47 Immersive Visualization for the Geological Sciencesweb.eng.ucsd.edu/~jschulze/publications/Sherman2014.pdf · 47Immersive Visualization for the Geological Sciences ... immersive

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47 Immersive Visualization for the Geological Sciences

William R. Sherman, Gary L. Kinsland, Christoph W. Borst, Eric Whiting, Jurgen P. Schulze, Philip Weber, Albert Y.M. Lin, Aashish Chaudhary, Simon Su, and Daniel S. Coming

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Contents

47.1 Introduction........................................................................................................................ 122847.2 CommonGeologyDataTypes............................................................................................ 1229

47.2.1 TerrainElevationData............................................................................................ 123047.2.2 SatelliteImagery..................................................................................................... 123047.2.3 2DFields(e.g.,GravityandMagnetism)................................................................ 123147.2.4 3DMaterialPropertyData(TomogramVolumes)................................................. 123347.2.5 Ground-PenetratingRadar(GPR)Data.................................................................. 123347.2.6 WellLogData......................................................................................................... 123347.2.7 VectorGeometryObjectswithAttributes.............................................................. 123447.2.8 BreadcrumbTrail.................................................................................................... 123447.2.9 PointCloudData..................................................................................................... 1234

47.3 GeologyVisualization........................................................................................................ 123447.3.1 RelevantIssuesinGeologyVisualization.............................................................. 123547.3.2 ApplyingStandardVisualizationTechniquestoGeology..................................... 123547.3.3 VisualizationTechniquesforGeology................................................................... 123647.3.4 TemporalData........................................................................................................ 123747.3.5 BenefitstoGeologicVisualizationfromtheImmersiveInterface......................... 1237

47.4 ExemplarApplicationsandLessonsLearned.................................................................... 123847.4.1 ApplicationsforExplorationofTerrains,AssociatedGeophysicalData,

andWellLogsattheUniversityofLouisianaatLafayette.................................... 123947.4.1.1 RenderingandInteractionforTerrainInterpretation.............................. 123947.4.1.2 WellLogVisualization............................................................................124047.4.1.3 3DLensImmersiveInterfaceTechnique................................................. 1241

47.4.2 LidarViewer:AnImmersivePointCloudVisualizationTool................................ 124347.4.2.1 VisualizationTechniques.........................................................................124447.4.2.2 ImmersiveInterfaceTechniques.............................................................. 124547.4.2.3 IdahoNationalLaboratoryLidarViewerWorkflow.................................124647.4.2.4 OutcomesandLessons............................................................................1246

47.4.3 VisualizationofMultimodalGeophysicalSurveyData......................................... 124747.4.3.1 VisualizationTechniques.........................................................................124847.4.3.2 ImmersiveVisualizationApplication...................................................... 124947.4.3.3 BenefitsDerived....................................................................................... 1251

47.4.4 DRILancasterSandDuneLayers.......................................................................... 125247.4.4.1 VisualizationTechniques......................................................................... 1253

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1228 Handbook of Virtual Environments: Design, Implementation, and Applications

47.1 IntroduCtIon

Onecharacteristicofgeologistsistheirabilitytothinkin3D.Whetherthisabilityisdevelopedastheyadvanceasgeologistsor theyweredrawntogeologybecause theypossessed thisability isanopenquestion.However,experiencedgeologistspossessthisabilitytosuchadegreethattwoormoregeologistsmayverballycommunicateaboutacomplex3D,real-worldproblem—eachofthembuildingandmodifyinga3Dimageintheirmindastheyreceiveinformationfromtheothers.Theycanverballyquerytheimageofanotherandasagroupmayactuallychangeperspectiveinthevirtual/mental3Dimage.Thatis,theyarecapableofagreeingtolookatthe3Dproblemfromdif-ferentpositionsinspace…ortime.Theyoftenneedtotracethedevelopmentofaparticulargeologicsituationthroughgeologictime.Thegroupiscapableofcomingtoconsensusandrunningaverysimilar3Dmovieintheirindividualminds.Thismovieistweakedbyevidentialoropinioninputfromthoseinvolved,developingacauseandeffect–basedplausibleexplanationofthepresentgeo-logicalsituation.Similarly,thegroupmayrunthismovieintothefuturetomakepredictions.Usingimmersive tools for visualizing the data is, therefore, both natural and powerful for geologists.Immersive3Dcomputergraphicstechnologiesaretoolsthatcombineanintuitive3Dviewingenvi-ronmentwitheasilylearnedinteractionmechanisms.Theseadvanced3Dtoolsrepresentanaturaloperatingplatformforgeologistswhoofnecessitymustthinkin3D.

Thefollowingstatementmaybeatautology;however,itisworthspecificallystatingforitsrel-evancetotheuseofimmersivevisualization:“Allinterpretationoccursinthemind.”Thespecificpointisthatbeforeinterpretationcanproceed,informationmustgetintothemind.Intheexampleearlier,theinformationiscommunicatedverbally.Inmanyothercases,informationentersthemindvisually.Often,thisinformationisintheformof2Dimages.Inthemind,2Dimagesfromsev-eraldifferentperspectivesandatdifferentscalesareoften(inthecaseofgeologists…veryoften)constructedinto3Dspaces.These3Dspaces,then,arethebasisformakinginterpretations.Whilegeologistsarequitegoodatassembling3Dspacesfromvariousinputformats,itismoreefficienttohavetheinformationenterthemindasalreadycorrectlyconstructed3Dimages.Thisisonegreatadvantageofimmersivevisualizationforgeologists.

Anotheradvantageistheabilityofoneorseveralgeologiststofreelymoveaboutinthe3Dvol-ume.Theeffectonthegeologistofbeingimmersedintheirdataissimilartobeinginthefieldwherecuriositydrivesmovement,inspection,andanalysis.Togeoscientists,thistypeofinteractionisoftenintuitive—if,whileinthefield,thegeoscientistwantstolookatsomefeaturethathasarousedcuri-osity,heorshesimplywalksoverthereandlooksatit(aspartoftheeducationandexperience,mostgeoscientistshavehadsomefieldwork).Contrastthiswithwhathappensinmanyotherenviron-mentswhereintheinvestigatormustmakedecisions,consciouslyorunconsciously,relatedtoeffortversusreward.Thatis,decisionsmustbemadeastowhetherinvestigationofthecuriousfeatureisworththeeffortofgatheringtheadditionaldatanecessarytoreachaconclusion.Ofcourse,inthisprocess,theconclusionisestimated,perhapsincorrectly,andcomparedtothecost.Onememberofourteam(Kinsland)experiencedthisfirsthand:hehadwrittenandpublishedapaperusing2Drep-resentationsofaerialtopographiclightdetectionandranging(LIDAR)datatointerprettheevolutionofaparticularriver.Later,hehaspresentedtheLIDARdataimpressively,allowinghimtomove,interact,andcontinuouslyqueryastolatitude,longitude,andelevation.Withinjustafewminutesofmindlesslybutcuriouslymovingaboutinthedatahesaid,“Ihavetowriteanotherpaper.Ihavefoundanalternativeexplanation.”Hehadpouredoverthe2Ddataformanymonthsbeforehewrotetheoriginalpaper.Lessthan20minofwalkingwithinthedata,asifhewereinthefield,resultedin

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47.4.4.2 ImmersiveInterfaceTechniques.............................................................. 125347.4.4.3 OutcomesandLessons............................................................................ 1253

47.5 Benefits............................................................................................................................... 125547.6 FutureandConclusion........................................................................................................ 1256References.................................................................................................................................... 1257

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1229Immersive Visualization for the Geological Sciences

thebasisforanotherpaper,whichwasalsopublished.AnotherexamplecomesfromtheworkdonewithdataovertheChicxulubimpactcrater(theonethatperhapskilledthedinosaurs)(Figure47.1).For severalyears,Kinslandmade2Dcontourmapsof the topographicdata,makingpseudo-3Dmapsofthedatawithcolor-codedelevationrepresentations.Allwereusefulinbuildingthemental3Dimage.However,indiscussingthedayhewasreintroducedtohisdatainahead-mounteddisplay(HMD)andallowedtowalkaboutwithinhisdata,herelaysthat“IwastotallyenthralledandstayedintheHMDlongerthanIlaterwastoldIshouldhave.To this day,yearslater,mymental3DimageofthetopographyovertheCraterislargelythatwhichIsawthatdayintheHMD.”

Oneoftheweaknessesofthehumanmindwhendealingwithmultiple3Ddatasetsfromthesamegeographicalareaisingettingthegeoreferencingdonecorrectly.Thatis,itisdifficulttomentallycom-paretwooverlapping3Ddatasetssuchastopographyandmagneticintensity.Theinterpreterisquitecapableofinspectingandinterpretingeach3Dmentalimagefromvariousperspectivesandatvariousscales.However,itisoftenusefultoquerythecorrelationoffeaturesbetweenthetwo.For example,doesavalleyinthetopographicdatacorrelatespatiallywithaparticularanomaly(outstandingfea-ture)inthemagneticdata?Georeferencingofdatasetsiswellhandledbycomputers,whichcanblendthedataintoasinglecoordinatesystem.Thetwoimagesmaythenbeimmersivelyviewed,compared,andinterpretedsimultaneouslyusinganyofseveraltechniquesdescribedinthischapter.

Inshort,theworldis3D,andgeologiststhinkin3D.Whyreducethe3Dworldto2Dimagesfor communication into minds where 3D images must then be reconstructed and interpreted?Immersiveenvironmentsarethekeytoaccurate3Dthinkingandofferimprovedunderstandingofalmostallgeology-relateddatasets.

47.2 Common GeoloGy data types

DataforthegeologicalsciencesofcoursearecenteredontheEarthandotherplanetarybodies.Nevertheless,therearestillmanydifferentformsthatgeologicaldatacantake.Geologydatacanbe1D,2D,or3D;itcanbestaticortimevarying;anditmightbespacedatregularintervalsorbeentirelyunstructured.

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FIGure 47.1 (See color insert.)Avarietyofimmersivetechnologies(akaVRinterfaces)enablethecre-ationofnewmethodsofinteractionusefulforgeologiststoinspectandinteractwiththeirdata.Here,GaryKinslandandstudentsfromtheUniversityofLouisianaatLafayettecollaborativelyexploretheChicxulubcraterwithareach-instyle(a)andlargerearprojection(b)technologies.(SeeSection47.4.1formoredetails.)

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In thissection,webrieflydescribesourcesofgeologicdataand theforms thosedata take intermsoftraditionalvisualizationtechniques(Figure47.2).

47.2.1 Terrain elevaTion DaTa

Afundamentaldatatypeforgeologyistheheightfieldofaterrain.Specifically,thetermterrain(orbare earth model)generallyreferstotheactualgroundsurfaceoftheplanet,whereassurfaceoftenincludesnatural(e.g.,forests)andman-made(e.g.,buildings)objectsprotrudingabovetheground.Theexpressionsdigital terrain model(DTM)anddigital surface model(DSM)areoftenusedtoexplicitlyindicatewhathasbeenmeasured.However,digitalelevationmodel(DEM)dataareambiguousandmayrefertoeither.

TerraindataareoftencollectedusingremotesensingtechnologiessuchasRADAR(collectedviasatellite)orspaceshuttleorairbornethatpassesovertheregionofinteresttakingLIDARscansandotherreadings.TerraindatamayalsobecollectedusingclassicalsurveyingtechniquesorevenbyGPStracking.Forthemostpart,themeasuringtechnologieswillcapturetheheightofmanypointsonthesurface,butthesepointsareirregularlyspaced.Often,thesepointsareprocessedintoaregular,2D(akaraster)pattern.TheDEMformatasspecifiedbytheU.S.GeologicalSurvey(USGS)isa2Dras-terdataformatwithelevationvaluesspecifiedinaregulargrid.ModernsourcesofDEMdataincludetheShuttleRadarTopographyMission(SRTM)datacollectedin2000(Farr&Kobrick,2000;Farretal.,2007)andtheAdvancedSpaceborneThermalEmissionandReflectionRadiometer(ASTER)datareleasedin2011(Yamaguchi,Kahle,Tsu,Kawakami,&Pniel,1998)(Figures47.3and47.4).

47.2.2 SaTelliTe imagery

Imagescapturedbyorbitingsatellitesareanotherquintessential(punintended)datatypeforgeol-ogy.Inthecasesofimagery,thedataaregenerallycapturedasregular2Drasterfieldsofspectralresponse.Capturedspectralfieldsgenerallyspanfromtheinfrared,throughthevisiblecolorsandintotheultravioletregionsoftheelectromagnetic(EM)spectrum.

Duetothenatureofanorbitingobjectscanningacurvedsurface,theimagedatarequirerepro-jectiontowarptheimagetomatcheitheraCartesianorpolarmapping.

