Curre nt T re nds a nd
De m a nds in V isua liza t ion
in t he Ge osc ie nc e s
Gordon Erle ba c he r
De pt . of M a t he m a t ic s
Florida St a t e U nive rsit y
T a lla ha sse e , FL 3 2 3 0 6 -4 1 2 0
Da vid A. Y ue n
Fa bie n Dubuffe t
M inne sot a Supe rc om put e r I nst it ut e
a nd De pt . of Ge ology a nd Ge ophysic s
U niv. M inne sot a
M inne a polis, M N 5 5 4 1 5 -1 2 2 0
To appear in Electronic Geosciences, also on www.msi.umn.edu/~heather
then click on the left electrongeo
ABST RACT
Geosciences, along with many other disciplines in science and engineering,
faces an exponential increase in the amount of data generated from observation,
experiment and large-scale, high-resolution 3-D numerical simulations. In this
communication we describe the fundamentals of visualization necessary to meet
these challenges. We present several alternative methodologies such as 2D/3D
feature extraction, segmentation methods, and flow topology, to help better
understand the physical structure of the data. We use AMIRA from TGS to
demonstrate our concepts. Examples are drawn from fields in computational fluid
dynamics, 3-D mantle convection and seismic tomography. Finally, we present our
perspective on the future of visualization.
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I nt roduc t ion
Today in the geosciences we are facing a very serious
problem of data flooding from very large data sets
produced by very high resolution numerical simulations ,
improved laboratory and experimental instrumentation, in
particular of terrain data gathered by satellites. For
instance, a state-of-the-art simulation in global
atmospheric circulation, mantle convection, geodynamo,
or 3-D earthquake modelling can easily generate up to one
to several gigabytes of data per time-step, because of the
many variables involved.
Terabytes of data can be produced each day from highresolution SAR data satellite coverage.. It is quite clear
that visualizing the data at full resolution is not a viable
option. In this communication we will discuss some
alternative routes to visualizing large data sets, not in full,
but by extracting and highlighting its salient features,
using currently available visualization packages.
Software ) can be employed to visualize high-resolution 3-D
mantle convection and seismic tomography.
Finally, we address the criteria for a successful visualization
system and comment on the future of visualization and its growing
multidisciplinary character.
Following the spirit of an online electronic journal, we will
present our ideas in the condensed format of overheads, where the
main ideas are encapsulated in bullet format, rather in long
paragraphs with complex wordy prose.
Our main objective is to find and reach an enthusiastic and eager
audience, who will avail themselves of the ideas presented herein
and will begin to apply these concepts to their own research and
/or educational endeavors.
First, we will discuss some of the fundamental paradigms
in visualization. This will be followed by a description of
an ideal visualization system suited to analysis and
comprehension of complex data sets drawn from diverse
disciplines, such as mantle convection, seismic
tomography, supersonic flows around aircraft and 3-D
biological structures. We will then discussed how the
volume-rendering algorithm implemented in the
commercial package AMIRA ( from Template Graphics
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We w ill …
Ø Discuss general visualization principles
Ø Some features of particular commercial visualization
package: Amira
Ø Visualization using Amira
We w ill not …
Ø Discuss specific algorithms
Ø Compare different visualization packages
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Pe rsona l ba c k ground
Gordon Erle ba c he r
Ø I have conducted research in Fluid Dynamics
and scientific visualization
§ Simulations of compressible transition and turbulence
§ Turbulence modeling
§ Numerical algorithms
Ø Work in Scientific Visualization
§ Vector fields
§ Interactivity
§ Distributed visualization
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Pe rsona l ba c k ground
Da vid A. Y ue n a nd Fa bie n Dubuffe t
ØWe are geophysicists
ØWe conducted large-scale numerical
simulations
ØWe are interested in the use of state-ofart techniques to help visualize and
understand 3-D numerical simulations in
the Earth’s mantle
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Possible Re se a rc h T opic s
in V isua liza t ion
Ø Visualization of time-dependent motion
Ø Change of topology
Ø Interactive feature extraction
Ø Interactive exploration
Ø Use of force feedback in visualization
Ø Handling of Multi-Gigabyte datasets
Ø Exploration of high-dimensional spaces
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Why V isua lize Da t a
Ø Numerical simulations and experiments produce
extremely large datasets
Ø The size of these datasets are increasing
exponentially fast
Ø Numerical output (e.g., tables) does not lend
itself to easy comprehension
Ø We need dynamical display of the fields for
unravelling new physics.
