maximizing the information return from ground penetrating radar

Ž .Journal of Applied Geophysics 43 2000 175–187www.elsevier.nlrlocaterjappgeo

Maximizing the information return from ground penetrating radar

Gary R. Olhoeft )

Department of Geophysics, Colorado School of Mines, Golden, CO 80401-1887 USA

Received 2 February 1999; received in revised form 23 April 1999; accepted 24 May 1999

Abstract

Ground penetrating radar data is not always easy to acquire, and sometimes the acquisition may be constrained byequipment availability, weather, legal or logistical constraints, safety or access considerations. Examples of these includearchaeological or geotechnical sites about to be excavated, contaminated lands undergoing remediation, hazardous areas suchas unexploded ordnance lands or active volcanoes, and difficult to visit locations such as Antarctica or the surface of Mars.These situations may result in only one chance at acquiring data. Thus, the data need to be acquired, processed and modeledwith the aim of maximizing the information return for the time, cost and hazard risked. This process begins by properlysetting up the survey with the expectation of the site conditions but allowing for flexibility and serendipity in the unknown.Not only are radar data acquired, but also calibration, orientation, location and other required parameters describing theequipment and survey are recorded. All of these parameters are used in the processing and modeling of the data. The finalresults will be not just a radar image as a pseudo-cross-section, but a corrected geometric cross-section, interpreted electricaland magnetic properties of the ground, location, orientation, size and shape of subsurface objects, and composition of theground and objects as inferred density, porosity, fluid saturation, and other relevant material occurrence properties. q 2000Elsevier Science B.V. All rights reserved.

Keywords: Acquisition; Processing; Modeling; Interpretation; Display; Utility detection

1. Introduction

For most of the history of ground penetratingradar, the instruments have been used to acquiredata that have been presented as distorted im-ages or pseudo-cross-sections of the subsurfaceŽ .Morey, 1974; Olhoeft, 1988 . Such images of-ten solve problems such as the horizontal loca-tion of some change or thing buried, withoutany further necessity of processing. However,many problems pose questions requiring an-swers with more detailed information, such as

) Fax: q1-303-273-3478; e-mail: [email protected]

what is the depth to a buried utility pipe and itssize, orientation, and composition? What are thedepth, size, shape and orientation of the unex-ploded ordnance? What is the density of com-paction of soil in the bridge approach? How isthe fluid saturation of contaminant perched onthis clay layer changing with time as the site isremediated? These questions require quantita-tive answers only obtained by properly acquir-ing the radar data, processing and modeling thedata, and interpreting the results, including adisplay in terms the person dealing with theproblem can understand. The physical processesand the equations to describe them have been

0926-9851r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved.Ž .PII: S0926-9851 99 00057-9

( )G.R. OlhoeftrJournal of Applied Geophysics 43 2000 175–187176

known for a long time. Only in the past decadehave they been applied to answer some of thequestions above, but they are still not routinelyapplied.

2. Locating a buried pipe

Fig. 1 illustrates a portion of a radar pseudo-section acquired with a GSSI SIR-10Aq radar

Ž 1.system using a 900-MHz in air center fre-quency bistatic bow-tie antenna over a 90-cm

Ždiameter steel pipe buried 37 cm deep surface. Žto top of pipe near Yuma, AZ Olhoeft et al.,

.1994 . The radar reflection from the pipe is theresult of electromagnetic wave propagation de-scribed by the radar equation and geometrythrough, among other things, the Fresnel reflec-tion coefficient in amplitude, Snell’s law inangle, and the Stokes scattering matrix in polar-

Žization Balanis, 1989; Powers, 1995; Smith,.1997 . The location and orientation of the pipe

were known, so the electric fields of the anten-nas were set horizontal, parallel to each other,parallel to the long axis of the pipe, and totraverse perpendicular across the pipe, maximiz-ing the coupling with respect to polarization.

ŽAcross the top of the image are marks small.white vertical bars used to locate the antenna

and later correct for variations of towing speed.Each mark indicates passage of the center of theantenna past a flag in a series spaced 1 m apartalong the antenna traverse path. The verticalblack line running through the middle of theimage is the location of the single scan wiggletrace plotted to the right. The two way traveltime vertical scale is also shown as an equiva-lent depth assuming a relative dielectric permit-tivity of four. The horizontal line across the topof the image currently displays nothing and will

1 Most GPR antennas are ground-loaded, lowering theircenter frequency when in contact with the ground by anamount determined by the antenna design and the electro-magnetic properties of the ground.

