28 September/October 2007 Published by the IEEE Computer Society 0272-1716/07/$25.00 © 2007 IEEE

Discovering the Unexpected

Each year, estimated losses from earth-quakes are measured in billions of dollars

and thousands of fatalities.1 While researchers havestudied earthquakes for a long period of time, recentadvances in computation now enable large-scale simu-lations of seismic wave propagation.

Because there’s no doubt about the utility of develop-ing a system to help us better understand earthquakes,we’ve done extensive work on what we call the Tera-Shake project (see http://epicenter.usc.edu/cmeportal/

TeraShake.html). In this project, weused a multitude of visualizations tointerpret simulation output, and werecently completed one of the firstlarge-scale simulations done on theSouthern San Andreas Fault.

Earthquake threatThe San Andreas Fault has a his-

tory of producing earthquakes withmoment magnitude exceeding 7.5.2

The most recent was the 1906 mag-nitude 7.9 event3 that caused tre-mendous damage to San Francisco.The 1857 (Fort Tejon) earthquakeruptured a 360-kilometer stretchfrom Parkfield to Wrightwood.

However, the San Bernardino Mountains segment and the Coachella Valley segment of the San AndreasFault haven’t seen a major event since 1812 and 1690,respectively.4

Average recurrence intervals for large earthquakeswith surface rupture on these segments are around 146�91 �60 years and 220 ±13 years, respectively.5 A majorcomponent of the seismic hazard in Southern Califor-nia and northern Mexico arises from the possibility of alarge earthquake on this part of the San Andreas Fault.6

Because no strike-slip earthquake of similar or largermagnitude has occurred since the first deployment ofstrong-motion instruments in Southern California,

much uncertainty exists about the ground motionsexpected from such an event. Compounding this prob-lem is the fact that this is a densely populated area. Wetherefore conducted our TeraShake earthquake simu-lation in an effort to try and reduce this uncertainty.7

TeraShake simulationTeraShake is a large-scale finite-difference (fourth-

order) simulation of an earthquake8 event based onOlsen’s Anelastic Wave Propagation Model code, conduct-ed in the context of the Southern California EarthquakeCenter (SCEC) Community Modeling Environment. The600 � 300 � 80-km simulation domain (see Figure 1)extends from the Ventura Basin and Tehachapi region tothe north and to Mexicali and Tijuana to the south. Itincludes all major population centers in Southern Cali-fornia, and is modeled at 200-meter resolution using arectangular, 1.8-giganode, 3,000 � 1,500 � 400 mesh.

The simulated duration is 250 seconds, with a tem-poral resolution of 0.011 seconds and a maximum fre-quency of 0.5 Hz, for a total of 22,727 time steps. Weconducted this study in two phases (see Figure 2). Wemodeled TeraShake-1 using a kinematic source descrip-tion (see the bottom image in Figure 2b) for the SanAndreas Fault rupture. Four production runs with differ-ent parameter and input settings were made for phaseone. TeraShake-2 (Figure 3b on page 30) added a physics-based dynamic rupture component to the simulation,which we ran at a high 100-meter resolution, to createthe fault’s earthquake source. This is physically morerealistic than the kinematic source description (see Fig-ure 2b) used in TeraShake-1. We ran three productionruns for TeraShake-2 with various parameter settings.

Data products and challengesTo date, all of the TeraShake simulations have con-

sumed 800,000 CPU hours on the San Diego Supercom-puter Center’s (SDSC’s) 15.6-teraflop Datastar super-computer and generated 100 Tbytes of output. TheTeraShake application runs on 240 to 1,600 processors

This study focuses on thevisualization of a series of largeearthquake simulationscollectively called TeraShake.The simulation series aims toassess the impact of SanAndreas Fault earthquakescenarios in SouthernCalifornia. We discuss the roleof visualization in gainingscientific insight and aidingunexpected discoveries.

Amit Chourasia, Steve Cutchin, Yifeng Cui, and

Reagan W. Moore

San Diego Supercomputer Center

Kim Olsen and Steven M. Day

San Diego State University

J. Bernard Minster

Scripps Institution of Oceanography

Philip Maechling and Thomas H. Jordan

Southern California Earthquake Center

Visual Insights intoHigh-ResolutionEarthquakeSimulations

and generates up to about 100,000 files and10 to 50 Tbytes of data per simulation.

