Modelling and Inversion of EMI data collected over ... · Modelling and Inversion of EMI data...
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UBCGeophysical Inversion FacilityModelling and Inversion of EMI data
collected over magnetic soils
UXO/Countermine/Range ForumAugust 26, 2009Len Pasion1, Kevin Kingdon1, Stephen Billings1, Douglas W. Oldenburg1
1. Sky Research, 2. University of British Columbia
Waikaloa, Hawaii
Waimea, Hawaii
Examples of EMI data acquired at sites with magnetic soils
• Geophysical Proveouts• Geonics EM63 Data• First time channel
Former Camp Sibert, Alabama
60.0 mV
51.0
41.9
32.9
23.9
14.8
5.8
-3.2
-10.0
50.0 mV
42.3
34.5
26.8
19.0
11.3
3.6
-4.2
-10.0
Map Data
A Typical Processing Flow1 Filter to Remove
Geologic Features2
Target Picking via Threshold on Filtered data
3Data Inversion/Discrimination Estimate dipole model parameters: • Location• Orientation• Polarizabilities
4
UBCGeophysical InversionFacilityCell 185Base Plate
Example of inverting for dipole polarizabilities
m=F -1 [d ]
Cell 64Scrap
L1 > L2 = L3
L1 ≠ L2 ≠ L3
InvertData
m=F -1 [d ]
InvertData
UBCGeophysical InversionFacility� At sites with a strong geologic signal, small spatial scale signals can result:
1. Position of SensorAbove Ground
2. Topography
Un-modeled correlated signal will negatively affect estimated dipole parameters
UBCGeophysical InversionFacilityOutline� Introduction1. Incorporating ground clearance when
processing EMI data2. Investigating the effects of magnetic
topography� Conclusion
UBCGeophysical InversionFacility
[ ] [ ] [ ]bgt pp,,, bgt FFF += θφrm
• When the target response and background response are additive we can rewrite the forward operator asBackgroundTarget
• We will use a dipole model
• The EM parameters of the background can vary laterally
χ(ω) χ(ω)= +
Target in a Background Host
Modeling EMI sensor data for a target in a magnetic host
UBCGeophysical InversionFacility� We have developed a fast method of modeling the sensor response due to changes in sensor position above magnetic soil� Assume response has form: V(x,y,t)=A(height,pitch,roll,yaw)G(χ)f(t)
Man Portable Vector (MPV) TEM Sensor Data
Sky Research UXO test plot in Ashland, OR
R2
R3R4
VerticalComponents
RadialComponents
AzimuthalComponents
Rx2,Rx3,Rx4
Modeling EMI sensor data for a magnetic host
UBCGeophysical InversionFacilityHeight Test
Front Center - coaxial
Height Above Ground (m)
Orientation Test
Front Center - coaxial
Sensor Tilt (degrees)
tilt
Modeling EMI sensor data for a magnetic host
UBCGeophysical InversionFacilityTwo approaches developed:1. Subtract soil response prior to inversion. Solve for a
smooth background susceptibility model, then subtracting the predicted from the smooth susceptibility model.� Approach successfully demonstrated using both frequency domain (GEM-3 at Fort Lowry Bombing and Gunnery Range) and time domain (Geonics EM63 at Camp Sibert) data2. Simultaneously solve for host parameters and target parameters� Approach successfully demonstrated using Geonics EM63 TEM data acquired at Camp Sibert, Alabama
Incorporating ground clearance when processing EMI data
UBCGeophysical InversionFacilityExample: Inversion of Geonics EM63
TEM data at Camp Sibert, AL
Channel 1 Channel 20
• ESTCP Discrimination Pilot Project• Data collected in cued mode• 4.2 inch mortarsMethod 1: Subtract soil response prior to inversion
UBCGeophysical InversionFacilityMethod 1: Subtract soil response prior to inversion • Use elevation to estimate height above ground• Solve for the property distribution G(x,y) with regularized inversion
( ) ( ) 2 2
soil soilminimize ( )bgd mFϕ β= − + obsm W d m W m
d =F [m]m=F-1 [d ]
• Small spatial wavelength features in the data can be modeled with a background host that has slowly spatially varying EM properties
UBCGeophysical InversionFacility- =
Corrected data can then be inverted for dipole model parameters
Background
χ(ω) χ(ω)
Target in a Background Host Target in Freespace
- =
UBCGeophysical InversionFacilityMethod 2. Simultaneous inversion for background and target parameters
Observed data Predicted data ResidualChannel 1
Channel 20
( ) ( )( ) 2
target soil( ) ( )t bgd F Fϕ = − +obsm W d m m
• Simultaneous inversion for dipole parameters plus a background susceptibility that varies spatially as a plane: G(x,y) = A + Bx + Cy
UBCGeophysical InversionFacilityChannel 20
Time (ms)
EM
63 R
esp
on
se (
mV
)
S1
S2
S2
S1
Method 2. Simultaneous inversion for background and target parameters
UBCGeophysical InversionFacilityPolarizations obtained from TEMTADS in-air measurements
Estimated polarizations when inverting data for 3 unique polarizations
Estimated polarizations when inverting data for 2 unique polarizations
Method 2. Simultaneous inversion for background and target parameters
UBCGeophysical InversionFacilityExample:� 37 mm projectiles buried at 20 cm depth� 60 mm mortars buried at 40 cm depth� Data synthetically generated by using IMU and GPS records from Camp Sibert Geonics EM63 survey data� Position error: σ = 1 cm, IMU error: σ = 1 degrees � Magnetic soil signal is approximately 15 mV in the first time channel
Using simulations to determine the importance of including ground clearance when processing data
Does modeling the soil response due to sensor movement improve the ability to discriminate?
