Citation: | Zhu K G,Wen J M,Fan M X,Yu Z N,Wang T,Marchetti D,Zhang Y Q,Chen W Q. 2024. Environmental response removal and pre-earthquake anomaly extraction of borehole strain data. Acta Seismologica Sinica,46(4):620−632. DOI: 10.11939/jass.20220210 |
China is one of the most affected countries by the earthquake disaster in the world. The earthquake not only damaged people’s lives and property safety, but also triggered a series of secondary disasters such as landslides, mud-rock flows and collapses. Earthquake prediction is one of the difficult scientific problems in the world which needs to be explored for a long time. Earthquake precursor research is the key to earthquake prediction.
Earthquake is the result of instability rupture after the strain on fault accumulates to the limit under the action of tectonic stress. The borehole strain gauge can detect the small stress load change before rock fracture, so borehole strain observation can not only record long period strain change, but also provide high frequency strain information, which is the basis of earthquake precursor research. In the borehole strain observation, the strain response caused by solid tide, air pressure, water level and other environmental factors will drown out the pre-earthquake strain anomaly. Therefore, it is of great significance to study the environmental response and anomaly extraction method of borehole strain data, and obtain the earthquake related strain anomaly accurately and reliably from the strain observation data.
At present, many scholars have conducted researches on environmental response removal and pre-earthquake anomaly extraction. This paper based on the environmental observation data, the strain response caused by solid tide, air pressure and water level was solved andremoved to obtain the crustal strain. solid tide is the periodic deformation of the earth under the gravitational force of the sun and the moon, which is the main reason for the periodic variation of diurnal wave and semi-diurnal wave in the borehole strain observation data. Air pressure and water level have immediate negative correlation to strain data. Time Series Decomposition is to decompose the time series with complex changes into several sub-components. The long-term background trend and periodic trend of the observed data before and after the Lushan earthquake (January 2011 to January 2014) were removed by Time Series Decomposition.Multi-channel Singular Spectrum Analysis can use the correlation between signals of different channels to decompose the data. In this paper, the trace matrix is constructed of borehole strainresidual, air pressure and water level three-channel measurement signal for Multi-channel Singular Spectrum Analysis, and obtain the strain response of air pressure and water level. The results show that Time Series Decomposition can effectively remove the periodic changes dominated by solid tides. The correlation coefficient of the water level and its strain response is −0.97, and in 96.1% of the days the absolute value of the correlation coefficient of the daily air pressure and its strain response is greater than 0.9, which verify removal of the environmental response the proposed method effectively.
Normal crustal strain, which is not affected by strong earthquakes and environment, is a short-period random signal. According to its characteristic of Gauss, this paper extracts the crustal strain data by day and accumulates the number of anomalies. It is found that the accumulation of negentropy anomalies of the Lushan earthquake showed a linear increase−acceleration increase−a small amount of anomalies−acceleration increase. Then the negentropy anomalies accumulation is compared with the Benioff strain accumulation. It is found that the negentropy anomaly accumulation trend is consistent with the Benioff strain accumulation trend. Combined with the fracture evolution process of rock stress loading (initial micro-fracture−extensional fracture−stress lock−earthquake eruption), it was guessed that the abnormal acceleration increase of the Lushan earthquake 4 to 6 months before the earthquake may be an earthquake precursor and related to the extensional fracture.
In order to further analyze the advantages of environmental response removal and negentropy anomaly extraction methods, this paper compared the method in this paper and difference processing. A large amount of precursor anomaly information is contained in the high-frequency components of the observed data, and the crustal strain is also dominated by high-frequency information. The difference process can remove the low-frequency information in the observed data, and one-dimensional difference processing is carried out on the standardized strain observation data to make a comparison with the crustal strain after removing the influence of environmental factors in this paper. The negentropy anomaly accumulation and non-Gaussiandistribution days accumulation showed the environmental response removal method can effectively remove the disturbance caused by air pressure, and the crustal strain data after the removalof environmental response can more effectively extract the earthquake precursor anomaly.
The above conclusions sufficiently indicate that the methods in this paper are effective for removing the environmental response of borehole strain observation data and extracting earthquake precursor anomalies.
