Better Together: Ensemble Learning for Earthquake Detection and Phase Picking

The detection and picking of seismic waves are the first step toward earthquake catalog building, earthquake monitoring, and seismic hazard management. Recent advances in deep learning (DL) have leveraged the amount of labeled seismic data to improve the capability of detecting and picking earthquake signals. While these DL methods have shown great promise, their success remains hindered by low generalizability and poor performance in low signal-to-noise ratios (SNRs) data. Here, we propose a new processing workflow that integrates pretrained DL models, multifrequency band predictions, and ensemble estimations to enhance the generalization of these algorithms. We test the performance of the ensemble model using three benchmark datasets, one of which is within-domain and has been used for training the DL models, and the other two being cross-domain test datasets. We explore the performance given data and model characteristics. We also compare an ensemble approach with a transfer-learning approach and discuss the benefits and drawbacks of these two approaches when deploying on continuous data. Our experiments demonstrate that ensemble learning can drastically improve generalization ability and hence alleviate the need for transfer learning in the case where no labeled datasets exist.