Putting the Commercial Cloud to Work for Seismology
Date: 4/25/2019
Time: 04:45 PM
Room: Elliott Bay
While most types of research in seismology still fit comfortably into a desktop computer, data-hungry algorithms such as quality assessment, signal detection, noise correlation, and certain kinds of machine learning can quickly become impractical over large data volumes. Traditionally approaches often involve prolonged data acquisition from a data center and substantial storage, but the commercial cloud offers an alternate workflow that has the possibility to accelerate data collection and analysis. We report on a streaming workflow for seismology, in which data are requested on-the-fly and not stored, that leverages private clusters in Amazon Web Services EC2 and the scientific Python ecosystem.
Presenting Author: Jonathan K. MacCarthy
Authors
Jonathan K MacCarthy jkmacc@lanl.gov Los Alamos National Laboratory, Los Alamos, New Mexico, United States Presenting Author
Corresponding Author
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Omar Marcillo omarcillo@lanl.gov Los Alamos National Laboratory, Los Alamos, New Mexico, United States |
Putting the Commercial Cloud to Work for Seismology
Category
Large Data Set Seismology: Strategies in Managing, Processing and Sharing Large Geophysical Data Sets