Thread: 2019 SSA Session - Large Data Set Seismology

Started: 2019-01-09 17:09:15
Last activity: 2019-01-09 17:09:15
Topics: SSA Meetings
Chad Trabant
2019-01-09 17:09:15

Dear Colleagues,

Have you ever processed large volumes of seismic data that required non-traditional strategies? Have you used HPC or cloud resources or new technologies and frameworks to orchestrate advanced workflows for such processing. If so, please consider submitting an abstract to our session at the upcoming SSA Annual Meeting April 2019 in Seattle, Washington to share your experiences. The abstract deadline is this Friday, January 11th.

Large Data Set Seismology: Strategies in Managing, Processing and Sharing Large Geophysical Data Sets

As seismology grows increasingly data rich, studies are being designed that use ever larger volumes of available data. The strategies for collecting, processing and sharing these data are evolving accordingly. In cases when the traditional research pattern of downloading, managing and processing data locally becomes untenably slow, new approaches are required. These strategies may include employing a compute cluster, either operated by a research group, an institutional HPC resource or a cloud computing provider. Researchers may use new technologies and frameworks to orchestrate more advanced processing workflows aimed at large scale computation, e.g. Hadoop. Furthermore, they may employ stream processing, where data are processed as it is collected from a center, thus mitigating the local storage issues. Ultimately, working with large data sets challenges researchers to be more informed and deliberate about computation, data transmission, compression and storage. This shift in data processing scale has a number of implications for both data providers and research processing pipelines and a variety of approaches are being used to address these changes. We invite researchers and data providers to describe their experiences in collecting, managing and processing large data sets.

Conveners
Jonathan K. MacCarthy, Los Alamos National Laboratory (jkmacc<at>lanl.gov <jkmacc<at>lanl.gov>)
Chad Trabant, IRIS Data Services (chad<at>iris.washington.edu <chad<at>iris.washington.edu>)
Page built 06:39:37 | v.fa0ab3d7