Toward Robust Seismic Phase Picking in Realistic Multi-Event Scenarios

Accurate and robust seismic phase picking remains a fundamental challenge in automated earthquake monitoring, particularly under complex waveform conditions. While recent deep learning models such as PhaseNet and EQTransformer have demonstrated strong performance on commonly used benchmark datasets, their architectural design choices introduce limitations in temporal resolution, sequence modeling, and generalization to more realistic seismic scenarios.

PhaseNet adopts a convolutional U-Net structure that is effective for waveform segmentation but is constrained by a fixed receptive field and limited dynamic temporal modeling. EQTransformer, in contrast, employs an encoder–attention–decoder architecture capable of capturing long-range dependencies, yet relies on aggressive temporal downsampling to alleviate the quadratic cost of self-attention. This heavy compression can discard fine-grained temporal information and degrade phase onset precision.

In this work, we present EQMamba, a sequence modeling framework for seismic phase picking that emphasizes temporal fidelity and efficient long-range dependency modeling. The proposed architecture integrates structured state-space models with efficient linear attention mechanisms, allowing long waveform sequences to be processed with minimal downsampling. By preserving high-resolution temporal information while maintaining computational tractability, EQMamba is designed to better reflect the continuous-time and dynamical nature of seismic signals.

Beyond controlled single-event settings, this study places particular emphasis on model behavior under more realistic waveform conditions, including event superposition, amplitude imbalance, and temporal interference between phases. We introduce a revised data construction and evaluation strategy to systematically probe robustness in multi-event and mainshock–aftershock scenarios, which are often underrepresented in standard benchmarks. Model performance is analyzed not only through conventional picking metrics, but also via error distributions, phase confusion patterns, and failure modes as waveform complexity increases.