3-d laser scanning and virtual photorealistic outcrops...

3-D Laser Scanning andVirtual Photorealistic Outcrops:

Acquisition, Visualization and Analysis

Carlos Aiken, Xueming Xu, John Thurmond, Mohamed AbdelsalamM. Iulia Olariu, Cornel Olariu, and Allie Thurmond

The University of Texas at DallasSaturday-Sunday, April 17-18, 2004

2004 AAPG Annual Convention, Dallas, TexasShort Course #3

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3D LASER SCANNING: ACQUISITION, VISUALIZATION AND ANALYSIS

The University of Texas at DallasSaturday-Sunday, April 17-18, 2004

Carlos Aiken, Xueming Xu, John Thurmond, Mohamed AbdelsalamM. Iulia Olariu, Cornel Olariu, and Allie Thurmond

3D LASER SCANNING: ACQUISITION, VISUALIZATION AND ANALYSIS

Center for Lithospheric StudiesDepartment of Geosciences

University of Texas at Dallas

• Carlos Aiken: 972-883-2450 (ofc), 214-535-6520 (cell)• Xueming Xu: 972 883 2405 (ofc)• John Thurmond: 214-718-2110 (cell)• Mohamed Abdelsalam: 972-883-2724 (ofc)• M. Iulia Olariu• Allie Thurmond• Sharon Edwards: 972-883-2424

– (Administrative Assistant - Center For Lithospheric Studies)

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Course Agenda

Time Activity Presenter .

8:30 A.M. Arrive9:00 A.M. Agenda and Overview Aiken

Emphasis on Team Approach And Case Histories

9:45 A.M. Model Building XuPost-processing, Modeling and Software

10:30 A.M. Break10:45 A.M. 3D Analysis Thumond, Olariu,

Geometric Analysis, Lithologic Mapping, Data Xu and AikenIntegration with Surface and Subsurface Data,

Surface Fitting

11:45 P.M. Review of Field Exercise at Preston Canyon Aiken12:00 P.M. Lunch off Campus at Sonny Bryan’s1:00 P.M. Go To Field—Preston Canyon1:30 P.M. Map Preston Canyon4:00 P.M. Return to Convention Center

Saturday, April 17, 2004:

Time Activity Presenter .

8:30 A.M. Arrive

9:00 A.M. Outline of Activities

9:15 A.M. Preston Canyon Xu

Review of Field Data Acquisition

Review of Data Processing

Review of Model Building

11:00 A.M. Break

11:15 A.M. Analysis of Preston Canyon Model Thurmond, Xu, AikenClass Exercise with Photomosaics3D Modeling of Structures

12:00 P.M. Lunch on Campus

Sunday Morning, April 18, 2004:

Course Agenda

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Time Activity Presenter .

1:00 P.M. Outline

1:15 P.M. Case HistoriesBig Rock Quarry, Arkansas Olariu

Spain Thurmond, Xu

Mineral Wells (Dobbs Valley) Aiken, Xu, Thurmond

Other Aiken, Thurmond

3:00 P.M. Break

3:15 P.M. Discussion All

4:00 P.M. Wrap-up and Return to Convention Center

Sunday Afternoon, April 18, 2004:

Course Agenda

OVERVIEWEmphasis on Team Approach and Case Histories

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Introduction• Mapping is the foundation of the geological

sciences.• Three basic elements in geologic map

– Location– Lithologic information– Spatial or geometrical relationships

• Ultimate goals: – Providing the digital data sets representing the real

earth so that a “virtual” earth can be simulated on the computer and then virtual model displayed and analyzed in “virtual reality” system.

CYBERMAPPING: Integration of GPS, Reflectorless Laser

Rangefinders, and other sensors.

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Technology

• GPS: global positioning to a centimeter.• Lasers: relative positioning of decimeter to

centimeter.• GIS: mapping functionalities.

GPS Techniques

Comparing accuracy, baseline, and time required for positions. SPS, PPS = autonomous position. OTF=on-the-fly kinematic post-processing.

RTK=real time OTF kinematic..

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Summary of the approximate accuracy of GPS positioning versus methods. (Modified from Featherstone, 1995)

RTK Base Station

Radio Transmitter GPS Base Antenna

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RTK Rover Station

Laser beam

with 3 milliradian div.

diffuse reflection

Target

Laser range-finder

receiver aperture

Not to scale

Diffuse reflection for reflectorless laser rangefinders

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Material DescriptionWinter Snow and IceVegetation (The Average Value of Many Types)SoilSiltSandGypsumClayDirtShale, CoralConcrete, AsphaltCoal Tar PitchPlywood, UnpaintedBrick, RedBark

DR.0.850.500.05 - 0.35 0.20 - 0.400.10 - 0.35 0.55 - 0.700.40 - 0.500.30 0.45 0.10 0.05 0.50 0.250.20 - 0.25

904 nm diffuse fractional reflections of common material

A B C

DE F

Common Type of Reflector Laser Rangefinder

A. Laser Atlanta Advantage CI B. Riegl Scout. C. Leica Binocs.D. Laser Atlanta Advantage CI with PRO XL DGPS. E. LaserTech Criterion. F. LaserTech Impulse.

