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SeisHub - Archiving, processing and simulation of multi-component data in seismology using Web Services

Overview

Data volumes in observational and computational seismology are rapidly expanding. This is due in part to the ever increasing amount of continuous data from global, regional and local permanent stations and in part to large scale experiments. A new contributor of growing importance, however, is the option to generate virtual observational data from highly accurate numerical simulations, which need to be stored with the same priority as real observations.

Furthermore, seismology is going beyond data reduction (e.g., by extracting travel times or surface wave phase velocities) towards complete waveform processing and simulation. It is commonly accepted, today, in the seismological community that the suite of database and processing tools developed in the past decade is now outdated and novel approaches are required.

In this pilot project we intend to develop a new paradigm with the intention to closely link data archiving, waveform processing and simulation infrastructure putting a strong emphasis on the field of seismology. These developments are carried out in close collaboration with major ongoing international projects in seismology (NERIES, SPICE, CIG, SCEC) as well as national initiatives (e.g., WebDC, GFZ Potsdam) and data centers (BGR/SZGRF, ORFEUS, IRIS). While the focus of those projects lies in the handling of real-time observations, automatic processing of large data sets and/or the provision of computational wave propagation algorithms, the main complementary developments envisaged in this project are:

  • an open source, modular, multi-component database with access to observational infrastructure (partly provided by NERIES);
  • a link between database, multi-component processing tools and executable simulation algorithms (provided by SPICE); and
  • the development of formats and standards to combine the joint storage and processing of observations and simulations (partly provided by FDSN and IRIS).

Acknowledgements

This project is funded by the German Science Foundation (DFG) via grant DFG IG 16/9-1.

by last modified 12. Apr 2007 12:50
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Printed 25. Feb 2020 20:07