A Knowledge-Driven Deep Learning System for Amplitude Window Selection in Seismic Signals

To characterize global seismic activity accurately, monitoring systems require the onset times and amplitudes of specific seismic phase arrivals in observed waveform data. However, phase amplitudes remain difficult to measure automatically, with current methods achieving only 64% accuracy and leaving 36% of windows to manual correction by experts, creating a major real-time bottleneck in seismic monitoring workflows. To address this limitation, we introduce two knowledge-driven deep learning systems that learn expert decision-making patterns from analyst-corrected seismic data, thus improving automated amplitude measurement. Our approach leverages a U-Net architecture and a novel hybrid loss function that encodes expert preferences for pixel accuracy, shape regularity, and temporal consistency in amplitude window selection. We evaluated 80,648 seismic signal windows to assess the capabilities of our deep learning models. The models achieved substantially higher accuracy in amplitude measurement than traditional rule-based systems. When their outputs diverged from analyst-selected labels, we observed considerable variability among analysts, highlighting the subjective nature of the task. However, independent seismologists showed a strong preference for our model outputs. These findings suggest that our deep learning models successfully capture expert decision-making patterns, demonstrating effective knowledge transfer in an inherently subjective domain.