Whilewewillseethattherearemanyopportunitiesforregisteringandviewingdifferentsourcesofdataintoasinglerepresentation,itisnaturaltobeginbycombiningimagerydatawithelevationdatatocreateabasicreconstructionoftherealworld.Evenwithnootherdataadded,thisinitselfcanbeagoodtoolforreconnoiteringgeologicexpeditionsintothefieldtoprepareabetterplanforwheretogatherdata(Figure47.5).

(a) (b)

FIGure 47.2 Geologists collectingGPRdataover a sanddune (a) and the associatedvisualization (b).(See Section47.4.4formoredetails.)

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47.2.3 2D FielDS (e.g., graviTy anD magneTiSm)

Inadditiontotheobviousterrain-heightdata,orevenspectralimagery,thatcorrespondwithlati-tudeandlongitude,thereareotherfieldsthatmaptothe2Dsurfaceoftheplanet.Thesedatamaybecollectedasaninputstreamrecordedbyamovingcraftsuchasanairplaneorboat,ortheymaybecollectedonthesurfacebymovingmeasurementequipmenttoareasofinterest.Twofieldsofparticularinteresttogeologyaregravityandmagnetism.

FIGure 47.3 GeophysicallogsofwellsarehungfromasurfacegeneratedfromSRTMterraindatafromNorthLouisiana.Inthissystem,thevirtualwandmaybeusedtohighlightandtoggleawelliconlocatedontheterrain.Togglingturnsthewelllogrepresentationon/offunderneaththeiconofthewell.Intheillu-minatedwelldatahere,yellowisSPandblueistheresistivity.Thoughnotyetimplemented,correlationoffeaturesintheselogswhilewithinthe3Denvironmentpromisestofacilitateandimprovetheinterpretationofsuchsubsurfacedatawhencomparedto2Dmethodsofcorrelation.(SeeSection47.4.1formoredetails.)

FIGure 47.4 AstereopairrepresentingterraincontoursnearMountRainier.Usingastereoscope,viewerscanseethereliefoftheterrainelevations.

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Magneticdataaretypicallycollectedthroughairborneinstrumentsflowninapatternovertheregionof interest (suchas theChicxulubcraterpresentedearlier).Airbornecollectionofgravitydata,however, lackssufficientprecisiontobeusefulfordetailedanalysis.Thus,gravitydataaremoretypicallycollectedbylaboriouslymovingmeasurementinstrumentsonthesurfacefromloca-tiontolocation.Theirregularlysampleddatamaythenbeinterpolatedintoaregularrasterformatmoreconvenientforsomevisualizationtechniques—forexample,usingtheKriginginterpolationtechniqueasdoneinthecasestudyinSection47.4.1.

Eachofthese,andother,fields(e.g.,radioactivity,resistivity,chemical,heatflow)isinterpretedbasedonknowledgeoftheregionalgeologyofthesurveyandthelikelytypesofsubsurfacemateri-als,whichwouldleadtoanomaliesinthedata.Anomaliesareareasofthedatathathavevaluesdis-tinctfromtheirsurroundings—implyingthatinthesubsurface,therearematerialsthataredistinctfromtheirsurroundings(Figure47.6).

FIGure 47.5 Here,ausermodifiesanEsrishapefilefromwithinthevirtualenvironment,presentedinafour-sidedCAVEsystem.

FIGure 47.6 Welllog.Thisisalogofawellastypicallyutilizedinthepetroleumindustrytoinvestigatethepropertiesofthesubsurface.Theheader,atthefarleft,containsinformationaboutthelocationofthewell,date logged,wellowner,etc.The logextendsfromleft torightfromtheshallowest to thedeepestdataand isusuallyviewedand interpretedvertically (rotated90degreesclockwise).Themeasurementsrepresentedbythesquiggly linesareidentifiedattheendsofthelinesasarethescalesforthevalues.Thevaluesinthislogare(frombottomtotopinthisimage)SP,resistivity,andconductivity(calculatedfromresistivity).

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47.2.4 3D maTerial ProPerTy DaTa (Tomogram volumeS)

Tomographytechniquescanbeusedtodetermineapropertyofmaterialwithinavolumebypassingasignalthroughthevolume.Thereareseveralsignaltypesthatcanbeusedtorevealdifferentprop-erties(andwhichhavedifferenteffectiverangesandresolutions).Inmedicalimaging,wegenerallythinkofCTscansthatusex-raysignals.CTscanscanalsobeperformedonsoilsandmineralstoanalyzepropertiessuchasporosity.Onalargerscale,seismictomographyisperformedwithsoundwaves(sentfromwelltowell).RADARandmanyotherwavesourcescanalsobeusedtoproduce3Dtomogramvolumes.Forexample,Muonabsorptiontomographyisbeingusedtoimagetheinte-riorstructuresofvolcanoes(Tanakaetal.,2007).

Seismic volumes are tomograms collected using seismic reflection surveying, whereby sig-nalsourcesandreceiversarelocatedonthesurfacewiththesignalreflectingoffhorizonsinthesubsurface. Processing the received signals yields representations of the distribution of variousseismicpropertieswithinthesubsurface.Themostcommonpropertyrepresentedisthereflection coefficient—ameasureofdifferencesinvelocityanddensitybetweenadjacentrocklayers.

47.2.5 grounD-PeneTraTing raDar (gPr) DaTa

Specializedradardevicesusetransmitandreceiveantennasseparatedontheorderof1mfromoneanothersendingandreceivingasignalaimeddownwardintothegroundandmeasuringthereturned/reflected time-responsesignalcausedbychanges in thedielectricconstantswhen tran-sitioningbetweenmaterials.Often,thecontrastsinthedielectricconstantwillcorrelatewiththeacousticimpedancecontrastsofaseismicreflectionsurvey.

Theresultofasinglesoundingisa1Dsignalresponsemeasuredovertime,wherethattimecor-relateswithdepth,thoughnotnecessarilyinanyspecificratio.Thegeneralmeansofvisualizationistoshow2Dverticalimages(e.g.,fence diagrams)comprisedofregularlyspacedsoundingsofthedata(e.g.,wherethatdatamaybeprocessedtoremovelow-frequencyeffects).Thus,thereal-timesignalfromthereceiverisplottedagainstdistancealongthesurfaceline.Arecenttrendinsomedomainshasbeentocreatea3Dtomogramvolumeconstructedbyperforminga2Dsurveyoverasurface,respectively,versusa1Dsurveyforthefencediagrams.

Ground-penetrating radar (GPR) is used as a tool for a variety of fields such as archaeol-ogy, environmental site characterization,hydrology, concrete and road inspection, sedimentol-ogy,andglaciology.Itisanondestructivesubsurfacemeasurementtechniquethat,inadditiontopreservingthesite,islessdifficultandexpensivethanexcavationordrilling(Bristow,Duller,&Lancaster,2007).

47.2.6 Well log DaTa

Datacollectedwhileawellholeisboredmaybecaptured.Thismaybeduringanactiveprocessofaccessingareserveofnaturalresources,orthecollectionofthedatamaybetheentiregoaloftheboringoperation(suchaswhensearchingfortheincreasedpresenceofamaterialsuchasgold).Thesewell logsorperhapsmultivariate well logs of physical properties down a borehole(Fröhlich,Barrass,Zehner,Plate,&Göbel,1999)are1Dinspacebutsamplemanydifferentprop-ertiesalongthepath.Itshouldbenotedthatwithmoderndrillingtechnology,thesewellsarenotnecessarilyinastraight-linepath,butcanbedirectedindifferentdirectionsalongthelengthofthewellpath.

Similartootherground-penetratingtechniques,anaccumulationofdatafrommultiplewellscanbeusedtoproducearegionallayoutofthesubsurfacestructureorascontrolpointstoconstrainandimprovemodelsofsubsurfacevolumes.

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47.2.7 vecTor geomeTry objecTS WiTh aTTribuTeS

Therearemanygeologicalapplicationsthatmakeuseofgeometricdataspecifiedona2Dmanifold(i.e.,surface)usingpoints,edges/lines,andpolygons/polylinestoindicateplacesandregionsonthesurface.Therealsoaremanyapplicationsofthesedatathatarelessthanscientificinnature,suchasforpoliticalorengineeringpurposes.Attributesaretypicallyassignedtothepoints,lines,andspacewithinapolygon.Additionally,astheoriginal2Dmanifold(e.g.,thesurfaceoftheEarth)isfrequentlynotplanarandmetadata(dataaboutthedata)arekeptofpropertiessuchaswhichprojec-tionsusedinmappingthedatafromthephysical3Dspaceontoaflatmap.Vectorgeometrydataforgeologymightincludefeaturessuchastopographiclevelsrepresentedascontourlines,alongwithnaturalandman-madefeaturessuchasrivers,roads,forests,anddams.

Amongthecommonformatsofgeospatial vector dataistheEsrishapefile.AlthoughEsriprod-uctsarecommercial,theformatoftheshapefileisbroadlyavailable,andmanyothercommercialaswellasfreesoftwaretoolsareabletoreadandwritethem.Thereareothercommonformatsaswell,includingtheWKT/WKBformats(akawell-knowntext/binary).Therearealsowebmarkuplan-guageswithvectorgeometryaimedasgeospatialdatarepresentationssuchasGeographyMarkupLanguage(GML)andKeyholeMarkupLanguage(KML).

47.2.8 breaDcrumb Trail

Collectinglocationdataasasubjectmovesandperformingasurveyandcollectingsamplesalongapathwayor justcapturingthepathitselfareexamplesofsurveytraverses.Inmanyrespects,abreadcrumbtrailcanbeconsideredaformofvectorgeometry(akageospatial vector data)wheretheattributesarethesamplescollectedatpointsalongthepath.Oneadditionalattributeisthatoftime.Thetimeattributecanthenbeusedtodeterminerateofmovementalongthepath.

Typically,aGPSsystemisusedtocaptureandlogthetrail.AnautonomousdifferentialGPScanbeusedtoachieve1mverticalresolutionand1mhorizontalresolution.

47.2.9 PoinT clouD DaTa

Pointclouddataareacollectionofspecificlocationsinspace(vertices)generallywithadditionalinformationforeachrecordedpoint,suchasintensity.Frequently,thepointlocationsexistwherethereissolidorliquidmatter.Thisisaconsequenceofthedatatypicallybeingcapturedbyscan-ningdevices that report the locationof surfaces struckby thescanningbeam.Afurtherconse-quenceisthatforscansofsolidobjects,thepointswillallbeonthesurfaceofthatobjectandthuscanbeusedtoderiveageometricshapefromthepoints.

Featuresofpointcloudsare that theyare irregular,andnotconstrained toa2Dmanifoldasvectorgeometriestypicallyare.Eachpointcanbemultivariate.Thatis,eachpointcanhavemanyattributes suchas intensity, color, temperature, aswell ascategoricaldata suchasvegetation orroad.Pointclouddataaremosteffectivewhentheyarerenderedingreatnumbers.

AcommonmethodofcapturingpointclouddataisthroughLIDAR technology.ALIDARscanconsistsofradiallycaptureddataasalasermountedonarotatingplatformwithaspinningmirrorsendsandreceivesthelightsignal.Thespeedsofthemirrorandplatformrotationaffecttheresolutionofthedatacollected.LIDARscanseasilycapturemillionstobillionsofpointdatainasinglescan.

TheLAS(AmericanSocietyforPhotogrammetryandRemoteSensing,2013)formatisaneffortbytheImagingandGeospatialInformationSociety(IGIS)totrytofacilitateastandardformatforLIDARdataexchangebetweenusers.

47.3 GeoloGy VIsualIzatIon

Many sciences work with specific datasets collected simultaneously, or simulated as a group,theresultofallbeinginthesamecoordinatespace.Thisscenarioisatypicalforgeologists,who

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generallygatherdatainavarietyofwaysandatdifferenttimes,resultingindatathatcanbedifficulttocorrelateandthusproperlyvisualize.Thegoalmightbetomodelandcharacterizeareservoirof natural resourcesor to “determine the stratigraphy fromadrill hole” (Fröhlich et al., 1999).Inthissection,wediscusstherelationshipofgeologicaldatatostandardandspecializedvisualiza-tiontechniques,leadinguptotechniquesspecifictoimmersiveinterfacetechnologies.

47.3.1 relevanT iSSueS in geology viSualizaTion

Geologyhasparticularneedswhenitcomestovisualizingdataforanalysis.Thisisnottoimplythatthetechniquesusedtomeettheseneedsarenecessarilyuniquetogeology,butthereareparticularissuesofgreatimportancetogeologists,andthereareparticularvisualizationmethodsthataddressthembetter.

Amajor issuewithgeology analysis is: “where is that?”For example, an anomalymightbeevidentinthedata,butdeterminingwherethatanomalyisinthephysicalworld(in the field)canbedifficult.Also,geologistsandparticularlygeophysicistsworkwithphysicalphenomenathatoccurhiddenfromview—underthesurfaceoftheearth—andthusaremoredifficulttomeasureandmoredifficulttosee.Thelimitedabilitytotakesubsurfacemeasurementsalsoleadstoirregularcollec-tionsofdata—notonlyspacedirregularly,butdatawithholesinit,andcollectedatvaryingtimes.

