Automatic inspection and analysis of digital waveform images by means of convolutional neural networks

Analyzing seismic data to get information about earthquakes has always been a major task for seismologists and, more in general, for geophysicists.
Recently, thanks to the technological development of observation systems, more and more data are available to perform such tasks. However, this data
“grow up” makes “human possibility” of data processing more complex in terms of required efforts and time demanding. That is why new technological
approaches such as artificial intelligence are becoming very popular and more and more exploited. In this work, we explore the possibility of interpreting seismic waveform segments by means of pre-trained deep learning. More specifically, we apply convolutional networks to seismological waveforms recorded at local or regional distances without any pre-elaboration or filtering. We show that such an approach can be very successful in determining if an earthquake is “included” in the seismic wave image and in estimating the distance between the earthquake epicenter and the recording station.