Scientists have empirical laws for how big an earthquake might be and when they might occur but they were missing a solution to the third part of their appearance - where they will occur. On a scale of accuracy that runs from 0 to 1 where .5 is essentially flipping a coin and 1 represents a perfectly accurate model at predicting earthquake aftershock locations- the previously most accurate method (Coulomb failure stress change model) scored 0.583, and the new AI system scored 0.849.
Although the results of the model are already very promising, the deep learning model isn't yet ready for real world use. Earthquake aftershocks can be caused by both static stress and dynamic stress. Aftershocks caused by permanent changes to the ground are known as static stress, while dynamic stress refers to aftershocks caused by rumblings in the ground. Currently, the neural network focuses only on aftershocks caused by static stress instead of both. The model also has other shortcomings, such as being too slow in its analysis to be used in real-time. Due to the nature of how neural networks improve with additional time and testing however, there remains incredible potential for its use in solving this problem.
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