Earthquake phase picking in out-of-distribution data conditions: Can synthetic data from earthquake simulations help?

Deep learning has improved automated seismic phase picking in recent years. However, many pickers are trained on a single dataset and often fail to generalize when deployed on out-of-distribution (OOD) data. Variations in earthquake magnitude, propagation distance, sensor instrumentation, and ambient noise between training and target domains lead to significant performance degradation under OOD conditions. To address this challenge, we propose a new framework to reduce performance degradation under OOD conditions based on a pipeline of seismic simulation -- phase labeling -- site-conditions simulation -- transfer learning. First, we use the seismic simulation tool AxiSEM3D to establish a 3-D waveform simulation, covering local, regional, and teleseismic scales. Next, we obtain precise P- and S-phase labels by computing theoretical arrival times from velocity models and refining these onsets with traditional automatic picking algorithms, ensuring high-fidelity phase annotations. Then, based on actual site conditions, we simulate instrument responses and synthesize ambient noise constrained by PPSD analysis. This gives us station-specific, noisy waveforms that closely match real observational conditions. Finally, we employ transfer learning to fine-tune a phase picker on this specific synthetic dataset, thereby enhancing the picker's performance in new conditions. The proposed framework aims to improve the ability of deep learning models to pick phases under OOD conditions. It enables reliable performance across regional variability and instrumentation differences without large-scale manual relabeling. It also reduces the amount of real data needed for training, making it useful for small datasets and leaving more data to analyse. Overall, this work introduces a transferable methodology for seismic phase picking under distribution shift and shows that physics-informed data augmentation combined with targeted transfer learning can effectively decrease OOD performance degradation, thereby increasing the applicability of deep-learning-based phase picking.