UBCGeophysical InversionFacilityTraditional MethodMedian filtered data
- Poor separation of features for the 60 and 37 mm
FAR = 0.68AUC = 0.84
60 mm
37 mm
Modeling the response due to sensor movement can improve our ability to discriminate between different target types
60 mm
37 mm
FAR = 0.00AUC = 1.00
Including soil response from sensor movement
- 60 mm and 37 feature clusters are well separated
UBCGeophysical InversionFacilityOutline� Introduction1. Incorporating ground clearance when
processing EMI data2. Investigating the effects of magnetic
topography� Conclusion
UBCGeophysical InversionFacility� EH3D Code developed at the University of British Columbia Geophysical Inversion Facility� Maxwell’s Equation solved numerical using a finite volume approach� Complex magnetic susceptibility used to represent soils with viscous remnant magnetization
Modeling the EMI response of topography using numerical modeling
UBCGeophysical InversionFacilityModelling the MPV response to a bump
Modeling the EMI response of topography using numerical modelling
UBCGeophysical InversionFacilityModelling the MPV response to a Trench
Modeling the EMI response of topography using numerical modelling
UBCGeophysical InversionFacilityInvestigating the effects of magnetic topography via simulationsExample:� 40 mm projectiles buried at 15 cm depth� Assume a bump running N-S� Position σ = 0.5 cm� Orientation σ= 0.5 degrees� 100 realizations
X comp Y comp Z comp
40 mm
Bump Response
X comp Y comp Z comp
UBCGeophysical InversionFacilityPolarizabilities Polarizability Sum
Time (ms) Time (ms)
L(t) Σ Li(t)
Investigating the effects of magnetic topography via simulationsCase 1: No magnetic soil topography� Accurate recovery of polarizabilities� Shape and Size information accurately recovered
UBCGeophysical InversionFacilityPolarizabilities Polarizability Sum
Time (ms) Time (ms)
L(t) Σ Li(t)
Case 2: Magnetic Bump� Magnetic properties typical of that found at the Ashland, Oregon Airport� Accuracy deteriorates at early and later times � Start to lose shape information� Size info accurate over most the time windowInvestigating the effects of magnetic topography via simulations
UBCGeophysical InversionFacilityPolarizabilities Polarizability Sum
Time (ms) Time (ms)
L(t) Σ Li(t)
Investigating the effects of magnetic topography via simulationsCase 3: A More Magnetic Bump� Magnetic properties 2 x that found at the Ashland Airport� Complete loss of shape information� Size information accurate over most the time window Z comp
UBCGeophysical InversionFacilityConclusions� Methodologies have been developed to estimate geologic and target parameters. � We have shown that a background with slowly spatially varying geologic properties can model the observed small spatial wavelength features in the data.� We have developed a method for modeling the EMI sensor response to topography� Recovery of the polarization tensor is less affected by topography than sensor position above the ground
This research is funded by the Strategic Environmental Research and Development Program (SERDP MM-1573)
UBCGeophysical InversionFacilityThis research is funded by the Strategic Environmental Research and Development Program (SERDP MM-1573)
Acknowledgements
UBCGeophysical InversionFacility
-100 -80 -60 -40 -20 0 20 40 60 80 1000
0.5
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5x 10
-3
Offset (cm)
Hz
6cm bump, 40cm loop10 cm bump, 40cm loop12 cm bump, 40cm loop14 cm bump, 40cm loop18cm bump, 40cm loop6cm bump, 100cm loop10 cm bump, 100cm loop12 cm bump, 100cm loop14 cm bump, 100cm loop18cm bump, 100cm loop
UBCGeophysical InversionFacilityModel based features:� Inversion of data for parameters of a physics-based model� These parameters reflect the size, shape, and material
properties of the target
Data based features:� Amplitude� Spatial extent
Sensor data: d
Model Parameters: m
d =F [m]
m=F-1 [d ]
Forward Operator
