Center for Subsurface Sensing &Imaging Systems

Center for Subsurface Sensing &Imaging Systems

NSF Year 6 Site VisitApril 5, 2006NSF Year 6 Site VisitApril 5, 2006

Wave-Based Modeling, Highlighting Underground

Sensing and Imaging

Wave-Based Modeling, Highlighting Underground

Sensing and Imaging

Research Thrust R1-B

Carey RappaportSara Wadia-FascettiMiriam Leeser

Carey RappaportSara Wadia-FascettiMiriam Leeser

Modeling as a Unifying ElementModeling as a Unifying Element

SoilBED Experiments

UndergroundDetect. Alg.

GPR/Acoust. Sensor Design

Forward Computational

Models

L2

ConceptualModels

Wave Model.Toolbox

R1

R2

L1, L2

L3

R1,R2

R2,R3R1,R2

L1

L1

CenSSIS Computational Modeling PrinciplesCenSSIS Computational Modeling Principles

Strengths

Models designed to address critical and realistic aspects of Underground Sensing problemIncorporate ground surface and lossy soil Models apply to other applications and modalities

Strengths

Models designed to address critical and realistic aspects of Underground Sensing problemIncorporate ground surface and lossy soil Models apply to other applications and modalities

Opportunities

Model refinement based on experimental observationsModel adaptability to MedBED, BioBED, and S3Accelerated computing by parallelizing intensive 3D codes.

Opportunities

Model refinement based on experimental observationsModel adaptability to MedBED, BioBED, and S3Accelerated computing by parallelizing intensive 3D codes.

R1-B Computational Modeling in CenSSISR1-B Computational Modeling in CenSSIS

R1-BComputational

Modeling

Fundamental ScienceL1

R3

Time Domain:Impulse Radar,Sonar

LPM

R2

Frequency Domain:Stepped Frequency, Light

MVT, MSD

FDTD Born FDFD

Born

SAMM

G. BeamG. Beam

R1

SDFMM

AcronymsAcronyms

FDTD – Finite difference time domain methodFDFD -- Finite difference frequency domain methodSAMM – Semi-Analytic Mode MatchingSDFMM – Steepest descent fast multipole methodG. Beam – Gaussian ray/beam methodFEM – Finite element methodMoM – Method of momentsFPGA – Field programmable gate array

FDTD – Finite difference time domain methodFDFD -- Finite difference frequency domain methodSAMM – Semi-Analytic Mode MatchingSDFMM – Steepest descent fast multipole methodG. Beam – Gaussian ray/beam methodFEM – Finite element methodMoM – Method of momentsFPGA – Field programmable gate array

Frequency Domain Computational Electromagnetic ModelsFrequency Domain Computational Electromagnetic Models

½-SPACE

BORN

SDFMM

2D FDFD

3D FDFD

3D SAMM

2D SAMM

2D G. BEAMNovel CenSSIS

Codes

ANAL

YTIC

BORN

FMM

3D MoM

2D MoM

Existing Codes

3D FEM

Chew ,Carin

Tsang, Canning

Sarabandi, Habashi

Paulsen, Jin, Burdett

Incr

easi

ng C

ompl

exit

y (L

og S

cale

)

Most Idealized Most Realistic

Effective Modeling for SoilBED Effective Modeling for SoilBED

Modeling for AnalysisImproved dispersive FDTD modelAccelerated FDFD

Modeling for InversionHalf space Born Approximation for cross borehole imagingPoint source borehole and above-ground SAMM Model-based automatic GPR target recognition

ExperimentsDNAPL detection: 3D (NU), 2D with flow (UPRM)Borehole tunnel detectionDual-wave Acoustic/GPR rebar stimulation and detectionMicrowave soil density meaurementMicrowave air sparging sensing

Modeling for AnalysisImproved dispersive FDTD modelAccelerated FDFD

Modeling for InversionHalf space Born Approximation for cross borehole imagingPoint source borehole and above-ground SAMM Model-based automatic GPR target recognition

