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. 地震学报, 42(5): 613-628. DOI: 10.11939/jass.20190173
引用本文: 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. 地震学报, 42(5): 613-628. DOI: 10.11939/jass.20190173
Yang Qianli, Wang Tingting, Bian Yinju. 2020: Recognition of earthquakes and explosions based on generalized S transform. Acta Seismologica Sinica, 42(5): 613-628. DOI: 10.11939/jass.20190173
Citation: Yang Qianli, Wang Tingting, Bian Yinju. 2020: Recognition of earthquakes and explosions based on generalized S transform. Acta Seismologica Sinica, 42(5): 613-628. DOI: 10.11939/jass.20190173

基于广义S变换的地震与爆炸识别

Recognition of earthquakes and explosions based on generalized S transform

  • 摘要: 为了进一步增强区域台网针对天然地震与人工爆炸事件的识别能力,本文利用广义S变换方法,围绕河北省三河采石场爆破以及周边发生的天然地震波形记录展开研究,总结了地震与爆炸在时频谱中的差异性。研究表明,受震源机制影响,天然地震时频谱图的频带范围更宽广,能量团分布也较人工爆炸更为复杂。为了消除震中距的影响,本文将求解的时频谱保存为规格相等的灰度图像,通过滑动窗口计算图像的灰度一致性得到新的识别判据−谱图二阶矩。对三河地区已知地震和爆炸事件的测试表明,谱图二阶矩对单台波形记录的识别率可达91%,多台求取平均值之后的识别率超过98%,因此该判据具有良好的应用前景。

     

    Abstract: In order to further enhance the ability of regional seismic network in distinguishing between natural earthquakes and artificial explosion events, the generalized S-transform method was used in this paper to study the waveform records of the explosions and the natural earthquakes occurred around Sanhe quarry of Hebei Province, and then the differences of time-frequency spectra between earthquakes and explosions were summarized. Affected by the focal mechanism, the time-frequency spectrograms of natural earthquakes show a wider frequency band range, and more complex distribution of energy clusters than those of artificial explosions. In order to eliminate the influence of epicentral distance, the time-frequency spectra obtained in this paper are saved as gray-scale images with the same size, and a new recognition criterion “the second-order moment of spectrum” is obtained by mathematical method. The test of the known earthquake and explosion events in Sanhe area suggest that the recognition rate of the second-order moment of spectrum for waveform records on single station reaches up 91%, and the recognition rate is more than 98% after taking the average of multiple stations, which indicates that the new recognition criteria has a good application prospect.

     

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