The output data consists of a range of dataproducts including surface velocity compo-nents (2D in space, 1D in time) and volumet-ric velocity components (3D in space, 1D intime). The system records surface data forevery time step and records volumetric datafor every 10th or 100th simulation time step.It derives other products (including velocitymagnitude, peak cumulative velocity, dis-placement, and spectral acceleration) fromthe output data.

This simulation data poses significant chal-lenges for analysis. The foremost need is tocomputationally verify the simulation prog-ress at runtime and thereafter seismological-ly assess the computed data. After completingthe simulation run, the results from the mul-tivariate data needs to be intuitively andquickly accessible to scientists at differentgeographical locations. Features of interestor key regions should be identified for furtheranalysis. This requires that we integrate thegeographical context with the data when it’s presented.

Visualization processFrom the beginning of the study, we’ve collaborated

on the visualization process. At the first stage, we colormapped the surface data, and it went through severaliterations of refinement, based on feedback from scien-tists, to capture and highlight the desired data range andfeatures. Because most of the data is bidirectional (veloc-ity components in positive and negative directions), wefinally chose the color ramp to have hot and cold colorson both ends. We decided that the uninteresting range inthe middle would be black (see Figure 3a).

The next step was to provide contextual information;this process also underwent several revisions as betteroverlay maps become available with time (see Figure 3b).Concurrently, we designed different transfer functions tocapture features of interest through direct volumetric ren-dering (see Figure 3c). Finally, we developed alternatemethods like topography deformation (see Figure 3d)and self-contouring (see Figure 4a) to aid in analysis.

We experienced several challenges along the way. Theforemost difficulty we experienced was handling the

IEEE Computer Graphics and Applications 29

1 The top right inset shows the simulation region 600 km long and 300 km wide,indicated by the red rectangle. In the center the topography, fault lines, and citylocations are visible. This also shows the domain decomposition of this region into 240 processors.

2 (a) Image comparing maps of maximum groundvelocity in two different simulated earthquake sce-narios on the San Andreas Fault. Simulation2 is forrupture toward the southeast, while Simulation3 isfor rupture toward the northwest. (b) Snap shots offault-plane velocity for TeraShake-2 (top) andTerashake-1 (bottom), respectively, showing thedifferences in source characteristics for the two cases.

(a) (b)▲ ▼

Discovering the Unexpected

30 September/October 2007

sheer size of data ranging from 10 to 50 Tbytes for eachsimulation. Keeping this data available for a prolongedperiod on a shared file system or moving it around wasimpractical. Interactive visualization tools for this capac-ity of temporal data are either virtually nonexistent orrequire specialized dedicated hardware. Furthermore,the disparate geographic location of scientists also madethese tasks difficult. Thus, we settled on providing pack-aged animations through the Web, which we refinedover time through feedback.

Visualization techniquesWe used existing visualization techniques and com-

bined them with off-the-shelf software to create mean-ingful imagery from the data set. We classify ourvisualization process into four categories: surface, topo-graphic, volumetric, and static maps.

Surface visualizationIn this technique, we processed the 2D surface data

via direct 24-bit color maps overlaid with contextual geo-graphic information. Annotations and captions provid-ed additional explanations. We encoded the temporalsequence of these images into an animation for generaldissemination. We used Adobe’s After Effects for com-positing and encoding the image sequences. Layeringthe geographic information with high-resolution simu-lation results provided precise, insightful, intuitive, andrapid access to complex information. Seismologists needthis to clearly identify ground motion wave-field patternsand the regions most likely to be affected in San AndreasFault earthquakes. We created surface visualizations forall 2D data products using this method.

We also needed to compare multiple earthquake sce-narios to understand rupture behavior. In Figure 2a, the

Topography deformation technique

Transfer function

Map overlay

Color scale and data range

(d)

(c)

(b)

(a)

3 Iterative refinements of the visualization incorporating feedback from scientists.

4 (a) Map showing the advantage of the self-contouring technique incontrast to the simple color mapping. In the region highlighted by theorange circle, scientists identified a star-burst pattern. This indicates anunusual radiation of energy worthy of further investigation, which wentunnoticed with simple color mapping. (b) Screen shot of the visualizationWeb portal. Users can select different data sets and change visualizationparameters to create data maps.

