数据分析的有力工具,而地震学是一门以数据为驱动力的学科,因此构建深度学习模型成为我们的首选,其中卷积神经网络因其特征提取的鲁棒性而被广泛应用。特别是在语音识别领域中(Sainath et al,2013;Tóth,2013;Qian et al,2016;Sercu et al,2016;Yu et al,2016),每个人的发音大不相同,卷积神经网络因为有局部滤波和最大池化技术可以有效地消除这种差异,有利于语音的声学建模,并且可以提高训练效果。考虑到地震动数据在很多方面与语音数据相似,例如都需要介质,都是通过振动传播,都是非平稳时间序列信号等,本文拟采用卷积神经网络模型基于地震动记录识别地震的大致震级。
如图2b所示,模型中卷积层共有三层,每一层只有一个卷积层,没有池化层。每层分为五个部分,分别对应于每个样本的五段输入,也就是采样过程中的五段数据。第一层每段输入数据形状为1×400,核心数为4,卷积窗尺寸为1×5,步长为5;第二层卷积层将第一层卷积层的输出作为输入,核心数为8,卷积窗尺寸为1×4,步长为4;第三层卷积层将第二层输出作为输入,核心数为16,卷积窗尺寸为1×2,步长为2。上述卷积层的所有激活函数都使用ReLu激活函数(Krizhevsky et al,2017),这是因为ReLu激活函数可以有效地避免梯度消失和过度拟合的问题,并且具有样本导数形式,可以加快训练速度。ReLu激活函数如下:
$$ {\rm{ReLu}} ( x ) =\max ( 0,x ) {\text{.}}$$ (4) 样本每次经过卷积层时,数据长度都会以卷积步长为倍数而减少,数据宽度会以核心数为倍数而增加。在三层卷积层之后,样本的五段数据形状为16×10。
因为全连接神经网络的输入是线性的,所以需要对卷积层的输出结果进行线性处理。将卷积层输出的结果经过两个全连接层之后合并为一条线性数据,然后在三个全连接层之后输出两个结果,它们分别代表模型判断样本为大震或小震的概率。除最后一层之外,所有层的激活函数均使用ReLu激活函数。最后一层不使用激活函数,但是在计算损失函数时会添加一个softmax [ 式(5) ] 层。Softmax激活函数在规范化过程中使用指数形式,这意味着较大的值更大而较小的值会更小,增加了区分的对比度,可使模型训练更加有效,这对于分类问题尤其重要。
$$ {\rm{softmax}} ( {x_i} ) = \frac{{{{\rm{e}}^{{x_i}}}}}{{\displaystyle\sum\limits_{j = 1}^n {{{\rm{e}}^{{x_j}}}} }} ,\qquad i=1, 2, \cdots, n {\text{.}}$$ (5) 本文使用自适应矩估计优化器(adaptive moment estimation optimizer,缩写为Adam)来训练模型。该优化器针对AdaGrad和RMSProp的缺点弥补而来,具有以下优点:实现简单且计算效率较高,几乎无需调整超参数,可以自动调节学习速度,非常适合大型数据和参数模型训练。
本文模型所用训练集和验证集数据来自K-NET和KiK-net,共有11万9 760个地震记录,涉及1 698个台站。按时间顺序排列,将9万1 488个记录作为训练集,2万8 272个记录作为验证集,共6万8 580个大震记录和5万1 180个小震记录。
3. 超参数调整
模型训练过程中,超参数的调整对训练效率和训练结果均会产生影响,本文涉及两个超参数:学习率和批量(batch size)。
1) 学习率。学习率是模型每次减小损失函数值的程度,学习率参数值设置得较大,则模型前几次可能会很快收敛,但学习率设置得过大,可能使模型无法达到全局最优,学习率参数值较小对训练效率也会产生较大的影响。在保证其它变量相同的情况下用控制变量法测得模型训练过程中的准确率,结果如图3所示。可见:当学习率为0.001 (橙线)时,随着训练次数的增加,训练集的准确率逐步提高,测试集的准确率反而下降,出现了过拟合现象,也就是模型过度拟合训练集,因而导致该模型在其它数据集上的表现不佳;当学习率为0.01 (蓝线)和0.000 1 (绿线)时,训练集的准确率表现基本持平,而学习率为0.01 (蓝线)时模型在测试集更胜一筹,所以面对其它数据学习率为0.01 (蓝线)的模型可能会有更好的效果。因此本文将学习率设置为0.01,每迭代一周学习率乘以0.99,随着训练次数增加,学习率逐渐降低,使模型损失更接近全局最小值。
2) 批量(batch size)。批量是指每次输入模型的样本数量,批量太小容易使模型收敛方向出现偏差,太大容易使模型困在局部最优而无法达到全局最优。根据训练集和测试集准确率的变化(图4),可以看到不同批量情况下均出现了程度不同的过拟合现象。考虑到模型以较强的泛化能力和最低过拟合现象为佳,我们选取批量为400,这种情况下模型在训练过程中过拟合现象最轻且在测试集效果最好,有较强的泛化能力。这样我们就可以选择出现过拟合现象前的模型作为最终结果。
4. 训练结果
本文提出使用CNN识别地震震级大小的方法。在对原始地震数据进行筛选和归一化之后,使用CNN模型对预处理后的地震记录进行识别和分类。经过100次训练后,将验证数据集上精度最高的模型用于分析。模型准确率统计流程如图5a所示,训练准确率随训练次数的变化如图5b所示,部分记录的识别结果展示在图5c中,图中的四个记录分别来自AIC010,A0M013,AKT021和AKT002台站,地震分别发生于2006年9月24日,2015年3月6日,2014年10月11日和2012年8月14日。结果显示:基于11万9 760个记录进行训练,以M5.5作为分界线进行分类,该模型在训练集上的平均准确率达到93.6%,在测试集上的平均准确率达到92.3%。根据统计,当地震震级处于5.0—5.9范围内即在M5.5附近时,模型的准确率仅为79.7%,这与平均准确率之间的差距较大,因此以M5.5作为地震震级分类界限是可行的。