Fig. 1. Raw radar data acquired with a GSSI SIR-10AqŽ .using a bistatic 900 MHz in air center frequency antenna

towed across a 90-cm diameter pipe buried 37 cm deep.

be explained in later figures when it showsfeatures in the data.

The data in the radar image exhibit severalproblems. The image is horizontally distorted

Žby uneven towing speed uneven spacing of the.marks across the top and vertically by an un-

known velocity of wave propagation. There ishorizontal banding running across the imagefrom less than optimal coupling of the antennato the ground and unwanted oscillatory ringingof the antenna. There are five vertical linescoming up from the bottom of the image causedby radio frequency interference from nearby

Žportable radios or cell phones see Appendix.A . Nonetheless, about a third of the way down

from the top, a reflection caused by a layer inthe geology may be seen to run horizontallyacross the image, broken in the middle by thetrench created to bury the pipe, and exhibiting

( )G.R. OlhoeftrJournal of Applied Geophysics 43 2000 175–187 177

the characteristic ‘‘hyperbola’’ scattering shapecaused by the metal pipe. If the problem wereutility location, then the problem is solved atthis point by noting the presence of a metallicreflector at the horizontal position of the top ofthe hyperbola. By rotating the antenna electricfield orientation while centered above the pipelocation indicated by the hyperbola, the az-imuthal strike orientation of the pipe may alsobe quickly determined from the change in polar-ization response. By measuring the change inpolarization with different antenna geometriesŽoften called HH, VV and HV polarizations;

.van Zyl and Ulaby, 1990 and solving for theStokes–Mueller polarization matrices, both thestrike and dip of the pipe may be determined.For the remainder of this example, polarizationwill be ignored as the data acquisition was setupto be maximally coupled between the antennaelectric field and the long axis of the pipe.

3. Estimating the pipe depth and size

However, if more information is required, thefirst processing step will be to remove the arti-facts in the data. In Fig. 2, the time zero hasbeen set at the first energy of arrival. Time zerois a function of the system timing, cable lengths,and antenna positioning. The horizontal blackline is positioned at time zero and also showsthe position of the line plotted across the top ofthe image, which now shows the uneven marklocations. The average of all the scans has been

Žaccumulated and removed background re-.moval to eliminate the antenna ringing and

horizontal banding across the image. The back-ground removal also removes other horizontalfeatures such as flat lying geology and thesurface of the Earth, so time zero had to belocated first.

ŽIn Fig. 3, a median gradient filter Paeth,.1990; Pratt, 1991; Weeks et al., 1993 has been

applied to remove the radio frequency interfer-ence from nearby wireless phone and portable

Ž .radio transmissions see Appendix A . In Fig. 4,

Fig. 2. The data of Fig. 1 with time zero determined andŽ .the average scan removed background removal .

an image processing contrast enhancementŽ .stretch Pratt, 1991; Zuiderveld, 1994 has been

applied to bring out details in the image. Thislast step loses all the absolute amplitude infor-

Ž .mation that will be recovered later and en-Žhances not only geological details note the

appearance of several small hyperbolas caused. Žby rocks but also noise the radio frequency

interference as diagonals across the bottom ofthe image from the computer inside the SIR-

.10Aq control box . These steps are done toimprove the ability to clearly see the tails of thepipe hyperbola, to be used in determining veloc-ity and size of the pipe.

In Fig. 5, the image has changed size andshape slightly as a spline rubber sheeting pro-

Ž .cess Bochicchio, 1988 is used with the loca-tions of the marks to correct the horizontalgeometry of the image. This allows the scansnumbered across the bottom of the image to


Fig. 3. The data of Fig. 2 with a median gradient filterapplied to remove radio noise.

also be labelled as horizontal traverse distancein meters. This may also be done in 3D tocorrect for topographic relief, but requires thevertical time scale first be converted to a depthscale. The survey in this case is across flatground. In the process of doing the rubbersheeting, it was found that some scans weremissing in making a uniformly spaced image, soan interpolation was used to fill in, with thelocations of the filling indicated across the top

Žof the plot as small spikes two on the left half.and seven on the right half of the image . This

interpolative filling is just to make a betterlooking image.