(a)

(b)

IEEE Computer Graphics and Applications 31

left image shows the areas affected by a rupture travel-ing northwest to southeast; the right image shows thecorresponding results for a rupture moving southeastto northwest. Both images have geographical and con-textual information (fault lines and freeways) overlain.We also show seismic information of time, peak veloci-ty, and instantaneous location visually via a graphicalcursor and in text. The goal is to gain an understandingof the region most heavily impacted by such an earth-quake and the degree of damage.

The side-by-side approach for this visualization pro-vides scientists with an important visual intuitionregarding similarities and differences between faultrupture scenarios. In particular, such comparisonsreveal significant differences in the ground motion pat-tern for different rupture directions, and in one case, itshows wave-guide effects leading to strong, localizedamplification. By using animations, the scientists wereable in some instances to detect instabilities in theabsorbing boundary conditions, and to identify alter-nate conditions to remedy the problem (see Figure 2b).In other instances, specific physical behaviors wereobserved—for example, that the rupture velocity wasexceeding the S wave speed at some locations. Suchsupershear rupturing is of great interest to seismolo-gists, because it generates different types of groundmotions than subshear ruptures.9

Topographic visualizationThis process uses dual encoding of the surface veloc-

ity data as both color mapping and as displacementmapping. We used the surface velocity data to create acolor-mapped image; and the displacement magnitudecalculated from the surface velocity data to generate agray-scale image. The system uses the gray-scale imageas a displacement map to create terrain deformationalong the vertical axis (see Figure 5a) using Autodesk’sMaya (see http://www.autodesk.com/maya).

The animation corresponding to Figure 5a lets the sci-entist gain a better understanding of the kind of wavespropagating the model. Another example is use of cross-sectional views (see Figure 5b). In this view, we lowerthe southwest side of the fault’s surface to the bottomso that we can see the rupture on the fault. This kind ofvisualization helps seismologists connect surprising fea-tures in the ground motion with features in the rupturepropagation. Currently, we’re working to develop amethod to display true 3D deformations based on threecomponents of surface velocity data to provide morerealistic insight.

Volumetric visualizationThe bulk of the data our simulation yielded is volu-

metric. This is by far the most significant for analysis, asit holds the most information content. We performeddirect volume rendering10 of the volumetric data set andcomposited it with contextual information to provide aholistic view of the earthquake rupture and radiatedwaves (see Figure 6).

Our initial work helped the seismologists see the gen-eral depth extent of the waves. For example, dependingon the wavefield’s component, waves propagating pre-

dominantly in the shallow layers can be identified assurface waves. Such waves typically contain large ampli-tude and long duration and can be particularly danger-ous to certain structures. However, more research inaddition to existing work11-13 needs to be done to repre-sent multivariate data in a unified visual format.

Additional challenges in volumetric visualizationinclude how to visually present to the user a globalunderstanding of the seismic waves’ behavior while atthe same time allowing them to examine and focus onlocalized seismic activity. This challenge is important,as often only reviewing the global behavior of the seis-mic wave hides important localized behaviors while atthe same time simply focusing on localized activity oftenhides how this localized motion impacts the waves’ over-all behavior.

Static mapsSome data products like spectral acceleration depict

the ground’s vibration characteristics at different fre-

5 (a) Snap shot showing deformation along the vertical axis of the terrain-based velocity magnitudes. (b) Cross-sectional view showing the slip rateon the vertical fault plane; the velocity magnitude and surface topographyare cut in two halves on the horizontal plane.

(a)

(b)

6 Snap shot showing volume-rendered velocity in y (shorter horizontal)direction. The z axis (vertical) has been scaled twice.

Discovering the Unexpected

32 September/October 2007

quencies (see Figure 4a). Peak ground velocities andpeak ground displacements are nontemporal andrequire visual representation for better understanding.

Self-contoured maps. We developed a technique tohighlight features in 2D by using bump mapping. (Wijk14

provides a detailed analysis of thisapproach.) Encoding the spectralacceleration levels using bothcolor maps and bump mapsreveals subtle transitions betweenground motion levels within local-ized regions with similar spectralacceleration properties. The colorand bump encoding techniquebrought out variations in the datathat weren’t previously visible (seeFigure 4a).

Web portal. We wanted toensure that TeraShake could offer scientists hands-onanalysis capabilities. This is important, because scien-tists need to be able to take this kind of approach to gaina better understanding of the output data. However, thesize of TeraShake’s data poses a significant problem foraccessibility and analysis. We therefore developed aWeb front end where scientists can download the dataand create custom visualizations over the Web directlyfrom the surface data. The portal uses Linux, Apache,PHP, and Java technology for Web middleware. On theback end it relies on specialized programs to fetch datafrom the archive, visualize, composite, annotate, andmake it available to the client browser.

