Learning Transferable Noise-Environment Representations for Cross-Domain Time-Series Restoration

Time-series restoration under domain shift is challenging because degradation patterns vary across sensing environments, while conventional models learn a single mapping from corrupted to clean observations. We study whether observable background context can provide transferable representations of degradation conditions. We formulate restoration as noise-environment conditioned learning and introduce PCD-Net, which encodes background segments into environment representations and learns a differentiable routing distribution over a prototype memory to condition waveform reconstruction through cross-attention. This separation between environment representation and restoration enables selective cross-domain adaptation, where degradation-related representations are updated together with a lightweight reconstruction adjustment module while the general restoration backbone is preserved. Experiments across seismic, mine microseismic, and industrial waveform domains show that the learned environment representations improve restoration under varying noise conditions and provide an effective basis for parameter-efficient cross-domain adaptation. These results suggest that explicitly modeling observable degradation context can enhance the transferability of time-series restoration models.