Perhapsthemostsignificantissueisregisteringdatacollectedbydifferenttechnologies,atdiffer-enttimes,andfromdifferentlocationssuchthatcomparisonsofattributescanbemadewithdatasetsalignedtooneanother.Bringingmultiplegeologydatasetssuchasmineralcontent,watercontent,andmagnetismtogetherisanimportantmeansforcomparingdatasetsinsearchoffeatureslinkedbetweenthem.Also, thedifferingframesofreferencewilloftenbemappedintoCartesianspaceusingdifferentmathematicalprojections.Thus,thedatamustbereprojectedintoacommonprojec-tionaswellastranslated/scaled/rotatedtobecoregistered.Wecantakethisonestepfurtherandalignthemtoastandardearth-basedframeofreference,whichisreferredtoasgeoreferencingthedata.

47.3.2 aPPlying STanDarD viSualizaTion TechniqueS To geology

Thesciencessharemanyattributes,suchastheuseofmathematicstoexplain(andpredict)observedphenomena.Thus, it isnotsurprising that therearemanystandard scientificvisualization tech-niquesthatworkwellforanalyzingandexplaininggeologicaldata.

Acommon technique forgeology is theuseofheight-fieldmaps (orwarping)wherebya3Dsurfaceiscreatedfromscalarvaluesusedtodisplaceanotherwiseflatplaneproportionatetothedata field. The displacement is performed orthogonal to the plane in either positive or negativedirections.Themostintuitiveuseofthisistomapterrain-heightdatatoheightmapping,butany2Dscalarfieldcanberepresentedthiswayinordertoquicklyidentifypeaks,valleys,andgradients.Another2Dtechniqueusedwithscalarvaluesistoapplyamappingfromdataintocolors.Insomecases,thecolorsmaycomefromactualphotographiccaptureandrepresentthephysicalappearanceoftheterrain.Thisneednotbethecaseofcourse,andmappinganarbitraryselectionofcolorsontoarepresentationisthusreferredtoaspseudocoloring.Further,a2Dcoloringofonedatafieldcanbedrapedovera2Dheight-mappedfieldofanotherdataset,allowingtheresearchertomoreeasilycomparetherelationshipbetweenthetwovalues.

For3Dscalarfields,suchasthosecreatedthroughtomographictechniquesorbuildingupavol-umefrom1DsignalssuchasGPR,therearethreecommonvisualizationtechniques:isosurfaces,volumerendering,andslices.

Isosurfacesareanextensionoftheisocontourlinetechniquethatproducesasurfacewithina3Dvolumeoverwhichallthevaluesareconstant.Aswithcontourlines,theremaybemanydisjointsurfacesofthegivenvalue.Also,multiplevaluescanbeselectedtoshowmoreoftheinternalstruc-tureofthedata,asisdonewithterraincontourlines.Isosurfacescanbeusedtoshowboundariesbetweensubsurfacelayersorperhapstoshowtheshapeofasubsurfacereservoir.

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Volume rendering is an alternative 3D visualization technique that can be used to see mul-tiplefeatureswithinavolume.Asopposedto isosurfaces,wherethe3Ddataareconvertedintosolidstructureswithinthevolume,volumerenderingtreatsthedataasanebulouscloud.Theviewthrough thecloud containscolorsbasedon thevaluesalong the lineof sight.Colorsaswellasopacitiesaremappedontospecificdatavalues.Thisisreferredtoasthetransfer function.Throughtheuseofpartialopaqueness(i.e.,partialtransparency),particularvalueswithinthedatacanbehighlightedtodifferingdegrees,whichthusleadtothenebulous-stylerendering.

Reducing the volume to specific slices through it is another way to peer into a 3D volume.A commonvisualizationmethodistoslicethroughoneofthecardinalplanes(i.e.,orthogonaltooneoftheprimaryaxes).Aslightlymorecomplexalgorithmcanbeusedtoslicethroughthedataatanyangle.Ineithercase,ininteractivevisualizations,theuserisgenerallygiventheopportunitytomovetheslicearound,givingabetterperceptionoftheentiredataset.Amethodmorespecifictogeologyvisualizationistotakeaslicealongatraverse(seeSection47.3.3).

Pointclouddatacanbeconvertedintogeometricshapeswithtechniquessuchasalpha shape and Delaunay triangulation(Edelsbrunner&Mucke,1994).Butwithmodernhardwarerenderingcapabilities,amorecommonmethodhasbecometosimplyrenderall,oralargesubsetof,thepointsthemselves.Eachpointmaybecoloredbyanyoftheassociatedmultivariatedata(actualcolor,timeofflight,classification,etc.).

Thecoloringtechniquediscussedearlierwiththe2Dvisualizationtechniquescanequallybeappliedto1Dand3Dscalarfieldsaswell.Thechoiceofhowtomapvaluesintocolorsisaveryimportantone,thoughalltoooften,adefaultrainbowcolormappingisused.Poorchoiceoncolor mappingcandisguiseimportantfeaturesorcreatetheappearanceofaninterestingphenomenonthatdoesn’texist(Rogowitz&Trenish,1998).

Therepresentationofvectordatasuchasfromfloworgradientfieldscanbedoneinahandfulofways,affectedbywhetherthefieldisinsteadystateorchangesovertime.Acommonandeasytocomprehendflowfieldrepresentationismoving particlesthatarereleasedfromstrategiclocationsandadvectedthroughthefield.Streamlinesandstreaklinesrepresentthepathsthatparticlesfollowovertimeorpathsofparticlesthatareinfinitelyfast.Line interval convolution(LIC)isatechniquethatshowstheentiretyofasteady-stateflowfieldbywarpingawhitenoisetexture.TheLICtech-niqueworksbestasa2Dtechnique(perhapsona2Dslicethrougha3Dfield),whereastheothertechniquesworkequallywellfor3Ddata.

47.3.3 viSualizaTion TechniqueS For geology

Thenatureofgeologicalphenomenontakingplacebelowtheplanet’ssurface(thoughsometimesmadevisiblethroughgeologicorhumanactivity)isprimarilyreflectedbymeansofdatacollectionandthensubsequentlyhowtolinksubsurfacevisualizationstorecognizablesurfacelandmarks.

Giventhatsomefeaturesofaplanet’sgeologyarereflectedin the topographyof thesurface,anearlyvisualizationtechniquewastheuseofisocontour linesdrawnatregularintervalstogivebothaquantitativemeasureoftheheightoftheterrainaswellasaqualitativeviewofthegradi-entofthesurface.Thiswasalsoextendedtobeanearlyuseofstereoscopicdataanalysisthroughthecreationofstereo-pairtopographiccontourmaps(Blee,1940;Gay,1971)andbeforethataerialphotography(Bagley,1917;MacLeod,1919).(Althoughtheuseofstereotoviewterrainfeatureshassincebecomecommonplace,theuseofstandard[flat]topographic contour and terrain mapsremainsprevalent.)

Thevector geometry objects with attributedatadiscussedinSection47.2.7alsoleadtospecificvisualizationtechniquesthatcanbethoughtofascartographicinnature.Differentpolygonalshaperegionsmightbecoloreddifferentlybasedonvegetation,landuse,orgeologicera.

ThenatureofwelllogorGPRdata(atleastwhenlookingatasingletransmissionandreturn)mapseasilyintorepresentationsofthedataasasignalsimilarinnaturetotherecordingofasingleseismictrace—a1D needle vibration movement plotted over time.Ofcourse,forwelllogorGPR

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data, the data are sensory responses plotted over depth. The 3D nature of the measured objectbeginstobecomeapparentthroughmultipleplotsofwelllogsorGPRrecordings.

Fence diagrams,whichare2Ddataslicestakenalongasurfacetraverse,areageologicalvisual-izationtechniquethatisusedtoviewsubsurfacedatathatcutsacrossanimportantterrainfeature.AsmentionedearlierinSection47.3.2,fencediagramsaresimilartosomestandardslicevisual-izationtechniquesusedinotherfieldsofresearch.Becauseweknowthatdataoneithersideoftheslicewillgenerallybesimilartothesliceitself,itallowstheresearchertomakeoverallconclusionsregardingtheneighboringgeologicmaterial.

Techniquesthatcut awaypartofthesolidrepresentationoftheplanet’ssurfaceareclearlyveryimportant when working with data measured from beneath the terrain surface. Similar to howsomemedicalvisualizationsmight showanotherwisecompleteexternalviewofaperson,withcertainlayersofskin,muscle,etc.,removedtoshowtheinternalorgansinaparticularlocation,thesamecanbedonefortheEarthorothercelestialbody.Wecanstripawaythetopsoiltoseewhat’sbeneath.Oftentheedgesoftheholecreatedbythecutawaywillthemselvesshowsubsurfacedata,usingtheslicetechniquesdescribedearlier.Theninternally,techniquessuchasisosurfacesorvol-umerenderingmightshowreservoirformationsormineralcontent.

47.3.4 TemPoral DaTa

Whileit’seasytojumptotheconclusionthatthetimescaleofgeologicprocesseslimitstheneedfordatasetsinvolvingmorethanasingletimestep,thisisofcoursenotthecase.Forstarters,therearegeologicevents thathappen inhuman-scale time:activevolcanicactivity,earthquakeshifts,landslides,aswellasslowererosiveactivitiestonamefour.Furthermore,thereareunboundedareasinwhichgeologicprocessesmightbecomputationallysimulated,allowingforthepredictionofhowlandmasses,etc.,changeinmuchlargertimescales.Smallsimulationsmighthavehundredsoftimesteps,andgivensufficientcomputeresources,thousandsormoretimestepsmightbeproduced.

Wethinkoftime-lapsemoviesasoneswhereslowlychangingphenomenaarebroughttolifethroughlong-intervalphotographypresentedbackinrapidsuccession.In thisway,wecanmoreeasilyseebehaviorandflowthatwasdifficulttoperceiveatrealtime.Likewise,geologicandotherscientificdatacanbecollectedovertimewithlargetimeintervalsbetweencollections,andthesedatacanbeviewedinrapidpresentation.Twoareaswherethisismorecommoninthegeologicsciencesareintime-lapse seismic survey dataandmultipleLIDARcapturesusedfordetectingandmeasuringchangessuchasbeforeandafterearthquakesandlandslides.TheapplicationinthecasestudyinSection47.4.2(LidarViewer)isfrequentlyusedwithjustthistypeofdata.

Butingeology,evenstaticdatarepresentchangeovertime.Depositedsedimentsbuiltupoverlongperiodsoftimeformingundergroundrocklayersandotherphenomena.Workingthroughthelayers,geologistsdrawconclusions,whichthenallowthecreationoftemporalmodelsofgeologi-calactivity.OnesuchexampleishighlightedinthecasestudyinSection47.4.4wherethechangesin sand dunes are analyzed through an application with an immersive interface and tools thatspecificallymakeuseof the6-DOF inputsaffordedby that interface.Similarly,archaeologistsgenerallycorrelatethedepthinwhichanartifactisfoundwithincreaseinageasexemplifiedinSection47.4.3.

47.3.5 beneFiTS To geologic viSualizaTion From The immerSive inTerFace

Therearebothgeneralandspecificwaysinwhichimmersivetechnologiescanbenefitthevisualiza-tionofscientificdata.Someofthegeneralbenefitsderivemerelyfromtheaccidentalpropertiesofageneralimmersivesystem:substantialcomputepoweranddisplaysizeandresolution,whereasthebenefitofperspectiverenderingisspecificallypartofthenatureofimmersivesystems.

Perspectiverenderingiswhatmakesimmersivesystems(virtualreality[VR])unique.Itisthemeansbywhich,whenanimmersedviewermovestheirhead,theworldrespondsasthoughitwere

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actuallytherebeforethem.Thisfeatureprovidesimprovedperceptionoftherelationshipbetweenobjectsbasedonthatabilityofmovingone’sheadtoseetheworldfromanewperspective.

Mostimmersivedisplaysystemshavealargerfield of regardthanatypicaldesktoporlaptopdisplay.Thefieldofregardisameasureofhowmanydirectionsyoucanlooktoseethevirtualworld.Largerscreens,multiplescreens,andscreensthatfollowyouaretypicalwaysofincreas-ingthefield.Increasedfieldofregardbenefitsvisualizationtoolsbyprovidingmorerealestatetoplacethedata.Geologicallyscaledsimulationscanoftengeneratelargedatasets,andhavingtheabilitytoputasmuchdatainspaceaspossiblecanbehelpful.Relatedtothat,whenfocusedonaparticularphenomenon,itcanbehelpfultoseeitinitslargercontext.Thelargescreenspaceallows for the larger contextwhile placing important details in front and center, especially forhigher-resolutionsystems.Ontheotherhand,whensurroundingtheuserwithdata,itcanbecomedifficultforthemtofindtheuserinterface.Somesystemsthereforeallowtheusertocontroltheplacementoftheinterface,aswiththeLidarViewerapplicationinSection47.4.2.Anothersolu-tionistouseaninterfacethatisalwaysfrontandcenterwhensummoned,suchastheradialmenudiscussedinSection47.4.1.

