SEE-GRID-SCI Seismo logy VO

1. SEE-GRID-SCI Seismology VO Can Özturan Seismic VO Leader The SEE-GRID-SCI initiative is co-funded by the European Commission under the FP7 Research Infrastructures contract no. 211338

2. Background/Overview • Objectives: • Gridify some applications • Serve seismic data that is mirrored from national seismology centres using a high level interface that is easy to use/adapt. • Seek collaboration with other seismology related groups/organizations • There exists other organizations/projects (e.g.) : • ORFEUS, NERIES • In SEEGRID-SCI, we should avoid avoid duplicate work and contribute complementary work • Differentiate ourself by : • The grid platform • Performance aspects – (high performance computing, high performance access to massive data)

3. Background/Overview • High level interface to data: avoid requiring the users of seismic data to: • learn a lot of new tools • write data location dependent code • modify existing applications drastically • NERIES (closely related project): • Based on Web platform (provide data though portal and web services) • More comprehensive/larger scope

4. Collaborating Organizations

5. Seismology VO Platform Form of data: • In Seismology VO, as data, • (i) official lists of earthquakes and stations, and • (ii) massive seismic waveform data from various South Eastern European countries • are planned to becollected and served. • To realise the aims of the Seismology VO, the following are carried out: • Distributed storage of seismic data from different partner countries, • 2. Logical organization, indexing and update of distributed seismic data, • 3. Programming tools that will provide easy access to seismic data, • 4. Gridification of various seismology applications: SRA, NMMC3D, FPS, ELF, • MDSSP-WA ,and SDS Time 2005 2002 2004 2003 Station1 Station 2 Station 3 Station 4

6. Seismology VO Platform Earthquake and seismic waveform data (country 2) Earthquake and seismic waveform data (country n) Earthquake and seismic waveform data (country 1) Applications Programming Tools(C++Iterators) Distributed storageand indexing of data on grid (using distributed storage elements, LFC and AMGA)

7. Applications

8. SEISMIC RISK ASSESMENT (SRA) • Seismic Risk Assessment is very important for public safety and hazards • mitigation. • It is also important for the correct determination of earthquake insurance • premiums and also for understanding the social and psychological • effects of earthquakes. • Our aim is to develop an application framework to allow us to embed • alternative (deterministic, probabilistic etc.) models. • SRA application can be grouped into four main categories: • (i) Accessing Earthquake Catalogue, • (ii) Earthquake Source Model • (iii) Seismic Hazard Models • (iv) Producing Seismic Hazard Maps

9. Numerical Modeling of MantlE Convection – NMMC3D • The outer part of the Earth consist of moving, rotating and interacting • plates. • The motion of these plates suggest a large convective system in the • Earth's 2900 thick layer, the mantle. • The numerical calculations suggested that the convective cells are • formed by sheet-like elongated downwellings (subduction zones) and • narrow, cylindrical upwellings (mantle plumes, at the hotspots). • The main goal of our research is the quantitative study of the structure • and surface manifestation of mantle plumes and to make systematic • investigation of the parameters influencing the character of mantle • convection in 3D.

10. Fault Plane Solution (FPS) • Computes earthquake source parameters (strike, slip, dip) • Inputs: • Crust model: layer thicknesses, seismic velocities, densities, q-factor • Actual seismic waveform data (in SAC format) • Output: • Fault paramtheters • Useful for identifying tectonic structures that are not visible on earth’s surface • Computationally intensive application • A typical run that uses data from 50 stations takes 8 hours on a PC • Implemented in Fortran/C

11. Massive Digital Seismological Signal Processing with the Wavelet Analysis(MDSSP-WA) • Wavelet theory has matured in past years as new mathematical tool for time series analysis. • The continuous or discrete wavelet transforms and relevant plotting of the results in coordinate • system, scales versus time, shows striking similarity of the wavelet images, between different seismic • records, coming from the same source region ornoticeabledifference for records of earthquakes • occurred in different source region. • We assume that, those similar image patterns are due to same underlying geological setting while the • differences (usually for smaller scale) is due to different source mechanism and finer geological • structures. • In the first approximation of geological structure, similarities of the image patterns in domain of large • scale are noticeable even for the records from different source regions. • With massive processing of earthquake records we can define: (i) Common features of the • propagation path for the given seismic source region or to define empirical transfer function of the • media (ii) Calculation of the artificial seismograms, (iii) Determine the source region based on a • single earthquakes record (iv) Determine the more realistic attenuation curve of the selected feature • (parameter), very much needed in seismic hazard and risk analysis, (v) Mapping (coding) of the given • earthquakeprone region in terms of selected parameters (vi)Seismic source parameters

12. Earthquake Location Finding (ELF) • This application is based on HYPO71 and finds the location of earthquakes by scanning • seismic waveform data. • This application is not compute intensive , but it is data intensive.The application can • be parallelized by scanning data files in parallel by multiple using worker nodes. • A workflow can be generated automatically by a program corresponding to the time • intervals in which to look for earthquake

13. Seismic Data Server (SDS) Application Service • SDSAS is a JRA1 service that serves massive seismic data that are archived from national seismology centers using a high level interface that is easy to use/adapt. It servesofficial lists of earthquakes, stations , sensor information. • Itkeeps the details of where the data files reside are hidden by mapping high level user specifications (dates, hours, location etc.) to appropriate pathnames. • The SDSAS implementation will be done by using scripts to collect and organizing the seismic data by utilizing storage elements, LFC and AMGA . • C++ iteratorscan be used by applications to access station data, earthquake data and information about seismic waveform files.

14. Seismic Data Server (SDS) Application Service • Example • #include "sds.h" • #include <iostream> • using namespace sds ; • main() • { • SDS_Initsdsinit ; • SDS_Date_Rangedr( SDS_Date(2003,Jan,10), • SDS_Date(2003,Feb,11) ) ; • SDS_Quakes q(SDS_All,dr) ; • SDS_Quakes_Iteratorqend = q.end() ; • for (SDS_Quakes_Iteratori = q.begin(); i != qend ; i++) { • cout << (*i).latitude << endl; • } • q.kml("kmlfile") ; • }

15. SDS Web Interface • Serving of seismic data present in AMGA tables (station data, earthquake data and • information about seismic waveform files - not waveform files themselves) • through a web interface that utilizes kml and Google Earth api.