Li X Y,Bian Y J,Hou X L,Wang T T,Zhang Y X. 2025. Recognition of small magnitude seismic events type based on time-frequency features and machine learning. Acta Seismologica Sinica47(0):1−16. DOI: 10.11939/jass.20240012
Citation: Li X Y,Bian Y J,Hou X L,Wang T T,Zhang Y X. 2025. Recognition of small magnitude seismic events type based on time-frequency features and machine learning. Acta Seismologica Sinica47(0):1−16. DOI: 10.11939/jass.20240012

Recognition of small magnitude seismic events type based on time-frequency features and machine learning

More Information
  • Received Date: January 22, 2024
  • Revised Date: May 24, 2024
  • Accepted Date: May 28, 2024
  • Available Online: January 21, 2025
  • The identification and classification of seismic events hold significant importance in seismic monitoring and earthquake disaster mitigation. This research primarily focuses on 1935 seismic event data with low magnitude (ML≤3.0) in the North China region, encompassing three distinct types of events: natural earthquakes, artificial explosions, and mining collapses. Preliminary analysis involved the geographical distribution examination, annual trends, and magnitude distribution of these events. Preprocessing of raw seismic data included amplitude normalization, detrending, mean removal, and band-pass filtering (0.5—20 Hz). Additionally, short-time Fourier transform analysis was utilized to visualize waveform and spectrogram characteristics, facilitating the observation and analysis of both time and frequency domain features. Based on the analysis results, 62 features across 10 categories, including time, P/S amplitude ratio, frequency, zero-crossing rate, peak amplitude, peak ground acceleration, energy, signal characteristics, angle, and other ratios, were extracted as the foundation for classification.

    This research employed K-Nearest Neighbors (KNN), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LGBM) algorithms to train models using the extracted 62 features for binary and ternary classification tasks of natural earthquakes, artificial explosions, and mining collapses. The basic principles of KNN, AdaBoost, and LGBM algorithms were initially introduced, followed by a description of the training process for the classification models. To ensure balanced sample distribution for each event type, data were selected based on uniform distribution of time and geographical location. Ultimately, 545 events for each event type, totaling 1635 seismic events, were chosen as the sample data for building the classification models. The dataset was divided into training and testing sets using a holdout method, with 75% of the data used for model construction and validation, and 25% for evaluating model performance. The training data covered the main geographical range of the North China region (109.3°—123.5°E, 34.1°—43.7°N), ensuring the models could capture the region’s diversity and complexity. The testing data covered a slightly different geographical range (110.8°—124.1°E, 34.9°—42.7°N).

    The 62 features were used to train classification models by KNN, AdaBoost, and LGBM algorithms. Models were trained with number 0 representing natural earthquakes, number 1 representing artificial explosions, and number 2 representing mining collapses. Various classification models were evaluated using KNN, AdaBoost, and LGBM, with each model trained and tested 100 times for 0−1, 0−2, 1−2, and 0−1−2 classification tasks. AdaBoost and LGBM demonstrated superior performance compared to KNN across all classification tasks, especially in 0−1 and 0−1−2 classification task. LGBM consistently exhibited the best overall performance, maintaining an accuracy of over 95% and showing high stability. In different classification tasks, 0−2 classification yielded the most outstanding results, followed by 1−2 classification.

    Following the training of classification models, the focus shifted to comprehensive evaluation of these models using testing data. Each model was used to identify the event types in the testing data, yielding performance results for each model across different classification tasks. Confusion matrices were generated based on identification results, demonstrating excellent performance for each classification task, particularly in the 0−2 classification using three different classification algorithms.

    Based on confusion matrices, performance evaluation metrics, including accuracy, precision, recall, and F1 score, were calculated. In the 0−1 classification task, AdaBoost performed the best, achieving an accuracy of 96.69%. In the 0−2 classification task, all three algorithms performed well, with metrics exceeding 99.26%. In the 1−2 and 0−1−2 classifications, LGBM exhibited the best performance. Overall, each classification model demonstrated excellent performance, with accuracy, precision, recall, and F1 score all exceeding 89.71%.

    LGBM exhibited superior overall performance, maintaining an accuracy of over 95.90% and demonstrating high stability. KNN still has significant room for improvement, possibly due to its sensitivity to data, resulting in relatively weaker performance compared to AdaBoost and LGBM. AdaBoost’s overall performance lies between LGBM and KNN.

