Training specific deep learning models for particular tasks is common across various domains within seismology.
Overall, our study provides for the first time a prototype self-consistent all-in-one system of simultaneous seismic phase picking, association, and location, which has the potential for next-generation autonomous earthquake monitoring.
Reading emotions precisely from segments of neural activity is crucial for the development of emotional brain-computer interfaces.
The subsequent processing results show that our method can significantly improve S/N and effectively solve the problem of lack of energy in dead band.
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise.