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doi:10.1016/j.ca

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Computers & Geosciences ] (]]]]) ]]]–]]]

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Integration of regional to outcrop digital data: 3D visualisationof multi-scale geological models

R.R. Jonesa,b,�, K.J.W. McCaffreyc, P. Cleggc, R.W. Wilsonc, N.S. Hollimanb,R.E. Holdsworthc, J. Imberc, S. Waggottd

aGeospatial Research Ltd., Department of Earth Sciences, University of Durham, Durham DH1 3LE, UKbe-Science Research Institute, University of Durham, Durham DH1 3LE, UK

cReactivation Research Group, Department of Earth Sciences, University of Durham, Durham DH1 3LE, UKdHalcrow Group Ltd., Arndale House, Otley Road, Leeds LS6 2UL, UK

Received 26 September 2007

Abstract

Multi-scale geological models contain three-dimensional, spatially referenced data, typically spanning at least six orders

of magnitude from outcrop to regional scale. A large number of different geological and geophysical data sources can be

combined into a single model. Established 3D visualisation methods that are widely used in hydrocarbon exploration and

production for sub-surface data have been adapted for onshore surface geology through a combination of methods for

digital data acquisition, 3D visualisation, and geospatial analysis. The integration of georeferenced data across a wider

than normal range in scale helps to address several of the existing limitations that are inherent in traditional methods

of map production and publishing. The primary advantage of a multi-scale approach is that spatial precision and

dimensionality (which are generally degraded when data are displayed in 2D at a single scale) can be preserved at all

scales.

Real-time, immersive, interactive software, based on a ‘‘3D geospatial’’ graphical user interface (GUI), allows complex

geological architectures to be depicted, and is more inherently intuitive than software based on a standard ‘‘desktop’’ GUI

metaphor. The continuing convergence of different kinds of geo-modelling, GIS, and visualisation software, as well as

industry acceptance of standardised middleware, has helped to make multi-scale geological models a practical reality. This

is illustrated with two case studies from NE England and NW Scotland.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Virtual outcrop models; Digital geological mapping; Terrestrial laser-scanning; Immersive visualisation; User interaction

e front matter r 2007 Elsevier Ltd. All rights reserved

geo.2007.09.007

ing author. Geospatial Research Ltd., Depart-

Sciences, University of Durham, Durham DH1

+44 191 334 4294; fax: +44 191 334 2301.

ess: richard@geospatial-research.co.uk

s article as: Jones, R.R., et al., Integration of regional to

puters and Geosciences (2008), doi:10.1016/j.cageo.2007

1. Introduction

Geological structures and architectures are in-herently three-dimensional (3D) in nature, and arethe product of geological processes that operatewithin an additional temporal dimension. Priorto rapid improvements in visualisation hardwarein the late 1980s, and the release of suitable software

.

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in the 1990s, geoscientists had few ways in whichto display the full dimensionality of their 3D or 4Ddata. By the mid-1990s, real-time 3D visualisationhad been shown to offer excellent return oninvestment for many different work processes with-in the hydrocarbon exploration and productionvalue chain (e.g. Bethel et al., 1996; Jones, 1996),and the industry rapidly became one of the maindriving forces (along with defence, aerospace,medical, and automotive sectors) that influencedmanufacturers of visualisation hardware and soft-ware. Today, high-performance 3D visualisation iscommonplace in all major petroleum companiesand has become an integral part of standardexploration and production activities.

Despite this, many field geologists (e.g. students,academics, and national survey workers) still relyupon standard methodologies for recording obser-vations and disseminating published informationthat are fundamentally 1D or 2D in character.These methods have remained largely unchangedsince the birth of geology as a scientific disciplinetwo centuries ago. The innate limitation of the two-dimensional nature of paper (in the form of fieldnotebook, printed scientific journal or publishedmap) inevitably requires a compromise in the waythat 3D data are depicted. In order to show 3Ddata in a way that minimised distortion ofgeospatial relationships, traditional approachesnecessarily resorted to using 2D maps and cross-sections (i.e. involving a loss of dimension). A useful3D perspective was often gained by showingschematic block diagrams, though these generallyinvolve a loss of spatial precision, and the 2Dsections shown on the faces of the blocks still onlyshow a small proportion of the actual 3D rockvolume. Another fundamental limitation inherent topaper-based methods is that individual interpreta-tions must be prepared and drafted for display ata single fixed scale. This has a strong tendency tomask scaling relationships that may exist betweenstructures at different orders of magnitude.

In recent years, widespread utilisation of desktopcomputers and rapid development of dedicatedgraphics hardware and software has brought afford-able, high-performance real-time 3D graphics cap-ability to standard desktop and portable computers,so that modern geological field-based investigationcan benefit from 3D visualisation throughout anentire workflow, from primary digital acquisition offield data through to analysis and interpretation(e.g. Brodaric, 1997; Brimhall et al., 2002; Jones

Please cite this article as: Jones, R.R., et al., Integration of regional to

models. Computers and Geosciences (2008), doi:10.1016/j.cageo.2007

et al., 2004; Hodgetts et al., 2004; Pringle et al.,2004; Wilson et al., 2005; McCaffrey et al., 2005; DeDonatis and Bruciatelli, 2006; Clegg et al., 2006).The digitalisation of geological field mapping hashelped to overcome the limited dimensionality oftraditional methods, and also allows integrationof data across a very wide range of scales.Integrated digital models can combine data gath-ered at outcrop and embed these within regionaldatasets, to allow on-the-fly rendering across abroad scale range. The critical advantage of thismethod is that it preserves both the dimensionalityand the scale relationships of the data. This givesthe user immediate access to contiguous informa-tion ranging in scale from detailed outcrop up toregional overview, all integrated within a singlemodel, and accessed via a 3D graphical userinterface (GUI). The aims of this paper are firstly toprovide a conceptual framework that discusses thismulti-scale approach in more detail and secondly, toprovide a practical introduction to the integration ofdifferent types of geoscientific data into a single 3Dmodel. We illustrate the concepts with two casestudies, one from the Carboniferous basins of north-ern England and the other from the Lewisianbasement terrane of NW Scotland.

2. 3D visualisation

Earth scientists realised at an early stage thepotential offered by 3D computer graphics for thedisplay of spatially distributed geoscientific data,and 3D visualisation became a joint area of focusfor separate groups working in structural modellingor GIS (e.g. Mallet, 1988, 1989; Van Driel, 1989;Turner, 1992 and papers therein; Keller et al., 1995;Rhyne, 1997; Verbree et al., 1999). At that stage,desktop PCs were only powerful enough to render asmall number of graphic primitives in real-time, andworkstations with dedicated graphics capabilitywere needed to display all but the most simplistic3D models. Over the last decade, 3D visualisationtechnology has been progressively assimilated intomainstream computing. There is currently a widevariety of levels of sophistication in visualisationfunctionality, from simple 3D plots of graphs ina spreadsheet program, to interactive, immersive,real-time graphics (‘‘virtual reality’’) at the moresophisticated end of the visualisation spectrum.Rapid development of dedicated graphic chips(driven aggressively by the computer games market),has now given desktop PC graphics a remarkable

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performance/price ratio. With the use of stereo-scopic glasses, and more recently auto-stereoscopiccomputer screens (e.g. Holliman, 2004, 2006), it isnow commonplace to incorporate relatively ad-vanced semi-immersive 3D visualisation into stan-dard desktop PCs.

2.1. 3D geospatial GUI

Many different kinds of geoscientific softwarepackages have 3D visualisation capability. As withother scientific disciplines and market sectors, themajority of these software packages provide specific3D functionality within the overall context of thestandard GUI, i.e. the 3D model is rendered in a

Fig. 1. Examples of 3D visualisation, highlighting different graphical u

standard GUI (‘‘desktop’’ metaphor). User navigates within the 3D wor

Example shown is ESRI’s ArcScene; (B) early example of a basic 3D geo

user to navigate and interact with data is via actual 3D model, includ

immersive 3D geospatial GUI makes navigation and interaction much si

large, complex models). Example shown is Schlumberger’s Inside Realit

model and file-system hierarchy. Example shown: Schlumberger’s Petre

Please cite this article as: Jones, R.R., et al., Integration of regional to

models. Computers and Geosciences (2008), doi:10.1016/j.cageo.2007

normal GUI window (Fig. 1A), and the userinteracts with the model in ways that correspondto standard behaviour for the GUI. In other words,this type of 3D visualisation exists entirely within auser interface based on windows, icons, menu bar,etc.—the familiar ‘‘desktop metaphor’’ utilised inGUIs such as Macintosh and Microsoft Windows.This type of GUI provides the user with a porthole/window through which to view the virtual 3Dworld, and hence is generally considered as semi-immersive. Central to desktop-based GUIs is thedepiction of the file system as a hierarchy of iconicfolders and files. To work with a given dataset, theuser must navigate to the correct file by traversingthe file-system hierarchy.

ser interface (GUI) metaphors; (A) 3D model is rendered within a

ld, but there is little or no interaction with data via the 3D model.

spatial GUI, based on ‘‘virtual world’’ metaphor: primary way for

ing context-sensitive pop-up menus (from Jones 1996); (C) fully

mpler (as user interaction via a file-based hierarchy is unwieldy for

y; (D) hybrid GUI, in which user interacts with data via both 3D

l.

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In contrast, some software packages provideimmersive 3D visualisation in which the main visualcue for the user to locate a dataset is the positionof the data in 3D space, rather than its location inthe file system. This is a significant extension of thestandard desktop-based GUI, and represents adifferent metaphor for human–computer interac-tion—the ‘‘3D geospatial’’ GUI, based on a ‘‘virtualworld’’ metaphor (Fig. 1B; Jones, 1996). In short,the model does not exist within a user interface, themodel is the interface. To locate a given dataset, onenavigates in 3D space to the appropriate position inthe virtual model, rather than via folder icons in thefile-system hierarchy. Data that are geospatial innature will be rendered in their correct position aspart of the 3D model. The model also acts asthe interface through which the user can accessadditional data that are not inherently geospatial(e.g. metadata, photos, literature references, etc.),but which are tied to a given locality or object(Fig. 1B). This gives the user a rapid and intuitiveway to access vast amounts of data in large andcomplex models (Fig. 1C), without needing to befamiliar with the structure of an extensive, unwieldyfile hierarchy.

In practice, there is a gradation between thedesktop and virtual world GUI metaphors (Fig. 2).Comparable to the way in which operating systemssuch as Linux have blurred the distinction betweencommand-line and desktop-based GUIs, manygeoscientific software tools successfully combineaspects of both desktop-based and 3D geospatial

Fig. 2. Spectrum of 3D visualisation programs, showing variation in

‘‘virtual world’’ GUI.

Please cite this article as: Jones, R.R., et al., Integration of regional to

models. Computers and Geosciences (2008), doi:10.1016/j.cageo.2007

GUI functionality. Petrel and GoCad are represen-tative examples of such applications, and combine ahigh degree of interactivity via the 3D model, as wellas interaction through standard desktop-based GUIwidgets (Fig. 1D). In this way there is a positiveredundancy so that at any one time the userhas alternative methods (i.e. via different GUImetaphors) to access data in the model. Theadditional programming effort that this requires isgenerally rewarded with improved usability anduser-friendliness. From a GUI-design perspective,this hybrid approach is comparable to tried and testedGUIs used in GIS software, which render geospatialdata in an interactive map window (a ‘‘2D geospatialGUI’’), as well as allowing the user to interact withdata via the file hierarchy (e.g. ArcCatalog, and theArcMap ‘‘table of contents’’ window).

2.2. Visualisation vs. analysis: data formats

Although 3D visualisation of geological architec-tures can sometimes be useful in its own right(for example, to help students to improve theirspatial understanding, e.g. Hesthammer et al., 2002;McCaffrey et al., 2003), more commonly visualisa-tion represents an aid to interpretation that is builtinto various workflows for the detailed analysis andprocessing of data. In an ideal situation, it would bepossible to collate all available data for a givenproject into a single multi-scale geospatial model,and then perform any desired analysis upon thedata within one software environment. However,

reliance upon ‘‘desktop’’ GUI metaphor, and increasing use of

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because data analysis methods tend to be highlyspecialised for particular purposes, there is a largevariety of different kinds of software tools with-in the general domain of 3D geoscientific analysis(Fig. 3). This often makes it necessary to transfergeospatial data between a number of separatesoftware packages within a single analysis work-flow. Inevitably, most of the software tools forgeospatial analysis tend to use propriety dataformats. This was previously a major obstacle tobeing able to adopt an efficient multi-scale geo-scientific approach to analysis, since there wasalways a large overhead involved in transferringdata between different formats. While new stan-dards for 3D graphics (e.g. OpenGL) certainly madeit easier for software developers to include 3Dfunctionality, the initiatives did little to alleviatedata compatibility issues, since visualisation func-tionality was integrated separately into each ven-dor’s analysis software. Similarly, generic standardsaiming to facilitate data sharing (e.g. XML;Houlding, 2001) and specific standards for a singlecommon geoscientific data format (e.g. ISO CD

Fig. 3. Key types of application software that have influenced develop

specific programs are given, though many others exist. Progressive con

often have overlapping functionality.

Please cite this article as: Jones, R.R., et al., Integration of regional to

models. Computers and Geosciences (2008), doi:10.1016/j.cageo.2007

19115.3; CEN/TC287–ISO/TC211; Open GIS Con-sortium’s Geography Mark-up Language) have hadrelatively minor impacts to date.

While issues of data format and data transferremain a target for potential improvement, signifi-cant developments have already been made in anumber of ways. Firstly, there is continuingconvergence of different kinds of geoscientific soft-ware, so that fewer separate tools are needed tosatisfy the specific needs of many workflows (e.g.geostatistical functionality has been incorporatedinto later versions of GoCad and 3DMove).Secondly, most analysis software tools now includeextensive capabilities for import and export of awide range of different formats. Thirdly, higher-level graphics formats (e.g. VRML, VTK) arebecoming more widely adopted and supported.Fourthly, major improvements in data interconnec-tivity have been realised by middleware brokersoftware (e.g. OpenSpirit, http://www.openspirit.com/) that facilitate cross-platform and inter-application data exchange, including support forGIS, geological and geophysical software.

ment of 3D visualisation in geosciences. Only a few examples of

vergence of software now means that different types of program

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3. Multiple scales

Field-based geoscientists often need to considergeological phenomena over eight or nine orders ofmagnitude, from outcrop observations and mea-surements at the centimetre scale, up to inferencesabout geological architectures (e.g. rift basins,drainage systems, orogenic belts) that can spanhundreds or thousands of kilometres. Our under-standing of the way in which some geologicalprocesses operate across such a wide range of scalesremains relatively poor, and further work is neededto investigate the relationships between featuresobserved at outcrop and global-scale plate tectonicprocesses (e.g. Ramsay and Lisle, 2000, preface;Jones et al., 2005). Multi-scale digital modelsrepresent one tool that can help to improve under-standing, and are already being utilised duringpetroleum exploration and production (Roberts,D.M., personal communication, June 2004; Thur-mond et al., 2005). By combining different types ofspatially referenced data into a single geospatialmodel in an immersive visualisation facility with a3D geospatial GUI, the user has immediate accessto any given sub-set of the data at any availablescale of observation. The widespread success andmainstream acceptance of Google Earth (http://earth.google.com/) has rapidly demonstrated theusefulness of this approach with surface topo-graphic data: use of a 3D geospatial GUI allowsthis concept to be extended into the sub-surface.This overcomes many of the limitations of tradi-tional approaches in which separate fixed-scalemaps and sections are drawn to show regionallocation and local geology. The ability to zoomrapidly through a wide range of scales as seamlesslyas possible provides an immediate overview to localareas of geology, so that both broad regionalcontext and highly detailed outcrop informationare available simultaneously. Scaling relationships,such as variations in the geometry of structures atdifferent scales of observation are easier to detect,and spatial alignment between local outcrop ob-servations and regional lineations or geophysicalanomalies can be understood at a glance.

3.1. Integration of regional to outcrop data

In constructing multi-scale models, it is usuallycost-effective to re-use as much legacy data aspossible. Many different kinds of data, includingpublished geological and geophysical information,

Please cite this article as: Jones, R.R., et al., Integration of regional to

models. Computers and Geosciences (2008), doi:10.1016/j.cageo.2007

can be incorporated (Table 1). While various kindsof regional data are typically available within thepublic domain, reliable sources of digital geologicaldata at larger scales are much less common, andnew survey data often need to be acquired. This iscertainly the case for detailed outcrop geology,where sources of georeferenced data remain practi-cally non-existent in the public domain. Althoughsmall amounts of vector data at 1:50,000 to 1:10,000are gradually starting to become available, thequality of the data is usually unclear. Error ingeospatial positioning is just one of several sourcesof uncertainty associated with most publishedgeology maps (Jones et al., 2004), but since it isuncommon for any attempt to be made to quantifythese sources of error, it may sometimes bepreferable to acquire new field data of knownquality, despite the high cost typically associatedwith field acquisition.

Digital geological mapping is a rapidly develop-ing discipline, and a range of different approaches isnow used to capture georeferenced data withspecific spatial precision and resolution (Table 1).An in-depth analysis of current status is given byMcCaffrey et al. (2005) and Pringle et al. (2006).A modern system suitable for general geologicalmapping typically uses a highly portable pocketGPS and a hand-held PDA or ruggedised laptopcomputer (Wilson et al., 2005; De Donatis andBruciatelli, 2006). More detailed survey usingdifferential GPS is able to acquire data withcentimetre precision (Xu et al., 2000; Adams et al.,2004; Jones et al., 2004; McCaffrey et al., 2005).Highly detailed data representing entire outcropscan be acquired using terrestrial laser-ranging(Løseth et al., 2003; Xu et al., 2001), terrestriallaser-scanning (Ahlgren and Holmlund, 2002;Jennette and Bellian, 2003; Løseth et al., 2004; Joneset al., 2004; Bellian et al., 2005; Clegg et al., 2005),and digital photogrammetry (Pringle et al., 2001,2004; Hodgetts et al., 2004). These technologiesoffer highly efficient data capture, and are usuallythe starting point in the construction of realisticvirtual outcrop models.

Outcrop data are included in those parts of themodel where exposure exists and fieldwork has beencarried out. Intervening areas between outcrops arepopulated in the model with aerial photos drapedonto a DTM. Except in areas of exceptionallyhigh levels of continuous exposure, it is usually onlypossible to capture small amounts of data at theoutcrop scale. In practice, limitations of hardware

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Table 1

Examples of common sources of data typically used in multi-scale geological models

Category Source Typical coverage Typical resolution Type

Legacy data

Elevation data Digital Terrain Model (DTM) data 104–102m 101m horizontal

100m vertical

Point grid

Airborne LiDAR 104–101m 100–10�1m Point grid

Digitised contours 104–102m 100m Vectora

Aerial images Satellite, remote sensing 107–103m 101m Rasterb

Aerial photos 104–102m 101–10�1m Rasterb

Published maps Topographic maps 106–102m 101–10�2m Vector or raster

Geological maps and sections 106–103m 101m Mostly raster

Geophysics Regional seismic 106–105m 102m Mixedd

Regional gravity, magnetics 106–105m 102m Rasterc

New survey data

Elevation data DTM derived from photogrammetric

plot

104–102m 100m Point grid

DTM using real-time kinematic

(RTK) GPS

102–100m 10�2–10�3m Point grid

Aerial images Photos from kite or radio-controlled

plane

102–101m 10�2m Raster

Photos from boom, portable mast or

pole

101–100m 10�3m Raster

Photogrammetric plot 104–102m 101–10�1m Vector

Outcrop capture Terrestrial laser-scanning 104–100m 10�2m Mixede

Multi-view photogrammetry 103–101m 100–10�2m Mixede

Geology mapping General digital geological mappingf 104–102m 101–100m Vector

Detailed digital geological surveyf 103–100m 10�1–10�3m Vector

Sub-surface geology Mining records 103–102m 101–10�1m Mostly raster

Well core 103–101m 10�2–10�3m Mixed

Interpolated surfaces/volumes/point

clouds

104–100m 102–100m Vector or voxel

Geophysics 2D, 3D and 4D seismic 104–102m 101m Mixedd

Ground-penetrating radar (GPR) 102–101m 100–10�1m Mixedd

Borehole wireline logs 103–101m 10�2–10�3m Vector or raster

aFrom which DTM grid can be derived.bDraped on DTM topography.cDraped on DTM topography, or shown on horizontal plane, e.g. at +5000m elevation.dRaw seismic shown as raster or voxel, with interpretations as raster or vector.ePoint grid or cloud (similar to DTM) with digital image draped onto the surface.fMethodology described in detail in McCaffrey et al. (2005).

R.R. Jones et al. / Computers & Geosciences ] (]]]]) ]]]–]]] 7

will in any case always place a restriction on theamount of detailed data that can be renderedsmoothly in real-time. By showing delimited areasof exposure within a more regional area, multi-scalemodels help to emphasise the sparseness of availableraw observational data, and highlight the qualitativenature of existing published maps and interpretations(cf. Jones et al., 2004).

3.2. Extending outcrop data into 3D

Since the laser beam used in terrestrial laser-scanning is essentially a non-penetrative measure-

Please cite this article as: Jones, R.R., et al., Integration of regional to

models. Computers and Geosciences (2008), doi:10.1016/j.cageo.2007

ment, laser-scanning is only able to provideinformation about surface topography. Becausetopographic data represent an undulating 2D sur-face within 3D space, the dimensionality of datasuch as DEM/DTM and laser-scan data is some-times termed ‘‘2.5D’’ (e.g. Longley et al., 2001).Where possible, 2.5D outcrop models can beextended to be 3D by adding sub-surface (and/orsub-aerial) data. This can be achieved using anumber of different sources of additional data,including quarrying and mining records, shallowboreholes (Løseth et al., 2004; Hodgetts et al., 2004)and ground penetrating radar (Dehls et al., 2000;

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Pringle et al., 2001, 2003, 2004). Interpolationof sparse surface data into the sub-surface can beachieved using geometrical methods (Mallet, 1989;Trinks et al., 2005), rule-based or fuzzy-basedapproaches (Jones et al., 2004), and stochasticpopulation of fractured volumes (Ahlgren et al.,2002).

4. Case Study 1—the Carboniferous basins of

northern England

4.1. Geological overview

In northern England, the Northumberland sub-basin is located to the north of the Alston structuralhigh. This architecture is believed to reflect Dinan-tian back-arc extension north of a Variscansubduction zone, which produced a series of base-ment-influenced grabens that controlled fluvial-dominated siliciclastic and marginal marine carbo-nate facies distributions (Woodcock and Strachan,2000). Basin geometry and subsequent deformationhas strongly influenced the distribution of basemetal and coal resources throughout NE England;these have been exploited since early historic timesand have been of major importance to the region’seconomy during the 19th and 20th centuries.

4.2. Model construction

A multi-scale model was constructed by integrat-ing data from regional to outcrop scales. Regional-scale (100 km) topographical context is givenby legacy satellite imagery (http://visibleearth.nasa.gov/; http://www.mimas.ac.uk/). At this scale, thetopographic relief of the whole region is negligible(less than 103m height range) in comparison withthe lateral extent of the area (105m), so it isgenerally superfluous to use a wide-area DEMunless there is a specific reason for doing so (e.g.to integrate local outcrop geology with analysisof regional geomorphology). Geological surfaceinformation at a comparable scale (Fig. 4A) wasderived from published maps (Robson, 1981;Johnson, 1995). To provide sub-surface depthcontrol on overall basin architecture, isopachmaps of the depth to basement (based on Chadwicket al., 1995) were converted to a continuous surface(Fig. 4A) using ‘‘triangulated irregular network’’(TIN) interpolation methods in ArcGIS.

At a sub-regional scale (104m), coal mining plansand borehole records have been digitised to provide

Please cite this article as: Jones, R.R., et al., Integration of regional to

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spatial constraint on individual coal seams, tomatch and interpolate faults that displace multi-ple seams, and to track regional unconformitysurfaces (Fig. 4B). At a similar scale, digitaltopographic map data from the Ordnance Survey(http://www.digimap.com/) were combined withelevation data captured using airborne LiDAR.Commercially available DEM data based on air-borne LiDAR (e.g. http://www.intermap.com/corporate/greatBrit.cfm) are currently expensive,but provide an unprecedented level of topographicdetail. In the UK there is little freely availableairborne LiDAR data, while in the USA a fewpublic domain datasets have started to becomeavailable for general download (http://www.geongrid.org/).

Many of the best exposures in the Northumber-land basin occur along the coastline. New digitalgeological map data were acquired at Cullercoatsand Tynemouth, using the methodology describedin McCaffrey et al. (2005). This targeted coastalexposures of Carboniferous and Permian rocks inboth hangingwall and footwall to the 90 Fathom/Stublick Fault Zone (see trace on Fig 4A). A moredetailed view of this dataset is given in Fig. 4 ofClegg et al. (2006). Fig. 4C shows an aerialphotograph (from http://www.getmapping.com/),draped over a detailed DEM constructed frommanually digitised Ordnance Survey 5m contourdata, superimposed with the main geologicalboundaries.