    Finally, ROC curves were plotted to visualize the recognition of the testing dataset using three different classification algorithms (KNN, AdaBoost, LGBM). While KNN algorithm performance for 0−1 and 1−2 classifications requires optimization, all other models performed exceptionally well in the ternary classification scenario. Confusion matrices and evaluation metrics indicate that the constructed classification models perform well on testing data, with ROC curve analysis further confirming the excellent performance of the classification models in various tasks and revealing the applicability of different algorithms in their respective tasks, providing strong support for the practical application of the models.

  • 边银菊. 2005. Fisher方法在震级比mb/MS判据识别爆炸中的应用研究[J]. 地震学报,27(4):414–422. doi: 10.3321/j.issn:0253-3782.2005.04.008
    Bian Y J. 2005. Application of Fisher method to discriminating earthquakes and explosions using criterion mb/MS[J]. Acta Seismologica Sinica,27(4):414–422 (in Chinese).
    蔡杏辉,廖诗荣,张燕明,陈惠芳,林彬华. 2021. 基于支持向量机的地震事件类型自动识别及应用[J]. 华南地震,41(2):27–35.
    Cai X H,Liao S R,Zhang Y M,Chen H F,Lin B H. 2021. Automatic identification of earthquake event types based on support vector machine and its application[J]. South China Journal of Seismology,41(2):27–35 (in Chinese).
    陈润航,黄汉明,柴慧敏. 2018. 地震和爆破事件源波形信号的卷积神经网络分类研究[J]. 地球物理学进展,33(4):1331–1338. doi: 10.6038/pg2018BB0326
    Chen R H,Huang H M,Chai H M. 2018. Study on the discrimination of seismic waveform signals between earthquake and explosion events by convolutional neural network[J]. Progress in Geophysics,33(4):1331–1338 (in Chinese).
    范晓易,曲均浩,刘方斌,周少辉. 2020. 使用支持向量机识别地震类型的影响因素分析[J]. 大地测量与地球动力学,40(10):1034–1038.
    Fan X Y,Qu J H,Liu F B,Zhou S H. 2020. Analysis of influencing factors in use of support vector machine method to identify earthquake types[J]. Journal of Geodesy and Geodynamics,40(10):1034–1038 (in Chinese).
    工业和信息化部. 2011. “十二五”民用爆炸物品行业发展规划[S].
    工业和信息化部. 2016. “十三五”民用爆炸物品行业发展规划[S].
    靳玉贞,林木金,范晓瑜,刘晓萍,何佳,杨世英,孟彩菊. 2015. 山西地区爆破、塌陷(矿震)特殊地震动特征识别[J]. 地震地磁观测与研究,36(3):63–66. doi: 10.3969/j.issn.1003-3246.2015.03.012
    Jin Y Z,Lin M J,Fan X Y,Liu X P,He J,Yang S Y,Meng C J. 2015. Feature recognition of explosion,collapse (mine earthquake) to specific ground motion in Shanxi region[J]. Seismological and Geomagnetic Observation and Research,36(3):63–66 (in Chinese).
    靳玉贞,刘炜,何佳,杨世英,孟彩菊,刘晓萍. 2019. 太原基准地震台核爆震相识别[J]. 地震地磁观测与研究,40(4):59–64. doi: 10.3969/j.issn.1003-3246.2019.04.009
    Jin Y Z,Liu W,He J,Yang S Y,Meng C J,Liu X P. 2019. Identification of nuclear explosion waveform recorded by Taiyuan Fundamental Seismic Station[J]. Seismological and Geomagnetic Observation and Research,40(4):59–64 (in Chinese).
    梁皓,孙丽,陈姝荞,支明. 2023. 基于支持向量机算法的地震事件分类研究:以东北地区为例[J]. 地球物理学报,66(12):5030–5040. doi: 10.6038/cjg2022Q0829
    Liang H,Sun L,Chen S Q,Zhi M. 2023. Research on seismic event classification based on SVM algorithm:An application in Northeast China[J]. Chinese Journal of Geophysics,66(12):5030–5040 (in Chinese).
    刘莎,杨建思,田宝峰,郑钰,姜旭东,徐志强. 2012. 首都圈地区爆破、矿塌和天然地震的识别研究[J]. 地震学报,34(2):202–214. doi: 10.3969/j.issn.0253-3782.2012.02.007
    Liu S,Yang J S,Tian B F,Zheng Y,Jiang X D,Xu Z Q. 2012. Discrimination between explosions,mine collapses and earthquakes in capital region of China[J]. Acta Seismologica Sinica,34(2):202–214 (in Chinese).
    任涛,林梦楠,陈宏峰,王冉冉,李松威,刘晓雨,刘杰. 2019. 基于Bagging集成学习算法的地震事件性质识别分类[J]. 地球物理学报,62(1):383–392. doi: 10.6038/cjg2019M0380
