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 datasets 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.

Characterizing the large M4.7+ seismic events during the 2018 Kīlauea eruption is important to understand the complex subsurface deformation at the Kīlauea summit. The first 12 events (May 17 - May 26) are associated with long-duration seismic signals and the remaining 50 events (May 29 - August 02) are accompanied by large-scale caldera collapses. Resolving the source location and mechanism is challenging because of the shallow source depth, significant non double-couple components, and complex velocity structure. We demonstrate that combining multiple geophysical data from broadband seismometers, accelerometers and infrasound is essential to resolve different aspects of the seismic source. Seismic moment tensor solutions using near-field summit stations show the early events are highly volumetric. Infrasound data and particle motion analysis identify the inflation source as the Halema’uma’u reservoir. For the later collapse events, two independent moment tensor inversions using local and global stations consistently show that asymmetric slips occur on inward-dipping normal faults along the northwest corner of the caldera. While the source mechanism from May 29 onwards is not fully resolvable seismically using far-field stations, infrasound records and simulations suggest there may be inflation during the collapse. The summit events are characterized by both inflation and asymmetric slip, which are consistent with geodetic data. Based on the location of the slip and microseismicity, the caldera may have failed in a ‘see-saw’ manner: small continuous slips in the form of microseismicity on the southeast corner of the caldera, compensated by large slips on the northwest during the large collapse events.