Post on 01-Aug-2020
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)