alltem uxo detection sensitivity & inversions...

David L. Wright, Theodore H. Asch, Craig W. Moulton, Trevor P. IronsU.S. Geological Survey, Denver, CO

Misac N. Nabighian, Colorado School of Mines, Golden, CO

ALLTEM UXO DETECTION SENSITIVITY & INVERSIONS FOR TARGET PARAMETERS FROM YUMA PROVING GROUND AND TEST STAND DATAColorado School of Mines

SERDP Project Number MM-1328 (CLOSING)

Figure 2. The cart is made almost entirely of non-metallic materials. The three orthogonaltransmitting coils and some of the printed circuit board receiving coils are visible.

ALLTEM at the Yuma Proving Ground, AZ

YPG Calibration Grid as of June 2005

3638350

3638360

3638370

3638380

3638390

3638400

757660 757670 757680 757690 757700 757710 757720Easting, m

Nort

hing

, m

12-gage loop

16-gage loop

12-Shot

18-gage loop

20-gage loop

37mm P20mm M55

40mm M385

Blu-26

57mm M86

60mm M49A3

2.75in. M230

60mm&Clutter

30&60CM Plate

105mm M456

105mm M60

155mm M483

M4240mm MK11

BDU-28

81mm M374

MK118

M75

8-Shot

17

1

M

A

GeologicMagneticAnomaly Area

5002enuJfosa,dnuorGgnivorPamuYroftuoyaLsenaLnoitarbilaCderugifnoceR7161514131211101987654321

M 71M61M51M41M31M21M11M01M9M8M7M6M5M4M3M2M1M M

L 71L61L51L41L31L21L11L01L9L8L7L6L5L4L3L2L1L LN gam

K 71K61K51K41K31K21K11K01K9K8K7K6K5K4K3K2K1K K

J 71J61J51J41J31J21J11J01J9J8J7J6J5J4J3J2J1J J

WOR I 71I61I51I41I31I21I11I01I9I8I7I6I5I4I3I2I1I I

H 71H61H51H41H31H21H11H01H9H8H7H6H5H4H3H2H1H H

G 7G6G5G4G3G2G1G 51G41G31G21G11G01G9G8G 71G61G G

F 71F61F51F41F31F21F11F01F9F8F7F6F5F4F3F2F1F F

E 71E61E51E41E31E21E11E01E9E8E7E6E5E4E3E2E1E E

yeK D 71D61D51D41D31D21D11D01D9D8D7D6D5D4D3D2D1D D.1 )stuptohs(srekraM.2 CdirGretemenO 71C61C51C41C31C21C11C01C9C8C7C6C5C4C3C2C1C C

B 71B61B51B41B31B21B11B01B9B8B7B6B5B4B3B2B1B B

A 71A61A51A41A31A21A11A01A9A8A7A6A5A4A3A2A1A A

7161514131211101987654321

ENALFigure 4. ALLTEM in operation over the Calibration Grid in May, 2006. The smalltractor carries an electric generator in front and a computer and instrumentation rack at the rear.

Figure 6. Locations and identifications of the targets buried in the Calibration Grid.Figures 5 and 6 courtesy of USAEC and ATC.

TEST STAND STUDIES

Figure 9. This amplitude difference map is from filtered and processedALLTEM data. With only a few exceptions the noise is only about 1 mV.Some targets buried as deep as 17 times their diameter were detected. This map is from vertically polarized excitation and observation directions.

.

Figure 5. Calibration Grid Lane and row designations.

Figure 11. This map is from “y” directed excitation and observation (parallel to the survey lines). The spatial patterns for targets with a vertical axis of symmetry are simply rotated by 90 degrees (as, for example, the marker balls in the blue ellipses), but the spatial patternsfor targets that are not rotationally symmetric about a vertical axis have different spatial patterns for the two horizontal polarizations (as,for example, the 105 mm target in the red ellipses that is buriedhorizontally with its long axis along grid N-S). The inversionalgorithm makes use of these pattern differences to solve for targetshapes and orientations.

Figure 7. This raw data map illustrates the importance of filtering and appropriately time picking the data. This map was made from the same data as that of Figure 9, below, but without filtering and with the earlier time pick shown in the following figure. This map shows system drift,response to ground topography, and some other line-to-lineinconsistencies. Almost all of these disappear with the filtering and laterinitial time used for the amplitude difference maps. Several targets cannot be discerned in this map that are clear in Figure 9. In addition, some of the ground response in this map might be mistaken for targets.

