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.
PANG Cong,LIAO Chengwang,JIANG Yong et al. Research on Identification of Seismic Event Properties Based on Least Squares Support Vector Machine and Entropy Feature[J]. jgg, 2022, 42(6): 655-660.