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0.01;0.01;0.01Theodno100-200May Total Station?;0.2;0.15nono700Can Riegl

0.5;0.1;0.15noyes100noLeica

0.5;0.2;0.15noyes2200noLaser Tech.

0.01;0.01;0.15Theodno1000yesALS(MDL+Riegl)

0.01;0.01;0.15Theodno500yesALS(MDL+LA)

0.1-0.5;0.2;0.15yesyes100-500yesMDL

0.1-0.5;0.2;0.15yesyes500yesLaser Atlanta

Hor., Ver; Range(m)

EncoderComp.Max. Range(m)

Con.Manufacturer

Characteristics of common types of laser rangefinders

ALS = MDL theodolite with a Laser Atlanta (LA)or Riegl laser rangefinder.

SOFTWARE PRODUCTS

http://www.scanning.fh-mainz.de/

http://www.zf-uk.com/Light Form ModellerZ+F UK Ltd.

http://www.sdrc.com/Imageware SurfacerSDRC

http://www.geomagic.com/Geomagic Studio Raindrop Geomagic

http://www.paraform.com/ParaformParaform

http://www.octocom.de/octoCADoctocom AG

www.metrologic.frMetrolog IImetrologic group

www.kubit.dePointCloudkubit GmbH

Basilicahttp://www.iuav.it/OrthoLaserInstituto Universitario di Architetturadi Venezia

http://www.rapidform.com/RapidFormINUS Technology

CryptCarvings

http://www.innovmetric.com/Polyworks ModelerInnovMetric Software

http://www.topotek.it/Reconstructor, SurveyorInn.Tec s.r.l.

ApplicationsLink to ProducerTypeProducer

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http://www.zf-uk.com/Light Form ModellerZ+F UK Ltd.

http://www.sdrc.com/Imageware SurfacerSDRC

http://www.geomagic.com/Geomagic Studio Raindrop Geomagic

http://www.paraform.com/ParaformParaform

http://www.octocom.de/octoCADoctocom AG

www.metrologic.frMetrolog IImetrologic group

www.kubit.dePointCloudkubit GmbH

Basilicahttp://www.iuav.it/OrthoLaserInstituto Universitario diArchitettura di Venezia

http://www.rapidform.com/RapidFormINUS Technology

CryptCarvingshttp://www.innovmetric.com/Polyworks ModelerInnovMetric Software

http://www.topotek.it/Reconstructor, SurveyorInn.Tec s.r.l.

ApplicationsLink to ProducerTypeProducer

Ranging Scanners (measuring distance by time-of-flight or phase difference)

http://www.zofre.de/Light Form Modeller55IMAGER 5003

Zoller+FroehlichGmbH

http://www.3d-gurus.com/3Dguru363DguruVisi Image

ChurchPrioryBridge deformationRoman bridgeNymphaea

http://www.riegl.com/3D-RiSCAN 1000LMS-Zxxxseries

Riegl Laser Measurement Systems

http://www.optech.on.ca/ILRIS-3D Parser800ILRIS-3DOptech

http://www.metricvision.com/60MV200 / LR200

MetricVision/LeicaGeosystems

http://www.mensi.com/3Dipsos,

RealWorks100GS 100 Mensi

http://www.isite3d.com/I-SiTE Studio 400I-SiTE 4400 I-SiTE Pty Ltd

http://www.iqsun.com/http://www.iqvolution.com/

iQscene78iQsuniQsun GmbH, iQvolution AG

ChapelColiseumPriorySacristyAncient theatre

http://www.cyra.com/Cyclone, CloudWorx

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HDS 4500HDS 3000HDS 2500 (former Cyrax2500)

Leica Geosystems/Cyra Technologies

http://www.callidus.de/3D-Extractor80CallidusCallidus Precision Systems

MonticelloMonticello

http://www.3rdtech.com/12DeltaSphere3rdTech

ApplicationsLink to ProducerSoftwareRange (m)

ScannerProducer

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CyberMapping Design Flowchart

Laser Mapping System

Laserrangefinder

EncoderPortable computer

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REDUCTION TO THE ELLIPSOID

h

NH

REarth Radius

6,372,161 m20,906,000 ft.