Theotheraccidentalbenefitofusingimmersivesystemsistheincreasedcomputationandren-deringcapabilitiesthatareoftenincludedaspartoftheintegratedsolution.Thus,moredatacanbeheldinmemory,processed,andrenderedthanwithatypicallaptopordesktopcomputer.

Thereareamyriadofspecificuserinterfacefeaturesthatcanbeaddedtoanimmersiveapplica-tion.Atthispoint,therehasbeenonlyminimalcoalescencearoundcommonuserinterfacetech-niquesorlibraries.Thisstewofinterfacesisnotnecessarilyagoodthinginthelongrun,butasthestillquitenascenttechnologyevolves,thisflexibilityprovidesopportunitiesfornewandquiteuseful innovations.Someof these innovativeuser interfacesarediscussed in thecasestudiesofSection47.4.InSection47.4.1,theuseof3D magic lensesisdemonstratedasameanstocorrelateinformationfromdifferingbutspatiallyoverlappingdatasets.InSection47.4.2,ahandheldpaintingoperationisprovidedinordertoquickly,yetspecifically,selectpointsthatbelongtoaparticulargrouping.InSection47.4.4,avirtual Brunton compasswascreatedtomimictheutilityoftherealarticlebutappliedwithinthevirtualworld.

47.4 exemplar applICatIons and lessons learned

As immersive technologies began maturing in the late 1990s, opportunities for visualizationresearchers toworkwithgeologistsbegan tomaterialize.Twogoodexampleswerereportedatthe1999conferenceonvisualizationhostedbytheIEEE(InstituteofElectricalandElectronicsEngineers).TheseexamplesincludeworkbyFröhlichetal.(1999),whichprovidedauniqueinter-faceforgeologiststoviewseismicvolumes,andwelllogsusingisosurface,andslice-planetech-niques controlled by a newly created 6-DOF input device dubbed the Cube-Mouse. The otherrelatedprojectfromthe1999visualizationconferenceisbyWinkler,Bosquet,Cavin,andPaul(1999)wheretheyextendedanexistinggeosciencevisualizationprogramtoworkinanimmer-sive display system. Experimentation continued in the first decade of the new millennium asexemplifiedbyaboreholedrillingplanningapplication(Dorn,Touysinhthiphonexay,Bradley,&Jamieson,2001),aswellastheuseofGPRtoconstructanundergroundantcolonynetworkbasedontheboundarybetweensoilandairunderthesurface(LaFayette,Parke,Pierce,Nakamura,&Simpson,2008).

Inthesubsectionsthatfollow,wewilltakeaquicklookatfourapplicationsthatmakeuseofimmersivetechnologiestoassist intheprocessofgaininginsight intodataaboveandbelowtheplanetarysurface.Theseapplicationswerewrittenincollaborationwithresearchgeologistswhowereseekingbetterwaystounderstandtheirdata.Ingeneral,we’llgivealittlebackgroundinfor-mation,discussthevisualizationtechniquesused,presentthespecificwaysimmersivetechnologieswereused,anddiscusssomeofthebenefitsandotheroutcomesoftheproject.

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47.4.1 aPPlicaTionS For exPloraTion oF TerrainS, aSSociaTeD geoPhySical DaTa, anD Well logS aT The univerSiTy oF louiSiana aT laFayeTTe

BorstandKinslandappliedVRenvironments toexploregeosciencedatasets including(1)acol-lectionof3DdatasetsovertheChicxulubimpactcrater—forexample,BorstandKinsland(2005);(2)dataforinsightintowaterdrainageintheLafayette,Louisianaregion(Kinsland,Borst,Best,&Baiyya,2007);and(3)welllogdatafromtheNorthLouisianaCoalbedMethanedatabase—forexample, Kinsland, Borst, Tiesel, and Bishop (2008). They visualized these datasets in HMDs,single-wallstereoscopicprojectiondisplayswithtrackedviewpoint,aCAVE-likedisplay,areach-in-style fishtank display (a desktop display that provides a haptic stylus tool in a fishtank-sizeddisplayvolume),andalow-cost3DTV-basedenvironment.ThesystemshavebeenusedbothforgeologicalinvestigationsbyKinslandandcolleaguesaswellasforeducationalpurposesingeologyclasses(seeFigure47.7).

47.4.1.1 rendering and Interaction for terrain InterpretationGraphicalrenderinganddisplayofthesedatasetsforVRwasrelativelysimplecomparedtosup-portinguserinteraction.Earlyversionsofthesystemsachievedreal-timerenderingwithmeshtil-ingandculling,carefuluseofgraphicscardfeatures,andreduced-resolutionmeshestexturedwithcolorsandshadingfromhigher-resolutionversions.ToolkitssuchasOpenSceneGraph(Wang&Qian, 2010) andVRJuggler (Bierbaumet al., 2001)provided commongraphicsoperations andaccess to standardVRdevices inaportablemanner (withextensionsnecessary for thefishtankdevices).Toolkitsfor3Dinteractionand3Dinteractiontechniques,ingeneral,arelessdeveloped,leadingtotrade-offsbetweeninterfacequalityanddevelopmenttime.Forgeneralsystemcontrol,

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FIGure 47.7 UsersviewingChicxulubcraterdata.Twodisplaysareshown:alargeprojectiondisplayattheLouisianaImmersiveTechnologiesEnterprise(LITE)andafishtankdisplay(left)thatallowsausertoreachintoa3Dvolumewithahapticstylusunderamirror.ThetwosystemswerenetworkedforademonstrationattheGulfCoastAssociationofGeologicalSocieties(GCAGS)2006geologyconvention.Twomovableboxesarevisibleinthemaindisplay:oneselectsasourceregionofthemainmesh,andtheothershowsasecondarydatasetfromthatsourceregion.Aradialmenucanbeseennearthetopcenter.Annotativemarksofvariouscolorsappear,arrangedalongconcentricarcsnearthestandingusers.

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amenutoolkitwasdevelopedasacompromisebetweennaïveinterfaces(e.g.,functionsmappedtomanybuttons)andspecialized3Dinteractiontechniques(e.g.,directorwidget-basedmanipulationofobjects).Userstudiesoftheray-basedmenuinteractionsupporttheuseoflargeradiallayouts,contextuallocationofmenusatobjectsofinterest,andautomaticscalingforconstantprojectedsize(Das&Borst,2010a,2010b).Contextuallocationcancausemenuplacementbehindotherobjects,sothemenuswererenderedinawaythatreveals themthroughoccludingobjects,withreducedintensity.

Theability todirectlymove through thedatawithnaturalnavigation techniques isofprimeimportance for data exploration. Kinsland notes a personal affinity for navigation with naturalmovement(headmotionandwalking)inHMDandCAVEdisplays.Thisisaugmentedwithwand-based flying, grab the world, and scaling interactions to provide a greater range of movement.Unconstrainednavigationcanleadtouncomfortableviews,especiallywhenobjectsaretooclosetotheviewer,wherestereoscopicimagesaredifficulttofuse.Thus,whentheuserwantstoseealoca-tionontheterraininmoredetail,itcanbebettertoscaleupthesceneaboutthepointofinterestthantoflycloser.Thedifferenceisnotalwaysclearto,oradequatelycontrolledby,users—especiallythosenotaccustomedtostereoscopicviewing.Insingle-sideddisplays,rangingfromsmall-scalefishtankdisplaystolargerprojectionwalls,gooddistanceanddatasetorientationcanbemaintainedwithcenter of workspace–styleconstraints(Wareetal.,2001).InBorstandKinsland’ssystems,thisisachievedinpartbyaconstrainedgrabwheretranslationsareconstrainedtolateralslidinginaworkspace-centereddataset-alignedcoordinateframeandrotationsareconstrainedtofitanazimuthelevationmodelforthecenteredframe.

Withagoodnavigationalschemeinplace,thenextfeatureofimportanceistheabilitytoplaceandadjustannotativemarkers.IntheimpactcraterandLIDARterrainsystems,usersplaceandeditmarkersonsurfacestoannotateinterestingfeatures.Theapproachvarieddependingondis-play type. Ina single-walldisplay, themarkingandeditingusually involved ray-basedpointingat targets. InearlierCAVEandHMDimplementations,usersmoved theirhand (wand)directlyovertargetstodropmarkersinplace.Theray-basedmanipulationrequireslessnavigation,astheusercanbefurtherfromthetarget,butitislesspreciseatagivendatasetscaleduetofundamen-talproblemsoftheimprecisionoflong-rangeraypointing.Inthefishtankdisplayconfiguration,annotationscanbemoresophisticatedandpreciselyplaced:itspen-liketoolhasstablemechanicaltrackingandcanbeusedtodrawdirectlyondatasets.Thepenalsosupportsforcefeedbackthatallowsuserstofeelvirtualsurfaces.Itwasshownthattheforcefeedbackimprovesperformanceofuserstracingpathsonterrains,particularlywhenpathsfollowterrainfeaturesratherthancrossoverbumps(Raghupathy&Borst,2012).Researcherspreviouslysuggestedvariouswaysforforcefeed-backtoaidinteractioningeoscience-typeapplications—forexample,Harding,Kakadiaris,Casey,andLoftin(2002)andKomerskaandWare(2004).Forexample,forcecuescouldsupportpointorterrainediting,menuselection,andinteractive3Dwidgets.Othershavediscussedvariousbenefitsoffishtankconfigurationsoverfully immersivedisplays(Demiralp,Jackson,Karelitz,Zhang,&Laidlaw,2006;Mulder&Boschker,2004).Theyhaverelativelylow-costandspacerequirements,highangularresolution,and(forthereach-inapproach)afocalplanesuitedtohand-basedinterac-tionswithvisualizedobjects.

47.4.1.2 Well log VisualizationInthewelllogvisualizationsystem,usersviewverticalplotsofresistivityandspontaneouspoten-tial(SP)underaterrainandcanselectaplotorwelltomovemarkersalongitsaxis.Alloranysubsetofthewelllogsmaybedisplayedsimultaneously.Theirverticalscaleisadjustablesothattheusermightinvestigatethesignaturesoftheindividuallogsorofaselectionoflogsatvariousscalestohighlightfeatures.Correlationmarkersthatweredeterminedforeachwellinaseparate2Dsystemarealsodisplayedinthissystem.

Thissystem,asitis,servestoconvey3Drelationshipsofthewellsinawaythatpseudo-3Dren-deringsmaynot.Wheninsidethedata,ageologistfeelsmoreasiftheyareviewingandinterpreting

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fromwithintheearth.Ifmanywelllogsarevisiblesimultaneouslyatvariousdepths,thestereo-scopicvisualsmayhelpdisambiguateoverlappingplots.Beingabletooverviewmultiplewelllogscanbehelpfulfordeterminingsubsurfacestructures,lithologies,fluidcontents,faults,etc.,basedonplotfeaturesacrosswelllogs.Inthefuture,segmentsofmultiplewellscouldbeselected,moved,andscaledforcomparison(correlation).Correlationpointsmarkedonmultiplewellscouldinflu-enceacomputer-generated3Dsurfaceorvolumeinterpolated/extrapolatedthroughoutthedatasettoaidfurtherinvestigation(thistypeofinterpreter/computerinteractioniscommoninpackagesthataidinterpretationofseismicdatavolumes).VRwouldhelppreserve,correct,andmaintainconstantvisibilitybetweengeospatialrelationshipsoffeatures,reducingmentaltransformationsbetween2Dphysicalimagesand3Dmentalimages.

Togetanoverviewofundergroundfeatures,ausercanviewmanyplotsatonce.Thispresentsa problem for ray-based selection when desired targets are partially or fully occluded by otherplots.Toaidthisselection,theresearchersdevelopedHandymap,whichusesthumbmotiononthetouchsurfaceofatrackediPodTouchtoallowmoreprecisetargetingandselectiondepthcontrol(Prachyabrued,Ducrest,&Borst,2011).Thisapproachisadditionallymotivatedbyitsbenefitsforlow-costVRenvironmentswithreducedtrackingaccuracy:itdoesnotrequireverypreciseorstabletracking(Figure47.8).Thetouchsurfacecouldadditionallybeusefulfortaskssuchasmenuselec-tionorsymbolicinput(textentry).

