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Abstract By providing unrivaled resolution in both time and space, volcano seismicity helps to chronicle and interpret eruptions. Standard earthquake detection methods are often insufficient as the eruption itself produces continuous seismic waves that obscure earthquake signals. We address this problem by developing an earthquake processing workflow specific to a high‐noise volcanic environment and applying it to the explosive 2008 Okmok Volcano eruption. This process includes applying single‐channel template matching combined with machine‐learning and fingerprint‐based techniques to expand the existing earthquake catalog of the eruption. We detected an order of magnitude more earthquakes, then located, relocated, determined locally calibrated magnitudes, and classified the events in the enhanced catalog. This new high‐resolution earthquake catalog increases the number of observations by about a factor of 10 and enables the detailed spatiotemporal seismic analysis during a large eruption.
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Volcanic earthquake catalog enhancement using integrated detection, matched-filtering, and relocation tools
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.
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- Award ID(s):
- 1855126
- PAR ID:
- 10474298
- Publisher / Repository:
- Frontiers in Earth Science
- Date Published:
- Journal Name:
- Frontiers in Earth Science
- Volume:
- 11
- ISSN:
- 2296-6463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary The matched-filter technique is an effective way to detect repeats, or near-repeats, of a seismic source, but prior identification of an event from that source to use as a template is required. We propose a recursive matched-filter approach to systematically explore earthquake swarms, here applied to a swarm of volcanic long-period seismicity beneath Mount Sidley in Antarctica. We start with a single visually chosen template event with a high signal-to-noise ratio. We then extend our template database by selecting new templates to use in a subsequent matched-filter search from the newly detected set of events, allowing us to recursively expand the number of templates. We demonstrate that each iteration of the matched-filter search progressively extends the spatial coverage of our set of templates away from the original template event. In such a way, our proposed method overcomes the matched-filter search’s strictest constraint: that an event must already be identified to detect other similar events. Our recursive matched-filtering approach is well suited for the systematic exploration of earthquake swarms in both volcanic and tectonic contexts.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/feart.2023.1158442