Inverse Operator
UBCGeophysical InversionFacilityMedian filtered data
Pre-filter data by subtracting modelled soil response
Simultaneous inversion for dipole parameters and soil spatial variability
Principlepolarizability Secondary polarizabilities
UBCGeophysical InversionFacilityExample: MTADS EM61 TEM Array - Camp Sibert, Alabama
MTADS first time channel - Detrended
EM61 mV Channel 1
Cell 644• An approximately 40 mV anomaly was
detected in the NS lines• The detrended elevation suggests that
there is a variation of approximately 13 cm in the ground clearance
Ground Clearance Estimated from Elevation Data
meters
Modeling the sensor response due to magnetic soils (Task 1.2)
UBCGeophysical InversionFacility
Line 76
Predicted Geologic ResponseMTADS first time channel - Detrended
EM61 mV Channel 1
Line 15 Line 518
Modeling the sensor response due to magnetic soils (Task 1.2)
Example: MTADS EM61 TEM Array - Camp Sibert, Alabama
UBCGeophysical InversionFacilityHx
0 1 2
0
0.5
1
1.5
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-5
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x 10-4
Hx
0 1 2
0
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-5
Hx
0 1 2
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x 10-6
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Hz
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x 10-5
Example: Modelling the response of the TEMTADS to a lump of magnetic soil
Backgroundσ = 10-1 S/mχ(ω) 18cm18cm30cm
UBCGeophysical InversionFacilityTask 2.2: Include appropriate interaction components into modeling and inversion
• Different geologic scenarios have been modelled using a finite volume numerical modelling code for Maxwell’s Equations (EH3D)
• We have confirmed the ability of EH3D to correctly model the viscous remnant magnetization (VRM) response
χχχχ(ωωωω)
Magnetic susceptibility model • Based on lab measurements of
Kaho’olawe soil (MM1414)
Real
Imag
Frequency (Hz)
Real
Imag
Frequency (Hz)
Example: Modelling the VRM Response
UBCGeophysical InversionFacilityProcessing method FAR AUCMedian Filter data directly 0.68 0.84Pre-filter assuming plane 0.04 1.00Simultaneous inversion 0.00 1.00
Modeling the soil response due to sensor movement improves our ability to discriminate between different target types
FAR = 0.68AUC = 0.84
UBCGeophysical InversionFacilityBackgroundσ = 10-1 S/mχ(ω)
1m1mBump: 10x10x50cm(also ran 6,12,14,18cm)10cm10cm30cm
Backgroundσ = 10-1 S/mχ(ω) Trench: σ = 10-9 S/m
10cm10cm30cmHx
Hx Hy Hz
HzHy
Modelling different topographic features
UBCGeophysical InversionFacilityMedian filtered data
60 mm
37 mm
Modeling the response due to sensor movement can improve our ability to discriminate between different target types
Pre-filter data by subtracting modelled soil response
Simultaneous inversion for dipole parameters and soil spatial variability
FAR = 0.68AUC = 0.84
FAR = 0.04AUC = 1.00
FAR = 0.00AUC = 1.00
UBCGeophysical InversionFacilityTransmitter
( )( )
( )( )
0 030 020 0 1
L t
t L t
L t
=
ML1: Axial Polarization
L2 =L3 : Transverse Polarization
The Point Dipole Model
L1
L2 =L3
L1 ≠ L2 ≠ L3
Axi-symmetric (UXO-like) No Axial Symmetry (e.g. scrap)
L1 > L2 = L3
L3
L2L1
• The size and shape of a target is reflected in the elements of the diagonalized polarization tensor:
UBCGeophysical InversionFacility
Northing (m)
Example: Camp Sibert, Alabama• Data collected for the ESTCP Discrimination Pilot project • 4.2 inch mortars
UBCGeophysical InversionFacilityIncorporating Ground Clearance when Inverting EMI Data�Methodologies have been developed to estimate
geologic and target parameters. �We have developed a fast method of modeling the sensor response due to changes in sensor position above a magnetic earth�We have shown that a background with slowly spatially varying geologic properties can model the observed small spatial wavelength features in the data. �Techniques have been applied to synthetic and field data