47.4.1.3 3d lens Immersive Interface techniqueWhiledevelopingthesystemsdescribedearlier,BorstandKinslandexplored3Dlens-basedtech-niques formanagingmultipledatasetviewsandfilteringoptions inVRsystems for interpretinggeosciencedata.In2Ddesktopinterfaces,windowingsystemsareawell-establishedstandardfororganizingmultipleviews,forexample,showingadatasetinmultipleviewsorwithmultiplevisual-izationoptions,multiplerelatedorcolocateddatasets,collaborativeviewsthatcommunicateinfor-mationfromremotecollaboratorsduringnetworkedoperation,ormultipletemporalviews(differenttimepointsoranimatedviews).Thismotivatedvariousresearcherstoincorporaterelatedmecha-nisms into3D interfaces.PlumleeandWare (2006)explainedhowsuchwindowingcan reduce

FIGure 47.8 Low-costwelllogvisualizationsystem(Mitsubishi3DDLPTVandiPodTouchwithmarkersforOptiTrackcamera-basedtracking)showingwelllogs(curves)hangingunderneathaterraingeneratedfromSRTMdata.TheiPodTouchpresentsanoverviewofthewelllogscenethatresolvesocclusioninthemainviewandsupportsrapidtouch-basedselection.Amiddleverticallinerepresentsavirtualrayinthemainview,whichislockedduringaselectionstepaftercoarseraypointing.Right:(Constructedconceptualillustration)Welllogpicksillustratedashorizontallineswithassociateddepthandtextannotation.Ahighlightedpickontheleftlogisbeingassociatedwithapickontherightlogbyadraggesture.

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errorsrelatedtovisualworkingmemorylimitswhencomparedtoalternativenavigationtechniquessuchaszooming.

Somebasicapproachestowindowinginstereoscopic3Denvironmentsincludeplacing2D-stylewindowsinto3Dspaces—forexample,Task Gallery (Robertson,etal.,2000)—orusingportal-likewindowsintoother3DscenesorviewsaswiththeSEAMSproject(Schmalstieg&Schaufler,1999) or tunnel windows (Kiyokawa & Takemura, 2005). Another approach is Itoh et al.’s 3DWorldBottles, which are containers for separate scenes that are each drawn on their containersurface(Itoh,Ohigashi,&Tanaka,2006).WorldBottlessupportsoperationssuchastransportingobjectsbetweenbottlesandtransportingusersintobottles.

Incontrast,BorstandKinslandextendedrenderingmethodsandapplicationofvolumetric(3D)lenses,originallyintroducedbyViega,Conway,Williams,andPausch(1996)asa3Dversionof2Dlenstechniques(Bier,Stone,Fishkin,Buxton,&Baudel,1994;Perlin&Fox,1993).Describedsimply,these3Dlensesareboxescontaining3Dobjectsdifferentfromthemainscenerendering.Alenseffectisapparentwhenthechosencontentappearstomodifythesurroundingmainview,forexample,whenthelenscontainsaversionofthemaindatasetwithadifferentcolormapapplied(Figure47.9).Themostrecentrenderingtechniquesforsuchlensesin3Dincludemechanismsforcomposingeffectswhenmultiplelensesintersect,forexample,combiningacolormapwithamag-nifyingeffect.AcomparisonoflensrenderingtechniquesisgiveninBorst,Tiesel,andBest(2010).

Inaddition to lens-typeeffects, 3D lenses canbeusedmoregenerally as containers for sec-ondarydatasetsorviews,leadingBorstetal.tousethetermvolumetric windows(Borst,Baiyya,Best,&Kinsland,2007).Asimilarterm,volume windows,waspreviouslyusedtorefertoa3Dwindowingsystemproposedforholographicdisplays(Kurtenbach,Fitzmaurice,&Balakrishnan,2002).Volumetriclensescanalsobeusedtocut outportionsofenvironments,resemblingclippingvolumes—forexample,Weiskopf,Engle,andErtl(2003).Lensesconfiguredtocontainminiatur-izedversionsofa3Denvironmentresembleworld-in-miniaturetechniques(Stoakley,Conway,&Pausch,1995).

Comparedtoothersecondaryviewtechniques,volumetriclensesmaybetterpreserveviewingandinteractionfor3Denvironments.Stereoscopicandmotionparallaxeffectsarepresent,unlike

FIGure 47.9 Twolensesapplyeffectsinconstrainedregionsonaterrain.Abox-shapedlensappliesacol-ormap,andadistancetoollensaddsconcentriccontoursaboutitscenter.Forclarity,thelensesarelargeandthemainviewiszoomedout.

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for2D-in-3Dwindows.Comparedtoportalsorflatlensesin3D,avolumetriclensviewcanbemorestablebecause thesamedataset region isaffectedby(contained in) the lens regardlessofview-point.RaghupathyandBorst(2011)explainedthatthissimplifiesplacementofsecondaryviewsinaterrain-markingapplication.Theydemonstratedperformanceandsubjectiveimprovementswithaboxviewcomparedtoportal-ormirror-typeviewsinasmall-scaleVRenvironmentfordrawingon terrains.Theirevaluationalsounderscores the importanceofdirect interaction in secondaryviews:usersshouldbeabletoannotatevisualizedfeaturesdirectlyinsecondaryviewsratherthanusethemonlyasavisualguidewhileannotatinginamainview.

BorstandKinslandinitiallyapplied3Dlensestodealwithmultipledatasetsoverasingleregion.Kinsland’sgeologyresearchtaskwastomarkextremaandridges(e.g.,lowpointsfromlimestonesinkholes)onaterrainandcheckforcorrespondingfeaturesinagravitydatasurface.Interpretivemarks placed on one dataset were projected to the other to help check consistency of features.Priortoincorporatingmultipleviews,muchtimewasspentswitchingbetweendatasets,manipulat-ingviewpoints,andtogglingfilteringoptions.Forexample,toexamineminima,Kinslandappliedlarge-magnitude negative scale or flipped a mesh upside down to view the bottom. Long andrepeatedsequencesofsuchmanipulationsoccurredasKinslandsoughtthebestmarkerplacement.Alenssystemwasaddedthatalloweduserstosee throughonedatasettoanotherwithalens,grabandmovecopiesofdatasetregionsinboxestoarrangemultipleviewsinthescene,changefilteringandviewoptionsinlensesindividually,andreachintoanyofthelensestoplaceoradjustannotativemarks.Basedoninformalobservations,therewassometimespentsettingupthelensviews,butduringconsistencychecking,thissubsequentlyreducedtheswitchingandmanipulationofdatasets.Theabilitytofine-tuneannotativemarkswasenhancedbecausetheusercouldimmediatelyreachintoanyofmultipleviewstoadjustmarkers.Kinslandnotesthatseeing throughonemeshtoanotherinamovablelenssupportscomparisonbetweendatasetswithlittleoveralldistractionorfocusshift:thelensboxcanbemovedbackandforthwhiletheuserremainsfocusedontheareaofinterestandthesurroundingcontextislargelypreserved.

Othertaskshandledbywindowsofconventional2Dvisualizationsystemsmaybeaddressedin3Dby3Dlenssystems.Forexample,3Dlensesmaybeusefulforcommunicatingaremotecollabo-rator’sfilteringoptionsorviewofadataset.However,differencesbetweenlocalandremoteview-inggeometryinhead-trackedimmersivevisualizationmakeitdifficulttoreplicatearemoteuser’sviewpreciselywhilestillprovidingstereoscopicviewingandperspectiveappropriatetothelocalviewer.Recently,Borstetal.haverenderedtime warplensesthatnavigatetime-varyingdatasetsbyallowingausertoselecttime,relativetimeoffset,oranimatedplaybackinsideofconstrainedregionsselectedbylensvolumes(Borst,Tiesel,Habib,&Das,2011)(Figure47.10).Futureworkcanimprove3Dlensandwindowsystemswithbetterinteractionandarrangementtechniquesandintegrationintoscenegraphtools.

47.4.2 liDarvieWer: an immerSive PoinT clouD viSualizaTion Tool

The LidarViewer application (Kreylos, Bawden, & Kellogg, 2008) is a LIDAR data visualiza-tiontoolthatrunsonawiderangeofhardwareplatformssupportedbytheVruiVRintegrationlibrary(Kreylos,2008).LidarViewerdependsontheVruiVRintegrationtoolkitforitsimmersivefunctionalitysupport,which integratesdisplayconfigurationwith3Dinteractiondevices.Whenproperlyconfigured,theVruitoolkitlayerallowstheLidarViewerapplicationtoprovidethesamefunctionality on either a desktop platform or VR platform (CAVE-style or head-based display)(Figure47.11).

LidarViewer was developed under the auspices of the KeckCAVES lab at the University ofCalifornia,Davis,DepartmentofGeology.Notsurprisingly,theKeckCAVESlabisdedicatedtothevisualizationandanalysisofgeologicdatathroughtheuseofimmersiveinterfacesandotheradvancedtechniques.ThelabservesclientswithintheUCDavisDepartmentofGeologyandfromotherorganizationssuchastheUSGS.

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47.4.2.1 Visualization techniquesLIDAR capture devices generate a large set of points—a point cloud—with the 3D location oftheoptical signal returnalongwithotherdata suchas intensity and timeofflight.Often, colordataarecapturedthroughasecondarysensorandassignedasanattributetoalocationdatapoint.LidarViewerthenpresentsthesepointcloudsusinganoptimizedrenderingtechniquethatbuildsanoctreestructurefromthedatasetbringingpointsintothesceneasneededtopresentthefullresolu-tionwithinthenearbyfieldofview(Figure47.12).

FIGure 47.10 Auserinaprojectionenvironmentusesray-basedinteractiontosetuparelativetimewarplens.Thecolorinthisparticularlensindicatesincreasedrainfall6hbeyondthecontext.

FIGure 47.11 DatafromanairborneLIDARscancapturingthepathofapowerlinegridarepresentedusingLidarViewerinaCAVEdisplay.

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Withthedataloadedandvisibleinthe3Dimmersiveenvironment,theusercanselectasubsetofdatapoints,determinethedistancebetweenpointsorplanes,aswellasperformreal-time3Dnaviga-tionthroughthedataset.Thesedatainteractionandvisualizationtechniquesprovidetheuserwithatrueimmersiveexperience.Theuservisualizesthedataasthoughactuallypresentinthelocationwherethedatawerecollected,therebyprovidingtheuserwithinsightintotheirdatanototherwisepossible.

Inpreparationforusingtheimmersivedatavisualizationtool,LidarViewerprovidesapairofdataconversionandpreprocessingutilities.TheLidarPreprocessorutilitycomeswithdozensofoptionaldirectivesandconvertsastandardLIDARdataformatintoabinaryformatsuitablefortheoctreerenderingalgorithm.DataprocessedintoLidarViewerformatcanbefurtherenhancedwiththeLidarIlluminatorutility.TheprimaryenhancementperformedbyLidarIlluminatoristhecalculationofnormalsforeachofthepointsinthecloud.ThenormaldatacanthenbeusedbytheLidarViewerrenderingalgorithmtoproduceimprovedlightingeffects.

Menus within LidarViewer enable the user to control shading and other rendering options.Shadingoptionsincluderenderingpointsattheirgivenintensitylevel,usinganyRGBvaluesthatmayhavebeenassignedtothepointsorapseudosunreflectionmodelthatmakesiteasiertoseesurfacefeaturessuchas roughness.Pointscanalsobecoloredbasedon theirdistanceaboveanassignedgroundplane.Additionally,thenumberandsizeofthepointscanbecontrolledinordertobalancebetweenrenderingqualityandrenderingspeed.

47.4.2.2 Immersive Interface techniquesWrittenwiththeVruiVRlibrary,LidarViewerinheritsseveralstandardVruiinteractionmodes,includingabuilt-inmenuingsystem,whichincludesachoiceofmethodsfornavigatingthroughtheworld.TheVruimenusandwidgetsaredesignedtoworkreasonablywellinconfigurationsrangingfromadesktopworkstationuptolarge-scaleCAVEimmersivesystems.LidarViewerusesthemenusystemtobringupdialogcontrolsforshadingandothereffects,butLidarVieweralsohasinterac-tionspecifictoolsthatmakeuseoftheimmersiveinterface.

FIGure 47.12 UsingLidarViewerinaCAVEsystemtoviewchangesinterrainfromnaturalevents,sea-sonalchanges,andhumanactivity.AirborneLIDARscansfromtwotimeframesarerepresented,withpointsfromtheinitialscaninredandpointsfromthesubsequentscaningreen. AQ12

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AkeyfeatureofLidarVieweristheabilitytoselectpointsusing3Dpaintingandselectionges-tures.Allpointswithinacertainregionofthehandheldcontrollerarehighlightedastheyareaddedtotheselectedgroupofpoints.Thesizeoftheselectionregioncanbeadjustedthroughamenudialog.Itisalsopossibletoremovepointsfromtheselectionasdesired.Onceanacceptablesetofpointshavebeenselected,amathematicaldescriptionofthepointscanbecalculated.Thedescrip-tionisbasedonasetofsimpleprimitiveshapessuchasaplane,asphere,oracylinder.Themath-ematicaldescriptioncanthenbeusedtomakemeasurementsorotherannotationswithinthedata.

47.4.2.3 Idaho national laboratory lidarViewer WorkflowAsanopen-sourceproject,LidarViewerisalsousedatotherimmersivevisualizationcenters.OnesuchgroupistheCenterforAdvancedEnergyStudies(CAES)inIdahoFalls,Idaho,whohavesuc-cessfullyusedLIDARdataandimmersiveenvironmentstobetterunderstandandquantifygeologi-calchange.OnesuccessfulmethodinvolvesoverlayinggeoreferencedLIDARdatasetsacquiredatdifferenttimes,coloringthetime-seriespointcloudstohighlightchangesovertime.