In Fig. 6, a mathematical function has beenfitted to the hyperbola shape in the data. Thefunction is a slice from a conic section that isoften called ‘‘hyperbola’’. In the earlier figures,note the hyperbola contains considerable varia-tion in amplitude along its locus as well as some

deviation from the ideal hyperbola shape. Theseare caused by geological heterogeneity and maybe used to describe that heterogeneity. Theseare also causes of problems in attempting torefocus the hyperbola by synthetic aperture,phase unwrapping, or migration processingŽYilmaz, 1987; Figs. 43 and 44 of Powers,

.1995; Ghiglia and Pritt, 1998 . The slopes of theasymptotes on either side are controlled by thevelocity of propagation and thus calibrate thedielectric permittivity between the antenna andthe pipe, and give a calibration and conversionof the two-way travel time into depth. Theradius of curvature at the peak of the hyperbola

Žand the lengths of the asymptotes by taking.into account the antenna pattern give the size

of the object causing it. The ellipse drawn withinthe hyperbola indicates the size of the object,assuming the object is a circular cylinder withaxis perpendicular to the plane of the data im-

Fig. 4. The data of Fig. 3 with an image processinghistogram contrast stretch applied.


Fig. 5. The data of Fig. 5 after spline rubber sheeting tothe marks along horizontal traverse and in fill interpola-tion.

age. The circle is distorted into an ellipse byvertical exaggeration, as no correction has beenperformed to make the vertical and horizontalaxes the same scale dimension. Also indicatedin Fig. 6 is the position of the near-field of theantenna, as the hyperbola shape is fit with afar-field ray tracing model assumption. At thispoint, the data processing and hyperbola fittingindicate a pipe centered 2.78 m from the begin-ning of the traverse, at a depth of 0.34 m in asoil with relative dielectric permittivity of 4.0,and with a diameter of 0.41 m. These numberscan be further refined.

In Fig. 7, an image processing hyperbolamask has been applied to the data to collapse or

Žfocus the hyperbola using a process similar to.migration; Yilmaz, 1987 . The image now shows

only the scattering cross-section of the visibleradius of curvature of the pipe. By looking at

Žother hyperbolas in the image such as those.from the rocks , their over or under migration

Žfocusing residual hyperbolic shapes pointed up-.wards or downwards indicates the variability of

the velocity and hence dielectric permittivitythroughout the section.

4. Refining the pipe depth

In Fig. 8, the data scan under the verticalblack line at the location of the peak of thehyperbola in Fig. 6 has been extracted andplotted as the dashed line. This is the originalraw data scan before all the processing above.The processing was performed to produce aclear image and better fit to the hyperbola. Thesolid line in the main portion of the plot is a fullwaveform model generated through the radar

ŽFig. 6. Hyperbola fitting to estimate permittivity and.hence turn two way travel time into depth and to estimate

object size.


Ž .Fig. 7. Image processing hyperbola masking migration tofocus hyperbolas and to estimate velocity variationthroughout the image.

Žequation for a layered earth Duke, 1990; Pow-.ers and Olhoeft, 1995 from the estimated per-

mittivity and depth of the hyperbola fit, andusing the parameters versus depth shown on theright side of the plot. The key to the parametersis across the top.

There are three such sets of parameters, ofwhich one is shown in Fig. 8. This one is for thecomplex dielectric permittivity in terms of thefour Cole–Cole dielectric relaxation parametersŽ .Olhoeft and Capron, 1994; Olhoeft, 1998a : ´r1

is the low frequency limit of the relative dielec-tric permittivity, ´ is the high frequency limit,r̀

t´ is the time constant, and a´ is the breadthparameter for the log-normal Cole–Cole distri-bution of time constants. Values of 4.0, 4.0, 0.0,and 1.0 indicate a real permittivity with noimaginary part and no frequency dependence as´ s´ . The values of y1.0 in the next layerr1 r̀

tell the model to use the properties of a metal.There is a similar set for the complex magneticpermeability, which will be assumed to be thatof free space in this model, m sm s1.0.r1 r̀

The third set describes the low frequency limit-Žing electrical conductivity seen in the next

.figure .The label at the top of the figure tells where

the field scan is located. On the top right are theŽmodel frequency using a Ricker wavelet Sheriff,