Earth Center

S

D

S = D x R R + h

h = N + H

S = D xR + N + H R

REDUCTION TO GRID

Sg = S (Geodetic Distance) x k (Grid Scale Factor)

Sg = 1010.366 x 0.99991176

= 1010.277 meters

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REDUCTION TO ELLIPSOID

S = D x [R / (R + h)] D = 1010.387 meters (Measured Horizontal Distance) R = 6,372,162 meters (Mean Radius of the Earth) h = H + N (H = 158 m, N = - 24 m) = 134 meters (Ellipsoidal Height)

S = 1010.387 [6,372,162 / 6,372,162 + 134] S = 1010.387 x 0.999978971 S = 1010.366 meters

COMBINED FACTOR

CF = Ellipsoidal Reduction x Grid Scale Factor (k)

= 0. 0.999978971 x 0.99991176

= 0.999890733

CF x D = Sg

0.999890733 x 1010.387 = 1010.277 meters

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Preston Canyon, Austin Chalk, Plano

• Initial proof of concept of digital mapping.– Integration of GPS, lasers and portable

computers.

• Digital terrain by GPS and lasers.• Real time strike and dip determination.• Initial test of oblique photography.

Looking west just east of Preston Road, Plano, Texas

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Map view of the mapping of layer and faults. Continuously shooting Topcon total station

North side of cut, vertical section mapped by GPS controlled Topcon reflectorless laser mapping.

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Failed attempt to drape oblique photo onto terrain by rubbersheeting with ESRI software. Orthophoto draped

onto terrain.

Looking east along cut with draped orthoquadand laser mapped geology..

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Utah Muddy Creek Mapping Project

3D Perspective View of Surveyed Points

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Rotated Perspective View of Ferron Growth Faults

Two Key Bounding Surfaces and Terrain

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Digital Mapping at Corbula Gulch outcrop, Utah

• Locate 2D and 3D GPR surveys, coreholes, measured stratigraphic sections by RTK.

• Map the beds along the cliff faces by laser rangefinders.

• Interpolate the 3D geometry of the sedimentary bodies.

• Build the 3D geological model for visualization, analysis and interpretation.

3D Perspective View of Survey Data Layout

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GPR Cubes and Profiles of Depth Sections

Volume of bounding surfaces

Topography

CIncl 0

Incl 1

Unit 1 Upper unit

Incl 7Incl 6 Incl 5

Incl 4

Incl 3

Incl 2

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Mapping Subtle regional Structures in Wyoming

Problem

• Distribution of sandstone in foreland basins controlled

by structure.

• Structures are poorly defined because of low

amplitude.

• Modern structures are also folded.

• Use digital mapping approach to document subtle

changes in elevation related to basin warping.

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Data Points on Terrain Map

Powder River, Wyoming

3D Perspective View of Mapped Horizons

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Objectives

• Render the geological outcrop photorealistically in three-dimensions– Close-range– obliquely

• Capture the entire outcrop in three-dimensions and taking back to office– Additional 3D mapping and measurements made back

in office.• RESULT: a 3D Virtual World• Standard methods for vertical angles not valid

Building Photorealistic Virtual Outcrop

• Objectives• Previous Work• Image Registration• Test Case

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Objectives

• Rendering the geological outcrop photorealistically in three-dimensions

• Capturing the entire outcrop in three-dimensions and taking back to office

• Additional 3D mapping and measurements back in office.

Traditional Image Registration

• Apply the low order polynomial function to map the image coordinates into world space.

• Only appropriate with a perpendicular perspective and relatively small relief

• Poor accuracy

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The georeferenceddigital geological map

The DEM

Geological map draped onto terrain surface

Traditional Image Registration

Geometric Measurement

• Rely on stereo image sequences.• Require a large amount of correspondence

points.• Time consuming.• Not good for natural surfaces.

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Stereo Image Sequence Capture System

Image Registration

• Rely on pinhole model.• Project the ray of a point into camera space.• Project to image plane by projective

transform.• Linear initialization and non-linear

optimization.

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Camera Geometry(Pinhole Model)

Perspective View of Surveyed Points

Using GPS and total station mapped layers (yellow), faults (red) and Terrain (white)

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Initial Test

1) Several natural marks identified on the photo, and surveyed using laser

2) Transformation established using non-linear iterative method

3) Those points back projected into photo-space

Initial Test Result

1.45e-4-5.87e-51.46e-6-5.5e-40.7223.19YesPhoto5

-1.52e-42.35e-61.17e-4-5.23e-40.6323.18YesPhoto4

-4.99e-4-3.92e-51.55e-6-5.54e-41.4523.59YesPhoto3

5.62e-41.60e-4-2.80e-7-2.17e-42.4123.87NoPhoto2

4.73e-4-4.03e-4-5.25e-72.88e-52.4323.11NoPhoto1

P2P1K2K1Std. Error

(Pixel)

F (mm)ReflectorPhoto

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Photo Mosaic

Comparison with Direct Laser Tracing

1) Pink and blue points are bedding surface and fault traced by laser

2) Green and blue points are the control points for photograph for transformation

3) Points of bedding and faults back projected to photo space

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Re-Digitized Key Beds and Faults

Photorealistic Outcrop

Dr. Xueming Xu reinterprets the Austin Chalk at Norsk Hydro

Digital 3D outcrop data may imported into an immersive visualization environment.