The LidarViewer application creates a dramatic immersive scene allowing viewers to gaininsightsforavarietyofgeologicandnaturalresourcesincludingthefollowing:

1.Landmasschangeduetonaturalearthmovementsuchaslandslides,earthquakes,erosion,orsimilarchanges(Glenn,Streutker,Chadwick,Thackray,&Dorsch,2006)

2.Mechanicalman-inducedchangessuchasexcavation,construction,orroadbuilding 3.Seasonalvegetationchangessuchasthoseexperiencedinagricultureandwildlandfire 4.Short-andlong-termchangesinvegetationcanopyofagivenforestorrangelanddueto

naturalgrowth,fire,beetleinfestation,orforestryoperations 5.Changesinwaterlevelssuchaslakes,ponds,rivers,orsnowfields

Thesechangeshavebeenobservedintime-seriesdatasetsfromairborneLIDAR,aswellaster-restriallaserscanning(TLS)data.Thesedatasetscanalsobefusedtoprovidebothhigh-reso-lution(mmtocm)pointclouds(fromtheTLS),coupledwiththelargegeographicextents(fromairborneLIDAR) (Murgoitio,Shrestha,Glenn,&Spaete,2013).Additionally,LIDARdatasetscanbefusedwithhigh-resolutiondigitalimagery,includinghyperspectraldatatoprovidebothtrueandfalse-colorperspectivesofthepointcloud(Mooreetal.,2011;Olsoyetal.,2012).Theuniquecapabilityofimmersiveenvironmentstoviewthis3Dpointclouddatainitsnative3Dfor-matmakesthisrepresentationveryintuitiveforexploring,understanding,andquantifyingchange(Figure47.13).

ThecapabilityofdemonstratingtimevarianceusingtraditionalgeographicallyorientedLIDARdatasetsleadstoanexcitingextensionofthistechnologyinthechangedetectionofbuildings,infra-structure,bridges,andotherimportantfeatures.TheabilityofLIDARtocapturehighlyaccuratedata regarding current state and then capture feature evolution through subsequent scans opensthedoortoawholenewworldofveryimportanthistoricalpreservationactivities.TheCAEShasacquired a very-high-fidelity scan of the nearby Experimental Breeder Reactor No. 1 NationalHistoricalLandmark.Thefirstscanwillserveasthebaseline,whilesubsequentscanswillbestud-iedforchangedetectionactivities.

ResearchersaffiliatedwithCAEShavesuccessfullycombinedLIDARdatafromairbornesen-sorswith terrestrial-basedsensors, therebycreatingan impressivemultiresolution representationofaforest.Theair-basedscansgivetheoverallperspectiveandorienttheviewertothesurround-ingarea,whiletheground-basedscanprovidesauniqueviewofdetailsinareaswheredetailsarerequired.

47.4.2.4 outcomes and lessonsTheexperienceatIdahoNationalLaboratoryandIdahoStateUniversityisjustonesitethatmakesuseoftheLidarViewerapplicationtointeractwithpointclouddata.ResearchersatKeckCAVES

AQ13

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andtheUSGShaveanalyzedandmeasuredphenomenonsuchastheshiftcausedbyearthquakeactivity or the movement of structures whose foundations have been compromised by landslideactivity.

Inthecaseofearthquakeanalysis,asitealongtheSanAndreasFaultforwhichLIDARscanshadbeentakenbothbeforeandafteralargeearthquakeallowedscientiststocalculatebeforeandaftermeasurementsofvarioussurfacefeatures(Gold,Cowgill,Kreylos,&Gold,2012).Intheland-slideusagecase,ahousewithmuchofthefoundationwashedawaywasmeasuredfromthesamelocationonadailybasis,andbydeterminingthemathematicaldescriptionof thenearwall, themovementofthehousecouldbedeterminedtomillimeteraccuracy(Kreylosetal.,2006).

Theabilitytovisit,orrevisit,sitesofinterestinandofitselfisagreatbenefittothegeologistwhoselaboratorycanbeanywhereontheplanet,includingsitesthatcanbeexpensiveandother-wiseimpracticaltovisitonarecurringbasis.Thus,theabilitytovirtuallyvisitthesesiteswithcon-siderabledetailisagreatboon.Andwhilemeasurementscanbedirectlyobtainedwhileinthefield,theabilitytotakethemintheimmersiveenvironmentisfareasier(giventhatit’spossibletoflyorrescaletheworld)andalsocanbemoreaccuratethroughtheassistanceofbasingmeasurementsonalargecollectionofpoints.Consequently,LidarViewerisnotjustforrevisitinggeologicalandothersites,butcanbebetterthanthosevisitsbyallowingthevisitortotakemoremeasurements,explorethesitefaster,andleavebehindannotationsofintriguingdiscoveries.

47.4.3 viSualizaTion oF mulTimoDal geoPhySical Survey DaTa

Recognizingthatgeophysicalexplorationproduceslargeamountsofdata,generatedbyavarietyofacquisitiondevices,ateamofarchaeologistsandcomputerscientistsattheCaliforniaInstituteforTelecommunicationsandInformationTechnology(CALIT2)createdaprojecttofuseaerialandsat-elliteimageryandelevation;infrared,magnetic,andEMsurfacedata;GPR;photographsofartifactsandman-madestructures;andadatabasewithinformationaboutfoundartifacts(suchas3Dloca-tion,type,radiocarbondates,andpictures;Linetal.,2011).Someoftheacquireddatawerefurther

FIGure 47.13 Auserexploresawoodedareacapturedbyhigh-resolutionLIDARscanspresented inaCAVEwithLidarViewer.

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processed into derived data products: extensive image sets of 3D structures on the ground havebeenprocessedwithastructure-from-motion(SfM)algorithm(UniversityofWashington’sBundler;Snavely,Seitz,&Szeliski,2006)toderiveacolored3Dpointcloudofthestructure,and3Dmodelswerecreatedmanuallyforstructuresthatnolongerexistbutareassumedtohaveexistedinthepast.

Archaeologistscanoftenverifytheirhypothesesbydiggingupwhattheybelievetobeburiedsomewhere.ThisprincipledoesnotholdforCALIT2’sarchaeologistsworkinginMongoliatofindGenghisKhan’stombandotherhistoricalsites,becausetheMongoliangovernmentdoesnotallowthemtoexcavate.Thismeansthattheyhavetorelyentirelyonwhatisreadilyaccessiblefromthesurfaceanddatathatcanbeacquiredaboutsubsurfaceobjects.

TheStarCAVE(DeFantietal.,2009)atCALIT2isa34-megapixelimmersiveVRsystemandauniquetoolforarchaeologists,becauseitallowsthemtonotonlyvisitasiteofinterestonanothercontinentwithout traveling to itbutalso toshowall theavailabledataabout thesite in itsgeo-spatiallycorrectlocations.ThegoalofthiseffortistoexploretheuseoftheStarCAVEtoenablenoninvasivevirtual excavationthroughthe3DVRreconstructionofgeophysicalsurveydataofanarchaeologicalsitethathasbeeninvestigatedsinceJuly2010asacomponentoftheValleyoftheKhansproject,anoninvasiveremotesensingsurveyforburialsitesinNorthernMongolia.

Forthearchaeologists,workingwiththisVRtoolgivesthemtheuniqueabilitytoviewnotonlyaspatiallycorrectvisualizationoftheirdatabutalsoonethatfusesallthedatatypestheygenerate.Thisisbecausenooff-the-shelftoolsexistthatsolvethisproblemforthem.Understandingspatialcorrelationsbetweenthemultitudeofdatatypesthearchaeologistscollectiskeytomakingnewdiscoveries.

Therearenooff-the-shelfsolutionsforVRsystems,whichcandisplayallofthedatatypesthissystemsupports.AEGISEasy3D(AegisInstruments,2013)supportsGPRdatabutdoesnotruninaCAVE.ExistingGPRvisualizationsoftwareforVR,suchasBillenetal.(2008),doesnotallowpointvisualizationwiththedatapointsfollowingtheterrainunderwhichtheyarelocated(neededforapreciseanalysisofthedata).CommercialtoolssuchasEON’ssoftwaresuite(EONReality,2013)orVTK(Kitware,Inc.VTK,2013)arenotflexibleenoughtosupportallthedatatypesandvisualizationmodes,withtherequiredscalabilityduetothesizeofthedata.

47.4.3.1 Visualization techniquesThegeneralideaofthevisualizationtoolistodisplayallthedataintheirrespectivelocationsonavirtualreconstructionofthesiteofinterest.Inordertodisplaygeospatialdatacorrectlyonater-rainmap,wehadtogetaccurateterrainheight.ThiswasdonebyprocessingtheheightdatafromaTrimbleVXSpatialStationtotessellateaterrainsurface.Thisprovidedaterrainelevationasthemissingcoordinateforthe3Ddisplayofanydatasetarrays.Onevisualizationmode(Hill)showstheterrainwithaheightmaponit,whereaterrainheightiscolorcodedontothesurface.

Magnetic(Aitken,1958;Becker,1995)andEM(Fröhlich&Lancaster,1986;Tabbagh,1986)conductivityare2Ddatatypes,astheyresultinsurfacemapsofvariationsoftheirrespectivephysi-calproperties.Amagneticsurveyisapassivedetectionofcontrastsinthemagneticpropertiesofdiffering materials, whereas EM measures the conductivity and magnetic susceptibility of soil.A deriveddatatypeisbasedonthedensityofartifactsfoundin3×3mgridcellsaroundthesur-veyedsite,definedbythelocationsofmeasurementprobes,anddisplayedasafalsecolormapontheterrain.Ineachcase,asuitablecolorgradientisusedtomapthedatavaluestocolors.

GPR(Goodman,Nishimura,&Rogers,1995;Novo,Grasmueck,Viggiano,&Lorenzo,2008)andelectricalresistivitytomography(ERT)(DOE,2000)generate3Ddataarrays.GPRtransmitsanEMpulseandmeasuresareflectedsignalthatisdependentuponthedielectricpropertiesofsub-surfacematerial.ERTderivessubsurfacestructuresofresistivematerialsfromelectricalmeasure-mentsmadebyelectrodes.Thearchaeologistsusea1mgridfortheelectrodes.Notethatthedepthinformationinbothdataarraysisfordistancefromthesurface,whichmeansthatneighboringdatasampleswiththesamedepthvaluearenotnormallyonthesameplanebutfollowthecurvatureoftheterrain.

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Anotherimportantdatatypeusedinthisworkisphotographs.Picturesoftheprobesandtheirsurroundingareasaredisplayedonthegroundintheirrespectivelocations.Locationsofthevariousartifactsthatwerefoundintheareaarerepresentedasblueorredcubes.Theredcubesrepresentartifactsthathavebeenradiocarbondated(withdatesshownabovethecube).Bothtypesofcubescanbeclickedon,whichwilldisplayapictureoftheartifactinawindowfloatinginfrontoftheuser.

TwooftheobjectsatthesiteweredigitizedwithanSfMapproach:manyoverlappingphoto-graphs were taken from different viewing angles, and then the Bundler algorithm analyzed thepicturesandcomputedthecamerapositionstheyweretakenfromin3Dspace(Snavelyetal.,2006).ThisallowedthePMVS2algorithm(Furukawa&Ponce,2009)tocalculateadense,coloredpointcloudindicatingthegeometryofthedigitizedobject.Oneoftheseobjectsisacollectionofbricksfoundontheground;theotherisamodernshrinebuiltbylocalresidents.Thelatterservesmoreasalandmarkthantounderstandhistory.

Finally,a3DmodelingtoolwasusedtocreateatypicaltemplefromtheYuanDynasty,whichexistedatthesitearoundthetimetheradiocarbondatesindicate.Thetemplewasplacedandori-entedonaparticularlydensecollectionofbricksandsubsurfacematerialtoindicatethatthisisthelikelylocationofsuchastructure.

47.4.3.2 Immersive Visualization applicationThisimmersivevisualizationtoolwasimplementedasaplug-infortheVRmiddlewaresoftwareCalVR(Schulze,Prudhomme,Weber,&Defanti,2013).CalVRisbasedontheOpenSceneGraphAPI(Wang&Qian,2010)andrunsonLinux-basedvisualizationclusters.

Figure47.14 shows the applicationdisplayed inCALIT2’sStarCAVE.The imageon the leftshowsthesitewiththemagneticdatasurfacelayer(mostlygreen,withsomeyellowandredareas),the3Dtemplemodel(white),andtheprobesiteimages.Itcanbeseenthattheprobeswerearrangedona3×3mgrid.Thepictureontherightshowsthepointcloudofamodernshrineonthesite,whichservesasareferencepointforthearchaeologists.Thispointcloudwasnottessellatedinordertostayastruetotheacquireddataaspossible.

