Data Challenges in Seismology
Luca Trani, Alessandro Spinuso, Torild van Eck
What data?
Parameter data, event data, historical data
significance: public information, hazard, research, …
organisations/projects:
EMSC, GEM (EUCENTER/ETHZ-SHARE),
.
Waveform data
significance: basic research, exploration, hazard, …
Organisations/projects:
ORFEUS, MEREDIAN, NERIES, NERA, EPOS, …
- high (dynamic range) quality (BB)
- Short Period (many local networks)
- Special: Arrays, boreholes, etc.
- Analogue recordings (older systems)
- Acceleration (seismology and engineering)
Data challenges in Europe
Gathering, Archival, Access and Exchange
Facilitate data integration
- modelling
- multidisciplinary data
Analysis and Interpretation tools
Some basic constraints to overcome:
-
Geography / distributed observations
Technical
Financial
Political boundaries
Data open accessible for research
CTBTO International Monitoring System (IMS)
Data available for NDC and only restrictively open for research
Broadband Stations in Europe and surroundings
Observatories:
>100 networks
Integrated
data access:
~ 50%
Funding:
National public,
Hazard/Risk,
Projects.
Occasionally
Research
No EU funds!
Political aspects on
data exchange:
Middle East
Russia
Northern Africa
Comparing Europe and
its surroundings with
Northern America.
Seismological RI
Temporary deployments
2000 - 2005
Sources:
IRIS-DMC, GFZ,
SEIS-UK, UJF
Comparing Europe and
its surroundings with
Northern America.
Seismological RI
Temporary networks
Deployments 2000 - 2010
Sources:
IRIS-DMC, GFZ,
SEIS-UK, UJF,
TopoIberia
Integration of small (special purpose networks)!
near-fault observatory initiatives in NERA
Valais
South Iceland Seismic Zone
Marmara Sea
Alto Tiberina
Irpinia Fault System
Corinth Rift
Data Integration:
Observations, modelling, GPS, SAR, geology…
EPOS initiative
www.epos-eu.org
Modelling, topography, geology
Horizontal and vertical displacements
Aftershock locations
Source:
INGV,
ASI-SIGRIS
Seismic hazard modelling and
time varying seismic hazard
Global seismic hazard model
www.globalquakemodel.org
Time varying seismic hazard estimates
Data Integration:
Collection of data services and data discovery tools
NERIES web portal
www.seismicportal.eu
Data Integration:
Web-based seismic waveforms analysis
Distributed
Data and resources
Source:
RapidSeis,
Edinburgh Univ,
Liverpool Univ.
The need for e-infrastructures
l Gigantic Earth Science Data Volumes require the development
of new approaches to web-based data and model exchange,
data mining and visualization
(500 seismometers yield ≈17 GB/day and 6.2 TB/year)
l “Virtual Earth Laboratory” - Hypothesis testing will make
increasingly use of high-performance simulation technology of
Earth’s dynamic behaviour
l “software is infrastructure” – scientific simulation technology
needs to be adapted and maintained for wide use by the
community
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The need for e-infrastructures
l “data rich” Elements: Web-based superstructure linking Earth
Science Data Centres, standardize multi-disciplinary data and
model exchange
l “cpu rich” Elements: Simulation and processing technology
needs to be professionally engineered, linked to the European
High-Performance Computing infrastructure and the scientific
data infrastructure
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EPOS: data life cycle
Derived Data (Level II)
Seismic picks, amplitudes,
automatic Magnitudes
Moment Tensors
Users
Raw Data (Level I)
Data centres
Data archives
Model libraries
Level III - Data Processing,
Visualization Tools,
Simulation & modelling
libraries
Access
Data storage & processing + Web Portal infrastructure
EU HPC- Supercomputing Infrastructure
Grid
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22/11/10
Seismology: More data more discoveries?
Definitively yes!
Before 2000 only event digital data
 Earthquake and event oriented studies
After 2000 continuous digital data:
- Noise data used for detecting and identifying:

- Glacier movement and fracturing
- Velocity structures
- Large and small slow seismic events
- Non volcanic tremors
- Localised time varying velocities
- Earth hum (low frequency constant background noise)
Processing
Query params (time, sta, ch, ...)
Data Aggregator
Splitter
Data
Access
PE
SQL
Cloud – Grid
- HPC
PE
SQL
PE
SQL
PE WF Sychronization
PE WF Decimation
PE WF Filtering
These PEs are close to the
data
PEs implementation with
ObsPy
Working Together!