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New Cybermapping System

• Laser rangefinder/Scanner: • Acquire digital terrain surface• High-end and low-end cost (determined by required accuracy,

resolution , and speed)

• GPS• RTK system: provides global correlation for the laser offsets

• Digital Camera– Determined by interchangeable lens and CCD size

• Consumer-based (non interchangeable, small CCD size, large lens distortion)

• Professional-based

Laser-scan of quarry

Jackfork Sandstone, Bigrock Quarry, Arkansas

3D Photorealistic data for interpretation of deep water facies.

Face-On view of outcrop

Courtesy of Veritas

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Jackfork Sandstone, Bigrock Quarry, Arkansas

3D Photorealistic data for interpretation of deep water facies.

Courtesy of Veritas

Digital TerrainDigital TerrainPerspective View

A. Three-dimensional model of the Big Rock Quarry outcrop. B. Three-dimensional view of a channel connecting the two sides of the quarry.

Most paleocurrents suggest a SW flow direction; few of them have SE markers. However in most cases it was only possible to tell the direction of the flow and not its sense.

C. Channel boundaries highlighted on the 3D model allow reconstruction of submarine channelcomplex architecture.

Big Rock Quarry

Channel

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Ainsa data integrated with available airborne imagery and DEM

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Applications of 3D Outcrop DataJohn Thurmond

Immersive Environment (CAVE)

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LASER SCANNERS• Imaging system provides a user with a dense set of three-

dimensional vectors to unknown points relative to the scanner location

• Unprecedented density of geospatial information coverage • Return-beam detection device• Beam deflection mechanism. • Controlled by laptop computer that is also used for data

acquisition. • Range measurement is derived from the two-way travel

time of a laser pulse • Orientation (elevation and azimuth) of the transmitted• Pulse is measured by the beam deflection system • Energy of the return pulse is also recorded • color (RGB) is also recorded

LASER SCANNER ACCURACY

• BOEHLER, Vincent and Marbs, 2003.• Tested scanners for accuracy• Application was for cultural heritage

applications• Manufacturers specs not comparable

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Accuracy

• Angular accuracy– Angles from combination of deflection of

rotating mirrors and rotation about a mechanical axis

– Provides position with range position• Range accuracy

– Time of flight or phase comparison between outgoing and returning signal

– Noise-fuzz of points on a flat surface

Resolution

• Resolution– User—ability to detect an object in point cloud– Two specs contribute

• Smallest increment of angle between successive points (can manually set)

• Size of laser spot

• Edge effects– When a spot hits and edge and gets 2 locations

and or 2 materials

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Other effects on accuracy

• Surface reflectivity– Distance, atmospheric, incidence angle– Albedo (ability to reflect)

• White strong, black weak• Depends on spectra of the laser (green, red, near IR)• Shiny-poor reflector

– Effects accuracy-range errors larger than specs

Environmental conditions

• Temperature-check specs• Atmosphere-

– changes propagation speed slightly– Dust, mist, fog-- a problem

• Interfering radiation– Sunlight strong relative to signal

• Influence or prevent (don’t shoot into sun)

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Other considerations

• Measuring speed• Range limits• Field of view• Laser class—eye safe?• Can register?• Can transform into coordinates?• Logistics-weight, batteries etc.

Measuring noise in range direction. Riegl Z420 is comparable to Z360

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Resolution test

Resolution test

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Advantages and Disadvantages of mid-range laser scanners

LASER SCANNERS (From POB magazine)

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Other characteristics of previous

More scanners

43

44

SCANNER SOFTWARE

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More on scanner software

Using fast laser scanners• Changed many of our procedures

– Millions of points instead of thousands– Software limitations– Hardware limitations– BUT– Point clouds could integrate internally– Features such as vegetation definable– Scan at 1000to 24000 points/sec so much faster– Map at more detail– Can use the points in a cloud to position a location

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Mineral Wells OutcropNote the detail - yet the terrain interval is several decimeters—the photo brings in millions of pixels in detail.

Dobbs Valley Growth fault outcrop.Geology by McLinjoy.

Mapping of data onto 3D modelby Erik Brandlin.

Left: entire outcrop.

