A method for classifying an earthquake event using an attention-based convolutional neural network includes: centering and pre-treating input earthquake data; extracting a feature map by non-linearly converting the pre-treated earthquake data through at least three plurality of convention layers; measuring an importance of a feature trained based on an attention scheme for modeling mutual dependency between channels of a feature map; correcting a feature value by multiplying a measured importance value by the trained feature map with respect to each element; performing down-sampling through max-pooling, based on the feature value; and classifying the earthquake event by normalizing the down-sampled feature value. Accordingly, main features included in a larger amount of complex materials are extracted through the attention-based deep learning. Accordingly, the limitation of a conventional technology of sensing fine earthquakes is overcome, such that the earthquake is sensed even under a lower signal to noise ratio (SNR) environment.
EARTHQUAKE EVENT CLASSIFICATION METHOD USING ATTENTION-BASED CONVOLUTIONAL NEURAL NETWORK RECORDING MEDIUM AND DEVICE FOR PERFORMING THE METHOD