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Sc ie nt ific V isua liza t ion
Ge ne ra l Princ iple s
Ø Maximize comprehension
Ø Maximize information
Ø Maximize accuracy
Ø Minimize clutter
Ø Maximize interactivity
Ø I ndependence of underlying meshing
Ø Minimize program response time
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Sc ie nt ific V isua liza t ion
Ø Extract from large datasets more meaningful
components (called data extracts)
§ Isosurface, streamlines, streaklines, vector field
topology, vortex tubes, cracks, fault lines, etc.
§ Sedimentation layers, free-surfaces, edge and surface
extraction
Ø Render this data with comprehension of the
physics in mind, as opposed to visual realism
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Com put e r Gra phic s
M ode ling
input
input
Camera
Modeling
Geometric
Models
input
Light
Modeling
Rendering
output
input
Image Storage
and Display
Animation
Parameters
Textures
input
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T a x onom y
Ø Dimensionality of domain
§ 1D (x) à 4D (x,y,z,t)
§ N-D (e.g., phylogeny)
Ø Dimensionality of range R n
§ Scalar, vector, tensor fields
Ø Domain connectivity
§ (Un)Structured, points, graphs
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Dom a in Conne c t ivit y (2 D)
Cartesian
Tree
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Curvilinear
Unstructured
Graph
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Aux ilia ry V e rt e x Da t a
Ø Coordinates (2,3,or 4)
Ø Color
Ø Normals (for lighting)
Ø Temperature, conductivity, viscosity, etc.
Aux ilia ry Edge Da t a
Ø Heat Flux, forces
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V isua liza t ion in Ge osc ie nc e s
Com pone nt s
Ø Vector fields (velocity)
Ø Gradient fields (temperature gradient)
Ø Tensor fields (stress, strain-rate, anisotropic
energy spectra, momentum flux)
Ø Multiple scales in time and space
Ø Multi-domain, curvilinear and tetrahedral grids
Ø Time dependent structures (plumes, crack
propagation)
Ø Interactive data exploration in space and time
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V isua liza t ion H a rdw a re
De sire d Fe a t ure s
Ø Large framebuffer memory
§ Double buffering (smooth animation)
§ Stereo (left/ right buffers)
§ Z-buffering (hidden line removal)
x 1600x1200 frame with 32 bit color: 7.7 Mbytes
x Double buffering: 15 Mbytes
x Stereo: 30 Mbytes
x Even higher with alpha, stencil, z-buffers
§ Large texture memory
x
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Used by many modern visualization algorithms
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For $ 3 ,7 0 0 , N ove m be r,
2001 …
Dell Precision Workstation 530
Dual pentium: 1.7 Ghz cpu
1 Gbyte memory (400 Mhz)
21 inch screen
80 Gbyte disk
Read/ Write CD-rom (read/ write DVD is better)
Quadro2-Pro graphics card (can not handle dual
monitors and stereo)
Ø Linux/ Windows X
Ø
Ø
Ø
Ø
Ø
Ø
Ø
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I m m e rsive Environm e nt s
Ø PowerWalls and other large-scale displays
§ (PICTURE) 6’x8’ and larger
§ Rear or front projection
§ Enables 5-20 people to view and interact with the data
simultaneously.