Lower: with McLinjoy data

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Example of McLinjoy mapping color coded (lines)and 3D analysis (surfaces)

Digitized McLinjoy data boundaries (lines) and interpreted surfaces

Mapped features (lines) and analysis (surfacefits

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Mt. Rushmore, South Dakota

• Mapped like tourists with big equipmentscanning from 3 locations in visitor area-two half daysThree photos over the 2 days10 million points, 2-3cm accuracy and resolution with LPM 3800 Riegl

Will return to add more data from top of heads

Entire model—merged scans

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Surface fit to scanned points

Final photorealistic model

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LPM scanning of outcropLower left—intensity plot of a scanLower right—3D photorealistic model

Will see this again in stereo.

Shorter Range scanner- 100 m - 24 KHz- ~ 1.2 cm Accuracy

Using faster scanner at Salina tunnel and Mt. Rushmore

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Z360 in action in Salina tunnel.

Z360 in action

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Salina Tunnel, Utah(note fault)

I-35 Arbuckle Outcrops

• Mapping series of famous outcrops along I-35 in Oklahoma– Faulted anticline—in process– Unconformity—shown here in Polyworks

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I-35 unconformity model

Surface fit to point cloud

3D Virtual model

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MODEL BUILDINGPost-processing, Modeling and Software

Different Sensors

• Scanners • Local coordinate system

• Cameras• Local camera coordinate system

• GPS• Global coordinate system

55

Coordinate Systems

• Individual local scanner coordinates (each scan)

• Object coordinate system (single coordinate system aligning all scans)

• Camera coordinate system (each photograph)

• Global coordinates

Scanner Coordinate

• Individual scanner local coordinate

– Not necessary to levelY

X

Z

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Y

X

Z

Camera Coordinate SystemEach photographhas its ownCoordinatesUnits: mm or

pixel

Putting it together

• From individual scan coordinates to object coordinates

• From object (or global) coordinates to camera coordinates

• From object coordinates to global coordinates

57

Individual coordinates to object coordinates (1/2)

• Traditional survey approaches– Need to level the scanner– set up backsight– Knowing scanner location and backsight angle

• transform each point to the object coordinate system, usually global.

– Advantage: • easy to set up• one-step from local to global coordinates.

– Disadvantage:• problem in generating mesh models.

From individual coordinates to object coordinates (2/2)

• Use mesh alignment techniques (Polyworks)– No need to level.– Requires overlap with common

features to minimize the distance.Z

X

Ysc1 sc2

T =

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From Object to Camera (1/2)

• Two approaches– Polynomial fit (rubber sheeting)

• Low accuracy,• No need to know camera intrinsic parameters

– Projection transform (pinhole model)• High accuracy

From Object to Camera (2/2)1. From object to camera

coordinate system (pin hole model)

2. Perspective projection to convert to image coordinates (uv, pixel, or mm)

6 unknowns assuming known fNonlinear-needs initial value

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Camera Calibration

• Correct lens distortion– Radial distortion– Tangential distortion

– Calculate f, k1, k2, p2, p2 in the lab for each lens.

Example of the calibration (Canon 17mm)

1 2

3

1) Radial distortion2) Tangential distortion3) Complete model

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ExampleIteration = 8

Residualspts51 = -0.0027 -0.0065pts50 = 0.0045 0.0085pts2034 = 0.0050 0.0087pts 2010 = -0.0066 -0.0100

omage:0.08839938218814phi:1.36816786714242kappa: 1.45634479894558X: -0.975Y: 0.519Z: -0.013

Bundle Adjustment

Adjust the bundle of light raysto fit each photo

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Bundle Adjustment (2/2)Photo no : 7734

pt no U V201 -0.000 -0.000202 0.000 0.003203 -0.000 -0.00614 -0.003 0.01215 0.000 0.001204 0.001 -0.004205 0.001 -0.009302 0.001 0.004

Photo no omega phi kappa X Y Z7733 3.5147 78.25411 85.03737 -1.031 0.628 0.0467734 21.026 79.86519 68.09084 0.419 14.735 -1.055

Photo no : 7735pt no U V14 0.003 -0.00615 0.003 -0.001204 -0.001 0.004205 -0.001 0.00916 0.017 0.005206 0.000 0.001207 -0.001 -0.009208 0.001 0.010302 -0.006 -0.009

From Object to Global (1/2)

• 7-parameter conformal transformation

s

Where m11 = cos(phi) * cos(kappa);m12 = -cos(phi) * sin(kappa);m13 = sin(phi)m21 = cos(omega) * sin(kappa) + sin(omage) * sin(phi) * cos(kappa);m22 = cos(omage) * cos(kappa) – sin(omega) * sin(phi) * sin(kappa);m23 = -sin(omage) * cos(phi);m31 = sin(omage) * sin(kappa) – cos(omage) * sin(phi) * cos(kappa);m32 = siin(omage) * cos(kappa) + cos(omage) * sin(phi) * sin(kappa);m33 = cos(omage) * cos(phi);and s is scale factor

