Abstract
Abstract
Recent advances in sensing technologies, particularly Distributed Acoustic Sensing (DAS), have significantly improved the collection and analysis of seismological data. DAS is a powerful method for detecting vibrations from various sources, including earthquakes. However, the vast amount of data produced by DAS requires sophisticated analytical methods to differentiate between signals of interest and noise, such as traffic signals. We introduce an innovative approach by extending the Noise2Self framework to effectively remove unwanted, structured coherent noise from DAS data. By creating masks based on the characteristics of traffic signals, we isolate and preserve earthquake signals while maintaining the denoising performance of the original Noise2Self approach, which reduces noise without requiring clean reference data. To evaluate our method, we used synthetic data generated from seismic recordings of closely spaced seismometers and then applied our approach to data from a DAS array crossing the Alpine Fault near Haast, New Zealand. Our results demonstrate that our model successfully removes traffic noise and other non‐coherent noise while preserving seismic signals, leading to improvements in both Signal‐to‐Noise Ratio (SNR) and waveform coherence. Evaluations on real‐world DAS data further confirm the robustness of our method, positioning it as a valuable tool for analyzing large‐scale DAS data sets in various geoscientific contexts. This approach, which we refer to as “CoherentNoise2Self” to emphasize the extension of Noise2Self to coherent noise, advances the capabilities for near real‐time monitoring and analysis of seismic DAS data.