Visualization tools and resultsWe used SDSC’s volume rendering tool Vista, based

on the Scalable Visualization Toolkit, for visualizationrendering. Vista employs ray casting10 with early ray ter-mination for performing volumetric renderings. Wevisualized surface and volume data with different vari-ables (velocities and displacements) and data ranges inmultiple modes. The resulting animations have provenvaluable not only to domain scientists but also to abroader audience by providing an intuitive way tounderstand the results. The visualizations required sig-nificant computational resources. So far, the visualiza-tions alone have consumed more than 10,000 CPUhours and more than 30,000 CPU hours on SDSC’sDatastar and TeraGrid IA-64, respectively.

The use of multiple data sets with different visual rep-resentations (see Figures 2a, 2b, 4a, 4b, 5, and 6) helpthe seismologists understand key earthquake conceptslike seismic wave propagation, rupture directivity, peakground motions, and the duration of shaking. The strongvisual impact leads the viewer from the global contextof earthquake hazards to the hazards in a specific regionand then into the details about a specific earthquake sim-ulation. Watching the earthquake simulation evolve overtime helps viewers gain insight into both wave propaga-tion and fault rupture processes. The simulation alsoillustrates the earthquake phenomena in an effective wayto nonscientists. We’ve performed more than 100 visual-

ization runs, each utilizing between 8 and 256 proces-sors in a distributed manner. The results have producedmore than 130,000 images and more than 60 unique animations (available at http://visservices.sdsc.edu/projects/scec/terashake/).

Insights gainedthrough visualization

Large-scale simulations areplaying an increasing role in theunderstanding of regionalearthquake hazard and risk.These developments are compa-rable to those in climate studies,where the largest, most complexgeneral circulation models arebeing used to predict the effectsof anthropogenic global change.By visualizing the TeraShakesimulations, scientists have been

able to gain the following insights:15

■ In a northwestward propagating rupture scenario,the wave propagation is strongly guided toward theLos Angeles basin after leaving the San Andreas Fault(unexpected). (See the image on the right in Figure2b and Figure 5a.)

■ The sediment-filled basin acts as an amplificationsource for trapped waves. A strong amplification isobserved in the LA basin long after the initial rupture(unexpected). (See the image on the right in Figure2b and Figure 5a.)

■ Contiguous basins act as energy channels, enhancingground motion in parts of the San Gabriel and LosAngeles basins (see the image on the right in Figure2b and Figure 5a).

■ The visualizations identify regions with particularlystrong shaking (see Figure 2).

■ The visualizations validate an input rupture modeland instability identification.

■ The visualizations observe star-burst patterns in thespectral amplification maps (unexpected). (See Fig-ure 4a.)

DiscussionThe role of contextual information has been pivotal;

the surface visualizations have been quite helpful to sci-entists. However, encoding the rendered image sequenceinto animations has been a bottleneck, because of theserial, time-consuming, and lossy compression process.Further, it requires carefully selecting codecs for broad-er accessibility by scientists on different platforms.

In the past, researchers have successfully used somevisualization techniques—such as plotting the surfacepeak ground motions on a map—to analyze wave prop-agation simulations. Thus, such basic techniques haveproven to provide useful scientific information and areuseful for initial simulation assessment. However, with-out the exploratory visualizations applied to theTeraShake simulations, such as the color and bumpencoding technique, the origin of some features in theresults would have been unclear. These new methods

Watching the earthquake

simulation evolve over time

helps viewers gain insight into

both wave propagation

and fault rupture processes.

IEEE Computer Graphics and Applications 33

should be considered in future large-scale simulationsof earthquake phenomena. While these methods areunder development, future efforts should concentrateon documentation of the procedures to promote wide-spread use.

In addition to the scientific insight gained, these meth-ods can provide important instructional learning mate-rials to the public. For instance, Project 3D-View, aNASA-funded program, uses a TeraShake animation forteacher training; TeraShake could potentially become apart of the curriculum in thousands of classrooms. Also,the National Geographic Channel featured one of theanimations in their documentary, LA’s Future Quake.

