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Article(id=1322941180459229353, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322940959071285581, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.07.310, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1719763200000, receivedDateStr=2024-07-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793343356480, onlineDateStr=2026-10-30, pubDate=1749916800000, pubDateStr=2025-06-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793343356480, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793343356480, creator=13701087609, updateTime=1793343356480, updator=13701087609, issue=Issue{id=1322940959071285581, tenantId=1146029695717560320, journalId=1281212831689347082, year='2025', volume='45', issue='6', pageStart='551', pageEnd='660', issueExtLink='null', onlineDate='null', pubDate='1749916800000', pubDateStr='2025-06-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793343303697, creator='13701087609', updateTime=1793343303697, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=568, endPage=573, ext={EN=ArticleExt(id=1322941181885292714, articleId=1322941180459229353, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Characterization of the Abnormal Response of Strain Time Series to Earthquakes, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The strain monitoring data from the Guza station in 2022 to 2023 were analyzed using time series analysis. By comparing the difference in strain signals at the observation site during the seismic and non-seismic periods, it is concluded that during earthquakes above MS4.5, there are obvious trend anomalies in both the detrended surface strain and the corrected principal strain direction. The likelihood of earthquakes increases when the detrended surface strain decreases at 1.1 times and the variance increases significantly. The trend of the corrected principal strain directions during seismicity is similar to the drug metabolism concentration curve model. The fluctuation period is small and the amplitude is large, which is significantly different from the nearly straight-line trend during the steady state.

, authors=Yibin ZHAO1, Yanfang ZHANG1, *, Zhitong JIN1, Fuchang WANG1, Lei TANG2, authorsList=Yibin ZHAO, Yanfang ZHANG, Zhitong JIN, Fuchang WANG, Lei TANG, authorCompany=null, correspAuthors=Yanfang ZHANG, authorNote=

ZHAO Yibin, professor, majors in system modeling and simulation, data mining and feature extraction, earthquake disaster loss prediction and assessment, E-mail:

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以2022—2023年姑咱台应变监测数据为研究对象，利用时间序列分析方法，通过比较观测点地表在地震发生和未发生时段的应变信号差异，认为MS4.5以上地震发生过程中去趋势的面应变和矫正后的主应变方向都有明显的趋势异常。具体来说，去趋势的面应变在以稳定状态1.1倍的速率持续减小，且方差明显增大时，发生地震的可能性比较大；矫正后的主应变方向在地震发生过程中的变化趋势类似于药物代谢浓度变化曲线模型，且波动周期小，振幅大，与稳定状态时的近似直线趋势明显不同。

, authors=赵宜宾1, 张艳芳1, *, 靳志同1, 王福昌1, 唐磊2, authorsList=赵宜宾, 张艳芳, 靳志同, 王福昌, 唐磊, authorCompany=null, correspAuthors=张艳芳, authorNote=

赵宜宾, 教授, 主要从事系统建模与仿真、数据挖掘与特征提取、地震灾害损失预测与评估方面研究, E-mail:

, correspAuthorsNote=

张艳芳, 教授, 主要从事系统建模仿真、运筹优化、大数据统计分析相关领域研究, E-mail:

ZHANG Yanfang, professor, majors in system modeling and simulation, operational research and optimization, big data statistical analysis, E-mail:

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ZHAO Yibin, professor, majors in system modeling and simulation, data mining and feature extraction, earthquake disaster loss prediction and assessment, E-mail:

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赵宜宾, 教授, 主要从事系统建模与仿真、数据挖掘与特征提取、地震灾害损失预测与评估方面研究, E-mail:

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赵宜宾, 教授, 主要从事系统建模与仿真、数据挖掘与特征提取、地震灾害损失预测与评估方面研究, E-mail:

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Comparison of de-trended strain indicators from early May to early June