Fig. 4D shows a very detailed digital model of theoutcrop at approximately 1:1 scale, in which small-scale structures in the damage zone to the 90Fathom Fault can be mapped. The outcrop data area dense, true-colour point cloud acquired usingterrestrial laser-scanning (ground-based LiDAR).The data shown were gathered using a Riegl LMS-Z360i scanner with a tightly calibrated Nikon D70digital SLR camera precision-mounted on top(http://www.riegl.com/). This equipment is shownin operation at Cullercoats in Fig. 2A of Jones et al.(2004). We also have additional laser-scan datafrom this locality that were captured using aTrimble GS-200 scanner (http://www.trimble.com/3dlaserscanners.shtml), as well as photogrammetricmontages constructed using SiroVision (http://www.sirovision.com/). Collectively, these high-resolution datasets form the core of a detailed digitaloutcrop model, upon which additional geologicalobservations, data, and interpretations have beensuperimposed (see below).

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Fig. 4. Example of integrated multi-scale 3D geological model from NE England, spanning seven orders of magnitude: (A) regional scale,

showing solid geology at Earth’s surface (from Johnson, 1995), above a surface showing basement topography: field of view ca.

1.6� 105m. X and Y mark traces of 90 Fathom/Stublick fault system on the surface and at top basement, respectively; (B) sub-surface data

at sub-regional scale; Maudlin coal seam (lower surface) and Carboniferous–Permian unconformity (upper surface); width of sub-surface

area covered ca. 2.5� 104m. These surfaces are shown embedded within a cut-away geology map of Tyneside–Teesside; (C) local scale

with integration of geological boundaries draped onto local topography: X is trace of 90 Fathom fault on fore shore. Field of view ca.

5� 102m, location shown in (B); (D) outcrop scale, showing interpretation of detailed terrestrial laser-scan data in an immersive 3D

visualisation facility: field of view ca. 3� 101m, location given in (C); laser-scan point spacing ca. 2� 10�2m.

R.R. Jones et al. / Computers & Geosciences ] (]]]]) ]]]–]]] 9

4.3. Strategies used and problems encountered

In this case study, our preferred strategy forhandling the regional and sub-regional data was tostart by integrating the different datasets into asingle GIS project, in order to cross-check that eachdataset was correctly georeferenced. Where neces-sary, datasets were re-projected at this stage so thatthey shared a common coordinate system. Oncegeoreferencing was complete, the GIS project wasdiscarded, and the individual datasets were im-ported into object-based 3D modelling software,since GIS software was unable to handle thedetailed 3D outcrop data.

Preliminary attempts to use GoCad as theprimary modelling tool were initially hampered

Please cite this article as: Jones, R.R., et al., Integration of regional to

models. Computers and Geosciences (2008), doi:10.1016/j.cageo.2007

by problems encountered with the large range inscales of data: the software was not able to displayoutcrop datasets with adequate sub-centimetrepositional precision, when X or Y location attri-butes for points had a large integer component.For example, locations at Cullercoats have BritishNational Grid northing values in excess of 571,000,so software needs to allocate sufficient memory tostore at least six integer digits and three floatingpoint digits to preserve the spatial precision for eachpoint. For this problem, one useful work-aroundis to offset all the datasets so that false northingand/or easting values are used that require fewerdigits. Comparable problems with display of high-precision outcrop data are also typical of somevoxel-based software, and it is worth checking

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before starting to build a model to ensure thatavailable tools are not limited to integer precision,or that they do not snap data points to the centreof the nearest voxel in a grid that is too coarse topreserve the detail in one’s data.

Most existing object-based geological modellingtools (e.g. Petrel, GoCad, Inside Reality) are care-fully designed for optimal manipulation of geologi-cal surfaces, together with display of 3D seismicdata and other datasets typically utilised in tradi-tional hydrocarbon exploration and production.However, these tools are generally not optimised forthe display of very large point-cloud datasets (e.g.105 to 108 individual points). When importing pointdata, software that instantiates a new object foreach point (rather than streaming the points intoone contiguous array) will generally consume hugeamounts of RAM and use vast CPU resources.Checks should be made using small sub-sets of pointdata to ensure that your tool of choice will handlethe available data satisfactorily. Dedicated softwareto handle large point clouds is available from laser-scanner manufacturers (e.g. Riegl RiScanPro,Trimble RealWorks Survey, Leica Cyclone, http://www.leica-geosystems.com/) and third-party devel-opers (e.g. Polyworks, http://www.innovmetric.com/;Pointshop3D, http://graphics.ethz.ch/pointshop3d/).We used RiScanPro software to acquire and processthe laser-scan data, since it has powerful function-ality for georeferencing and stitching multiple scanstogether, colouring the scans using calibrated andorthorectified digital photos, and picking relevantfeatures such as geological lines and planes from theoutcrop surface. Most importantly, the software hasfunctionality for filtering, decimating, and meshingthe data, so that very large point-cloud datasets canbe reduced to a more manageable meshed surface.Once processing in RiScanPro was complete, thedata were exported as meshed surfaces, ready forimporting into the multi-scale digital model.

