EARTHQUAKE EVENT CLASSIFICATION METHOD USING ATTENTION-BASED CONVOLUTIONAL NEURAL NETWORK, RECORDING MEDIUM AND DEVICE FOR PERFORMING THE METHOD

摘要

A seismic event classification method using a state-based neural network includes: centering and preprocessing input seismic data; extracting a feature map by non-linearly transforming the preprocessed seismic data through a plurality of convolutional layers of three or more; Measuring the importance of the learned feature based on the attention technique that models the interdependence between channels of the feature map on the nonlinearly transformed seismic data; correcting the feature value through element-by-element multiplication of the measured importance value with the learned feature map; down-sampling through max-pooling based on the feature value; and classifying earthquake events by normalizing the down-sampled feature values. Accordingly, through attention-based deep learning, key features inherent in large amounts of/complex data are extracted, thereby overcoming the limitations of existing micro-seismic sensing technologies and enabling seismic detection even in low SNR environments.