, figureFileSmall=null, figureFileBig=null, tableContent=
时间 均值/10－6 标准差/10－6 平均速率/10－6·h－1
2022年 －0.529 0.694 0.020
2023年 －0.086 0.475 0.018
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5月上旬至6月上旬去趋势面应变指标对比

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时间 均值/10－6 标准差/10－6 平均速率/10－6·h－1
2022年 －0.529 0.694 0.020
2023年 －0.086 0.475 0.018
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Comparison of de-trended strain indicators from July to October

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时间 均值/10－6 标准差/10－6 平均速率/10－6·h－1
2022年 0.081 0.787 0.019
2023年 －0.919 0.437 0.018
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7—10月份去趋势面应变指标对比

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时间 均值/10－6 标准差/10－6 平均速率/10－6·h－1
2022年 0.081 0.787 0.019
2023年 －0.919 0.437 0.018
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Statistical indicators in the principal strain direction at different stages in 2023

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时间 均值/(°) 标准差/(°) 平均速率/(°)·h－1
3—6月份 5.459 2.173 0.174
7—10月份 23.661 9.622 0.538
11—12月份 10.670 0.966 0.116
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2023年不同阶段主应变方向统计指标

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时间 均值/(°) 标准差/(°) 平均速率/(°)·h－1
3—6月份 5.459 2.173 0.174
7—10月份 23.661 9.622 0.538
11—12月份 10.670 0.966 0.116
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Statistical indicators in the principal strain direction at different stages in 2022

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时间 均值/(°) 标准差/(°) 平均速率/(°)·h－1
03-01—05-12 10.455 1.685 0.243
05-13—06-07 45.070 21.716 0.884
07-05—10-30 23.990 10.150 0.311
11—12月份 11.434 0.946 0.093
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2022年不同阶段主应变方向统计指标

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时间 均值/(°) 标准差/(°) 平均速率/(°)·h－1
03-01—05-12 10.455 1.685 0.243
05-13—06-07 45.070 21.716 0.884
07-05—10-30 23.990 10.150 0.311
11—12月份 11.434 0.946 0.093
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应变时间序列对地震的异常响应特征分析
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赵宜宾 1 , 张艳芳 1, * , 靳志同 1 , 王福昌 1 , 唐磊 2
大地测量与地球动力学 | 地震地质 2025,45(6): 568-573
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大地测量与地球动力学 |地震地质 2025 , 45 (6) : 568 -573
应变时间序列对地震的异常响应特征分析
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赵宜宾1 , 张艳芳1, * , 靳志同1, 王福昌1, 唐磊2
作者信息
  • 1 防灾科技学院理学院, 河北省三河市学院街465号, 065201
  • 2 中国地震台网中心, 北京市三里河南横街5号, 100045
通讯作者:

张艳芳, 教授, 主要从事系统建模仿真、运筹优化、大数据统计分析相关领域研究, E-mail:

ZHANG Yanfang, professor, majors in system modeling and simulation, operational research and optimization, big data statistical analysis, E-mail:

作者简介:

赵宜宾, 教授, 主要从事系统建模与仿真、数据挖掘与特征提取、地震灾害损失预测与评估方面研究, E-mail:

Characterization of the Abnormal Response of Strain Time Series to Earthquakes
Yibin ZHAO1 , Yanfang ZHANG1, * , Zhitong JIN1, Fuchang WANG1, Lei TANG2
Affiliations
  • 1 College of Science, Institute of Disaster Prevention, 465 Xueyuan Street, Sanhe 065201, China
  • 2 China Earthquake Networks Center, 5 Nanheng Street, Sanlihe, Beijing 100045, China

    • ZHAO Yibin, professor, majors in system modeling and simulation, data mining and feature extraction, earthquake disaster loss prediction and assessment, E-mail:

出版时间: 2025-06-15 doi: 10.14075/j.jgg.2024.07.310
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摘要
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以2022—2023年姑咱台应变监测数据为研究对象，利用时间序列分析方法，通过比较观测点地表在地震发生和未发生时段的应变信号差异，认为MS4.5以上地震发生过程中去趋势的面应变和矫正后的主应变方向都有明显的趋势异常。具体来说，去趋势的面应变在以稳定状态1.1倍的速率持续减小，且方差明显增大时，发生地震的可能性比较大；矫正后的主应变方向在地震发生过程中的变化趋势类似于药物代谢浓度变化曲线模型，且波动周期小，振幅大，与稳定状态时的近似直线趋势明显不同。

应变换算  /  主应变方向校正  /  时间序列  /  关联分析  /  异常响应

The strain monitoring data from the Guza station in 2022 to 2023 were analyzed using time series analysis. By comparing the difference in strain signals at the observation site during the seismic and non-seismic periods, it is concluded that during earthquakes above MS4.5, there are obvious trend anomalies in both the detrended surface strain and the corrected principal strain direction. The likelihood of earthquakes increases when the detrended surface strain decreases at 1.1 times and the variance increases significantly. The trend of the corrected principal strain directions during seismicity is similar to the drug metabolism concentration curve model. The fluctuation period is small and the amplitude is large, which is significantly different from the nearly straight-line trend during the steady state.