ExperimentsDNAPL detection: 3D (NU), 2D with flow (UPRM)Borehole tunnel detectionDual-wave Acoustic/GPR rebar stimulation and detectionMicrowave soil density meaurementMicrowave air sparging sensing

SoilBED consists of: Problem Definition, Sensor/Geometry Model, Computational Reconstruction, Experimental Validation

Synthetic Farfield 2D Modal DistributionInversion of Clumped Mitochondria

Synthetic field generated by FDTD, converted to farfield12 views, 30o apart

Ground truth refractive index contrast Reconstructed contrast

S1Is it possible to optically distinguish clumped Mitochondria?

SoilBED Controlled Experimental Facility SoilBED Controlled Experimental Facility

Transmitters/Receivers

ContrastingPermittivit

Ground Surface

Receiver

Half Space Green’s Function Concept for Borehole (Underground) SourcesHalf Space Green’s Function Concept for Borehole (Underground) Sources

source

scatterers

air

soil

Γ3 Γ4 Γ5Γ2Γ1

Experimental Validation of Half Space Green’s Function Model: Published/Calibrated Soil ValuesExperimental Validation of Half Space Green’s Function Model: Published/Calibrated Soil Values

Transmitter depth: 11”Receiver depth 11”Separation 8.5”

Transmitter depth: 9”Receiver depth 13”Separation 12”

Network analyzer measurementsHalf-Space Lossy Green’s Function, initial soil calibrationOptimal soil selection: Excellent Agreement w/ Measurements

Object Localization and Reconstruction Using Half Space Born Approx.Object Localization and Reconstruction Using Half Space Born Approx.

Initial guess volumeReconstructed volume

[xo,yo,zo, lx, ly, lz] Ground truth: [4.764 4.764 0.40 4.169 4.169 0.250]cmEstimated result: [4.764 4.764 0.40 4.169 4.169 0.250]cm

Ground truth: DNAPL Pool

Linear array of T/R 7 vertical depths at 4 cornersf = 1.5GHz ; εr,sand = 20 – 0.14j, εr,obj = 2.6 – 0.001j, Additive Gaussian noise SNR = 20dB

Linear array of T/R 7 vertical depths at 4 cornersf = 1.5GHz ; εr,sand = 20 – 0.14j, εr,obj = 2.6 – 0.001j, Additive Gaussian noise SNR = 20dB

Semi-Analytic Mode Matching (SAMM): Modeling Objects Under Rough SurfacesSemi-Analytic Mode Matching (SAMM): Modeling Objects Under Rough Surfaces

target

groundair

d

R

0^

^

^0

x

x

yimage target center

0

C

C'

C C2C1

L=R+d

surface perturbation centers

S5

S3

3D SAMM vs. Half Space Born Approx. for Borehole Scattering3D SAMM vs. Half Space Born Approx. for Borehole Scattering

SCATTERER

DIPOLE SOURCE

f = 1 GHz

Slice taken at x = 5.2 cm

Source at z = - 20.1 cm

Scatterer: 1.6cm radius sphere at z = - 10.3 cm

z

y

x

AIR

Accomplishments: –Point Source SAMM below/above ground–Excellent computational validation between methods

Future Directions for Underground Sensing and ImagingFuture Directions for Underground Sensing and Imaging

Progress in Underground Contaminant Sensing•Antennas optimized•Experiments are repeatable•Modeling results validated with experiment•Inversion progressing successfully•BUT…DOE INL has reoriented away from contaminant detection

SoilBED has expanded into Civil Infrastructure & Nat’l Security

•Bridge deck and pavement deterioration monitoring•Tunnel detection: perimeter security, smuggling prevention•UXO detection: land mines, IEDs, suicide bombers

Underground SSI Researchers: 10 faculty, 13 grad., 8 UG (13 women, 7 min.)

