Classification of tectonic and non-tectonic seismicity based on convolutional neural network
Xinliang Liu, Tao Ren, Hongfeng Chen, Yufeng Chen
Abstract
SUMMARY In this paper, convolutional neural networks (CNNs) were used to distinguish between tectonic and non-tectonic seismicity. The proposed CNNs consisted of seven convolutional layers with small kernels and one fully connected layer, which only relied on the acoustic waveform without extracting features manually. For a single station, the accuracy of the model was 0.90, and the event accuracy could reach 0.93. The proposed model was tested using data from January 2019 to August 2019 in China. The event accuracy could reach 0.92, showing that the proposed model could distinguish between tectonic and non-tectonic seismicity.
Topics & Concepts
Induced seismicityConvolutional neural networkTectonicsGeologySeismologyEvent (particle physics)Pattern recognition (psychology)Artificial intelligenceComputer sciencePhysicsQuantum mechanicsSeismology and Earthquake StudiesEarthquake Detection and AnalysisSeismic Waves and Analysis