strain conversion  /  principal strain direction correction  /  time series  /  correlation analysis  /  anomalous response
赵宜宾, 张艳芳, 靳志同, 王福昌, 唐磊. 应变时间序列对地震的异常响应特征分析. 大地测量与地球动力学, 2025 , 45 (6) : 568 -573 . DOI: 10.14075/j.jgg.2024.07.310
Yibin ZHAO, Yanfang ZHANG, Zhitong JIN, Fuchang WANG, Lei TANG. Characterization of the Abnormal Response of Strain Time Series to Earthquakes[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (6) : 568 -573 . DOI: 10.14075/j.jgg.2024.07.310
正文
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钻孔应变仪能够对地壳活动进行全频段监测，在解析地震孕育过程、解决地震预报等科学问题上有着重要意义。由我国首创的水平四分量钻孔应变仪可通过数据自检确定仪器的工作状态，推断数据的可靠性，是应变监测的主要仪器。通过对应变观测数据进行分析，众多学者对地震的孕育及发生过程中的应变异常信号特征进行了多角度研究[1-6]。应变监测数据是地壳构造运动在地壳形变上的反映，在地质条件稳定的情况下，固定区域的应变信号呈周期变化。如果地质结构在应力作用下发生改变(如破裂、移位等)，那么在地质结构未发生改变和改变的过程中，应变监测会出现明显的异常信号。通过对应变信号与地震的关联性进行分析，可以挖掘出与地震相关的异常信号特征，进而为有效的地震预警或地震预测模型的建立提供科学的前兆参数。
本文以姑咱台2022—2023年应变观测数据为研究对象，通过分析应变观测台周边200 km范围内的历史震例与应变特征趋势之间的关系，对区域内地震与应变异常之间的联系进行初步探讨。
1 应变换算基本理论概述
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YRY型四分量钻应变仪是将间隔45°的4个传感器放在一个密封的圆柱形套桶中，通过监测套桶内径的变化来记录地表形变状态。传感器记录的数据Si(i=1, 2, 3, 4)是相应方向上内径的增量，由应变换算理论可知，元件的直接观测值与真实增量之间相差一个常量[7]，因此应变分析时要用观测值的增量，Si(i=1, 2, 3, 4)的增量记为ri(i=1, 2, 3, 4)。为方便叙述，本文依然将第i个传感器测量值的增量ri称为测量数据。
测量数据ri(i=1, 2, 3, 4)与二维应变状态描述指标(ε1, ε2, φ)之间的关系式为：
$r_i=A\left(\varepsilon_1+\varepsilon_2\right)+B\left(\varepsilon_1-\varepsilon_2\right) \cos 2\left(\theta_i-\varphi\right)$
式中，ε1为最大主应变，ε2为最小主应变，两者方向垂直；εi>0(i=1, 2)表示地表变形拉长，反之表示缩短；ε1+ε2为面应变，用以描述观测点地表平面面积的形变状态；φ为最大主应变方向；θi为第i个传感器对应的方向角；A, B为耦合系数，与制作仪器的材料及周围岩石的性质有关。
1.1 观测数据矫正
在确定耦合系数A、B之前，要检查仪器工作状态是否良好。由于4个传感器依次间隔45°，根据式(1)及三角函数关系可知：
$r_1+r_3=r_2+r_4=2 A\left(\varepsilon_1+\varepsilon_2\right)$
式(2)为四分量钻孔应变观测的自洽方程。如果仪器工作状态良好，4组测量数据ri在误差允许范围内是满足自洽方程的，此时可直接利用观测数据进行应变换算。由于受外界干扰因素影响，4个传感器的灵敏度与设计标准可能会有偏差，导致监测数据不满足自洽方程，此时需要对传感器灵敏度进行校正。关于传感器灵敏度的校正方案详见参考文献[8]。分别用Ki、Ri(i=1, 2, 3, 4)表示校正后的灵敏度和数据，并规定若不需要数据矫正，则取Ki=1。
$R_i=K_i r_i(i=1, 2, 3, 4)$
1.2 耦合系数的确定
对矫正后的数据Ri(i=1, 2, 3, 4)给定如下公式：
$\left\{\begin{array}{l}R_{13}=R_1-R_3 \\R_{24}=R_2-R_4 \\R_a=\left(R_1+R_3+R_2+R_4\right) / 2\end{array}\right.$