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Transform to Global (2/2)

Object GPS

Iteration:5scale: 0.998986 (*****)omega: 0.22279535phi: -0.04740587 kappa: 1.45393837X trans: 24.834 Y trans: 11.698Z trans: 2.142

Pt: 1, X -0.012 Y 0.042 Z 0.010Pt: 2, X 0.008 Y -0.004 Z -0.012Pt: 3, X 0.011 Y 0.012 Z 0.004Pt: 4, X -0.017 Y -0.032 Z -0.007Pt: 5, X 0.010 Y -0.018 Z 0.006

Surface Generation

• Through merge process in Polyworks• Through fitting through GoCad• Through direct triangulation (Delauney

triangulation, TIN)

63

Surface cleaning (in Polyworks)

• The single most time consuming part of entire process (90% of time).– Filling the holes

(because of scan shadow)

– Correct triangles

Summarize

1

Photorealistic Interpretation Techniques

3D Data Types

• GPS/Laser Mapping

• Remote Sensing/DEM Draping

• Photorealistic Data

2

Interpretation of 3D Data

• Laser/GPS Data

– Direct ‘in the field’ interpretation

– Point ‘clouds’

– Line/Surface fitting

3

Interpretation of 3D Data

• Remote Sensing/DEM Draping

– Similar to GPS mapping• Point clouds, lines and

surfaces

– Point density varies with DEM resolution

– Fails on vertical cliffs

4

Photorealistic Data Interpretation

• Can span a variety of scales– Small scale single outcrop– Complex canyon systems

• Quality depends on “three dimensionality” of outcrops

• Fundamental difference – data can be reinterpreted any time.

3D Digitization

5

3D Digitization – Surface Fitting

6

The Power of Surfaces

• Correlation– Do the surfaces match?

• Orientation– Strike and Dip– Fault/Fold orientations– Fault offset/rotation

• Paleogeomorphology

7

Integration with other data

Integration with Geophysical Data

8

Summary

• Photorealistic data is the only option to ‘take the outcrop home’

• Instead of mapping individual surfaces, all surfaces can be captured simultaneously

• Multiple interpretations can be made of one photorealistic example

• Integration of other data sets can be a powerful tool for subsurface-to-surface interpretation.

1

Case History: Big Rock Quarry, Arkansas

Development of an Analytical Method for Close-in 3-D Photorealistic and Multispectral Mapping:

Deep-water deposits at Big Rock Quarry, Arkansas

ByIulia Olariu

2

Overview?Techniques

3-D modellingMultispectral imaging

?Contributions to geologic analysisApplication to deep-water deposits at Big Rock

• 3-D modeling/analysis of submarine channels• Spectral analysis - Reflection spectra

- Thermal spectra

?Considerations in data collection/processingInfrared camerasReflective and thermal imagesMultispectral acquisition and 3-D modeling

?Conclusions

? How much does 3-D image analysis of geology improve conventional 2-D analysis?

Big Rock Quarry 2-D Photomosaic. Key bedding boundaries are outlined in black on outcrop wall. From Douglas et al., 1993

THE PROBLEM

Big Rock Quarry 3-D photorealistic model

3

THE PROBLEMHow much does multi-spectral imaging/analysis helps in mapping geology ?

A principal component was performed on the three visible bands and the three PC images combined in red, green and blue. The color PC image displays spectral variation in the shale (blue) and sandstone (purple).

False Color Image of Big Rock Quarry

Natural Color Image of Big Rock Quarry

? Oblique close-in photography is acquired with digital cameras

? Centimeter-digital terrain models are generated by laser scanning the outcrop

? Digital photos are integrated with terrain data and converted into a 3-D digital model of the outcrop

? 3-D photorealistic methodology is used to build 3-D multi-spectral models

? Analyses and interpretation methods are developed for these 3-D multi-spectral models

Three Dimensional Modeling

4

Leica 500 GPS Base(1-2 cm accuracy)

Leica 500 GPS Rover(1-2 cm accuracy)

Digital Acquisition of the Outcrop

Radio

Signal

Topcon Total Station(1 cm accuracy)

Atlanta Laser (5 cm)Riegl LPM3800 (2.5 cm)Riegl LMS-Z360 (0.5 cm

accuracy)

Fuji S1ProCanon D60IndigoDigital Cameras

Outcrop

CORS Station

1) All data points are merged in global coordinates2) A surface is built based on raw data points3) Image information is added to the surface

3-D Modeling

5

Multispectral Imaging

? Remotely sense lithology at outcrop scale

• obliquely at close range • with an increased spatial and spectral resolution

? Collect images using appropriate narrow band filters where spectral features are present in the spectral curve of rock samples

? Co-register individual bands and display them in RGB color space

? Drape the false color images onto the three-dimensionaldigital model of the outcrop