    Ren T,Lin M N,Chen H F,Wang R R,Li S W,Liu X Y,Liu J. 2019. Seismic event classification based on bagging ensemble learning algorithm[J]. Chinese Journal of Geophysics,62(1):383–392 (in Chinese).
    王婷婷,边银菊. 2011. 识别天然地震和人工爆破的判据选择[J]. 地震地磁观测与研究,32(6):62–67. doi: 10.3969/j.issn.1003-3246.2011.06.012
    Wang T T,Bian Y J. 2011. Criterion selection of earthquake and explosion recognition[J]. Seismological and Geomagnetic Observation and Research,32(6):62–67 (in Chinese).
    魏富胜,黎明. 2003. 震源性质的倒谱分析[J]. 地震学报,25(1):47–54. doi: 10.3321/j.issn:0253-3782.2003.01.006
    Wei F S,Li M. 2003. Cepstrum analysis of source character[J]. Acta Seismologica Sinica,25(1):47–54 (in Chinese).
    隗永刚,杨千里,王婷婷,蒋长胜,边银菊. 2019. 基于深度学习残差网络模型的地震和爆破识别[J]. 地震学报,41(5):646–657. doi: 10.11939/jass.20190030
    Wei Y G,Yang Q L,Wang T T,Jiang C S,Bian Y J. 2019. Earthquake and explosion identification based on Deep Learning residual network model[J]. Acta Seismologica Sinica,41(5):646–657 (in Chinese).
    吴顺川,甘一雄,任义,郑立夫. 2020. 基于RA与AF值的声发射指标在隧道监测中的可行性[J]. 工程科学学报,42(6):723–730.
    Wu S C,Gan Y X,Ren Y,Zheng L F. 2020. Feasibility research of AE monitoring index in tunnel based on RA and AF[J]. Chinese Journal of Engineering,42(6):723–730 (in Chinese).
    吴涛,庞聪,江勇,丁炜,廖成旺. 2022. 基于随机子空间和AdaBoost集成学习的地震事件性质辨识研究[J]. 地球物理学进展,37(3):981–988. doi: 10.6038/pg2022FF0234
    Wu T,Pang C,Jiang Y,Ding W,Liao C W. 2022. Identification and classification of earthquake types based on Cov-SSI and AdaBoost ensemble learning algorithm[J]. Progress in Geophysics,37(3):981–988 (in Chinese).
    薛思敏,黄汉明,施佳鹏,袁雪梅,黎炳君. 2022. 基于EMD的IMF时域统计特征提取及其应用于震动事件源类型识别研究[J]. 地震工程学报,44(1):100–107.
    Xue S M,Huang H M,Shi J P,Yuan X M,Li B J. 2022. Extraction of IMF time-domain features based on EMD and its application to recognition of vibration event source type[J]. China Earthquake Engineering Journal,44(1):100–107 (in Chinese).
    杨千里,王婷婷,边银菊. 2020. 基于广义S变换的地震与爆炸识别[J]. 地震学报,42(5):613–628.
    Yang Q L,Wang T T,Bian Y J. 2020. Recognition of earthquakes and explosions based on generalized S transform[J]. Acta Seismologica Sinica,42(5):613–628 (in Chinese).
    赵永,刘卫红,高艳玲. 1995. 北京地区地震、爆破和矿震的记录图识别[J]. 地震地磁观测与研究,16(4):48–54.
    Zhao Y,Liu W H,Gao Y L. 1995. Distinguishing earthquake,explosion and mine earthquake in Beijing area[J]. Seismological and Geomagnetic Observation and Research,16(4):48–54 (in Chinese).
    Badawy A,Gamal M,Farid W,Soliman M S. 2019. Decontamination of earthquake catalog from quarry blast events in northern Egypt[J]. J Seismol,23(6):1357–1372. doi: 10.1007/s10950-019-09873-8
    Barama L,Williams J,Newman A V,Peng Z G. 2023. Global nuclear explosion discrimination using a convolutional neural network[J]. Geophys Res Lett,50(17):e2022GL101528. doi: 10.1029/2022GL101528
    Chiang A,Dreger D S,Ford S R,Walter W R. 2014. Source characterization of underground explosions from combined regional moment tensor and first-motion analysis[J]. Bull Seismol Soc Am,104(4):1587–1600. doi: 10.1785/0120130228
    Chiang A,Ichinose G A,Dreger D S,Ford S R,Matzel E M,Myers S C,Walter W R. 2018. Moment tensor source-type analysis for the Democratic People’s Republic of Korea-declared nuclear explosions (2006−2017) and 3 September 2017 collapse event[J]. Seismol Res Lett,89(6):2152–2165.
    Dargahi-Noubary G R. 1998. Statistical estimation of corner frequency and its application to seismic event-identification[J]. Soil Dyn Earthq Eng,17(5):297–309. doi: 10.1016/S0267-7261(98)00016-5