依据固体潮理论可得到观测点的二维应变张量理论值(εθθ, ελλ, ελθ)[9]，采用下式求解得到耦合系数A、B[10]：
$\left\{\begin{aligned}A & =\frac{R_a}{2\left(\varepsilon_{\theta \theta}+\varepsilon_{\lambda \lambda}\right)} \\B & =\sqrt{\frac{R_{13}^2+R_{24}^2}{4\left[4 \varepsilon_{\lambda \theta}^2+\left(\varepsilon_{\lambda \lambda}-\varepsilon_{\theta \theta}\right)^2\right]}}\end{aligned}\right.$
式中，εθθ、ελλ、ελθ分别为NS、EW方向的正应变和NE方向的剪应变。
确定A、B后，用Ri替换式(1)中的ri(i=1, 2, 3, 4)，(ε1, ε2, φ)可表示为[11]：
$\left\{\begin{array}{l}\varepsilon_1=\frac{1}{4 A} R_a+\frac{1}{4 B} \sqrt{R_{13}^2+R_{24}^2} \\\varepsilon_2=\frac{1}{4 A} R_a-\frac{1}{4 B} \sqrt{R_{13}^2+R_{24}^2} \\\varphi=\frac{1}{2} \arctan \left(\frac{R_{24}}{R_{13}}\right)+\theta_1\end{array}\right.$
基于上述方案，可完成监测数据与二维应变状态描述指标(ε1, ε2, φ)之间的换算。
1.3 最大主应变方向校正
最大主应变方向φ对于正确描述观测点地表受力后的形变状态至关重要，但依据式(6)得到的φ可能是最大主应变方向，也有可能与之相差90°[11]。唐磊等[12]对不同应力环境下主应变方向φ的矫正方法进行了详细描述：首先根据ε1、ε2的取值确定地表受力情况，再借助R13、R24的符号及应变仪传感器位置确定φ的取值区间，最后观察$ \varphi, \varphi \pm \frac{\pi}{2}$三个值中哪个在确定区间内，校正后的φ就取在确定区间内的那个值。校正后的主应变方向用φ*表示，依据(ε1, ε2, φ*)的时序变化趋势可分析观测点地表在地震孕育及发生过程中的形变状态，进而挖掘地震与应变异常信号之间的联系。
本文主要聚焦于对震前应变异常变化趋势的特征描述。由于受天体运动的影响，固体潮是呈周期变化的，因此观测点的地表形变也呈一定的周期性。本文取周期长度为1 a，并假定去除随机因素的影响后，若没有地震，每年相同时段的应变具有相同的变化趋势。在上述假设条件下，通过比较发生地震年份与未发生地震年份相应时段应变信号的趋势差异，可以较好地描述应变在地震孕育及发生过程中的异常反应。
2 监测数据换算与基本特征分析
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2.1 姑咱台地理位置特征概述
姑咱台YRY四分量钻孔应变仪处于NW向鲜水河断裂带与NE向龙门山断裂带交会处以北地区，距离大渡河仅300 m(图1)。应变仪安放于总长为205 m的水平山洞中，安装点距入口大于60 m，垂直方向距地表大于70 m，洞内日温差小于0.03℃，年温差小于0.3℃。应变仪4个传感器相对位置如图1子图所示，监测数据记为Si(i=1, 2, 3, 4)。
2022年龙门山断裂带发生了MS6.1芦山地震，鲜水河断裂带发生了MS6.8泸定地震，这2个地震的震中距离姑咱应变观测台都不超过80 km。由于2021年姑咱台的应变观测仪工作状态不稳定，而2023年姑咱台周边没有剧烈的地质活动，本文以2023年观测结果作为应变稳定的变化趋势，来描述2022年地震发生过程中的应变异常特征。
2.2 监测数据应变换算与基本特征分析
以小时为采样频率，从中国地震台网中心获取姑咱台2022—2023年的应变观测数据，以每年01-01 00:00的数据为基准点，4个传感器在各时间点相对于基准点的增量分别记为ri(i=1, 2, 3, 4)。根据式(2)分别对2022年和2023年的增量数据ri(i=1, 2, 3, 4)进行自洽验证，得到r1+r3与r2+r4两条趋势完全一致的曲线(图2)，两者相关系数分别为0.998和0.999。因此可认为，监测数据满足自洽原理，4个探头的灵敏度满足设计标准，不必进行数据矫正。依据式(4)和式(5)，直接利用ri可求得耦合系数A=0.123 6, B=0.836 4。
根据式(6)，分别计算2022年和2023年观测点地表在水平方向上的应变状态分量(ε1, ε2, φ)，并依据参考文献[12]的方案完成对主应变方向的校正，最终得到用于描述姑咱台周边地表水平应变状态的分量(ε1, ε2, φ*)。
