Volcanic earthquake catalog enhancement using integrated detection, matched-filtering, and relocation tools

Frontiers in Earth Science

Earth Science

Darren Tan¹  David Fee¹  Társilo Girona¹  Matthew M. Haney²  John A. Power²  Alicia J. Hotovec-Ellis³  Jeremy D. Pesicek⁴ 

[1] Geophysical Institute, Alaska Volcano Observatory, University of Alaska Fairbanks, Fairbanks, AK, United States;United States Geological Survey, Alaska Volcano Observatory, Anchorage, AK, United States;United States Geological Survey, California Volcano Observatory, Moffett Field, CA, United States;United States Geological Survey, Volcano Disaster Assistance Program, Vancouver, WA, United States;

关键词: volcano seismology;    volcano monitoring;    matched filter;    relative relocation;    cross correlation;    Redoubt Volcano;    Augustine Volcano;    Alaska;   

DOI  :  10.3389/feart.2023.1158442

 received in 2023-02-03, accepted in 2023-04-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

Volcanic earthquake catalogs are an essential data product used to interpret subsurface volcanic activity and forecast eruptions. Advances in detection techniques (e.g., matched-filtering, machine learning) and relative relocation tools have improved catalog completeness and refined event locations. However, most volcano observatories have yet to incorporate these techniques into their catalog-building workflows. This is due in part to complexities in operationalizing, automating, and calibrating these techniques in a satisfactory way for disparate volcano networks and their varied seismicity. In an effort to streamline the integration of catalog-enhancing tools at the Alaska Volcano Observatory (AVO), we have integrated four popular open-source tools: REDPy, EQcorrscan, HypoDD, and GrowClust. The combination of these tools offers the capability of adding seismic event detections and relocating events in a single workflow. The workflow relies on a combination of standard triggering and cross-correlation clustering (REDPy) to consolidate representative templates used in matched-filtering (EQcorrscan). The templates and their detections are then relocated using the differential time methods provided by HypoDD and/or GrowClust. Our workflow also provides codes to incorporate campaign data at appropriate junctures, and calculate magnitude and frequency index for valid events. We apply this workflow to three datasets: the 2012–2013 seismic swarm sequence at Mammoth Mountain (California), the 2009 eruption of Redoubt Volcano (Alaska), and the 2006 eruption of Augustine Volcano (Alaska); and compare our results with previous studies at each volcano. In general, our workflow provides a significant increase in the number of events and improved locations, and we relate the event clusters and temporal progressions to relevant volcanic activity. We also discuss workflow implementation best practices, particularly in applying these tools to sparse volcano seismic networks. We envision that our workflow and the datasets presented here will be useful for detailed volcano analyses in monitoring and research efforts.

【 授权许可】

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Copyright © 2023 Tan, Fee, Hotovec-Ellis, Pesicek, Haney, Power and Girona.