BryanLavigne

Emmett Bishop

Faculty Grad Students Undergrad Students

Center forSubsurface Sensing & Imaging Systems

Center forSubsurface Sensing & Imaging Systems

Advanced Sensing for Detection of Hidden Defects in Concrete

Structures

Prof. Sara Wadia-FascettiNortheastern University

Advanced Sensing for Detection of Hidden Defects in Concrete

Structures

Prof. Sara Wadia-FascettiNortheastern University

Advanced Sensing for Detection of Hidden Defects in Concrete StructuresAdvanced Sensing for Detection of Hidden Defects in Concrete Structures

Void

Magnitude of the ProblemMagnitude of the Problem

Maintenance / Rehabilitation of Bridges• 500,000 bridges• Deck Replacement = $110 / ft2

• 75 ft x 50 ft span costs $412,500• $212.5 Billion replacement

Highway System• 427,000 miles• Similar costs• Inspection / repair is a $Billion problem• Interpreting and managing are more $Billions

Current Technologies & NeedCurrent Technologies & Need

• Defect Sensing Technologies• Visual• Infrared thermography• Acoustic / impact echo / chain drag• Ground penetrating radar (GPR)

Advanced modeling & processing algorithms are needed for high resolution automatic quantitative assessment.

Available sensors have sufficient fidelity to provide signal information to, in principle, reveal damage & defects, BUT…

Grand Island Bridge - Case StudyGrand Island Bridge - Case Study

Data collected by Infrasense Inc.

GPR Response: What gets collected requires 1000s of analysis hoursGPR Response: What gets collected requires 1000s of analysis hours

Surface Reflection

Vehicle Path

Note change in bridge structure

National Need: Can CenSSIS Contribute?National Need: Can CenSSIS Contribute?

Questions:

1. Are the steel reinforcements good?

2. Are there delaminations?

3. What is the condition of boundary between the slab and the subgrade?

4. Can other sensors aid deterioration analysis?

Needs:

1. Collect data & interpret at highway speeds

2. Real time feature interpretation

3. Reliable results

4. High resolution reconstruct. of suspicious regions

Relation to Strategic PlanRelation to Strategic Plan

R1

R2Fundamental

ScienceFundamental

Science

ValidatingTestBEDsValidatingTestBEDs

L1L1

L2L2

L3L3

R3

S1 S4 S5S3S2Bio-Med Enviro-Civil

S1 S3S2Bio-Med

Fundamental ScienceFundamental Science

ValidatingTestBEDsValidatingTestBEDs

L1L1

L2L2L3L3S4 S5Enviro-Civil

Linking to the Strategic Research PlanLinking to the Strategic Research Plan

R1• High resolution FDTD

Modeling• Synthetic data

generation• Model-based assessment

R2• Automatic feature detection• Anomaly reconstruction• Change detection• Registration & mosaicing

R3• Real time FPGA

acceleration• Image database• Massive data handling

• Rapid feature ID

• Rapid data collection

• Reliable detection

• High resolution

Pavement GPR Signal Analysis Based on FDTD ModelPavement GPR Signal Analysis Based on FDTD Model

air

concrete

rebar

asphalt

Motivation: Improve image interpretation with full-wave computational model – predict:•material dielectric characteristics•rebar position/integrity•layer thickness•imperfections

New SoilBED Project: GPR Interrogation of Acoustically Excited Pavement*New SoilBED Project: GPR Interrogation of Acoustically Excited Pavement*

•Shake pavement on surface with sinusoidal variation

•Observe differential movement of rebar as indication of corrosion/voids

•Model with FDTD

•Validate in Controlled SoilBED Experimental Facility

Time

Sig

nal

* Dual Wave Sensing

Center forSubsurface Sensing & Imaging Systems

Center forSubsurface Sensing & Imaging Systems

Field Programmable Gate Arrays to Accelerate Sub-Surface Imaging Problems

Prof. Miriam LeeserNortheastern University

Field Programmable Gate Arrays to Accelerate Sub-Surface Imaging Problems

Prof. Miriam LeeserNortheastern University

Why FPGAs for CenSSIS Algorithms?Why FPGAs for CenSSIS Algorithms?

