Benchmarking Study of EQTransformer Autopicker for Seismic Phase Identification in Northern Chile

One of the vital open questions in seismology is rapid, high quality phase identification and picking. Measurements of earthquake arrival time or phase picking are often done by expert judgment with many years of experience. Due to advances in technology and seismometer deployment, the amount of recorded data has increased dramatically in the previous decade, leading up to a point, where it has become almost impossible for humans to deal with this amount of data flow. Therefore, automatic picking algorithms are being used.  In recent years multiple machine learning algorithms have been introduced that bear the potential to combine both, high picking accuracy and the capability of processing large amounts of data. 
In this contribution, we demonstrate the performance of the EQTransformer autopicker, when applied to continuous seismic data from the Northern Chilean subduction zone. To test this deep neural network, we chose a random day and carefully hand picked the continuous data on 18 IPOC stations, selecting only combinations of picks which should lead to locatable events (e.g. with at least five picks). This results in the identification of  3040 P and 2310 S picks. We compare the results of two different training versions of EQTransformer with hand-picked data and with the IPOC seismicity catalog. As it turns out, the comparison is not straightforward, because the evaluation of the picks is highly complicated, given that the true number and type of phase arrivals is and remains unknown. However, the autopicker is able to detect most of the hand-picked phases and arrival times. It outperforms the IPOC catalog by a factor of about 10-15 and thusly it appears to be a valid alternative for advanced seismic catalog construction.