Deep-water Deposits at Big Rock Quarry, Arkansas

Contributions to Geologic Analysis

6

USA, Arkansas

Pulaski County

Location of Study Area

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Scott

Marche

Roland Gibson

Parkers

Woodson

Ferndale

PinnacleMcAlmontSherwood

Maumelle

Mabelvale

Sweet Home

Little Rock

Iron SpringsWrightsville

Sylvan HillsGravel Ridge Jacksonville

Cammack VillageNorth Little Rock

0 10 205

Miles

S t r e a m s

!( C ities

R o a d s

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Big Rock Quarry

USA, Arkansas

Pulaski County

BIG ROCK QUARRYBig Rock Quarry

Geologic Map of Arkansas

N

Prepared by the Arkansas Geological Commission and the United States Geological Survey, 1993

Big Rock Quarry

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Big Rock Quarry 3-D Photorealistic Model

N

The outcrop belt displays a nearly orthogonal cut across the downcurrentdirection to the southeast and a more oblique, dip-oriented profile to the north.The northwest end of the quarry displays a nearly cross-sectional view.

A. Three-dimensional model of the Big Rock Quarry outcrop. B. Three-dimensional view of a channel connecting the two sides of the quarry. C. Channel boundaries highlighted on the 3-D model allow reconstruction of

submarine channel complex architecture.

Big Rock Quarry

Channel

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Sedimentary Structures

Paleoflow direction inferred from direct measurements of paleocurrentson rocks exposed at the base of the outcropMost paleocurrents suggest a SW flow direction; few of them have SE markers. However in most cases it was only possible to tell the direction of the flow and not its sense.

Multispectral Analysis of Shale and Sandstone Samples from Big Rock Quarry

over the Visible Near Infrared Part of the Spectrum

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Multispectral Analysisof Shale and Sandstone Samples

from Big Rock Quarry in the Visible Near Infrared Part of the Spectrum

Apart from an overall albedochange due to variations in light intensity and to the roughness of surfaces the spectra did not significantly differ.

Log transformation helps to better visualize trends in the data set.

Logarithmic Transformation

Reflectivity Spectrum

Ref

lect

ance

(%

)R

efle

ctan

ce

VISIBLE NIR SWIR

MATLAB Algorithm Developed to Explore the Spectral Signature of Sandstone and Shale Samples

Each quadratic curve describes the general tendency in the data.It separates trends in illumination and reflectivity from spectral details. The trend components are subtracted from the observations.

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MATLAB Algorithm

• Higher order, but smooth components are modeled by Fourier series and removed. • The residual is smoothed with the running median smoother.

Nonlinear smoothers are used instead of linear smoothers, such as running means because they are resistant to outliers and will remove narrow “spikes”.

Fourier Transform Applied to Residuals Left after Fitting a 2nd Degree Polynomial

Nonlinear Smoothing Applied to ResidualsLeft after Fitting a Fourier Series

Multispectral Analysis

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Principal Component Analysis (PCA)

R-mode Scores

Shale• Sandstone

PCA was performed in order toreduce the dimensionality of the data set and possibly to isolatespectra typical of shale and sandstone rock types.

Each observation is converted toto a principal component score by projecting it onto the principal axes.

First and sixth principal components best separates thedominating features in the data set.

Loadings of Variables on the Principal Components

1900

1900

Loadings are weights associated with variables on each component and show how much correlation is between that PC and the original variable.

Wavelengths at 1900 nmhave high loadings on first and sixth principal components.

Possible locations for filtersare highlighted in red.

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Multispectral Analysis of Shale and Sandstone Samples from Big Rock Quarry

over the Thermal Part of the Spectrum

Multispectral Analysis in the Thermal Infrared Portion of the Electromagnetic Spectrum

Emission Spectrum Spectral Features of Shale and Sandstone Samples Log Transformation

There is a linear relationship between decreasing silica and increasing wavelength for the emission minimum. The silicate rocks show the typical emissivity minimum between 8.5 and 12 µm.

Reflected IRReflected IRThermal IR Thermal IR

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Nonlinear Smoothing (4253H) Applied to Residuals Left after Fitting a Fourier Series

Fourier Transform Applied to Residuals Leftafter Fitting a Second Degree Polynomial

Multispectral Analysis

8.5-9.7 µm 8.5-9.7 µm

Principal Component Analysis (PCA)

R-mode Scores

First and second principal components best separates the dominating features in the data set.

S1

S2

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Loadings of Variables on the Principal Components

Principal Component Analysis

Wavelengths at 9 -10 µm have high loadings on the first and second principal components. Possible locations for filters are highlighted in red.

Considerations in Experimental Design

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There is an inverse relationship between having high spatial resolution and high radiometric resolution when collecting thermal infrared data.

