Recipe For Regular Machine Learning-based Earthquake Cataloging: A Systematic Examination in New Zealand, from Local to Regional Scale

Machine learning-based algorithms are emerging in mining earthquake occurrences from continuous recordings, replacing some routine processes by human experts, e.g., phase picking and phase association. In this study, we explore the combination of phase picker and phase associator with challenging application scenarios: the complex seismogenic structure, wide study area (15 degrees of both longitude and latitude and a depth of 600 km), hundreds of stations, and intensive seismicity during the 2016 Mw7.8 Kaikōura earthquake that correlates with at least seven faults. The deep learning-based phase pickers usually follow the prototype of PhaseNet, which maps the phase arrivals into truncated Gaussian functions with a customized model. Recent studies have shown poor generalizability of the advanced models on data out of the training distribution. In this study, we argue that appropriate data augmentation enables the RED-PAN model, trained on the Taiwanese data, to generalize well on New Zealand data even under intense seismicity. We applied RED-PAN on year-long continuous recordings over 439 stations of the GeoNet during 2016 and 2017. RED-PAN produces approximately three million P-S pairs over the New Zealand-wide network, enabling the exploration of the advanced phase associators' robustness on local and regional scales and under intense seismicity, e.g., back-projection, GaMMA, and PyOcto. Finally, we developed a six-stage automatic pipeline producing a high-quality earthquake catalog: phase picking, phase association, 3-D absolute location by NonLinLoc, magnitude estimation, weighted template matching, and 3-D relative location by GrowClust.