Figure 8. This figure shows a waveform (black curve) when the systemis over one of the perimeter marker shots. The main difference between Figure 7 and Figure 7 is that Figure 7 is a map of the amplitude differences from the time of the green vertical cursor to that of the red cursor. The amplitude differences mapped in Figure 9, however, are from the blue cursor to the red cursor. Using the later time greatly improves the result because both the system primary signal response and the ground response have reached their final values by the time of the blue cursor whereas the target responses have longer time constants and have not yet reached their final value. The time that should be selected must be late enough so that most of the step response in the (selectable frequency) system analog low-pass filter has settled, but not so late that too much target signal amplitude is sacrificed.

THE IMPORTANCE OF TIME PICKS AND DATA FILTERING

DATA MAPSCALIBRATION GRID & TARGETS

MAPS ARE NICE, BUT CAN MOVING PLATFORM ALLTEM DATA BE INVERTED FOR TARGET PARAMETERS?

Figure 12. This figure and the next visually show a comparisonbetween the spatial signal amplitude patterns from four mostly vertical and two horizontal polarizations from measured field dataand forward modeled (outer panels) computations.

Figure 13. The target is the same 60 mm M49A3 as in the previousfigure, but the lines over the target are run parallel to the long axisof the target instead of perpendicular to it. The forward models areclose to the measured data as they need to be for good inversions.

Each of the tables below contains information about a 60 mm target buried in the Calibration Grid.The inversion calculates three orthogonal dipole polarizability moments (M1, M2, and M3) for a series or times plus the target’s location, depth, azimuth, and inclination. The first data row in each table is the ground truth and the second contains the values calculated by the inversion. The code always calculates values for azimuth and inclination, but for spheres these have no meaning and for targetsthat have a vertical axis of rotational symmetry only inclination is meaningful. The three orthogonal dipole moments, M1, M2, and M3 are functions of time, but we show in this table values at one particulartime. For spheres M1, M2, and M3 would ideally all be equal. For rod-like targets we expect one larger and two smaller and equal moments. “MSE” stands for “mean-squared error” and is a measure of how well the forward modeler matched the measured data (a small MSE is better). See Figures 12 and 13.

Polarization

Direction

Polarization

Direction

Figure 10. This amplitude difference map is from an “x” directed(perpendicular to the survey lines) excitation and observationdirection. Vertical and horizontal components are used in theinversions.

ALLTEM

“Trolley”

“Shuttle”

No Added Noise +/- 2 cm Random Noise +/- 5 cm Random Noise +/- 10 cm Random Noise

+/- 20 cm Random Noise

0 mV

2 mV

5 mV

10 mV

50 mV

ZZM Waveform

Effects of Spatial Data Density on Effects of Spatial Data Density on Inversions for M1, M2, and M3Inversions for M1, M2, and M3

1: 0.25 m Line Spacing,

0.12 m Along Line


0.20 m Along Line


0.40 m Along Line


1.0 m Along Line

Inversion over 60 mm M49A3 at grid location F10 with lines run east to west.

0.0350.390.382.190.015.75-0.27373.766695.387

****0.010.60-0.25373.609695.636

MSE M3 (m3)M2 (m3)M1 (m3)Inclination (degrees)Azimuth (degrees)Depth (m)Y (m)X (m)

0.0350.390.382.190.015.75-0.27373.766695.387

****0.010.60-0.25373.609695.636


Inversion over 60 mm M49A3 at grid location F10 with lines run south to north.

0.0260.350.431.992.916.80-0.26373.543695.636

****0.010.60-0.25373.609695.636


0.0260.350.431.992.916.80-0.26373.543695.636

****0.010.60-0.25373.609695.636


Inversion over 60 mm M49A3 at grid location M11.

0.0450.310.421.9849.82.88-0.47386.877700.225

****45.010.60-0.48387.001700.225


0.0450.310.421.9849.82.88-0.47386.877700.225

****45.010.60-0.48387.001700.225


║Yp-Y║/║Y║, where Yp is the predicted data vector and Y is the measured data, is thenormalized mean squared error (MSE) and measures the difference between the final forward model and the data. ALLTEM inversions are not overly sensitive to noise and in our experience an MSE below 0.1 suggests a good inversion. Failed inversions typically have an MSE well above 0.2, but there can be exceptions.