FPGAs provide fine-grained parallelism for acceleration of algorithmsWell suited for image and signal processing applications

We work closely with application developers

Achieve acceleration by matching hardware to algorithmExploit specifics of the problem

FPGAs provide fine-grained parallelism for acceleration of algorithmsWell suited for image and signal processing applications

We work closely with application developers

Achieve acceleration by matching hardware to algorithmExploit specifics of the problem

PCI

PCI BUS

WILDSTARTM-II PCI Pro

32/64 Bits 33/66/133 MHz

50

DDRDRAM

I/O 80 20

32 32

DDRII/QDRIISRAM

36

Switches

32 32

I/O

36 36

36 36 36

DDRDRAM

80

32 32

DifferentialPairsSingle Ended

50

DDRII/QDRIISRAM

DDRII/QDRIISRAM

DDRII/QDRIISRAM

DDRII/QDRIISRAM

DDRII/QDRIISRAM

DDRII/QDRIISRAM

36 36 36

DDRII/QDRIISRAM

DDRII/QDRIISRAM

36 36 36

DDRII/QDRIISRAM

DDRII/QDRIISRAM

DDRII/QDRIISRAM

20

PE 1VIRTEXTM II Pro

XC2VP 70,100,125

PE 2VIRTEXTM II Pro

XC2VP 70,100,125

Rocket IO

Multiple Problems -- Similar SolutionsMultiple Problems -- Similar Solutions

Current CenSSIS FPGA Projects

1. “Acceleration of the 3D FDTD Algorithm in Fixed-Point Arithmetic Using Reconfigurable Hardware”

Wang Chen, Miriam Leeser, Carey Rappaport2. “Phase Unwrapping for BioBED on the Annapolis

Wildstar II Pro”Sherman Braganza, Miriam Leeser, Charles DiMarzio, Carol Warner

3. “Automatic Sliding Window Operation Optimization for FPGA-Based Computing Boards”

Haiqian Yu, Miriam Leeser4. “FPGA Implementation of the

ISRA Algorithm”Javier Morales,Nayda Santiago UPRM

Current CenSSIS FPGA Projects

1. “Acceleration of the 3D FDTD Algorithm in Fixed-Point Arithmetic Using Reconfigurable Hardware”

Wang Chen, Miriam Leeser, Carey Rappaport2. “Phase Unwrapping for BioBED on the Annapolis

Wildstar II Pro”Sherman Braganza, Miriam Leeser, Charles DiMarzio, Carol Warner

3. “Automatic Sliding Window Operation Optimization for FPGA-Based Computing Boards”

Haiqian Yu, Miriam Leeser4. “FPGA Implementation of the

ISRA Algorithm”Javier Morales,Nayda Santiago UPRM

Phase Unwrap

FPGA to Accelerate FDTDFPGA to Accelerate FDTD

Our solution is small, fast and flexible 2D and 3D versionsFixed point: up to 10 times faster than floating point

Accurate results over a variety of materialsPML or Mur absorbing boundary conditions

We handle dispersive mediaSpecifically target subsurface problems

Our solution is small, fast and flexible 2D and 3D versionsFixed point: up to 10 times faster than floating point

Accurate results over a variety of materialsPML or Mur absorbing boundary conditions

We handle dispersive mediaSpecifically target subsurface problems

Buried Object Detection Forward ModelBuried Object Detection Forward Model

Breast Cancer Detection Forward ModelBreast Cancer Detection Forward Model

Geometry map

Simulated Model Space

3D Broadband Spiral Antenna Model3D Broadband Spiral Antenna Model

FDTD Simulated 2D SpaceSpiral Antenna Floorplan

3D FDTD Hardware Acceleration Results3D FDTD Hardware Acceleration Results

3D FDTD Test Model:Model space 50*50*50 cells, Iterate 500 time stepsTotal Computing Task: Updating 62.5 Million Nodes