§ Only one person controls the interaction
Ø Caves
§ Project in stereo onto 5 or 6 walls
§ Provides realistic display of data
§ Users interact using wands and other devices (one at a time)
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V isua liza t ion Algorit hm s
Ra nge T ype s
Ø Scalar
§ Isocontours (2D), isosurface (3D)
§ Volume rendering
Ø Vector
§
§
§
§
Streamlines, pathlines, streaklines
Line integral convolution (steady state)
LEA (Lagrangian-Eulerian Advection (time-dependent)
Critical points, vector field topology
Ø Tensor
§ Tensor field topology (symmetric and antisymmetric tensors)
§ Hyperstreamlines: streamlines along dominant eigenvector,
ellipsoidal cross-section normal to the streamline,
determined by other two eigenvalues
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V isua liza t ion Algorit hm
Cha lle nge s
Ø Strike balance between
§ High- resolution versus interactive speed
Ø How to do time-dependent visualization
Ø Describe and view change of data topology
§ Vector and scalar fields
§ Tensor fields
Ø How to navigate a Terabyte dataset?
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V olum e Re nde ring
Ø It is often difficult to choose isosurface values that
produce meaningful surfaces
Ø More often, it is a collection of isosurfaces that is
desired
Ø Need global techniques for complex datasets
Ø In the physical world
§ x-rays, translucent medium
Ø Solution
§ consider points of the physical domain as emitters and
absorbers of light
§ Composite points along rays through the volume to produce
final image
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V olum e Re nde ring
Ray casting
Texture compositing
Screen
Screen
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API s, Pa c k a ge s, T oolk it s
Ø Low Level Graphic APIs (OpenGL, Direct8X)
Ø Visualization APIs (Open Inventor)
Ø Visual Interfaces (Ensight, LightView)
Ø Flowcharting (OpenDX, Iris Explorer, Amira)
Ø Visualization Toolkits (VTK, NCAR)
Ø Free specialized Solutions (Vis5d)
Ø Commercial specialized solutions (Rivertools, …)
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Am ira
(from T GS)
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Am ira Fe a t ure s
Ø Flowcharts are created interactively by the user
Ø Each component has an associated user interface
Ø Software algorithms harness the latest graphic hardware
(SGI, Nvidia, ATI) to achieve good performance
Ø Flowcharts, called networks, can be saved for re-use
Ø Developer version allows users to create their own
modules for specialized visualization
Ø The user interface is based on Qt (free for academic
use); portable on wide array of architectures (including
PDA)
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Am ira Fe a t ure s
Ø Very Interactive
Ø Manipulators
§ Interact with the data
Ø Extensible
§ Users can write extension modules
§ API is very sophisticated
Ø Highly advanced algorithms
§ Isosurface, volume rendering, vector visualization, ima
§ Combinations of the above
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Ex a m ple
Ø Read in 3D file
Ø Generate several planar cross-sections
Ø Generate an iso-surface
Ø Generate a volumetric plot
Ø Combine techniques
Ø Query data (e.g., line cut, point probe)
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Ex a m ple s
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pow(x,3)+pow(y,3)-3*x*y+x*z+2*y*z*x
Opaque isosurfaces
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pow(x,3)+pow(y,3)-3*x*y+x*z+2*y*z*x
Transparent isosurfaces
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FEM a nd
3 D da t a visua liza t ion
Visualized with Amira
Vector Fields:
(courtesy TGS)
• illuminated field lines
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3 -D m a nt le c onve c t ion
2573 dataset
643
subsampling
Volume rendering of temperature fields
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Adiabatic heating distribution
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Local state of adiabaticity in convection
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Viscous heating distribution
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Viscous heating and illuminated streamlines
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Convection looking upward from the bottom.
Isothermal surface with planforms illuminated
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Isothermal surface with planforms
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Thermal fields volume-rendered with BOB
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3-D tomographic slice at 250 km depth
Taken from
Zhao,
EPSL,2001
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Zoom-in view of the whole mantle under Japan
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Integration of time-dependent vector field
animations within Amira using developer
version
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Interaction of a shock with a longitudinal vortex
Pressure isosurface and contours of pressure gradient
magnitude and Mach number.
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Interaction of a shock and a longitudinal
vortex (side view). Pressure isosurface,
velocity vectors.
