Deep Neural Network of Earthquake Signal Identification using Stridenet

Earthquake is one of the main causes of worldwide destruction. A seismogram is a time function recording of ground motion. In the next few years, there will be a massive increase in the volume of seismogram data generated by earthquake events sourced from the IRIS, USGS, and GEOFON seismic networks. From this twenty-four-hour record of data, we must draw a distinction between earthquake and earthquake noise. One of the applications of Artificial Intelligence can be used to identify between seismic events and noise. In this paper, we studied seismograms to identify an earthquake. Our main objective is to identify earthquake signals and earthquake noises with the combination of CNN, LSTM, and fully connected layer using established datasets and achieve accuracy even better than pretrain CNN architectures. Due to the limitation of hardware, we only trained a part of earthquake datasets. The average validation accuracy of the model when reaching saturation was 98,08% and validation loss was 14,76%.