Repeating Earthquake Detection final report

billhowe edited this page Sep 1, 2014 · 11 revisions

Key Personnel

  • Project Lead: Alicia Hotovec-Ellis, Graduate Researcher, Earth and Space Sciences
  • Advisor: John Vidale, Professor, Earth and Space Sciences
  • eScience Liaison: Jake Vanderplas, Director of Research - Physical Sciences, UW eScience Institute

Final Report

In this project, we aimed to provide an open-source tool for seismologists to cluster repeating earthquakes in continuous data. The primary focus was to do this in near real-time as part of network operations (e.g., for the Pacific Northwest Seismic Network (PNSN)), but also have the flexibility to work with archived data. Most processing of repeating earthquakes requires a priori knowledge of what the earthquakes look like, which is not possible in real-time.

In REDPy, we automatically detect and associate each new potential repeating earthquake. This is possible through an online clustering algorithm (IncOPTICS: Incremental Ordering Points To Investigate the Clustering Structure, Kriegel et al.), allowing us to more efficiently reduce the number of required calculations as the catalog grows. OPTICS also allows us to have flexible definitions of a cluster, and has minimal restrictions on how separated in time two repeats may be. We also utilize a database-like structure using PyTables to efficiently store and recall data.

The code is still in development, however, we have already made some strides in facilitating new research at Mount St. Helens. Once the code is completed and running at the PNSN, we will have increased automated monitoring of the active volcanoes in the Cascades. We also plan to share the code as an open source package to the rest of the seismological community, with the hope that it will offer a more standardized way of identifying repeating earthquakes in large datasets.

Figure 1 (below) is an example of a small test dataset from the beginning of the 2004 eruption of Mount St. Helens. We know from previous research that many, but not all, of the earthquakes during this time period are nearly identical to each other. Figure 2 is one of the outputs of REDPy, visualizing the identified repeating earthquakes in an ordering that makes clusters more visible. For example, the top ~50 rows comprise a cluster of highly similiar earthquakes, and a few smaller clusters are below that.

"Helicorder" plot of raw seismic data

Figure 1. "Helicorder" plot of six hours of raw seismic data during the lead-up to a volcanic eruption. Each line is 15 minutes worth of data, and each blip is a potential repeating earthquake we seek to automatically identify.

"Wiggle" plot, ordered with OPTICS

Figure 2. "Wiggle" plot of identified repeating earthquakes. Each row in the image corresponds to a single earthquake, white is zero and red/blue is negative/positive amplitude. The rows are ordered with OPTICS such that similar events are near each other in the ordering, and appear as vertical stripes where crests and troughs align.

We are continuing to develop the code and address some of the original goals in the proposal. We anticipate a publication in Seismological Research Letters as an advertisement for the code when it is scaling more efficiently.

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