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Interaction of a shock with a vortex ring. Pressure
isosurface, contours of density gradient magnitude
and Mach number.
Data: Ding, Hussaini, Erlebacher
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Shock interaction with an axisymmetric vortex
Data: Ding, Hussaini, Erlebacher
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Com put a t iona l St e e ring
a nd Co-proc e ssing
Ø Couple numerical simulations with scientific
visualization
Ø Drill down of image for data querying (i.e.,
visualizing metadata or underlying raw data)
Ø Raw data is often not on client: need robust
client/ server communication
Ø Would like to query a running simulation and
change its parameters (e.g., PV3, Cumulus,
SciRun, etc.)
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Fut ure of V isua liza t ion
Ø Visualization is/ has become multidisciplinary, involves
many fields.
Ø Successful visualization system must address
§
§
§
§
§
§
I/O
Maintainability
Flexibility (e.g., using plugins)
Accessibility (low cost and easy to use/ install)
Robust
Standardization
Ø The above features are not consistent with each other
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V isua liza t ion U biquit y
Ø Collaboration at a distance through visualization
Ø Office walls or ceilings become visualization
displays (E-Ink: thin, pliable medium capable of
electronic encoding)
Ø Exchange of visual data becomes as ubiquitous
as exchange of text documents and graphics in
2001
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An I de a l V isua liza t ion
Syst e m
Ø Reusable modules
Ø Flexible
Ø Ease of use
Ø Low memory
footprint
Ø Extensible
Ø Scriptable
Ø Good debugging
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Ø Portable
Ø Intelligent defaults
Ø Changeable
defaults
Ø Interpreted and
compiled modes
Ø Novice and expert
modes
Ø Mathematical text
editor
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Grid Se rvic e s
(Fox e t a l. 2 0 0 1 )
Ø Collaborative Portal
§ XML-based
§ Secure
Ø Coupling of
§
§
§
§
§
§
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Multi-scale numerical simulations / observational data
4D space-time domain (visualization)
Data mining
Efficient I/ O mechanisms
Computational Steering
Databases
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Se le c t e d Re fe re nc e s
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Geosciences
Balachandar, S., Yuen, D.A., and D. Reuteler, Viscous and adiabatic heating
effects in three-dimensional compressible convection at infinite Prandtl number,
Phys. Fluids, A vol.5 (11), 2938-2945, 1993.
Jordan, K.E., Yuen, D.A., Reuteler, D.M., Zhang, S. and R. Haimes, Parallel
interactive visualization of 3D mantle convection, IEEE Computational Science
and Engineering, Vol. 3 , No. 4, 29 - 37, 1996.
Matyska, C. and D.A. Yuen, Are mantle plumes adiabatic? Earth Planet. Sci. Lett.,
189, 165-176, 2001.
Yuen, D.A., Balachandar, S. and U. Hansen, Modeling mantle convection: A significant
challenge in geophysical fluid dynamics, in Geophysical and Astrophysical
Convection, , edited by P.A. Fox and R.M. Kerr, pp 257-293, Gordon and Breach
Publishers, 2000.
Zhao, D., “Seismic Structure and Origin of Hotspots and Mantle Plume”, Earth Planet.
Sci. Lett., 192, 251-265, 2001.
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Shock I nteractions
Ding, Z., Hussaini, M. Y., Erlebacher, G., and Krothapalli, G. A., "Self-Interaction of
Acoustic Wave due to Shock/ Vortex I nteraction," AI AA Journal, Vol. 38, No. 6, pp.
1002-1009.
Erlebacher, G., Hussaini, M. Y., and Shu, C.-W., "Interaction of a shock with a
longitudinal vortex," Journal Fluid Mechanics, Vol. 337, 1997, pp. 129-153.
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Vector and Tensor Field Visualization
Cabral, B. and Leedom, L. C., "Imaging Vector Fields Using Line Integral
Convolution," Computer Graphics Proceedings, ACM, 1993, pp. 263-269.