Physics and the ALLTEM System Michael Faraday and Heinrich Lenz formulatedthe laws of electromagnetic induction. Lenz’sLaw states that an induced electromagneticforce (EMF) will be in the direction such that the flux it creates will oppose the change in the flux that produced it. When a magnetic field from a transmitting coil changes, the resulting EMFinduces currents in conducting bodies. The orthogonal ellipses on Figure 3 represent a decomposition of induced currents flowing inthe body of a 60 mm mortar round. These currents decay with time constants that arecharacteristic of the electrical conductivity, mass, and shape of the object. For non-ferrous metal objects the secondary fields reflect onlythe currents that decay to zero (See the Aluminum response in Figure 1).

For ferrous objects, however, the primary field (H) not only induces currents in the object, italso aligns the magnetic domains in the bodyof the object producing an increase in the flux density (B) according to the familiar relationship B = mH. For a triangle wave dH/dt = constant except at the inflection points and thus dB/dt is also a constant after all induced currents in the body have died. Since our receiving induction coils produce voltages proportional to dB/dt, the late-time asymptotic voltage is a non-zero constant for ferrous targets. See the “steel” response in Figure 1.

Figure 3. The red ellipsesrepresent induced currents.M1(t), M2(t), and M3(t) aretime-dependent polarizabilitymoments that are unique toeach type of target.

INSTITUTE FOR DEFENSE ANALYSIS comments onALLTEM 2006 BTG results (rounded to the nearest 5%)

“O” stands for ordnance, “C” for clutter, and “B” indicatesthat a cell is blank (empty).

(O or C calls on cells containing O)/(total # of O) = 100%(O or C calls on cells containing B)/(total # of B) = 0 %(O calls on cells containing O)/(total # of O) = 90 %(O calls on cells containing C)/(total # of C) = 0 %(O calls on cells containing B)/(total # of B) = 0 %

You correctly identified the type of UXO (e.g. 37 mm,105 mm etc.) for about 90 % of the items you correctlyclassified as UXO.

We do not know how our results compare to thosefrom other systems and investigators over the BTG.Our results show that we need to change a bias in our classification methodology, but we think theseresults are strong, especially when it is considered thatthey are derived from data collected without stopping and with no attitude sensor data. We plan to add an attitude sensor in a demonstration/validationphase.

We ran our inversion program for the targets wedetected in the BTG and submitted a spreadsheetcontaining our evaluation of the contents of eachof the cells in the BTG to the Institute for DefenseAnalysis (IDA). Some of the analyses are givenbelow (in blue).

Clutter 1

M1 = 4.98

M2 = .001

M3 = 1.23

105 mm

M1 = 14

M2 = 3.8

M3 = 3.6

60 mm

M1 = 2.3

M2 = 0.4

M3 = 0.35

20 mm

M1 = 0.1

M2 = 0.01

M3 = 0.008

Clutter 1

20 mm

60 mm

105 mm

Steel and Aluminum Tubes, 203 mm long x 6.35 mm dia.Horizontal, 36 cm below Rx Antenna

-0.004

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

0.004

0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016 0.018

Seconds

Volts

Alum Steel No Target Drive/250

ALLTEM Concept and System Implementation

Inversion for Target Parameters from Moving Platform Data

ALLTEM IN OPERATION

The ability to identify targets as probable UXO or probable non-UXO with a high degree of confidence lags behind the ability to detect. This is understandable because discrimination is much more difficult than detection. For single axis EM systems studies have indicated that position information may need to be accurate to ~1 cm to achieve the necessary signal-to-noise ratio for accurate inversions. For this reason the “cued mode” in which sensors make measurements while stationary at a single point or on an accurately known grid of points has been investigated. But can one adequately invert for target parameters from a set of data acquired while in continuous motion with somewhat higher position uncertainty and other noise? A study by L. Collins indicated that data from multi-axis systems might be more tolerant of position errors. Inversions we made on 2006 Calibration Grid and Blind Test Grid field data support this conclusion. Recently, we have also acquired controlled test stand data and are using these data to more quantitatively assess the effects of position errors, sensor noise and data spatial density on inversions.

