Open Source DataTurbine for Tsunami Detection in Indian Ocean and other Environmental Observing Systems

1
Open Source DataTurbine for Tsunami Detection in
Indian Ocean and other Environmental Observing
Systems

• Sameer Tilak, Tony Fountain, Peter Shin, Brian
  McMahon, ArunAgarwal, K. V. Subbarao, Peter
  Arzberger

2
Streaming Data Middleware

• Common programming layer for real-time systems
• Enables integration of real-time components
• Provides abstractions over vendor-specific
  products
• Supports in-network processing (buffering, time
  synch )
• Make data streams first class objects
• Addressable
• Efficient operations
• Monitoring, QA/QC
• Event detection
• Replication and subscription
• Reliable transport

3
Open Source DataTurbine Initiativehttp//www.data
turbine.org

• In-network buffered data management and
  archiving for streaming data
• Scalable support for in-network intelligent
  routing, data processing, filtering, and topology
  management
• Robust bridge environment between diverse data
  sources and distributed data destinations
• Optimized for high-speed streaming data
• All-software solution (Java)
• Used in NSF, NASA, NOAA, DOE projects
• Developed by Creare Inc., http//www.creare.com/
• OPEN SOURCE SOFTWARE - Apache 2.0 License, Jan
  07
• NSF support from SDCI program (funding started
  on Sept 07)

4
DataTurbine Generalized Architecture
5
DataTurbine GoogleEarth Plug-in
Credit Matt Miller, Creare Inc.
6
System Architecture
Open Scalable, Modular architecture based on
OGC-SWE standards
7
Real-World Deployments

• GLEON
• CREON
• Animal Tracking
• Earthquake Engineering
• Smart Buildings
• NASA etc. etc.

8
Modeling and Prediction
Open Ocean Forecast
Online
Offline
9
Tsunami Sensors

• Incois uses data streams from tide gauges, bottom
  pressure readers (BPRs), and seismic stations to
  detect possible tsunami activity
• Potential events are checked against
  precalculated mathematical models to aid in
  decision making
• Integrating all of this data into a single
  DataTurbine server that can be mirrored and used
  for event detection

10
(No Transcript)
11
Tsunami and Storm Surges Observational Network
Infrastructure Details
Seismic Network
Bottom Pressure Recorders
Tide Guages
Complementary Observations

• 5 Coastal Radars
• 2 Current Meter Moorings
• 26 Surface Drifters
• 2 XBT Lines
• Surface, Met-Ocean observing platforms
• Observations from other Systems on Internet

Network of 12 Deep Ocean Assessment and
Reporting Systems (DOARS) for detection of
Tsunami Waves
Network of 17 Seismic stations with Central
Receiving Stations at IMD Delhi and INCOIS,
Hyderabad for monitoring the seismic activity
Network of 50 Tide Gauges for monitoring the
progress of Tsunami Waves
Buoy under Lab Test
12
Tsunami Modelling for Operational Early Warning
Epicenter (Assumed Epicenters) Depth of Fault
Top Edge (0, 20, 40, 60, 80, 100) Magnitude (5.5,
6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5) Fault
length (log L 0.55 M 2.19) Fault width (log
W 0.31 M 0.63) Displacement (log D 0.64 M
2.78) Strike angle (Parallel to Trench Worst
Case) Dip angle (45 deg Worst Case) Slip angle
(90 deg Worst Case)
Tsunami N2 Model
Database of Scenarios
Generation
Seismic Deformation
Propagation
Models Cannot be run during the event due to
large computing time and non-availability of
Fault Parameters in real-time from Seismic Wave
Form Data Hence for Tsunami Forecasting,
database of pre-run scenarios is essential
Bathymetry
Run up Heights and Inundation
Coastal Topography
GLOBAL RELATIONS BETWEEN SEISMIC FAULT PARAMETERS
AND MOMENT MAGNITUDE OF EARTHQUAKES Papazachos
B C, etal
13
PRIME student at Univ. of Hydebrad

• Set up a DataTurbine server at INCOIS with their
  tide gauge, bottom pressure reader (BRP) and
  seismic data streams feeding into it as sources.
• This server is mirrored to a DataTurbine server
  at the University of Hyderabad, where RDV is used
  to view the real time sensor data from INCOIS.
  Goal is to automate the process.
• Test to prove the setup is working.

14
Accomplishments

• Set up DataTurbine server at INCOIS and UoH
  (mirrored)
• Developed parser for various sensors. Real-time
  data acquisition and processing system was
  deployed at INCOIS for a variety of sensors
  including NOAA data.

15
People and groups in GLEON
GLEON 4 Lammi FI March 2007
GLEON 1 San Diego USA March 2005
GLEON 2 Hsinchu TW October 2006
GLEON 3 Townsville AU March 2006
16
A Typical GLEON Site Infrastructure
Portable Lake Metabolism Buoy North Temperate
Lakes LTER Wisconsin
Instrumented Platforms make high frequency
observations of key variables and send data to
the field-station
17
Status of DataTurbine GLEON Deployments
Freeway Serial Radio Link
Cellular Link
Lake Sunapee, NH
Lake Erken, Sweden
Northern Temperate Lake, Wi
Thanks to GLEON community!
18
Coral Reef Environmental Observatory Network
(CREON)
NOAA
GBR
UCSB
Taiwan
Source Stuart Kininmonth, AIMS Source
Fang-Pang Lin, NCHC
http//www.coralreefeon.org/
19
Network of Underwater Cameras at Kenting
Collaboration with NCHC, Thanks to Fang-Pang Lin,
Ebbe, and other staff members
20
Screen Capture of Acquired Video streams via RDV
21
Integration with Tile Display Wall (TDW)
TDW at UCSD showing real-time streaming data from
underwater cameras at Kenting
22
Moorea Coral Reef Deployment
23
Tsunami Detection at MCR
24
Acknowledgements

• INCOIS staff members, India
• University of Hyderabad, India
• Open Source DataTurbine Initiative Team and
  community
• Funding Agencies
• NSF
• Gordon and Betty Moore Foundation
• GLEON, CREON, communities
• Corporate Partners