    Freund Y,Schapire R E. 1997. A decision-theoretic generalization of on-line learning and an application to boosting[J]. J Comput Syst Sci,55(1):119–139. doi: 10.1006/jcss.1997.1504
    Gaebler P,Ceranna L,Nooshiri N,Barth A,Cesca S,Frei M,Grünberg I,Hartmann G,Koch K,Pilger C,Ross J O,Dahm T. 2019. A multi-technology analysis of the 2017 North Korean nuclear test[J]. Solid Earth,10(1):59–78. doi: 10.5194/se-10-59-2019
    Goforth T T,Hetzer C H,Stump B W. 2006. Characteristics of regional seismograms produced by delay-fired explosions at the Minntac iron mine,Minnesota[J]. Bull Seismol Soc Am,96(1):272–287. doi: 10.1785/0120050068
    Horasan G,Güney A B,Küsmezer A,Bekler F,Öğütçü Z,Musaoğlu N. 2009. Contamination of seismicity catalogs by quarry blasts:An example from İstanbul and its vicinity,northwestern Turkey[J]. J Asian Earth Sci,34(1):90–99. doi: 10.1016/j.jseaes.2008.03.012
    Jia L Z,Chen H F,Xing K. 2022. Rapid classification of local seismic events using machine learning[J]. J Seismol,26(5):897–912. doi: 10.1007/s10950-022-10109-5
    Kim G,Ku B,Ko H. 2021. Multifeature fusion-based earthquake event classification using transfer learning[J]. IEEE Geosci Remote Sens Lett,18(6):974–978. doi: 10.1109/LGRS.2020.2993302
    Kong Q K,Wang R J,Walter W R,Pyle M,Koper K,Schmandt B. 2022. Combining deep learning with physics based features in explosion-earthquake discrimination[J]. Geophys Res Lett,49(13):e2022GL098645. doi: 10.1029/2022GL098645
    Koper K D,Holt M M,Voyles J R,Burlacu R,Pyle M L,Wang R J,Schmandt B. 2021. Discrimination of small earthquakes and buried single-fired chemical explosions at local distances (<150 km) in the western United States from comparison of local magnitude (ML) and coda duration magnitude (MC)[J]. Bull Seismol Soc Am,111(1):558–570. doi: 10.1785/0120200188
    Laasri E H A,Akhouayri E S,Agliz D,Zonta D,Atmani A. 2015. A fuzzy expert system for automatic seismic signal classification[J]. Expert Syst Appl,42(3):1013–1027. doi: 10.1016/j.eswa.2014.08.023
    Levshin A L,Ritzwoller M H. 1995. Characteristics of surface waves generated by events on and near the Chinese nuclear test site[J]. Geophys J Int,123(1):131–148. doi: 10.1111/j.1365-246X.1995.tb06666.x
    Lindenbaum O,Rabin N,Bregman Y,Averbuch A. 2020. Seismic event discrimination using deep CCA[J]. IEEE Geosci Remote Sens Lett,17(11):1856–1860. doi: 10.1109/LGRS.2019.2959554
    Ma J,Zhao G Y,Dong L J,Chen G H,Zhang C X. 2015. A comparison of mine seismic discriminators based on features of source parameters to waveform characteristics[J]. Shock Vib,2015(1):919143.
    Mousavi S M,Horton S P,Langston C A,Samei B. 2016. Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression[J]. Geophys J Int,207(1):29–46. doi: 10.1093/gji/ggw258
    Peng K,Tang Z,Dong L J,Sun D Y. 2021. Machine learning based identification of microseismic signals using characteristic parameters[J]. Sensors,21(21):6967. doi: 10.3390/s21216967
    Pérez N,Venegas P,Benitez D,Grijalva F,Lara R,Ruiz M. 2022. Benchmarking seismic-based feature groups to classify the Cotopaxi volcanic activity[J]. IEEE Geosci Remote Sens Lett,19:7500505.
    Pomeroy P W,Best W J,McEvilly T V. 1982. Test ban treaty verification with regional data-A review[J]. Bull Seismol Soc Am,72(6B):S89–S129. doi: 10.1785/BSSA07206B0089
    Ren J Q,Zhou S H,Wang J Y,Yang S,Liu C. 2022. Research on identification of natural and unnatural earthquake events based on AlexNet convolutional neural network[J]. Wireless Commun Mobile Comput,2022(1):6782094.