● ALLTEM is an on-time, time domain, electromagnetic induction system that uses a continuous triangle wave current excitation in the transmitting (Tx) loops (Figure 1)● The received voltage step response is analyzed in the time domain.● Precedent -- the UTEM system developed at the University of Toronto for minerals exploration. (West, G.F., Macnae, J.C., and Lamontagne, Y., 1984, A time-domain electromagnetic system measuring the step response of the ground: Geophysics, vol 49, p. 1010-1026.)● Three orthogonal transmitting loops are used to sequentially excite targets while the time derivative (voltages) of spatial gradients of the magnetic fields from the target response is measured at several positions and polarizations by gradiometer coils. 19 Tx/Rx combinations are recorded.● The Tx and Rx coils are mounted on a 1 m cube on a non-metallic platform (Figure 2).

Figure 1. On-time measurements using triangle wave excitation distinguish between ferrous and non-ferrous metals because of opposite polarity and different late-time responses. Another advantage is that less dynamic range is required in the receiver. The ferrous object response becomes a constant proportional to the magnetic susceptibility of the target at late times which provides useful target information. Non-ferrous target responses are maximum at early time and decay to zero.

ALLTEM is a multi-axis electromagnetic induction system that uses a triangle wave current excitation in three orthogonal transmitting coils and an array of induction coil sensors. Use of the triangle wave excitation yields two advantages: first, responses from ferrous and non-ferrous metals have opposite polarities and, second, the late-time response for ferrous targets asymptotically approaches a non-zero value, yielding good late-time signal-to-noise ratios (SNR).

In May, 2006, ALLTEM was tested over the Calibration Grid and the Blind Test Grid in the Standardized UXO Test Area at the Yuma Proving Ground (YPG), Arizona. We find that filtering our data and forming amplitude differences at appropriate times almost eliminates system “drift” and response to the ground, resulting in much improved target SNR’s leading to cleaner target maps and more consistent inversions for target parameters -- even from survey mode moving-platform data.

High density test stand data are being used to assess how robust our inversions are against position errors, sensor noise, and data spatial density variations.

Introduction

The ability to identify targets as probable UXO or probable non-UXO with a high degree of confidence lags behind the ability to detect. This is understandable because discrimination is much more difficult than detection. For single axis EM systems studies have indicated that position information may need to be accurate to ~1 cm to achieve the necessary signal-to-noise ratio for accurate inversions. For this reason the “cued mode” in which sensors make measurements while stationary at a single point or on an accurately known grid of points has been investigated. But can one adequately invert for target parameters from a set of data acquired while in continuous motion with somewhat higher position uncertainty and other noise? A study by L. Collins indicated that data from multi-axis systems might be more tolerant of position errors. Inversions we made on 2006 Calibration Grid and Blind Test Grid field data support this conclusion. Recently, we have also acquired controlled test stand data and are using these data to more quantitatively assess the effects of position errors, sensor noise and data spatial density on inversions.

● ALLTEM is an on-time, time domain, electromagnetic induction system that uses a continuous triangle wave current excitation in the transmitting (Tx) loops (Figure 1)● The received voltage step response is analyzed in the time domain.● Precedent -- the UTEM system developed at the University of Toronto for minerals exploration. (West, G.F., Macnae, J.C., and Lamontagne, Y., 1984, A time-domain electromagnetic system measuring the step response of the ground: Geophysics, vol 49, p. 1010-1026.)● Three orthogonal transmitting loops are used to sequentially excite targets while the time derivative (voltages) of spatial gradients of the magnetic fields from the target response is measured at several positions and polarizations by gradiometer coils. 19 Tx/Rx combinations are recorded.● The Tx and Rx coils are mounted on a 1 m cube on a non-metallic platform (Figure 2).

Figure 1. On-time measurements using triangle wave excitation distinguish between ferrous and non-ferrous metals because of opposite polarity and different late-time responses. Another advantage is that less dynamic range is required in the receiver. The ferrous object response becomes a constant proportional to the magnetic susceptibility of the target at late times which provides useful target information. Non-ferrous target responses are maximum at early time and decay to zero.

ALLTEM is a multi-axis electromagnetic induction system that uses a triangle wave current excitation in three orthogonal transmitting coils and an array of induction coil sensors. Use of the triangle wave excitation yields two advantages: first, responses from ferrous and non-ferrous metals have opposite polarities and, second, the late-time response for ferrous targets asymptotically approaches a non-zero value, yielding good late-time signal-to-noise ratios (SNR).