陈学忠,李艳娥,陈丽娟. 2021. 鲁甸MS6.5地震前巧家台阵观测到的中小地震应变释放加速现象[J]. 地震,41(3):32–41. doi: 10.12196/j.issn.1000-3274.2021.03.003
|
Chen X Z,Li Y E,Chen L J. 2021. The accelerating seismic strain release observed by the Qiaojia seismic array prior to the Ludian M S6.5 mainshock[J]. Earthquake,41(3):32–41 (in Chinese).
|
池成全. 2020. 钻孔应变前兆观测数据分析与异常提取研究[D]. 长春:吉林大学:13−39,81−89.
|
Chi C Q. 2020. Analysis of Borehole Strain Precursory Observation Data and Research on Anomaly Extraction[D]. Changchun:Jilin University:13−39,81−89 (in Chinese).
|
贺小丹. 2022. 基于时变背景场的Swarm卫星磁场震前异常提取与统计分析[D]. 长春:吉林大学:48−50.
|
He X D. 2022. Pre-Earthquake Anomaly Detection and Statistical Analysis of Swarm Satellite Magnetic Field Based on Time-Varying Background Field[D]. Changchun:Jilin University:48−50 (in Chinese).
|
柳建菲. 2020. 基于时间序列分解的PM2.5浓度预测[D]. 兰州:兰州大学:7−8.
|
Liu J F. 2020. PM2.5 Concentration Prediction Based on Time Series Decomposition[D]. Lanzhou:Lanzhou University:7−8 (in Chinese).
|
刘琦,张晶. 2011. S变换在汶川地震前后应变变化分析中的应用[J]. 大地测量与地球动力学,31(4):6–9. doi: 10.3969/j.issn.1671-5942.2011.04.002
|
Liu Q,Zhang J. 2011. Application of S transform in analysis of strain changes before and after Wenchuan earthquake[J]. Jour nal of Geodesy and Geodynamics,31(4):6–9 (in Chinese).
|
娄家墅,田家勇. 2022. 基于高分辨率钻孔应变仪的地震应变波观测研究进展[J]. 地球物理学进展,37(1):51–58. doi: 10.6038/pg2022FF0050
|
Lou J S,Tian J Y. 2022. Review on seismic strain-wave observation based on high-resolution borehole strainmeters[J]. Progress in Geophysics,37(1):51–58 (in Chinese).
|
牛安福,赵静,苑争一,吉平. 2022. 汶川地震孕育过程中变形场变化特征研究[J]. 武汉大学学报·信息科学版,47(6):839–848.
|
Niu A F,Zhao J,Yuan Z Y,Ji P. 2022. Pre-seismic deformation related to the Wenchuan earthquake[J]. Geomatics and Infor mation Science of Wuhan University,47(6):839–848 (in Chinese).
|
邱泽华,周龙寿,池顺良. 2009. 用超限率分析法研究汶川地震的前兆应变变化[J]. 大地测量与地球动力学,29(4):1–4. doi: 10.3969/j.issn.1671-5942.2009.04.001
|
Qiu Z H,Zhou L S,Chi S L. 2009. Study on precursory strain changes of Wenchuan earthquake with ORA method[J]. Journal of Geodesy and Geodynamics,29(4):1–4 (in Chinese).
|
邱泽华. 2017. 钻孔应变观测理论和应用[M]. 北京:地震出版社:52−67,199−214.
|
Qiu Z H. 2017. Theory and Application of Borehole Strain Observation[M]. Beijing:Seismological Press:52−67,199−214 (in Chinese).
|
邱泽华,唐磊,赵树贤. 2021. 地震的确定性前兆及观测方法[J]. 地震地磁观测与研究,42(2):104–105. doi: 10.3969/j.issn.1003-3246.2021.02.016
|
Qiu Z H,Tang L,Zhao S X. 2021. Definite earthquake precursors and means to observe them[J]. Seismological and Geomagne tic Observation and Research,42(2):104–105 (in Chinese).
|
任天翔,杨少华,董培育,程惠红,石耀霖. 2018. 大渡河水位变化对四川姑咱台钻孔应变观测影响的数值分析[J]. 中国科学院大学学报,35(5):674–680. doi: 10.7523/j.issn.2095-6134.2018.05.014
|
Ren T X,Yang S H,Dong P Y,Cheng H H,Shi Y L. 2018. Numerical analysis of influence of water level fluctuation of Dadu River on Guzan borehole strain meter[J]. Journal of University of Chinese Academy of Sciences,35(5):674–680 (in Chinese).
|
王雪园. 2017. 基于多通道奇异谱分析的结构时域损伤识别方法[D]. 哈尔滨:哈尔滨工业大学:7−8.
|
Wang X Y. 2017. Time Domain Structural Damage Detection Based on Multichannel Singular Spectrum Analysis[D]. Harbin:Harbin Institute of Technology:7−8 (in Chinese).
|
徐克科,李伟. 2017. 利用GNSS基线分析芦山MS7.0级地震前后应变演变特征[J]. 武汉大学学报·信息科学版,42(8):1054–1060.
