Earthquake event classification method using attention-based convolutional neural network, recording medium and device for performing the method

    公开(公告)号:US11947061B2

    公开(公告)日:2024-04-02

    申请号:US16992246

    申请日:2020-08-13

    CPC classification number: G01V1/008 G06F18/21 G06F18/24 G06V10/82

    Abstract: An earthquake event classification method using an attention-based neural network includes: preprocessing input earthquake data by centering; extracting a feature map by nonlinearly converting the preprocessed earthquake data through a plurality of convolution layers having three or more layers; measuring importance of a learned feature of the nonlinear-converted earthquake data based on an attention technique in which interdependence of channels of the feature map is modeled; correcting a feature value of the measured importance value through element-wise multiply with the learned feature map; performing down-sampling through max-pooling based on the feature value; and classifying an earthquake event by regularizing the down-sampled feature value. Accordingly, main core features inherent in many/complex data are extracted through attention-based deep learning to overcome the limitations of the existing micro earthquake detection technology, thereby enabling earthquake detection even in low SNR environments.

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