4.4. New perspectives

Digital outcrop models have previously been usedto give improved 3D constraint of sedimentologicalarchitectures (e.g. Løseth et al., 2003, 2004; Pringleet al., 2003; Bellian et al., 2005). Our primary focusis the quantitative analysis of geological structuresat different scales. At Cullercoats detailed structuralmeasurements of fractures and folds have beengathered and integrated with the virtual outcropmodel, building further on the earlier structural

Please cite this article as: Jones, R.R., et al., Integration of regional to

models. Computers and Geosciences (2008), doi:10.1016/j.cageo.2007

interpretation of De Paola et al. (2005). Newlyacquired fold and fracture data were spatiallylocated with centimetre precision using real-timekinematic (RTK) differential GPS.

The detailed outcrop model has been used toderive an interpretative 3D fracture network for usein fluid flow simulations. The geometry of indivi-dual fractures was interpreted by extrapolatingsurface traces of fractures into (and out of) therock. 3D regression methods (e.g. Fernandez, 2005)are useful for interpolating planar fractures, but forcurved fractures we used spline surface interpola-tion techniques based on generic mapping tools(Smith and Wessel, 1990; Trinks et al. 2005). Thedigital outcrop model has also underpinned a studyinvestigating the spatial relationship between sub-surface coal extraction, Permian evaporate dissolu-tion, and subsidence at the surface, which is causingdamage to property and infrastructure.

Finally, we are using the model in ongoing workto analyse subtle fold geometries in the hangingwallto the 90 Fathom Fault, based on adaptation of themethods described in Pearce et al. (2006). Thegeometry of these long-wavelength low-amplitudestructures is difficult to measure using traditional(non-digital) methods. The aim of this work isto compare fold geometries with other localities inthe Northumberland basin, and to test whetherconsistent relationships exist between fold geome-try, regional faulting, and outcrop-scale fracturepatterns.

5. Case study 2—Lewisian Complex, NW Scotland

5.1. Geological overview

The Lewisian Complex in NW Scotland is aLate Archaean high-grade gneiss terrain that dis-plays a long record of Proterozoic and Phanerozoiccontinental assembly and dispersal (Park 1991).Despite ongoing debate regarding details of indivi-dual events, the region provides unique insights intoprocesses in the lower and middle continental crustand its response to repeated thermo-mechanicalloading and unloading.

5.2. Model construction

A multi-scale digital model, spanning nine ordersof magnitude was constructed centred on excellentcoastal exposures at Achmelvich Bay (Fig. 5A).Here, the Palaeoproterozoic Canisp shear zone

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Fig. 5. Multi-scale digital dataset from Canisp Shear Zone, NW Scotland: (A) reconnaissance-scale maps comprising remote-sensing data

and digitisation of pre-existing data; (B) regional maps constructed from aerial photograph analysis and digital field sampling; (C) field-

based digital geological mapping and attribute collection; (D) detailed outcrop mapping using survey-grade GPS, structural logs and

georeferenced photo-mosaics (inset shows extra photos hyperlinked to GPS localities); (E) 3D visualisation of digital map data, X and Y

show examples of surfaces fitted to major faults and shear zone boundaries, respectively (inset shows laser-scanned outcrop dataset);

(F) spatial and geostatistical analysis of attribute data; (G) oblique view of regional topographic model superimposed with structural data

in foreground.

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ARTICLE IN PRESSR.R. Jones et al. / Computers & Geosciences ] (]]]]) ]]]–]]]12

shows evidence for viscous and brittle reworking ofthe orthogneiss protoliths. At the regional scale, themodel combines remote-sensed data, surface geolo-gical maps, and aerial photographs with interpreta-tive overlays (Figs. 5A and B). Digital geologicalmapping (Fig. 5C) and detailed mapping oflithology, structural observations, and outcropphotographs in the vicinity of the shear zone(Fig. 5D) were georeferenced to ca. 30 cm precisionwith a differential GPS, and added to the model inthe field using ArcPad on a PDA. High-resolutiondatasets of fault networks were created fromoutcrop photographs of rock pavements using acamera mounted on a 5m pole. The photo-traverses(Fig. 5D) were located to centimetre precision withpost-processed GPS data. These analyses haverecently been supplemented by additional terrestriallaser-scan data from key localities within the shearzone.

5.3. Strategies used

Since the main focus of this study was thegeospatial analysis of fracture arrays (particularlyin relation to reactivation of pre-existing basementstructure), the primary software tool used wasArcGIS, because of its integrated functionality fortesting spatial correlation. Although the true 3Dvisualisation capabilities of ArcGIS are limited,reasonable 3D perspective was provided by extra-polation of fault planes above and below thetopographic surface (Fig. 5E), and the basicvisualisation capabilities of ArcScene were usuallyadequate. However, specific laser-scan software isneeded to process and interpret the point-clouddata, and future work to integrate these data intothe existing multi-scale model will probably exceedthe capabilities of the GIS and require us to changeto a dedicated geo-modelling package.