5. 讨论与结论
本文以归一化的地震动记录为数据集来构建卷积神经网络,分类效果良好,初步得到以下结论:① 模型进行了多次对比训练,均出现了不同程度的过拟合现象,可采用文中提到的早停法等策略解决;② 超参数中的学习率大小宜适中,学习率过大,模型不易收敛,过小则训练较慢,批量过大容易造成局部最小,过小则训练过程中波动太大;③ 模型能够识别经过归一化的地震动记录的震级大小,说明地震的加速度时程记录带有一定的地震震级信息。
尽管该模型具有比较好的训练效果,但仍有一些问题需要解决,还有需要优化的方面:
1) 如何选择分类分界线来区分大地震与小地震。我们使用M5.5作为模型的分类标准,仅基于简单的统计信息,且M5.0—5.9地震记录在模型中的平均准确率为79.7%,远低于整个数据集的平均准确率,所以我们初步判断M5.5作为分类界限有一定可行性,但可能会有更合适的震级界线来区分大小地震,应该也在M5.5左右,未来我们会不断尝试优化模型,找到一个最优震级作为分类界限;
2) 本文使用的数据来自K-NET和Kik-net,尚需验证该模型是否适用于其它地区。下一步会将来自其它国家地区的数据添加到训练数据中,以提高模型的泛化能力;
3) 未来我们会不断优化模型,并基于该模型测试常见的地震动模拟以及调整方法所得的模拟地震动或者经调整的小震记录能否被模型识别为大震,为这些方法的选择及模拟或调整效果提供参考。另一方面,基于该模型深入研究,不拘泥于二分类,可以做成大、中、小地震的三分类问题,甚至可以直接识别出大致震级等延展性工作。
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图 4 基于Crust1.0 (a)、Shen等(2016)模型(b)和联合反演模型(c)采用CAP方法反演随州ML4.7地震事件的震源机制解结果和理论合成波形(红线)与实际观测波形(黑线)的拟合结果
波形左侧字母为台站名、震中距(单位:km)、方位角,波形下方数字表示时移(单位:s)以及互相关系数,震源球上的小红叉表示台站方位角,下图同
Figure 4. Inversion results of the focal mechanism solution of the Suizhou ML4.7 earthquake event by using the CAP method based on Crust1.0 (a),Shen et al (2016) model (b),and joint inversion model (c),and the fitting results between the theoretical synthesized waveforms (red lines) and the actual observed ones (black lines)
The letters on the left side of the waveform represent different station names, the number represents the epicenter distance (in km),and the numbers below the azimuth waveform show the time-shift values (in s) and correlation coefficients. The small red crosses on the source ball show the azimuths of the station. The same below
图 1 湖北省构造背景以及中强地震和台站的分布
断层数据引自邓起东等(2003),黑色实线为秦岭—大别造山带和扬子克拉通分界线
Figure 1. Tectonic settings and distribution of moderate-strong earthquakes and stations of Hubei Province
The fault data refer to Deng et al (2003). The black solid line is the boundary between the Qinling-Dabie orogenic belt and the Yangtze craton
图 2 随州ML4.7 (a)和秭归MS4.5 (b)地震震源参数反演所用的P波和S波速度模型
红、绿线代表Crust1.0模型,黑、灰线代表Shen等(2016)模型,蓝、棕线代表本文联合反演模型
Figure 2. The P and S wave velocity models used for inversion of source parameters of Suizhou ML4.7 (a) and Zigui MS4.5 (b) earthquakes
The red and green lines represent the Crust1.0 model,the black and gray lines represent the model from Shen et al (2016),while the blue and brown lines represent the joint inversion model in this study