根据地表水平形变状态数据，可得姑咱台面应变(ε1+ε2)时序趋势(图3红线)。进一步对面应变进行回归分析(图3蓝线)，可知在年度评估周期中，姑咱台周边地表总体处于持续压缩形变状态，且整体来看，2022年和2023年的形变速率几乎一致。
校正前后主应变方向变化趋势见图4。本文分析的是姑咱台周边地表每年相对于01-01 00:00的应变状态，是对地表中长期相对形变的一种描述，而面应变趋势说明姑咱台周边地表是处于持续压缩状态的。由于在每年开始的一段时间内，主应变ε1、ε2在0附近随机波动，故主应变方向受随机因素影响变化范围较大(图4(c)和4(d))，该时段分析价值不大，故本文主要分析每年3月份后变化趋势较稳定的主应变方向对地震的异常响应。
3 地震与应变信号异常之间的关系
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为描述应变信号异常响应与地震之间的关系，从国家地震科学数据共享中心获取的正式地震目录中提取2022—2023年姑咱台周边200 km范围内发生的地震，并采用C-S空窗法[13]去除余震得到MS2.0以上地震104次，其中MS4.5以上地震3次，分别为汉源MS4.8、芦山MS6.1和泸定MS6.8地震，震中距离姑咱台均不超过80 km。
3.1 去趋势的面应变对地震的异常响应
分析可知，年度面应变时间序列是有明确趋势性的，为更准确地表述面应变与地震之间的关系，首先对2022年和2023年应变数据作回归分析，拟合出回归直线方程，再将观测数据与回归直线估计值作差，以完成对面应变的去趋势化处理。由于姑咱台处于多条断裂带交会地带，地质条件不稳定，受随机因素影响，微小地震发生频率很高。为能明确显示应变异常与地震之间的内在关系，本文对MS3.0以上地震与去趋势后的面应变时间序列作关联分析(图5)。
图5(b)显示，2023年姑咱台周边200 km范围内地震发生频率很低，且发生的地震基本都是MS3.5以下小震，地质活动相对平静。在去除固有趋势后，2023-01—07应变观测数据的面应变曲线在0附近小范围波动，这是一种稳定状态的表现；但2023-07—10应变曲线在0值线以下作相对稳定波动，这是因为姑咱台距离大渡河很近(图1)，7—10月份是大渡河的丰水期，河水会对地表形成外在压力，且上游的猴子岩水库距台站仅2 km左右，通过水库流量监测和应变趋势的关联性分析[4]证实，该时段应变异常是受河水影响，属于该地区特定时段的正常应变状态。由此可知，在去除应变时序的固有趋势后，图5(b)可对姑咱台地区年度平稳应变趋势进行有效展示。
将2022年去趋势后的面应变(图5(a))与年度稳定应变状态(图5(b))进行比较，可发现在MS4.8以上地震发生过程中，应变会出现明显的异常反应。
5月上旬至6月上旬，面应变的稳定状态(图5(b))应该是在0值线附近波动，振幅约为1×10-6。但2022年对应时段的面应变处于快速振荡下行趋势，整个时段的均值下降至稳定振幅的50%左右，速度约提升11%，且振荡时离中趋势变大，面应变异常信号明显，具体统计指标见表1。在面应变信号异常时段，龙门山断裂带发生了汉源MS4.8(05-20)和芦山MS6.1(06-01)2次较大地震。
7—10月份在10－6数量级上，面应变的稳定状态应该是在－1附近小振幅波动，但2022年对应时段的面应变曲线在0值线附近大幅度波动，标准差约是稳定状态的2倍，应变速率比稳定状态提升约5.5%，并且在9月份同样有一个以稳定状态1.1倍速率快速减小的应变过程，具体统计指标见表2。鲜水河断裂带发生的泸定MS6.8(09-05)地震恰在这一面应变异常时段发生。
由此可知，在地震发生过程中，去趋势后的面应变会有明显的异常响应，这些异常信号在均值、标准差或面应变变化速率等指标上有较为清晰的表现。
3.2 主应变方向对地震的异常响应
为减少随机因素对判断主应变方向对地震异常响应的影响，对姑咱台周边200 km范围内MS3.0以上地震与主应变方向在3—12月份的趋势变化关系进行分析，具体见图6。
对于地质活动平静的年份，姑咱台地表主应变方向分别在3条直线附近小幅度波动。图6(b)显示，2023-07之前主应变方向稳定在NE5.5°，波动振幅很小；7—10月份由于大渡河或上游水库的水压影响，主应变方向在NE23.6°附近以较大幅度波动，角度变化速率是7月份之前的3倍，偶有主应变方向极速改变的情况，但在极短时间内会恢复至原来的状态；11月份之后，主应变方向会在NE10.7°以极小的幅度和极低的变化率波动，具体统计指标见表3。
相对于大地震很少的2023年，2022年姑咱台地表主应变方向的异常特征十分明显：3—5月份主应变方向向E偏转了5°；5月中旬至10月份主应变方向经历了从短周期、大幅度振荡偏转到缓慢恢复的过程，其变化趋势与药物代谢浓度曲线模型类似，拟合曲线(图6(a)蓝线)与观测数据的相关系数为91.1%，变化趋势明显异于地质活动平静时期；11—12月份主应变方向与稳定状态趋势几乎一致(表3和4)，说明区域内地质活动相对平静。
