Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (10): 226-230.DOI: 10.3778/j.issn.1002-8331.1901-0276

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Anomaly Detection Method of Earthquake Precursor Observation Data Based on Negative Selection Algorithm

XIONG Yi, LIANG Yiwen, TAN Chengyu, ZHOU Wen   

  1. School of Computer Science, Wuhan University, Wuhan 430072, China
  • Online:2020-05-15 Published:2020-05-13



  1. 武汉大学 计算机学院,武汉 430072


In order to solve the problem of low detection accuracy caused by the lack of abnormal data in the existing precursor anomaly detection methods, a detection method based on negative selection is proposed. Firstly, self set and nonself set in seismic data are defined. Secondly, the randomly selected immature detector is matched with self set to generate a maturity detector with variable radius to cover nonself space. Then, the data to be detected are matched with the detector to determine whether the detection result is obtained in nonself space. Finally, compared with the existing seismic anomaly detection methods, BP neural network and support vector machine. The experimental results show that the negative selection is more effective for the anomaly detection of seismic precursor observation data.

Key words: negative selection, anomaly detection, earthquake precursor observation data, computer immune system



关键词: 反向选择, 异常检测, 地震前兆观测数据, 计算机免疫系统