A larger IFOV provides good radiometric resolution. The larger the IFOV, the poorer the ability to resolve fine spatial detail.

Ground Resolution Cell ( r ) = distance ( H ) x IFOV

A more intense thermal infrared signal can be obtained by getting the camera as close to the ground as practical.

Trade-off of Using Infrared Cameras

Indigo Infrared Cameras

Merlin IR cameras (320x256)

r = 50m x 0.15 mrad = 7.5 mm (lens diameter = 200 mm)

Merlin Uncooled camera (7.5-13.5 µm) [320x240]

r = 50m x 0.25 mrad = 12.5 mm (lens diameter = 200 mm)

Phoenix cameras (640x512)

r = 50m x 0.25 mrad = 12.5 mm (lens diameter = 100 mm)

Detector SizePhoenix MID

640x512 Phoenix NIR Phoenix LAB Phoenix Long

Phoenix NIR Phoenix MID320x256 Merlin NIR Merlin MID

Alpha NIR Merlin LAB Phoenix Long

320x240 Merlin Uncooled

NIR MIR TIR

0.9 - 1.7 µm 3 - 5 µm 7.5 - 13.5 µm

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Considerations in Design of Reflective Image Acquisition

? Timing of the data collection

- Direct illumination (solar elevation angle)- Indirect illumination

• Diffuse sky illumination (atmospheric conditions)• Scattered light from surrounding objects

?Target viewing and illumination geometry

- The overall brightness changes due to changes in the amount of shadow in the field-of-view of the camera

• As scene illumination varies, the ability to convey detail may be reduced– Increased brightness values tend to wash out features– Decreased brightness values remove any detail by darkening a part

of the scene

• The effects of these variations in brightness values can be removed by using band ratioing since ratio techniques compensate for factors that act equally on the various bands.

Band Ratioing

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Considerations in Design of Thermal Image Acquisition

? Timing of the data collection

• Nighttime images (show thermal properties of rocks)• Daytime images (are dominated by topography because of solar

heating and shadowing)

?Thermal rock characteristics

• Dominant wavelengths for rocks with a temperature of about 300K are at 9 -10 µm

• At these wavelengths the intensity of radiant energy is very low- A broad bandpass- Longer exposure time

Conclusions

? 3-D modeling/analysis allowed for reconstruction of submarine channel complex architecture in a deep water marine environment

?Close-in oblique multi-spectral imagery is useful in highlighting lithologic variation of the outcrop

? 3-D close-in multi-spectral mapping/analysis provide new tools for geologic mapping and interpretation

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Acknowledgments

?3D Virtual Geology UTD Consortium

?Gulf Coast Section SEPM Foundation

?Raed Aldouri and Randy G. Keller (UT El Paso)

?Mike Sampson (UTD Chemistry Department)

?David Williamson (Martin Marietta Materials)

?Brad Bankhead (Veritas Exploration Services)

1

Case History: Ainsa, Spain

Visualization to Utilization –Capturing and constructing a three-dimensional model

of the Ainsa turbidite system, northern Spain

John ThurmondUniversity of Texas at DallasWith contributions from:Carlos AikenXueming Xu, UTDTore M. LøsethOle MartinsenKristian SøegaardJan Rivenæs, Norsk Hydro

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Geology of the Ainsa Basin

Early-Middle Eocene turbidite basins of the southern Pyrenees

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Paleogeography of the Ainsa basin

?Basin Fill: 4000 m thick, regressive, from slope to alluvial.? Slope complex: Mudstones with coarser-grained lithosomes (turbidite systems)? Turbidite systems: straight-sided, trending downslope, 10’s km long, km’s wide, 800-80 m thick. Mostly formed by channel-fills.

General Eocene stratigraphy of the Ainsa basin (Arbués et al. 1998)

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Study Area

Cross-section of the southern Ainsa basin

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Growth turbidite strata during the piggy-back stage of the Ainsa basin

Outcrops of the Ainsa turbidite system

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Capturing the Ainsa-2 Channel

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Project Goals

• Capture outcrop data in 3D

• Integrate diverse data– Structural Data (Univ. of Barcelona)– Contextural Data

• Develop methods for direct construction of 3D reservoir models from outcrops

• Utilize for teaching and as an analog for offshore Angola

Outcrop Data Capture

+

=

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Data Integration

DEMs Aerial Photos Geologic Maps

Structural Surfaces Wells, Measured Sections, and Cross Sections

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Data Interpretation

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An effective teaching tool…

• All data same scale

• Can change scale seamlessly – regional to bed-scale

• Understanding outcrop data and upscalingcompromises

Conclusions/Lessons Learned

• Photorealistic data workflow very effective for collecting 3D outcrop data

• Data can be re-interpreted

• Quantitative reservoir models can be produced from outcrop data quickly

• An excellent resource prior to field schools