  Earthquakes, as natural phenomena, have continuously caused damage and loss
of human life historically. Earthquake prediction is an essential aspect of any
society's plans and can increase public preparedness and reduce damage to a
great extent. Nevertheless, due to the stochastic character of earthquakes and
the challenge of achieving an efficient and dependable model for earthquake
prediction, efforts have been insufficient thus far, and new methods are
required to solve this problem. Aware of these issues, this paper proposes a
novel prediction method based on attention mechanism (AM), convolution neural
network (CNN), and bi-directional long short-term memory (BiLSTM) models, which
can predict the number and maximum magnitude of earthquakes in each area of
mainland China-based on the earthquake catalog of the region. This model takes
advantage of LSTM and CNN with an attention mechanism to better focus on
effective earthquake characteristics and produce more accurate predictions.
Firstly, the zero-order hold technique is applied as pre-processing on
earthquake data, making the model's input data more proper. Secondly, to
effectively use spatial information and reduce dimensions of input data, the
CNN is used to capture the spatial dependencies between earthquake data.
Thirdly, the Bi-LSTM layer is employed to capture the temporal dependencies.
Fourthly, the AM layer is introduced to highlight its important features to
achieve better prediction performance. The results show that the proposed
method has better performance and generalize ability than other prediction
methods.