Figure47.15showstwoviewsofthesubsurfacedata.BothshowGPRpoints.Everyspherecor-respondstoasampleddatapoint.Thecolorsindicatevalue,normalizedtoacolorrangefromgreentoyellowtored.Theimageontheleftwastakenfromaviewpointonthesurface,showingonlytheGPRdatapointsatacertaindepth(setwitha3Dmenu).Theimageontherightshowsallavailabledatapointsfromacamerapositionbelowtheground.

Anartifactlocationcubeindicatingacarbondatedartifact,alongwithitsdatedtimerange,isshownintheleftpictureofFigure47.16.Thispicturealsoshowsthe3Ddigitizedgroundstructure,

AQ14

AQ15

(a) (b)

FIGure 47.14 StarCAVEuserhighabovethearchaeologicalsite(a)andlookingattheshrinemodel(b).

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donewiththeBundleralgorithm.Inthepictureontheright,onecanseethephotographofaprobelocationon thegroundand thephotographof the selectedartifact in the top leftcornerof theimage.Artifactsareselectedbypointingandclickingonthem.Selectinganartifactupdatesthepictureinthefixedframeatthetopleft.Theframeitselfcanbemovedaroundbyclickinganddraggingitwiththeinputdevice.ThepictureontherightalsoshowswhatatranslucentgroundtexturewithunderlyingGPRsphereslookslike.Thetransparencylevelcanbesetinthemenuwithaslider.

Theapplication’sVRmenuisshowninFigure47.17.Thethreesubimagesshowthreedifferentsubmenusoftheapplication.TheleftmostpictureshowstheModelsubmenu,themiddletheSurfacesubmenu,andontherighttheSubsurfacesubmenu.Ineachsubmenu,theusercantogglethedisplayoftherespectivevisualelementsaswellassettheirparameters.IntheSurfacemenu,thetranspar-encylevelforthesurfacetexturecanbeset.TheSubsurfacemenuallowsselectingalayerofspherestodisplayor toshowall spheres.TheDensityoption in thismenuallowssettingacutoffpoint,whichhidesallsphereswithadensitybelowthatvalue.Renderingallspherescreatesaverydenselypackedgrid,becauseourdataconsistofabout20millionGPRand400,000ERTdatapoints.

Allmenuadjustmentsresultinimmediatevisualfeedback.Theapplicationloadsallrequireddata intomemory at startup and, thanks tooptimized rendering algorithms,donot suffer from

(b)(a)

FIGure 47.15 Ground-penetratingdatavisualizedascoloredspheres(a).Warmercolorsindicatehighermeasureddensity(b).

(b)(a)

FIGure 47.16 SfMandradiocarbondates(a),imagesofthemagneticprobes,andartifactphotograph(b).

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loadinglagorotherdelaysoncethedatahavebeenloaded.Thisispossiblebydownsamplingthephotographstosizestheytypicallyaredisplayedatandafast,shader-basedpointrenderingalgo-rithmfortheGPRdata.Theterrainisnotcomplexenoughtorequiresophisticatedterrainrenderingalgorithmsandcanbeloadedintomemoryinitsentirety.

Themenuallowsselectingoneofthefoursurfacetypesandoneofthetwosubsurfacetypes,anditallowsdisplayinganynumberof3Dmodelsandpicturesdesired.Thisisbecausethelattercancoexistwithoutocclusion,whereastheformertwodatatypeswouldoccludeoneanother.

47.4.3.3 Benefits derivedThissuccessfulVRsoftwareapplicationforgeologistsandarchaeologistsfuses12differentdatatypesinoneinteractivevisualizationtool.Theabilitytoseeallthesedatatypesgeoreferencedonthesameterrainandgetanideaoftheageofthefoundartifactsaswellastheir3Dlocationsisnewforthedomainscientists.Itisveryvaluableforthemtobeabletospatiallycorrelateallthesedif-ferentdatatypes,whichismoreintuitivethroughtheimmersiveinterfacethanwiththeirexistingdesktopsoftwaretools.Additionally,theabilitytoseethedatato scaleisveryuseful,asistheabil-itytonavigatethroughthedataspacewitha3Dinputdevice.BothgeophysicistsandarchaeologistslikethatusingtheapplicationinCALIT2’sStarCAVE,whichcanaccommodateupto10peoplewhocancollaborativelyexplorethedataset.

(a) (b)

(c)

FIGure 47.17 (a–c)ThreesubmenusoftheVRapplication.

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Futuregoalsforthisapplicationaretosupportadditionaldatatypes,suchasLIDARpointdata,anddatasets that are larger than the sizeof thecomputers’RAM.Thiswill require integratingexistingout-of-coreapproaches,whichcouldperhapsbeimprovedbyoptimizingspatialaccessformultipledifferentdatatypes.

47.4.4 Dri lancaSTer SanD Dune layerS

Sgambatietal.alsoappliedimmersivevisualizationandanalysistoGPRdata(Sgambati,Koepnick,Coming,Lancaster,&Harris,2011)—inthiscase,datacapturingthesubsurfacelayerswithinsanddunes. They began with the immersive tool Toirt Samhlaigh (O’Leary, 2010) to volume renderstacksofGPRimagesinfour-sidedCAVEandsix-sidedCAVE-styledisplaysandonalow-costimmersivedisplay.During theircollaborationwithgeologistNickLancaster, they introducedanimmersivewidgetcalledavirtualBruntoncompass (Figure47.18) formeasuringdipandstrike(orientation)offeaturesinsidethevolumeandalsovisualizedthesemeasurementsandthegroundsurfaceincontextwiththevolume.

GatheringdatawithGPRworkswellonsanddunesbecausesandhasahighresistivity,allow-ingforgoodpenetrationofEMenergy.Thisenablesresearcherstolookdeepintothelarge-scalesedimentary structuresbelow the surface (Bristowet al., 2007).Analysis andannotationof theundergroundstructureshelps researchersunderstandandpresenthowgivensanddunesevolvedovertime.Buildingonthisdata,researcherscanextrapolateinformationonpastclimatesandwinddirectionsovertheregion.Depositsofancientsanddunesalsooccurintherockrecord.ManyoftheseancientAeoliansandstonesareimportantreservoirsforhydrocarbons.

Characterizingthesedimentsofmodernsanddunesinordertounderstandtheconditionsunderwhichtheywereformedbenefitsfromtakingmeasurementsoftheangleanddirectionofdipofprimaryandsecondarysedimentarystructures.Airflowoverthedunesaffectstheirshape,and thusbymeasuring theshapeofancientdunes, thewindflowpatternsof thepastcanbededuced.Workinginthefield,geologistsmeasurethedipandstrikeofsurfacesusingaBruntoncompass.Unfortunately,mostdesktopGPRvisualizationtoolsdonotofferagooduser

FIGure 47.18 ArealBruntoncompassandthevirtualversioninuse.

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interfaceallowingthegeologist,accomplishedintheuseofarealBruntoncompass,totransferthisskilltousingavirtualversionofthetool.

47.4.4.1 Visualization techniquesTheproject team leveragedToirtSamhlaigh for its immersivevolumevisualizationcapabilities.Theycreateda3D tomogramvolumeof thedata fromthestackof2Dsubsurfacecross-sectionimages from theGPR(seealsoFigure47.19andSection47.2.5).Workingwith the3Dvolume,thegeologist(Lancaster)manipulatedthevisualizationcontrolstohighlightfeaturesofscientificsignificance—thatis,hemanipulatedthetransferfunctions,lightingparameters,andsliceplane’spositionstobestviewthedata.Asemitransparentgroundsurfaceprovidescontext.Finally,thevir-tualBruntoncompasstoolwasaddedtoallowmeasurementandannotationofthedip/strikeacrossthedata.Thesemarkingsthenprovidedavisualizationofthenonuniformvectorfield,indicatingdirectionwithaconepointinginthedirectionofthesteepestgradient.

47.4.4.2 Immersive Interface techniquesLeveragingtheToirtSamhlaighimmersivevolume-renderingtool,theprojectteamhadafirmfoot-ingonwhichtoaddfeaturesspecificallyforvisualizationofGPR-sourceddatavolumes.AddressingtheshortcomingofmostdesktopGPRvisualizationtools,thevirtualBruntoncompasswasaddedtoenableuserstoefficientlyandpreciselymeasuredipandstrikeoffeatures.Theinterfacecom-prisesavirtualplane,withadynamicdipline,attachedtoa6-DOFwandthattheuseralignswithfeaturestomeasureorientation.Withoneclickandasmallhandmotion,theuseralignsthenextmeasurementand,withasecondclick,savesthatmeasurement.Thecollectionofallthedipandstrikemeasurementsisthensavedforoff-line(onthedesktop)analysis(Figure47.20).

47.4.4.3 outcomes and lessonsLancaster,thegeologistontheproject,statedthatthis“applicationimprovescurrenttoolsusedbyresearchersorpractitionerswhoare interested in thesedatasets.”He tested thisapplication inahigh-resolution(1920×1920)six-sidedCAVE-likedisplay,onastandardresolution(1050×1050)four-sidedCAVE,ona low-cost3DTV-based immersivedisplay(67″with1080pusingchecker-boardstereo)similartotheIQ-Station(Sherman,O’Leary,Whiting,Grover,&Wernert,2010),andonastandardnonimmersive(mouseandkeyboardonly)computer.Allwereusableforbasicvisu-alizationanddataexploration.The6-DOFinteractionofferedbytheimmersivedisplaysdemon-stratedanadvantageoverthemouse/keyboardinterfaceintheinteractiveuseofthevirtualBrunton

FIGure 47.19 UsingthemeasurementfeatureofToirtSamhlaigh,aresearchersimultaneouslymeasuresthelength,height,andwidthofthesandduneslope.

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compassandvirtualrulerfortakingdip,strike,anddistancemeasurements.Lancasternotedmajorpositivesthatincludedtheabilitytoquicklyviewthedatasetfromdifferentviewpointsinadditiontobeingabletointuitivelycollectquantitativeinformationondipandstrike.

Specifically,Lancasternoted that interactive tools“enableextractionofquantitative informa-tionfromdataset”andthat“Comparedtofieldinvestigations,workingintheCAVEisaloteasierandquicker.”Incomparingthetakingofmeasurementsintherealversusvirtualworlds,hesaid:“toperformactualmeasurementsofstrikeanddiponthebedsandsurfacesimagedbythesedata,wewouldhavetoexcavatetheduneandexposethesefeatures,whichwouldbelogisticallydiffi-cultandnotfeasibleinmostcases.”Whileusingtheimmersivetools,hewouldfrequentlychangeviewpointstoconfirmandrefineplacementoftheBruntoncompassmarkers.Duringhissessionsinthevariousimmersivesystems,thevisualizationscientistsontheteamnotedthedifferencesinhowmuchhewasabletotakeadvantageoftheiruniquequalities.Inparticular,theyfoundthatthewiderfieldofviewof theCAVE-styledisplaysover the low-cost3DTV-based systemsaffordedthescientisttheabilitytoplacemoreuserinterfaceelementsintheworldwithoutinterferingwiththevisualization,whereasthe3DTV-baseddisplaybecameincreasinglycumbersometouseasthesmallerscreenbecamecluttered.

Lancaster alsopointedout somedeficiencies in the tool. Inparticular, determining the scaleofthedatawasdifficult,whichledtosomeuncertainty in dip and strike measurements.Perhapsnotsurprisingly,takinganglemeasurementsonanonuniformlyscaledvolumecanbedisconcert-ing.Evenifcalculationsarecorrect,resultsarenonintuitive.Thisshortcomingcouldperhapsbereducedthroughgreaterpracticeorbyimprovingtheinterface.Anotherdeficiencyemanatesfromtheuseof2Dmenusandcontrols(widgets)containedwithina3Dworld.Thisproblemisexacer-batedwhenattemptingtocontrolsmallfeatureswithshakypositiontracking.Onepossiblesolutionistomovethe2Dwidgetsontoatablet,matchingthe2Dnatureofthetouchinterface,thoughthiscouldthenaddafatiguefactorfromprolongedholdingofthetablet.

Backonthepositiveside,basiccomputergraphicslightingalsoturnedouttobeimportantinunderstandingthestructureofthedata.Theetherealnatureofvolumerenderingisoftendifficult

FIGure 47.20 AuserinteractingwithavolumedatasetusingtheToirtSamhlaighapplicationrunningonaportableandlow-costIQ-Station.

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tofuseandreducestheeffectivenessofstereoscopicrendering.Motionparallaxisstillhelpful,butthroughtheuseofGPUlightingeffects,featureswithinthevolumecancometolifemore,provid-ingsomehooksthatthebraincanconnectwith,andmakethe3Dobjectmorecompelling.Also,themanipulationoflightingparametersinandofitselfcanprovidestructuralcuesasshadingonsurfaceschanges.

