The invention discloses a maximum
aftershock prediction method based on online enhancement, and belongs to the technical field of
earthquake prediction. The core of the method is that a personalized prediction model is dynamically generated for a current event through online retrieval of similar historical sequences and data enhancement aiming at a newly occurring main earthquake. Firstly, a historical seismic
database is constructed from a global seismic
directory, and an initial prediction model is trained. When a new main earthquake occurs, an online process is triggered immediately: based on the characteristics of the new main earthquake, K most similar sequences are quickly retrieved from historical data; then, the similar sequence data are enhanced (such as
noise adding and interpolation), so that a diversified enhanced
training set is constructed; and finally, performing rapid fine adjustment on the initial model by using the enhancement set to obtain an optimization model for the current main earthquake, and predicting the maximum
aftershock magnitude. According to the method, the limitation of one-time
cutting of the existing
static model is overcome, the problem of inaccurate prediction in an earthquake
small sample scene is effectively solved, and the accuracy, robustness and timeliness of prediction are remarkably improved.