Baseline design: Software in Fortran runs in 49 seconds

261Speedup

331.27Million nodes per second

1.949Runtime (seconds)

Hardware: Model: UPML Absorbing

Boundary cellsSoftware in

Fortran

Civil Infrastructure FDTD ModelCivil Infrastructure FDTD Model

•Goal: Apply 2D FDTD model to civil infrastructure problems•Characterize bridge deck and locate and quantify damage•Simulate concrete bridge deck with an asphalt overlay.•Layer of reinforcing steel (circles in 2D) •Source is approximately 14 inches off the deck.

AsphaltConcrete

Rebar steel

Pavement with Rebar FDTD ModelPavement with Rebar FDTD Model

Timestep 200 Timestep 600

Rebar steel Asphalt layer reverberation

2D FDTD Hardware Timing Results2D FDTD Hardware Timing Results

050

100150200250300350

400

A B

Performance Result

FPGAs Enable Solutions Not Currently Feasible

FPGAs Enable Solutions Not Currently Feasible

InversionCurrently do not use FDTD

because too slow

Real-time model based assessmentBridge deck anomaly assessmentReal-time cellular imagingImage-guided therapy

Implement on field systemcompact, inexpensive

InversionCurrently do not use FDTD

because too slow

Real-time model based assessmentBridge deck anomaly assessmentReal-time cellular imagingImage-guided therapy

Implement on field systemcompact, inexpensive

New SoilBED Project: Borehole GPR Tunnel Detection: 2D FDFD ModelingNew SoilBED Project: Borehole GPR Tunnel Detection: 2D FDFD Modeling

Scattered H-field, real part

Total H-field, magnitude

Motivation: perimeter security, smuggling path detection, underground caches

3D Half Space Born Approximation Model of Tunnel Scattered E-Field on Perpendicular Plane 3D Half Space Born Approximation Model of Tunnel Scattered E-Field on Perpendicular Plane

TUNNEL TUNNEL

Experimentally Measured Borehole Detection Data: Magnitude of Tunnel Scattered SignalExperimentally Measured Borehole Detection Data: Magnitude of Tunnel Scattered Signal

Transmitter Depth (cm)R

ecei

ver D

epth

(cm

)F=1.3 GHz

MeasuredModeled

Accomplishments and Plans for Continued Research Accomplishments and Plans for Continued Research

Modeling Innovations Since Inception• Production-version 2D FDFD• Accelerated 3D FDFD • SAMM analysis with plane wave and point sources, with

incorporation into inverse models• Half Space Born Approx. in lossy media• Gaussian Beam method for rough surface analysis• Implementation of 3D FDTD on FPGA• Weak scatterer distribution Modal expansion/reconstruction• 3D breast cancer models with time reversal reconstruction

Continuing SoilBED WorkDNAPL pool detectionHydrogeologic flow detectionBorehole tunnel detectionPavement health diagnosisMicrowave soil density analysis

Modeling Innovations Since Inception• Production-version 2D FDFD• Accelerated 3D FDFD • SAMM analysis with plane wave and point sources, with

incorporation into inverse models• Half Space Born Approx. in lossy media• Gaussian Beam method for rough surface analysis• Implementation of 3D FDTD on FPGA• Weak scatterer distribution Modal expansion/reconstruction• 3D breast cancer models with time reversal reconstruction

Continuing SoilBED WorkDNAPL pool detectionHydrogeologic flow detectionBorehole tunnel detectionPavement health diagnosisMicrowave soil density analysis

Industrial and Government S5 LinkagesIndustrial and Government S5 Linkages

www.geophysical.com

GSSI donated GPR equipment (SIR-3000)