|
Xu K K,Li W. 2017. Strain evolution characteristics before and after Lushan MS7.0 earthquake using GNSS baseline[J]. Geomatics and Information Science of Wuhan University,42(8):1054–1060 (in Chinese).
|
杨少华,任天翔,董培育,石耀霖. 2016. 姑咱台钻孔应变观测值年变化的数值模拟解释[J]. 地震地质,38(4):1137–1147. doi: 10.3969/j.issn.0253-4967.2016.04.026
|
Yang S H,Ren T X,Dong P Y,Shi Y L. 2016. Interpretation of borehole strain annual change at Guzan station by numerical simulation[J]. Seismology and Geology,38(4):1137–1147 (in Chinese).
|
于紫凝. 2021. 钻孔应变观测数据的震前异常提取与评价方法研究[D]. 长春:吉林大学:1−2,21−65.
|
Yu Z N. 2021. Pre-earthquake Anomaly Extraction and Evaluation of Borehole Strain Observations[D]. Changchun:Jilin University:1−2,21−65 (in Chinese).
|
赵然杭,甘甜,逄晓腾,王兴菊,苟伟娜,齐真. 2021. 基于时间序列分解的降雨数据挖掘与预测[J]. 中国农村水利水电,(11):116–122. doi: 10.3969/j.issn.1007-2284.2021.11.019
|
Zhao R H,Gan T,Pang X T,Wang X J,Gou W N,Qi Z. 2021. Rainfall data mining and forecasting based on time series decomposition[J]. China Rural Water and Hydropower,(11):116–122 (in Chinese).
|
周龙寿,邱泽华,唐磊,阚宝祥. 2009. 用小波方法系统检验强震“前驱波”[J]. 地震学报,31(1):1–12. doi: 10.3321/j.issn:0253-3782.2009.01.001
|
Zhou L S,Qiu Z H,Tang L,Kan B X. 2009. Systemically checking-up strong earthquake precursory waves with wavelet analysis[J]. Acta Seismologica Sinica,31(1):1–12 (in Chinese).
|
吉林大学. 一种去除钻孔应变数据环境影响因素的方法:中国,202111637566.1[P]. 2022−04−19.
|
Jilin University. A method for removing environmental factors of borehole strain data:China,202111637566.1[P]. 2022−04−19. (in Chinese).
|
Benioff H. 1949. Seismic evidence for the fault origin of oceanic deeps[J]. GSA Bull,60(12):1837–1856.
|
Chi C Q,Zhu K G,Yu Z N,Fan M X,Li K Y,Sun H H. 2019. Detecting earthquake-related borehole strain data anomalies with variational mode decomposition and principal component analysis:A case study of the Wenchuan earthquake[J]. IEEE Access,7:157997–158006.
|
Groth A,Ghil M. 2011. Multivariate singular spectrum analysis and the road to phase synchronization[J]. Phys Rev E,84(3):036206.
|
Groth A,Ghil M. 2015. Monte Carlo singular spectrum analysis (SSA) revisited:Detecting oscillator clusters in multivariate datasets[J]. J Climate,28(19):7873–7893.
|
Kojima H,Yoshino C,Nemoto K,Hattori K,Konishi T,Furuya R. 2020. Multi-channel singular spectrum analysis of underground Rn concentration at Asahi station,Boso Peninsula,Japan:Preliminary report on relation between the variation of underground Rn flux and the local seismic activity[J]. J Atmos Electr,39(1):46–51.
|
Yu Z N,Hattori K,Zhu K G,Chi C Q,Fan M X,He X D. 2020. Detecting earthquake-related anomalies of a borehole strain network based on multi-channel singular spectrum analysis[J]. Entropy,22(10):1086.
|
Yu Z N,Zhu K G,Hattori K,Chi C Q,Fan M X,He X D. 2021. Borehole strain observations based on a state-space model and ApNe analysis associated with the 2013 Lushan earthquake[J]. IEEE Access,9:12167–12179.
|
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闫小兵,梁瑞平,王伟君,郝雪景. 地脉动在系舟山北麓断裂次级断裂探测中的应用. 地震工程学报. 2023(02): 421-430 .
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2. |
李彩华,滕云田,周健超,胡星星,王喜珍,李小军,王玉石. 分布式地震数据采集器的高精度时间同步系统研制. 地震学报. 2022(06): 1111-1120 .
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3. |
鲁兵,陈以伦. 基于GPS观测同震位移场的汶川地震矩震级计算. 兰州文理学院学报(自然科学版). 2020(01): 41-44 .
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