    Saad O M,Shalaby A,Sayed M S. 2019. Automatic discrimination of earthquakes and quarry blasts using wavelet filter bank and support vector machine[J]. J Seismol,23(2):357–371. doi: 10.1007/s10950-018-9810-5
    Sanina I A,Nesterkina M A,Konstantinovskaya N L,Gabsatarova I P. 2021. Identification of the nature of seismic events that occurred in the East European platform as recorded by the Mikhnevo small-aperture seismic array at regional distances[J]. Seism Instr,57(1):38–54. doi: 10.3103/S0747923921010084
    Selby N D,Bowers D,Douglas A,Heyburn R,Porter D. 2005. Seismic discrimination in southern Xinjiang:The 13 March 2003 Lop Nor earthquake[J]. Bull Seismol Soc Am,95(1):197–211. doi: 10.1785/0120040040
    Shang X Y,Li X B,Morales-Esteban A,Chen G H. 2017. Improving microseismic event and quarry blast classification using Artificial Neural Networks based on Principal Component Analysis[J]. Soil Dyn Earthq Eng,99:142–149. doi: 10.1016/j.soildyn.2017.05.008
    Tan A,Horasan G,Kalafat D,Gülbağ A. 2021. Discrimination of earthquakes and quarries in the Edirne district (Turkey) and its vicinity by using a linear discriminate function method and artificial neural networks[J]. Acta Geophys,69(1):17–27. doi: 10.1007/s11600-020-00519-9
    Taylor S R,Denny M D,Vergino E S,Glaser R E. 1989. Regional discrimination between NTS explosions and western U. S. earthquakes[J]. Bull Seismol Soc Am,79(4):1142–1176. doi: 10.1785/BSSA0790041142
    Tibi R,Linville L,Young C,Brogan R. 2019. Classification of local seismic events in the Utah region:A comparison of amplitude ratio methods with a spectrogram-based machine learning approach[J]. Bull Seismol Soc Am,109(6):2532–2544. doi: 10.1785/0120190150
    Voyles J R,Holt M M,Hale J M,Koper K D,Burlacu R,Chambers D J A. 2020. A new catalog of explosion source parameters in the Utah region with application to ML-MC-based depth discrimination at local distances[J]. Seismol Res Lett,91(1):222–236. doi: 10.1785/0220190185
    Wang T T,Bian Y J,Yang Q L,Ren M Y. 2021. Correction of P/S Amplitude ratios for low-magnitude seismic events based on Bayesian Kriging method[J]. Bull Seismol Soc Am,111(5):2799–2813. doi: 10.1785/0120200293
    Wang T T,Bian Y J,Zhang Y X,Hou X L. 2023. Using artificial intelligence methods to classify different seismic events[J]. Seismol Res Lett,94(1):1–16. doi: 10.1785/0220220055
    Yıldırım E,Gülbağ A,Horasan G,Doğan E. 2011. Discrimination of quarry blasts and earthquakes in the vicinity of Istanbul using soft computing techniques[J]. Comput Geosci,37(9):1209–1217. doi: 10.1016/j.cageo.2010.09.005
    Yue L,Qu J H,Zhou S H,Qu B A,Zhang Y W,Xu Q F. 2023. Seismic event classification based on a two-step convolutional neural network[J]. J Seismol,27(3):527–535. doi: 10.1007/s10950-023-10153-9
    Zhang Y X,Wang T T,Bian Y J,Yang Q L. 2021. Features of different types of seismic events in China’s Capital Region[J]. Earthq Sci,34(6):489–506. doi: 10.29382/eqs-2021-0035
  • Cited by

    Periodical cited type(4)

    1. 李军,马念杰,王卫军,任建举,连小勇,马振凯. 蝶形破坏理论及其应用研究进展. 中国矿业大学学报. 2023(02): 209-228 .
    2. 师皓宇,黄辅琼,张超,杨长益,马龙,田益博. 微小应力对断裂带深部岩体应变能释放的触发过程模拟. 地震地磁观测与研究. 2021(S1): 11-13 .
    3. 师皓宇,马念杰,石建军,李楠,谈国文. 采场围岩能量演化及释放模拟研究. 煤炭科学技术. 2020(03): 106-111 .
    4. 师皓宇,黄辅琼,马念杰,王永建,马骥,邹光华,彭瑞. 基于岩体塑性位错理论的龙门山区域构造系统演化过程. 地质学报. 2020(12): 3581-3589 .

    Other cited types(5)

Catalog

    Article views (93) PDF downloads (36) Cited by(9)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return