5.4. New perspectives

The principal advantage afforded by this digitalmodel is the ability to carry out geospatial queriesusing attributes stored at each locality (Fig. 5F).This enables hypotheses to be quickly evaluated,which greatly helps the process of concept develop-ment in these complex tectonic systems. Being ableto test ideas at the outcrop scale and then quicklyzoom out to obtain a perspective on the likelyimpact at the regional scale is extremely powerful.

Please cite this article as: Jones, R.R., et al., Integration of regional to

models. Computers and Geosciences (2008), doi:10.1016/j.cageo.2007

6. Conclusions and recommendations

Multi-scale digital geological models are con-structed by integrating various kinds of geologicaland geophysical data that span many orders ofmagnitude. In our experience, immersive, interactivevisualisation of multi-scale models gives the user newinsights during interpretation. Software using a 3Dgeospatial GUI, based on a ‘‘virtual world’’ meta-phor, provides best user interactivity for complexgeological models. Currently, the most significantchallenges when integrating data into multi-scalemodels include the incompatibility of proprietarydata formats, the inability of some software packagesto preserve high spatial precision over several ordersof magnitude, and problems with some toolswhen importing new types of outcrop data such aslarge laser-scan point clouds. Other bottlenecksinclude difficulties in georeferencing data storedin different map projections, and extrapolation oflimited amounts of sparse outcrop data. Constructionof multi-scale models can be time-consuming, but thisis offset by the following advantages:

�

ou

.09.

much improved regional context that is immedi-ately accessible visually when analysing andinterpreting more localised field datasets;

� preservation of the inherent quality of each raw

dataset: no loss of spatial precision or dimen-sionality caused by simplifying the data for 2Ddisplay at a single scale;

� more seamless access to many different types of

spatially referenced data via a single 3D geospa-tial GUI (i.e. increased use of the ‘‘virtual world’’rather than ‘‘desktop’’ GUI metaphor);

� direct visual depiction of the level of exposure in

an area, and consequently, the degree of abstrac-tion and qualitative interpretation that exists inpublished maps (cf. Jones et al., 2004);

� easier understanding of spatial relationships

between sets of field observations distributedacross wide areas, such as map-scale channelsdefined by variation in facies types seen inneighbouring outcrops (e.g. Løseth et al., 2003,2004; Adams et al., 2004);

� increased appreciation of the significance of

scaling relationships between structures of dif-ferent characteristic wavelength (e.g. mesoscalefracture arrays and parasitic folds, related tohigher-order, map-scale fold or fault geometries);

� simplified spatial comparison of observations at

outcrop with more regional, remotely acquired

tcrop digital data: 3D visualisation of multi-scale geological

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ARTICLE IN PRESSR.R. Jones et al. / Computers & Geosciences ] (]]]]) ]]]–]]] 13

data (e.g. testing whether structures seen inoutcrop are spatially aligned with features visibleon satellite images, aerial photos, regionalgeophysics).

Further work is needed in the following areas toimprove the creation and usability of multi-scalemodels:

�

P

m

existing geo-modelling tools need to ensurethat they preserve sub-centimetre spatial preci-sion (i.e. avoid rounding errors and snapping ofpoints to voxel centres), to reflect the increasingavailability of high-precision spatial data fromdigital field acquisition;

� geo-modelling software packages also need to be

able to import and display very large point-clouddatasets;

� improved tools are needed to process and

manipulate digital outcrop data, as well as newfunctionality for 3D spatial analysis of theresultant virtual outcrop models;

� new algorithms should be explored for the

interpolation and extrapolation of geologicalsurfaces from sparse data, together with statis-tical methods to quantify the uncertainty of thepredicted geometry;

� new methods should be sought to provide

smoother on-the-fly adjustment of the level ofdetail needed to render the model with adequateresolution. This requires further research tooptimise the inter-relationship between extent ofthe geological model, the position of the viewerwithin the model, the screen size and resolutionof the visualisation hardware being used, and thedepiction of individual objects and datasetswithin the model. More work should focus onways of rapid abstraction and simplificationof complex datasets and objects so that theycan be rendered more clearly and efficiently whenviewed at far distances.

Acknowledgements

Mauro De Donatis and Andrea Zanchi arethanked for significant editorial input. Ruth Wight-man gathered the laser-scan data shown in the insetto Fig. 5E. Dave Stevenson, Gary Wilkinson andmembers of RRG and the e-Science ResearchInstitute at Durham have provided invaluabletechnical help and discussion. Parts of this workrelate to NERC Follow-on Fund (NE/C506964/1),

lease cite this article as: Jones, R.R., et al., Integration of regional to

odels. Computers and Geosciences (2008), doi:10.1016/j.cageo.2007

and Ocean Margins Link project (NER/T/S/2000/01018, NER/S/S/2001/06740) funded by Statoil UKLtd., BP and NERC. RRG gratefully thank Badleys,Midland Valley Exploration, and Schlumbergerfor non-commercial software licences and ongoingassistance.

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