Because large temporal data sets pose significant chal-lenges for analysis, it’s better if we can automate visual-ization techniques and methods applied in a plannedway. Interactive visualization of large temporal data setsseems useful but is nontrivial and often impractical.Domain-specific feature-capturing algorithms coupledwith visualization could play an important role foranalysis. Animations, though noninteractive, can oftenserve the purpose for gaining insight when created in athoughtful manner. Off-the-shelf software such as Mayacan augment scientific visualization tools. And, multi-ple representations of the same data products with dif-ferent techniques can be valuable assets. Usinghigh-dynamic-range (HDR) imagery for amplifyingfidelity and precision in visualizations seems promisingbut the lack of ubiquitous HDR display hardware andthe plethora of tone-mapping methods make this taskdifficult.

Future directionsPlanned future work includes online analysis of sur-

face data by remote Web clients plotting synthetic seis-mograms. We also plan to investigate data miningoperations, spectral analysis, and data subsetting.

The TeraShake simulation project has provided someinsights on the IT infrastructure needed to advance com-putational geosciences, which we’ll examine further.We would like to use 16-bit imagery instead of our cur-rent 8-bit imagery to increase visualization fidelity.Additionally, we want to integrate a GIS database tooverlay a variety of contextual information that wouldenable further analysis. We’re currently testing our inte-gration of surface imagery with virtual globes likeGoogle Earth and NASA’s World Wind. ■

AcknowledgmentsWe thank Marcio Faerman, Yuanfang Hu, Jing Zhu,

and Patrick Yau for their contributions. We also thank

the scientists and engineers from SDSC and SCEC whofunded this work and made it possible. SCEC is fundedby the US NSF Cooperative Agreement EAR-0106924and US Geological Survey Cooperative Agreement02HQAG0008. The SCEC contribution number for thisarticle is 1085.

References1. Federal Emergency Management Agency (FEMA),

“HAZUS 99 Estimated Annualized Earthquake Losses forthe United States,” FEMA Report 366, Feb. 2001, p. 33.

2. K. Sieh, “Prehistoric Large Earthquakes Produced by Slipon the San Andreas Fault at Pallett Creek, California,” J.Geophysical Research, vol. 83, no. B8, 1978, pp. 3907-3939.

3. B.A. Bolt, “The Focus of the 1906 California Earthquake,” Bul-letin Seismological Soc. Am., vol. 68, no. 1, 1968, pp. 457-471.

4. R. Weldon et al., “Wrightwood and the Earthquake Cycle:What a Long Recurrence Record Tells Us about How FaultsWork,” Geological Seismology Am. Today, vol. 14, no. 9,2004, pp. 4-10.

5. Working Group on California Earthquake Probabilities,“Seismic Hazards in Southern California: Probable Earth-quakes, 1994 to 2024,” Bulletin Seismological Soc. Am., vol.85, no. 2, 1995, pp. 379-439.

6. A.D. Frankel et al., “Documentation for the 2002 Update ofthe National Seismic Hazard Maps,” US Geological SurveyOpen File Report, 2002, pp. 020-420.

7. K.B. Olsen et al., “Strong Shaking in Los Angeles Expectedfrom Southern San Andreas Earthquake,” GeophysicalResearch Letters, vol. 33, citation no. L07305, 2006;doi:10.1029/2005GL025472.

8. Y. Cui et al., “Enabling Very-Large Scale Earthquake Sim-ulations on Parallel Machines,” Proc. Int’l Conf. Computa-tional Science, Part I, LNCS 4487, Springer, 2007, pp.46-53.

9. E.M. Dunham and R.J. Archuleta, “Near-Source GroundMotion from Steady State Dynamic Rupture Pulses,” Geo-physical Research Letters, vol. 32, citation no. L03302,2005; doi:10.1029/2004GL021793.

10. J.T. Kajiya and B.P.V. Herzen, “Ray Tracing Volume Densi-ties,” Proc. Siggraph, ACM Press, 1984, pp. 165-174.

11. P. Chopra, J. Meyer, and A. Fernandez, “Immersive Vol-ume Visualization of Seismic Simulations: A Case Studyof Techniques Invented and Lessons Learned,” Proc. IEEEVisualization, IEEE CS Press, 2002, pp. 171-178.

12. H. Yu, K.L. Ma, and J. Welling, “A Parallel VisualizationPipeline for Terascale Earthquake Simulations,” Proc.ACM/IEEE SC Conf., IEEE CS Press, 2004, p. 49.