进一步对5—10月份的异常阶段进行分析：从05-13到主应变方向达到峰值角度(81°)大约持续了25 d，速率约为稳定状态的5倍，标准差约为稳定状态的20倍，观测曲线小周期、大幅度波动上升。这段时间观测点周边的应力快速积累，主应变方向频繁大幅度变化，是地质活动的活跃期，汉源MS4.8和芦山MS6.1两次地震就发生在该时段。06-10以后，主应变方向开始振荡下行，随着应力的释放，地质活动逐渐趋于平静，对比表3和4对应指标可知，到11月份主应变方向恢复至构造应力场对应的平稳状态。
在缺少大渡河水流量数据的条件下，虽从07-05到10月底这段时间主应变方向的均值、标准差、变化速率与2023年相应指标基本一致，但异常应力完全释放的时间节点并不能准确确定。因为水压造成的主应变方向变化曲线应有很多尖峰(图6(b))，而图6(a)显示的2022-07中旬至9月主应变方向变化曲线不具备这样的特征，因此该时段主应变方向变化趋势可以理解为异常应力与水压共同作用的结果。基于上述分析可知，主应变方向对于2022-09-05发生的泸定MS6.8地震的异常响应并不明显。
从地理位置分析，姑咱台及汉源MS4.8、芦山MS6.1地震的震中都位于鲜水河断裂带东侧，且2次地震震中都位于龙门山断裂带上，而泸定MS6.8地震震中位于鲜水河断裂带西侧。在以小时为采样频率的应变观测数据分析中，汉源地震和芦山地震没有出现主应变方向阶跃变化现象，而泸定地震出现了明显的阶跃变化，主应变方向由震前的NE22.4°直接下降至NE12.5°(图6(a))。主应变方向对于鲜水河断裂带两侧地震差异响应的成因，是后续研究的重要内容。
4 结语
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本文以2022—2023年姑咱台应变观测数据为研究对象，通过比较地质活动平静的2023年与地震活动相对频繁的2022年对应时段的应变信号差异，分析应变时序对地震的异常响应特征。通过对应时段数据比较可得：在地震孕育及发生过程中，去趋势的面应变和主应变方向在变化趋势和重要统计指标上，都与通常的稳定状态有着非常明显的不同，这些异常表现对于借助应变信号进行地震预测有着重要价值。
鉴于数据源的单一性，本文研究结论具有一定的局限性。对于构建基于应变异常信号的地震预测模型，本文提取的异常特征指标是否具有规律性、与地震位置的地质条件是否相关、如何去除应变异常信号的随机因素等问题都需进行深入探讨。后续将通过随机过程模型结合统计分析方法，利用多台站、长时间的监测数据，挖掘各类地震不同特征的异常应变信号指标规律，为有效提取地震预测的应变异常指标提供基础。
基金
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  • 廊坊市科学技术研究与发展计划自筹经费项目(2023011072)
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2025年第45卷第6期
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doi: 10.14075/j.jgg.2024.07.310
  • 接收时间：2024-07-01
  • 首发时间：2026-10-30
  • 出版时间：2025-06-15
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  • 收稿日期：2024-07-01
基金
Science and Technology Research and Development Plan Self-Funded Projects of Langfang(2023011072)
廊坊市科学技术研究与发展计划自筹经费项目(2023011072)
作者信息
    1 防灾科技学院理学院, 河北省三河市学院街465号, 065201
    2 中国地震台网中心, 北京市三里河南横街5号, 100045

通讯作者:

张艳芳, 教授, 主要从事系统建模仿真、运筹优化、大数据统计分析相关领域研究, E-mail:

ZHANG Yanfang, professor, majors in system modeling and simulation, operational research and optimization, big data statistical analysis, E-mail:

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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