.1984 , and a display gain in decibels. Across thebottom are the time scale, the Offss1.81 indi-cates a 1.81-ns offset from the beginning of data

Žto locate time zero determined from the previ-.ous processing and the C s1.00 is a couplingr

ratio to describe changes in the antenna centerŽfrequency as it is loaded by the ground not

.used here . In the left half of the main plot, thesmaller vertical solid line indicates time zeroand the larger vertical solid line indicates theestimated position of the near-field boundary.This model assumes far-field, plane wave prop-agation with vertical incidence at horizontal lay-ering. Thus the air wave between the transmitter

Žand receiver antennas in the data between the.two vertical lines in the near-field is not mod-

eled. An example of near-field modeling andŽ .requirements may be found in Kirkendall 1998 .

The first excursion in the data at the left-mostedge of the plot is an internal radar system syncpulse and is also not modeled. Multiples be-tween the antenna and the pipe are modeled.Further details of this full waveform modeling

Ž .are published in Powers and Olhoeft 1995 .In Fig. 8, the first thing noticed about the

model attempted from the estimated parametersŽderived by the previous processing time zero,

.permittivity and depth are that the amplitudesare wrong. There is also a lot of high frequencyringing caused by truncation in computing

ŽFourier transforms which are performed onlyover the frequency range which contains signifi-

.cant amplitude in the data . The amplitude com-putation includes the effects of geometricspreading deduced from the processing derived

Ž .depth algorithm of May and Hron, 1978 , the


Fig. 8. Full waveform modeling of the scan directly over the pipe in the previous figures. This figure shows the modelingonly using the estimated permittivity and depth from the previous hyperbola fitting.

Fresnel reflection coefficient at the interface,and of the hardware range gain function thatwas recorded with the data, but it has not yetincluded the exponential material lossesŽ .Olhoeft, 1998a . In Fig. 9, on the right, the

electrical conductivity depth profile has beenadjusted to more realistically match the decay

Žshown in the data thus including part of the.exponential material losses , and to include the

conductivity of the metal pipe. To make the

Fig. 9. Improving the model fit by adding the effects of electrical conduction loss compared to the previous figure.


zero crossings between the model and the dataalign better, the original processing depth esti-mate is adjusted from 0.34 to 0.36 m.

5. Estimating the soil density and water con-tent

The main wavelet being reflected by the pipealso has some character beyond that of the idealRicker wavelet. In particular, the wavelet hasone up-going peak and two down-going peaks,with the ideal Ricker wavelet showing the twodown-going peaks to be the same amplitude.However, the recorded data show the twodown-going peaks to have different amplitudes.

ŽThis is caused by frequency dependence or.dispersion in the electromagnetic properties that

control the velocity and attenuation of propaga-Ž .tion Olhoeft, 1998a . The Cole–Cole parame-

ters in Fig. 10 are adjusted to match this ampli-tude difference, changing the shape of the modelwavelet to better fit the recorded data. In theprocess, the zero crossing match requires an-

other depth adjustment to 0.37 m. The depth tothe pipe has now been determined to a highaccuracy, and the model result agrees remark-

Ž .ably well with the known depth 0.37 m to thetop of the pipe. The remainder of the wiggles inthe field data are caused by radio frequency

Žnoise and should not be modeled this is deter-mined by looking at the texture and patterns in

.the 2D radar image in the earlier figures .Frequency dependences in geological materi-

als are dominantly caused by dielectric relax-ation processes related to the presence of water,and to a lesser extent by magnetic relaxationprocesses related to the presence of iron bearingminerals, and a variety of scattering processesŽ .Olhoeft and Capron, 1994; Olhoeft, 1998a .Assuming all of the frequency dependencecomes from the presence of water, the modelfitted Cole–Cole parameters indicate the re-quirement of about 1% or 2% water by volumein the soil between the antennas and the pipe tocause the required dispersion and subsequentchange in wavelet shape. The Bruggeman–

ŽHanai–Sen volumetric mixing formula Sen et

Fig. 10. Further improving the model fit over Fig. 9 by including the effects of a frequency dependent complex dielectricpermittivity.


