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
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
Open Source DataTurbine Initiative
http://www.dataturbine.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)
DataTurbine: Generalized Architecture
DataTurbine GoogleEarth Plug-in
Credit Matt Miller, Creare Inc.
System Architecture
Open Scalable, Modular architecture based on OGC-SWE standards
Real-World Deployments
 GLEON
 CREON
 Animal Tracking
 Earthquake Engineering
 Smart Buildings
 NASA etc. etc.
Open Ocean
Modeling
and Forecast
Prediction
Online
Offline
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
Observation Network in Indian Ocean (Earthquake & Sea Level)
Seismic Stations
National
International
Tsunami Buoys
National
International
Tide Gauges
National
International
Tsunami and Storm Surges
Observational Network
Infrastructure Details
Seismic Network
Bottom Pressure Recorders
Tide Guages
Complementary
Observations
Dharamshala
Samla
Dehradun
DELHI
Shillong
OKH KAND
VADINA
LA
A
PORABAN
GARPIP
DAR
VERA AVMAGD
ALLA
VAL
Bokaro
Bhuj
TB11
TB12
Bhopal
Pune
Vishakapattinam
HYDERABAD
INDIA
TB10
TB9
TB8
Goa
TB7
Chennai
Thiruvananthapuram
Minicoy
Diglipur
P
ort
Bl
air Cam
pbell
Bay
TB6
TB5
TB4
TB2
TB1
TB3
GARDE
N
CHAND
REACH
PARADIP
IPUR
DIAMON
(+1)
D
MUMBAI
VISHAKHAPA
ARBOU
(+1) JAIG
TNAM
MACHALIPA
R (+1)
ARH
TNAM
KAKINNIZAMPA
HALDIA
GOA
KAR
ENNO
ADA TNAM
(+1)
(+1)
CHENNA
RE
AERIALSAGAR
WAR
PONDICH
RANGAT
MANGALO
I (+1)
PORT
BAY
ANDR
ERRY
NAGAPATN
BAY (2)
RE (+1)BEYP
BLAIR
OTH
KAVARATT ORE
AM (+1)
RAMESH
(1+2)
TUTICORI
COCHIN
I (+1)
WARAM
N
(+1)
MINIC
(+1)
KANNIYAK
NANCO
OY VIZHIN
CAMPBEL
UMARI TIDE GAUGE
WRY
JAMEXISTING
L BAY
STATIONS
PROPOSED TIDE GAUGE
STATIONS
Network of 17 Seismic
stations
with
Central
Receiving Stations at
IMD Delhi and INCOIS,
Hyderabad for monitoring
the seismic activity
Network of 12 Deep
Ocean Assessment and
Reporting
Systems
(DOARS) for detection of
Tsunami Waves
Network of 50 Tide
Gauges for monitoring the
progress of Tsunami
Waves
5 Coastal Radars
2 Current Meter
Moorings
26 Surface Drifters
2 XBT Lines
Surface, Met-Ocean
observing platforms
Observations from
other Systems on
Internet
Buoy under Lab Test
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
Generation
Database
of
Scenarios
Seismic Deformation
Propagation
Bathymetry
Coastal Topography
Run up Heights
and
Inundation
GLOBAL RELATIONS BETWEEN SEISMIC FAULT PARAMETERS AND
MOMENT MAGNITUDE OF EARTHQUAKES – Papazachos B C, etal
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
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.
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.
People and groups in GLEON
15
GLEON 1
San Diego USA
March 2005
GLEON 4
Lammi FI
March 2007
GLEON 2
Hsinchu TW
October 2006
GLEON 3 Townsville AU
March 2006
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
Status of DataTurbine
GLEON Deployments
Freeway Serial Radio Link
Cellular Link
Lake Erken, Sweden Northern Temperate Lake, Wi
Thanks to GLEON community!
Lake Sunapee, NH
Coral Reef Environmental Observatory Network
(CREON)
GBR
Taiwan
UCSB
NOAA
Source: Stuart Kininmonth, AIMS
Source : Fang-Pang Lin, NCHC
http://www.coralreefeon.org/
Network of Underwater Cameras at Kenting
Collaboration with
NCHC, Thanks to
Fang-Pang Lin,
Ebbe, and other staff
members
Screen Capture of Acquired Video
streams via RDV
Integration with Tile Display Wall (TDW)
TDW at UCSD showing real-time streaming data from
underwater cameras at Kenting
Moorea Coral Reef Deployment
Tsunami Detection at MCR
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