13. A. Uemura, C. Watanabe, and K. Joe, “Visualization of Seis-mic Wave Data by Volume Rendering and Its Applicationto an Interactive Query Tool,” Proc. Parallel and DistributedProcessing Techniques and Applications, CSREA Press, 2004,pp. 366-372.

14. J.J. vanWijk and A.C. Telea, “Enridged Contour Maps,”Proc. IEEE Visualization, IEEE CS Press, 2001, pp. 69-74.

15. C. North, “Toward Measuring Visualization Insight,” IEEEComputer Graphics and Applications, vol. 26, no. 3, 2006,pp. 6-9.

An Earth-Shaking Moment

To see the rippling effect of a major earth-quake in Southern California visit http://csdl.computer.org/comp/mags/cg/2007/extras/g5028x1/mov.

Amit Chourasia is a visualizationscientist at the San Diego Supercom-puter Center (SDSC) at the Universi-ty of California, San Diego (UCSD).His research interests are in scientificvisualization, visual perception, andcomputer graphics. Chourasiareceived an MS in computer graphics

technology from Purdue University. He’s a member of theGeological Society of America. Contact him atamit@sdsc.edu.

Steve Cutchin manages the visual-ization services group at SDSC. Hisresearch interests include scientificvisualizations of natural phenomena,volume rendering, and techniques forimproved perceptual representationof large-scale data sets. Cutchinearned his doctorate studying collab-

orative systems at Purdue University. Contact him atcutchin@ sdsc.edu.

Yifeng Cui is a computational scien-tist at SDSC. His research interestsinclude high-performance computingon both massively parallel and vectormachines, large-scale distributedmodeling, data-intensive computing,performance optimization and eval-uation, tracer hydrology, climatology,

and hydroclimatology. Cui received a PhD in hydrologyfrom the University of Freiburg, Germany. Contact him atyfcui@sdsc.edu.

Reagan W. Moore is the director ofdata and knowledge systems at SDSC.He coordinates research efforts indata grids, digital libraries, and per-sistent archives. His current researchis on the development of rule-baseddata management systems. Moore isalso the principal investigator for the

development of the Storage Resource Broker data grid,which supports internationally shared collections. Moorealso has a PhD in plasma physics from UCSD. Contact himat moore@sdsc.edu.

Kim Olsen is an associate professorat the Department of Geological Sci-ences at San Diego State University(SDSU). His research interests include3D simulation of wave propagation,strong ground motion, earthquakedynamics, parallel and high-perfor-mance computing, and visualization.

Olsen has a PhD in geophysics from the University of Utah.He’s a member of the Seismological Society of America and the American Geophysical Union. Contact him atkbolsen@sciences.sdsu.edu.

Steven M. Day is the Eckis Professorof Seismology at SDSU and a visitingresearch geophysicist at UCSD. Hisprincipal research interest is compu-tational earthquake physics. Day hasa PhD in earth science from UCSD,and he serves on the board of theSouthern California Earthquake Cen-

ter (SCEC). Contact him at day@moho.sdsu.edu.

J. Bernard Minster is a professorof geophysics at the Institute of Geo-physics and Planetary Physics (IGPP)of the Scripps Institution of Oceanog-raphy, UCSD, and senior fellow at theSan Diego Supercomputer Center. Hisresearch interests include determiningthe structure of the Earth’s interior

and imaging the Earth’s mantle and crust using seismicwaves. Minster has a PhD in geophysics from the Califor-nia Institute of Technology. Contact him at jbminster@ucsd.edu.

Philip Maechling is the informa-tion technology architect at SCEC. Hisresearch interests include the applica-tion of high-performance computingto seismic hazard analysis, real-timeearthquake monitoring, and scientif-ic workflow technologies. Maechlingreceived his BS in applied physics from

Xavier University in Cincinnati, Ohio. He has authoredand coauthored journal articles and book chapters on com-puter science research topics including distributed objecttechnology, grid computing, and scientific workflow tech-nologies. Contact him at maechlin@usc.edu.

Thomas H. Jordan is the directorof the SCEC and the W.M. Keck Foun-dation Professor of Earth Sciences atthe University of Southern California.He studies earthquakes and earthstructure. Jordan received a PhD fromthe California Institute of Technology.Contact him at tjordan@usc.edu.

Discovering the Unexpected

34 September/October 2007

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