Forssell, L. K. and Cohen, S. D., "Using Line Integral Convolution for Flow
Visualization: Curvilinear Grids, Variable-Speed Animation, and Unsteady Flows,"
IEEE Transactions on Visualization and Computer Graphics, Vol. 1, No. 2, 1995, pp.
133-141.
Helman, J. L. and Hesselink, L., "Representation and Display of Vector Field
Topology in Fluid Flow Data Sets," Visualization in Scientific Computing 1989.
Jobard, B., Erlebacher, G., and Hussaini, M. Y., "Lagrangian-Eulerian Advection for
Unsteady Flow Visualization," Proceedings I EEE Visualization 2001, I EEE Computer
Society, New York, 2001.
Stalling, D. and Hege, H.-C., "Fast and Resolution I ndependent Line I ntegral
Convolution," Proceedings of SI GGRAPH '95, 1995, pp. 249-256.
Tricoche, X., Scheuermann, G., and Hagen, H., "Topology-Based Visualization of
Time-Dependent 2D Vector Fields,".
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Volumetric techniques
Kniss, J., Kindlmann, G., and Hansen, C., "I nteractive volume rendering using multidimensional transfer functions and direct manipulation widgets," Proceedings I EEE
Visualization 2001, I EEE Computer Society, New York, 2001.
Lichtenbelt, B., Crane, R., and Naqvi, S., I ntroduction to volume rendering, Prentice
Hall, New Jersey, 1998.
Lum, E. B., Ma, K.-L., and Clyne, J., "Texture Hardware Assisted Rendering of TimeVarying Volume Data," Proceedings Visualization 2001, IEEE Computer Society, New
York, 2001.
Levoy, M. S., "Display of Surfaces from Volume Data," Ph.D., Chapel Hill University,
1989, 91 pages.
Max, N. L., "Optical methods for direct volume rendering," IEEE Transactions on
Visualization and Computer Graphics, Vol. 1, No. 2, 1995, pp. 99-108.
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Computational Steering and Grid Services
Fox, G. C., Ko, S.-H., Pierce, M., Basloy, O., Kim, J., Lee, S., Kim, K., Oh, S., Rao, X.,
Varank, M., Bulut, H., Gunduz, G., Qiu, X., Pallickara, S., Uyar, A., and Youn, C.,
"Grid Services for Earthquake Science," ACES 2001: Special Issue of Concurrency
and Computation:Practice and Experience, 2001.
Haimes, R., PV3: A distributed system for large-scale unsteady CFD visualization.
AIAA paper 94-0321. 1994. AI AA.
Parker, Steven G. and Johnson, Christopher R., "SCI Run: A scientific programming
environment for computational steering",
http:/ / www.sci.utah.edu/ publications/ sc95_pj/ PARKER_JOHNSON.html.
Swann, J. Edward, Lanzaborta, Marco, Maxwell, Doug, Kuo, Eddy, Uhlmann, Jeff,
Anderson, Wendell, Shyu, Haw-Jye, and Smith, William, "A Computational Steering
System for Studying Microwave Interactions with Missile Bodies",
http:/ / www.ait.nrl.navy.mil/ vrlab/ projects/ CompSteering/ Vis_00_Comp_Steering_Slid
es.pdf.
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Useful Links
"OpenDX", www.opendx.org.
"Template Graphics Software", www.tgs.com.
The Visualization Toolkit", http:/ / public.kitware.com/ VTK.
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Acknowledgements
We would like to thank discussions with Geoffrey C.
Fox. This work was supported by the NSF visualization
grant NSF-0083792 and by the geophysics ( D.A.Y.) and
information science (G.E.) programs of the National
Science Foundation and the complex fluids program of
the Dept. of Energy. Fabien Dubuffet has been
supported by the Visiting Scholar program of the
Minnesota Supercomputing Institute. Finally we thank
ACES, GEM and SCEC , earthquake research
organizations for sponsoring Maui workshop, which laid
the seed for this interdisciplinary collaborative effort.
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