| 基于最小二乘支持向量机与熵特征的地震事件性质辨识研究 |
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| 引用本文： | 庞聪,廖成旺,江勇,程诚,吴涛,舒鹏,丁炜.基于最小二乘支持向量机与熵特征的地震事件性质辨识研究[J].大地测量与地球动力学,2022,42(6):655-660. |
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| 作者姓名： | 庞聪 廖成旺 江勇 程诚 吴涛 舒鹏 丁炜 |
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| 作者单位： | 中国地震局地震研究所,武汉市洪山侧路40 号,430071;地震预警湖北省重点实验室,武汉市洪山侧路48 号,430071;湖北省地震局,武汉市洪山侧路48 号,430071,运城学院数学与信息技术学院,山西省运城市复旦西街 1155 号,044031,南华大学计算机学院,湖南省衡阳市常胜西路 28 号,421001 |
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| 基金项目： | 中国地震局地震研究所;应急管理部国家自然灾害防治研究院基本科研业务费专项;中国大陆综合地球物理场观测仪器研发专项;国家自然科学基金;湖北省自然科学基金 |
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| 摘 要： | 为解决天然地震事件性质辨识依赖人工检测、自动化程度不高且误差较大的问题，利用机器学习中的最小二乘支持向量机（LSSVM）和信息论中的排列熵、近似熵及香农熵等特征参数，建立Entropy-LSSVM地震波形特征提取与事件性质辨识模型。基于2021年青海玛多MS7.4地震、云南漾濞地震事件及人工爆破干扰事件等共计500条波形数据，设计多个不同训练比例与测试比例的随机抽取子实验，采用准确率、召回率、特效度、精确度、F-measure验证该模型的有效性。实验结果表明，熵特征对天然地震和非天然地震事件波形的区分效果明显，且结合熵特征的LS-SVM模型整体性能优于QDA、LDA、朴素贝叶斯、决策树、LogitBoost及RobustBoost等方法，训练集与测试集比例为3∶2的辨识准确率和召回率分别达到99.00%和96.97%，即使训练集只有50条的辨识准确率也可达98%以上，这对天然地震事件的有效甄别有一定参考价值。
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| 关 键 词： | 地震事件辨识 最小二乘支持向量机 熵特征 玛多地震 漾濞地震 |
Research on Identification of Seismic Event Properties Based on Least Squares Support Vector Machine and Entropy Feature |
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| PANG Cong,LIAO Chengwang,JIANG Yong,CHENG Cheng,WU Tao,SHU Peng,DING Wei.Research on Identification of Seismic Event Properties Based on Least Squares Support Vector Machine and Entropy Feature[J].Journal of Geodesy and Geodynamics,2022,42(6):655-660. |
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| Authors: | PANG Cong LIAO Chengwang JIANG Yong CHENG Cheng WU Tao SHU Peng DING Wei |
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| Abstract: | Natural seismic event property recognition used to rely on manual detection of seismic waveforms, leading to insufficient automation and large errors. To solve this problem, using least squares support vector machine(LSSVM) in machine learning and feature parameters such as permutation entropy, approximate entropy and Shannon entropy in information theory, we develop the Entropy-LSSVM seismic waveform feature extraction and event property recognition model. Based on a total of 500 waveform data from the 2021 Qinghai Maduo MS7.4 earthquake, Yunnan Yangbi seismic event and an artificial blast disturbance event, we design several random extraction sub-experiments with different training and testing ratios to verify the effectiveness of the model using accuracy, recall, effectiveness, precision and F-measure. The experimental results show that the entropy feature is effective in distinguishing natural and non-natural seismic event waveforms, and the overall performance of the model is better than that of QDA, LDA, plain Bayes, decision tree, LogitBoost, and RobustBoost, etc. The recognition accuracy and recall of the training set/test set ratio of 3∶2 can reach 99.00% and 96.97%. The recognition accuracy can reach more than 98%, even with only 50 entries in the training set, which provides some reference value for the effective screening of natural seismic events. |
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| Keywords: | seismic event identification least squares support vector machine(LSSVM) entropy feature Maduo earthquake Yangbi earthquake |
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