International Federation of Digital Seismograph Networks

YL (2021-2022): MangyStaU ARray (MSUAR) Feasibility Study

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FDSN Network Information

FDSN code YL (2021-2022) Network name MangyStaU ARray (MSUAR) Feasibility Study (MSUAR Study)
Start year 2021 Operated by
  • Michigan State University (MSU), United States of America
End year 2022 Deployment region -

Portions of the Mangystau region of western Kazakhstan are exceptionally quiet from a seismological point of view and have excellent detection of events from local to teleseismic distances. The region seems an excellent candidate for the installation of a high quality seismic array that would improve quantitative seismological monitoring for regions of interest in Eurasia. However, the initial study of the noise characteristics of the region conducted in August 2016 was limited in time, and prior to installation of a permanent array, it is important to evaluate the seasonal variability of noise levels and detection thresholds. The MangyStaU ARray (MSUAR) Feasibility Study will accomplish this goal. Additionally, this study will further refine the optimal array location considering year-round noise levels, infrastructure, and site servicing requirements. For the MSUAR Feasibility Study, 16 broadband seismic stations will be installed in the Mangystau region of western Kazakhstan, roughly between the Caspian and Aral Seas. Stations will be installed for approximately one year to allow full evaluation of seasonal noise and detection thresholds. Installations will be concentrated in the region of previously determined quiet sites (Figure 1). Ten general locations will be installed. At two of the ten installations, we will install 4-element mini-arrays consisting of a central station surrounded by three additional stations at distances of several hundred meters. The intent of this is to allow some basic array processing and gain insight on potential array performance. We may relocate the surrounding array elements to other installed stations during the experiment if warranted by data.

Citation Information

Digital Object Identifier (DOI) 10.7914/SN/YL_2021
For more: DataCite ( JSON | XML | BibTeX )

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