In May, 2006, ALLTEM was tested over the Calibration Grid and the Blind Test Grid in the Standardized UXO Test Area at the Yuma Proving Ground (YPG), Arizona. We find that filtering our data and forming amplitude differences at appropriate times almost eliminates system “drift” and response to the ground, resulting in much improved target SNR’s leading to cleaner target maps and more consistent inversions for target parameters -- even from survey mode moving-platform data.

High density test stand data are being used to assess how robust our inversions are against position errors, sensor noise, and data spatial density variations.

Introduction

Figure 14. This figure shows the time history of the three calculated polarizability moments M1(t), M2(t), and M3(t) for a 60 mm mortar round. There is one large moment and two smaller and equal moments as expected. These results were obtained from moving platform data over the Calibration Grid at YPG. Results like these suggest that it is possible to obtain good inversion results from moving platform data containing some position error. Effects of position error on inversions are being investigated using our test stand data.

Figure 15. This figure shows the time history of calculated values of the largest polarizability moment for a range of targetsfrom a 20 mm projectile to a 105 mm projectile. Note that the sizes of the calculated values correspond to the physicaldimensions and mass of the targets as they should. The polarizability moments are the “fingerprints” of the type of targetand are independent of target location and orientation. Limits on the ability to successfully invert for the moments are beinginvestigated using test stand data.

Figure 16. This is an amplitude difference map over the Blind Test Grid atYPG produced with processed data for one polarization (vertical).

Figure 17. This test stand was built without using metal fasteners so that it could be used with ALLTEM and withmagnetic systems. Systems can be manually moved on the deck or ordnance can be moved underneath by a computercontrolled system.

Figure 18. This figure shows parts of the motor driven automated ordnance positioning system. Ordnance holderscan be locked at any angle in two axes. The ordnance holder rides on the moving “shuttle” that simulates runninga short line. The lines are varied by the long beam “trolley”. Recorded positions are accurate to +/- 3 mm.

Figure 19. This figure shows results of a study of the robustness of our inversion solutions for the three orthogonalpolarizability moments as increasing uniformly distributed random position noise is added. In this case the resultsare all tightly clustered until +/- 20 cm of random position errors were added.

Figure 20. In this study uniformly distributed random noise was added to sensor data as shown by the waveformsat the left. For each level of noise a corresponding amplitude difference map for one polarization is shownillustrating how the maps would be degraded by this noise. Inversions for M1, M2, and M3 for a particular time were surprisingly little perturbed by this type of noise. We hypothesize that the averaging inherent in our use ofmultiple spatial locations for the data used in our inversions makes the inversions more robust against noise.

Figure 21. In this study we varied the spatial density of data by changing line spacing and the density of data recordedalong lines to simulate possible effects of varying ALLTEM speed and line density in the field. The locations of the datathat went into each inversion are shown in panels 1, 2, 3, and 4 on the left side of the figure. The corresponding inverted values for M1, M2, and M3 remain closely clustered. Further analyses will be conducted to determine whether it is feasible to operate ALLTEM faster in the field and/or with more widely separated lines without degrading inversions.

Figure 22. This figure shows inversion results for 20 mm, 60 mm, 105 mm UXO’s and the clutter item shown. Inverted values for M2 and M3 for each UXO are very close together indicating rotational symmetry. For theclutter item, however, no two of the moments are close together. Note that the scale on the left hand side of the plot is logarithmic to accommodate the wide range of values for M2 and M3 for these targets.

CONCLUSIONS, FUTURE PLANS, AND ACKNOWLEDGEMENT1. Multi-axis systems provide additional information that significantly improves inversions for target identification and discrimination relative to single-axis commercial benchmark systems.2. Numerical inversions from ALLTEM data for target parameters appear to be relatively robust to position errors and sensor noise.3. Inversions for target parameters and therefore target classification with high confidence appear feasible even from moving platform field data.4. This project is transitioning to an ESTCP Demonstration/Validation phase. Further refinements to the hardware and software will be included in this new phase.5. This project was supported by SERDP. Use of trade, product, or firm names in this poster presentation is for identification purposes only and does not constitute endorsement by the U.S. Government.

alltem uxo detection sensitivity & inversions...

Documents