Discrimination of earthquake and microtremor based on generative adversarial network and random forest combination model

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (15) : 312-324.

LIU Heyi1,2, SONG Jindong1,2, LI Shanyou1,2

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Abstract

Seismic event discrimination is one of the basic tasks of earthquake monitoring. Especially with the construction of large-scale seismic stations, the accumulation of massive seismic data and the development of earthquake early warning (EEW), it is more important to distinguish earthquakes accurately and automatically from continuous waveform records. To accurately discriminating seismic events, this paper proposes a combined model for seismic event discrimination based on a generative adversarial network (GAN) and a Random Forest (RF). The model first uses a GAN to extract the waveform features, and then uses a random forest to transform the seismic event discrimination into the classification problem of earthquakes and microtremor based on the extracted waveform features. The results of the testing set which has 5378 data of earthquakes and microtremor show that, the classification accuracy of the combined model for classifying seismic events and microtremor can reach more than 99%, and the results has increased by 23.56% compared with the STA/LTA (short term averaging/long term averaging) algorithm, which proves that the model can accurately identify seismic events from microtremor and has application prospects in earthquake monitoring and EEW.
Key words: Generative adversarial network; Random Forest; Earthquake; Microtremor; Earthquake early warning

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Generative adversarial network / Random Forest / Earthquake / Microtremor / Earthquake early warning

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LIU Heyi1,2, SONG Jindong1,2, LI Shanyou1,2. Discrimination of earthquake and microtremor based on generative adversarial network and random forest combination model[J]. Journal of Vibration and Shock, 2022, 41(15): 312-324

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