Transfer Learning for Seismic Phase Picking With Significantly Higher Precision in Faraway Seismic Stations

Earthquake data recorded in Texas are dramatically different from other places because of the various types of noise caused by oil and gas production or anthropogenic activities. This causes a relatively lower signal-to-noise ratio (SNR) and a strong challenge to leverage a globally trained deep learning model for earthquake detection. To combat the challenging data characteristics when monitoring seismicity using deep learning, we propose to apply transfer learning to a globally optimal phase-picking model using regional earthquake data compiled from Texas. Specifically, we first train an advanced deep learning model based on the compact convolutional transformer (EQCCT) using a global earthquake dataset. Then, we construct individual datasets from each of the main basins in Texas and apply transfer learning to each basin-scale database, intending to obtain optimal picking performance in each basin. As a result, the precision, recall, and $F1$ -score significantly increased from the original EQCCT model to the fine-tuned model in the Delaware and Midland basins. The standard deviations of the picking errors of both P- and S-wave phases accordingly decrease significantly. The greatly improved EQCCT models help detect more P- and S-wave arrivals, facilitating a more successful association and location. Transfer learning models using Texas data and Texas basin-based transfer learning models with detailed documentation can be downloaded from https://github.com/omarmohamed15/Picking-Texas/tree/main.