Contrastedagainst thedifficultyofmanipulating2Dwidgets ina3Dspace,usingthe6-DOFpositiontrackingtoplaceandorientthevirtualBruntoncompassprovedtobeaverybigwin.Buteven some of the 2D interfaces provided benefits not available in other tools. In particular, theGaussiantransferfunctioneditorofToirtSamhlaighwasmucheasiertousethanthestandardpiece-wiselineartransferfunctioneditor.ThisbenefitresultsfromtheuniqueGaussiancurveeditorthatrequiresfewerpreciseselectionactionsbytheusertoobtainadesiredcurve.SlidingtheGaussianaroundbyitscenterprovidesaquickwaytoexploreanewdatasetforsalientfeatures.

OnefeatureoftheimmersiveGPRvisualizationtoolthatisnotsplashy,butcrucial,isthebasicabilitytosaveandrestorethestateofthevisualization.Notonlydoesthisallowtheresearchertoreturntoworkwheretheyleftoff,butthisalsoallowsthemtosaveaparticularlyinterestingsce-nariotosharewithcolleagues.

Overall,thegeologistfoundgreatbenefitfromtheGPRvisualizationtool,whichwasbasedonavolumevisualizationapplicationwithspecificextensionsforgeologyvisualization,includingavirtualBruntoncompasswidgetwithnatural6-DOFmanipulations.Inanecdotalcomparisonsofvariousdisplaytypes,thevisualizationresearchersnotehowtheincreasedrealestateofafour-orsix-sidedCAVEdisplayprovidedthespaceneededtoplacecontrolswithoutinterferingwiththedatarepresentations.

47.5 BeneFIts

While not universal, there are demonstrable benefits from the use of immersive technology inmanyareas,includingthevisualizationandanalysisofscientificdatafromthegeologicalsciences.Butdemonstrablebenefitsarenotasufficientcriterionindeterminingwhetheranewimmersivevisualizationprogramortoolwillpaydividends.Eventhemostproventoolmustovercomethehurdle of breaking into the regular workflow of the researcher. For larger immersive displays,thisproblemisexacerbatedbythedistancetheresearchermusttraveltomakeuseofthesystem.Smallerimmersivesystemscanbeavailableinthelab,andwhiletheirbenefitsmayfallbetweendesktopandlocation-basedimmersivesystems,proximitytotheresearchcanmakethemmuchmorevaluable.

Asinglefavorableanecdoteindicatesthatanimmersivesystemcanbeuseful,andtherearesev-eralsuchanecdotes,somediscussedinSection47.4.Forexample,inSection47.4.1,within20mininaCAVEsystem,geologistKinslanddiscoveredsomethingnewindatahehadbeenporingover.InSection47.4.2,researcherswereabletoexplorethatwhichcouldnototherwisebeexplored—hiddendataunderthesurfaceatasiteforwhichdiggingwasprohibited.Alsotheyfoundthatseeingtheirdataat scalewasmuchmoreusefulthanviewingasmallmodelonacomputerdesktop.InSection47.4.3,theLidarViewerapplicationhasbeenshowntobevaluableforseveralresearchersinitsabilitytonaturallyselectagroupofpointsandthenmeasurethemprecisely.AndinSection47.4.4,theimmersiveinterfacewashelpfulthroughthenaturalplacementtechniquesusedtoanno-tatethedip/strikewithinthesubsurfacedata,whichalsowasfromasitethatwouldhavebeentoobig(anddeep)todig.

Butbeyondanecdotes,thereisalsoexperimentalevidencethatdemonstratesadvantagesinusingimmersive technologies forscientificvisualization. In the realmofgeosciences,Gruchallaetal.demonstratedanimprovedabilityinnavigatingthroughacomplexwell-pathlandscape(Gruchalla,2004).Inthewiderfieldofimmersivevisualization,Prabhat’sexperimentonanalyzingfluidflowthrough a heart found immersive technologies provided increased performance over desktoptools (Prabhat,Forsberg,Katzourin,Wharton,&Slater,2008),andBradyetal.described tasks AQ16

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(following indistinctfibersthroughvolumedata)foundtobenearlyimpossibletoperformonthedesktopweredoableinaCAVEdisplay(Bradyetal.,1995).

Ofcourse,therearefieldsrelatedtothegeosciencesintheirneedtovisualizedatawithinthecontextofthephysicalworldthatalsobenefitfromtheseandsimilarimmersivetools.Archaeologyhasalreadybeenmentioned,plusotherareassuchascommunityplanning,whichhasbeenusedattheCAESfacilityusingthesameLidarViewertooldescribedinSection47.4.2.

Thebenefitsarenotautomatic,however—purchasinganewCAVEisnotsufficienttoguaranteegreatresults.Acquiringthehardwareisonlypartoftheprocess.Thereisgoodsoftwareavailable,butnottoanoverwhelmingdegree,andexpertiseisrequiredtogetthesoftwareinstalled,config-ured,andputtouse.Insomecases,therewon’tbeasoftwarepackagethatdirectlymeetsthescien-tists’needs,andsocustomsoftwarewillbeneeded,requiringmoreexpertiseaswellasmoretime.Andthenonceagain,onlyifthehurdlestousearelowenough,willscientistsbefullyengaged.

TheobservationofMarshallMcLuhan“I wouldn’t have seen it if I hadn’t believed it”servesasanothercautionaryconsideration.Theabilitytovaryscales(e.g.,humanversuscontinental)orselecthowdataarefilteredoffersthepossibilitytoseefeaturesthataren’tobviousinthefieldorwhenobservingtheunfiltereddata.Buttoavoidperceivingwhatisn’tthere,itisalsoimportanttogobacktothefieldortotheunfiltereddata.Forexample,asubtlefaultwhosearealextentisn’tvisibleinthefieldbecomesidentifiableasafaultbyitshorizontalformandextentinLIDARdataviewedimmersively.Backinthefield,thesubtletopographycanbeconfirmedtoclearlyidentifyafault.

In theend,VRisamediumthatcanassistoursearchfordetails revealing thenatureofourplanet.Aswepointedout in the introduction,mostgeology is intrinsically3D.Interpretingandcommunicatingabout3Ddataisthusmosteffectively(quickest,mostaccurate,etc.)donein3D,physicallyimmersive,viewing.Whilegeologistshavelearnedhowtointerpret2Drepresentationsinto3D,bypresentingthedatadirectlyinthreelivingdimensions,itbecomeseasierforthemindto ingest.Wealso largelyavoid thepitfallsofpreconceptions thatcanunintentionallycloud theinterpretation.

47.6 Future and ConClusIon

Interestandexcitementaroundimmersivetechnologies—Virtual Reality!—ebbsandflowsoverthecourseoftime.Ingeneral,ithasfollowedthetypicalpatternoftheGartner’stechnologyhypecurve(Fenn,1995)inthatafteraninitialspark,therewashugeinterestandmediaexposure,surpassingtheaccomplishedmeritofthetime.ForVR,thispeak of inflated expectationsoccurredinthemid-1990s.Followingthat,duringthetrough of disillusionment,thependulumgenerallyswingstoofartothenegative,andmanygiveuponthetechnology.Thosewhocontinueandotherswhograduallyjointheeffortmayperseverethroughtheslope of enlightenment,perhapstoeventuallyreachtheplateau of productivity.

Presently,wecanconsiderthetechnologyofimmersiveinterfaces(VR)tobesomewhereontheslope of enlightenmentwithvaluegraduallybeingdemonstratedtomoreandmoreusers.Thatdoesnotmeanthat therearenot thosewhotest thetechnologyandfindit insufficientfor theirneeds,forthatisthenatureofalltechnology.Butweareseeinganincreasingnumberofscientificresearcherswhohavefoundvalueinimmersivetechnologies,severalinfieldsrelatedtogeologicresearch.

Onetrendbolsteringimmersivetechnologiesistheincreasingavailabilityofneededtechnolo-giesthroughconsumerchannels.Mostprominently,theavailabilityof3Dtelevisiondisplayshasmadetheprospectsofbuildingalow-costimmersivesystempossible.Theubiquitousnatureof3Daccelerationinjustabouteverycomputingdeviceenablesinterestingvirtualworlds,andthelastmajorpieceofthepuzzleispositiontracking,whichcanrangefromhomebrewusingWii™remotecontrols or PlayStation Move™ controllers to professional tracking systems costing one or twoordersofmagnitudemore.Butevenprofessionalsystemsaresignificantlylessexpensiveandper-formbetterthanwhatwasavailableadecadeago.Integratingalow-costimmersivesystemcanbe

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doneonanindividualorsmallgroupbasis,buttherearebenefitstobegainedbyworkingtogetherinacommunity.TheIQ-Station(Shermanetal.,2010)projectisonesucheffortwherebyanopenrecipefor thehardwareisprovidedandrefined, takingintoaccount thechangingavailabilityofconsumerproducts(IQ-StationConsortium,2013).

Anotherprecursortowiderusageofimmersivetechnologiesforvisualizationaswellasotherusescomesfromthegrowthofend-usersoftwarethatisquicklyusable.Thisgrowthcanbefosteredintwoways.Onewayisthroughtheadoptionofimmersive,orVR,optionstoexistingvisualizationsoftware—particularlysoftwarethatisalreadyinusebytheusercommunity.SomeexamplesofvisualizationtoolsthatprovidethisoptionincludethecommercialpackagesAvizo(VisualizationSciencesGroup,2013)andEnsight(ComputationalEngineeringInternational,Inc.,2013),aswellasopentoolssuchasParaView(Kitware,Inc.ParaView,2013).

Theotherprimarywaytospurgrowthistoprovideauserinterfacethatisnotonlyeasytousebutwidelyadopted.When there is inconsistencybetweenhow touse immersivesoftware tools,thenmoreeffortisrequiredforenduserstobecomefamiliarandfluentwitheachtool.Consistencyallowsuserstoquicklymovebetweenapplications,andwhentoolsbecomeavailablewithneededfeatures,userscanreadilyjumptothenew(andprobablyimproved)tool.Thisabilitytokeepontheforefrontofsoftwaretechnologywillbetterholdtheinterestofthosewhousethetechnologybecauseithelpsthemimprovetheirresearch.

The ebb and flow in the amount of usage and attention garnered by immersive technologieswilllikelycontinue.Butevennow,overall,theevidencesuggeststhatthereisvalueinimmersivesystemsbenefitingmanyinthescientificcommunities,includingthegeologicalsciences.Weantici-patethatasproliferationofthetechnologycontinuestospread,theaudiencewillgrowsteadily,thehardwareandsoftwaresystemswillimprove,andsomewillbeavailableready-to-userightfromthetimeofpurchase.

All these advances, coupled with the ascension of new scientists with technology usage,engrainedintheirlives,lookingtotakeadvantageofanytechnologythatprovidesdividends,andwecanexpectusageofimmersivetechnologiesinthegeosciencesoverthecomingyearstobeevenmorefruitful.

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autHor QuerIes

[AQ1] PleasecheckwhetherthecitationsforFigures47.1through47.6,47.11through47.13,47.19,and47.20areappropriate.

[AQ2] Pleasecheckifedittothesentencestarting“Togeoscientists,this....”isokay.[AQ3] ArtworkofFigures47.1and47.7aresame.Pleasecheck.[AQ4] PleaseconfirmtheinsertedcitationforFigures47.1–47.6,7.11–7.13,and7.20.[AQ5] PleaseconfirmtheinsertedpartlabelsinthecaptionofFigure47.2.[AQ6] Pleasecheckifedittothesentencestarting“Specializedradardevices…”isokay. [AQ7] Pleasecheckthesentencestarting“Additionally,astheoriginal....”forcompleteness.[AQ8] Pleasecheckifedittothesentencestarting“Collectinglocationdata....”isokay.[AQ9] Pleasecheckifedittothesentencestarting“Streamlinesandstreaklines....”isokay.[AQ10] Pleaseprovidetheexpansionof“6-DOF,”ifappropriate.[AQ11] Pleaseconfirmtheidentifiedheadlevels.[AQ12] PleasecheckthementionofcolorinFigure47.12.[AQ13] PleasenotethattheyearinMooreetal.(2012)ischangedasperlist.[AQ14] Pleasecheckthementionofcolorinthesentencestarting“Theimageon....” [AQ15] Pleasecheckthementionofcolorinthesentencestarting“Thecolorsindicate....” [AQ16] Prabhat(2008)hasbeenchangedtoPrabhatetal.(2008)asperthereferencelist.Please

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[AQ18] Please provide publisher details and also confirm the inserted report number for DOE(2000).

[AQ19] PleaseprovidepublisherdetailsforFenn(1995).[AQ20] PleaseprovidevolumenumberandpagerangeforFurukawaandPonce(2009).[AQ21] PleaseconfirmtheinsertedvolumenumberandpagerangeforGay(1971).Alsoconfirm

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47 Immersive Visualization for the Geological Sciencesweb.eng.ucsd.edu/~jschulze/publications/Sherman2014.pdf · 47Immersive Visualization for the Geological Sciences ... immersive