Seismic Signal Discrimination of Earthquakes and Quarry Blasts in North-East Italy Using Deep Neural Networks

Separation of seismic sources of seismic events such as earthquakes and quarry blasts is a complex task and, in most cases, require manual inspection. In this study, artificial neural network models are developed to automatically identify the events that occurred in North-East Italy, where earthquakes and quarry blasts may share the same area. Due to the proximity of the locations of the active fault lines and mining sites, many blasts are registered as earthquakes that can contaminate earthquake catalogues. To be able to differentiate various sources of seismic events 11,821 seismic records from 1463 earthquakes detected by various seismic networks and 9822 seismic records of 727 blasts manually labelled by the Slovenian Environment Agency are used. Three-component seismic records with 90 s length and their frequency contents are used as an input. Ten different models are created by changing various features of the neural networks. Regardless of the features of the created models, results show that accuracy rates are always around 99 %. The performance of our models is compared with a previous study that also used artificial neural networks. It is found that our models show significantly better performance with respect to the models developed by the previous study which performs badly due to differences in the data. Our models perform slightly better than the new model created by using our dataset, but with the previous study's architecture. Developed model can be useful for the discrimination of the earthquakes from quarry blasts in North-East Italy, which may help us to monitor seismic events in the region.