图 3 初始反演S波模型以及接收函数与面波联合反演示意图
S波模型图中,绿色和蓝色虚线为初始反演S波模型,红色实线为最终反演结果;接收函数反演图中,蓝色、红色实线分别为观测、理论接收函数;面波反演图中黑点和红线分别代表观测值和理论值。 (a) 保康MS4.8;(b) 巴东MS5.1;(c) 随州ML4.7;(d) 应城MS4.9;(e) 秭归MS4.6;(f) 秭归MS4.5;(g) 咸丰M6¼;(h) 竹山M6½;(i) 麻城M6.0
Figure 3. Initial inversion S-wave models and schematic diagrams of receiving function and surface wave joint inversion
In the shear wave model subfigures,the green and blue dashed lines represent the initial S-wave models,while the red solid line represent the final inversion result. In the receiving function inversion subfigures,the blue and red solid lines represent the observed and theoretical receiver functions. In the surface wave inversion subfigures,the black dots and red lines represent observed and theoretical values. (a) Baokang MS4.8;(b) Badong MS5.1;(c) Suizhou ML4.7;(d) Yingcheng MS4.9;(e) Zigui MS4.6;(f) Zigui MS4.5;(g) Xianfeng M6¼;(h) Zhushan M6½;(i) Macheng M6.0
图 5 基于Crust1.0 (a)、Shen等(2016)模型(b)和联合反演模型(c)采用CAP方法反演2018年秭归MS4.5地震事件的震源机制解结果以及理论合成波形(红线)与实际观测波形(黑线)的拟合结果
Figure 5. Inversion results of the focal mechanism solution of the Zigui earthquake event by using the CAP method based on Crust1.0 (a),Shen et al (2016) model (b),and joint inversion model (c),and the fitting results between the theoretical synthesized waveforms (red lines) and the actual observed ones (black lines)
图 7 中强地震震中区下方S波速度与震源机制(黑色方框为该地震的震源深度)
(a) 巴东MS5.1;(b) 保康MS4.8;(c) 随州ML4.7;(d) 应城MS4.9;(e) 秭归MS4.6;(f) 秭归MS4.5;(g) 咸丰M6¼;(h) 竹山M6½;(i) 麻城M6.0
Figure 7. S-wave velocities beneath the epicentral areas of the moderate-strong earthquakes where the black boxes show the source depths of the earthquake,and the focal mechanism are also given
(a) Badong MS5.1;(b) Baokang MS4.8;(c) Suizhou ML4.7;(d) Yingcheng MS4.9;(e) Zigui MS4.6;(f) Zigui MS4.5;(g) Xianfeng M6¼;(h) Zhushan M6½;(i) Macheng M6.0
表 1 使用CAP方法求解的湖北省中强地震震源参数
Table 1 Focal parameters of moderate-strong earthquakes of Hubei Province estimated by the CAP method
地震事件 震中位置 MW 深度
/km节面Ⅰ 节面Ⅱ 震源参数来源 东经/° 北纬/° 走向/ o 倾向/ o 滑动角/ o 走向/ o 倾向/ o 滑动角/ o 2 006年随州地震 113.10 31.50 4.14 8.0 126 78 −30 223 61 −166 本文研究 2 013年巴东地震 110.40 31.09 4.9 4.6 73 58 168 169 80 32 Huang等(2 018) 2 014年秭归地震 110.77 30.92 4.6 (MS) 7.5 331 71 40 226 53 156 王秋良等(2 016) 2 018年秭归地震 110.47 31.03 4.37 5.0 155 84 32 61 58 173 本文研究 2 019年应城地震 113.40 30.87 4.67 7.5 149 68 15 53 76 157 赵凌云等(2 022) -
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