.al., 1981; Olhoeft, 1987 may be used to con-vert the dielectric permittivity into bulk density,giving a volume average density of 1.89 grcm3,28% porosity sand equivalent soil between theantennas and the pipe, right over the pipethrough the trenching disturbed soil. These val-ues are consistent with laboratory measurements

Žon soil samples from the site Olhoeft and.Capron, 1993 and on the measured variation of

frequency dependence with moisture content inŽsoils Kutrubes, 1986; Olhoeft, 1981, 1987;

.Canan, 1999 . These values are typically mea-sured in situ for soils using time domain reflec-

Ž .tometry O’Connor and Dowding, 1999 , whichrequires pushing probes into the ground, buthave been derived here noninvasively fromground penetrating radar data.

The processing and modeling just performedrequire a few minutes played like a video gameon a 32-bit laptop computer. They have yielded

Žconsiderable information about the pipe loca-. Žtion, size and depth and the soil density and

.water content . The processing, modeling, andŽ .display figure generation were all done with

Ž .the GRORADARe software Olhoeft, 1998b .

6. Discussion

The parameters derived by this processingare not necessarily unique, but they are con-strained. The largest constraint is in the qualityof the data. The hyperbolic scattering shapepeak gives the location of the pipe horizontallyand is limited in accuracy by the positioninginformation available to describe the antenna

Ž .position, orientation polarization and move-ment. Similarly, the shape of the hyperbola isdetermined by the accuracy of the positioning,and the degree of distortion caused by geologi-cal heterogeneity, thus limiting the ability todetermine dielectric permittivity from theasymptotic slopes. The use of the hyperbola

Žshape asymptote lengths and radius of curva-.ture at the peak to determine the size of the

object is constrained by the positioning, hetero-geneity, and the knowledge of the antenna pat-tern in the ground.

In the full waveform modeling, the con-straints are the assumptions about the far-field,

Ž .vertical or normal incidence, polarization, anddealing with both dielectric and magnetic relax-ation. The model presented here only deals withamplitude of reflection at a scatterer using theFresnel reflection coefficient, and neglects theangular dependence described by Snell’s law,and the polarization change described by the

Ž .Stokes matrix van Zyl and Ulaby, 1990 . It alsoneglects scattering from the sides, not in the

Žplane of the image not under the antenna tra-. Ž .verse Olhoeft, 1994b . Models exist to de-

scribe such things, but the computational re-quirements are no longer near real time norwithin the ability of most portable computers. Ifexploited, these would give additional informa-tion such as the strike and dip of the pipe.

There is also much more information thatmay be derived from a radar dataset in terms of

Ždescribing geological heterogeneity Olhoeft,1991, 1994a; Huffman, 1992; Lucius and Ol-

.hoeft, 1996 , multipath and waveguide modes ofŽ .propagation Olhoeft, 1993 , surface versus vol-

Ž .ume scattering Schaber et al., 1986 , polariza-Ž .tion Roberts and Daniels, 1996 , fluid occur-

Žrence and behavior Olhoeft, 1986, 1992;Sander, 1994; Sander et al., 1992; Brewster et

.al., 1995 , and more that can be found in otherpapers in the literature as referenced. Such mod-eling may also be very useful in predicting radarsystem performance prior to acquiring data, suchas more optimal detection of plastic land mines

Žin wet rather than dry soil Powers and Olhoeft,.1996a , or the relative ability to detect metal or

Žplastic pipes, and fluid leaks from pipes Powers.and Olhoeft, 1996b .

Appendix A

The Federal Communications Commissionhas initiated an inquiry into ultra-wideband is-


sues, which encompass ground penetrating radarand all electromagnetic transmissions above 9kHz. The following is an illustration of oneportion of the problem. A GSSI 500-MHz cen-

Ž .ter frequency in air short pulse antenna issetup in a 6=6=3 m high underground roomwithout exterior walls in the interior of a large

Žbuilding the Green Center on the Campus of.the Colorado School of Mines . This is done to

reduce the signal levels from the cell repeatersto an analog cell phone in order to investigatethe interference both ways between the cellphone and the radar system. With the radar off,the cell phone indicates a signal strength in theroom about 1r6 of that outside the building,and has no trouble obtaining service. With the

radar on and the antenna properly coupled to thelinoleum tile covered concrete floor, the cellphone cannot acquire service within about 1 mof the radar antenna, but once having acquiredservice outside that range, has no trouble keep-ing it right up to the cell phone touching theantenna. There is no noticeable change in thequality of the conversation up to contact. At thehigher levels outside the building, the cell phonehas no problems acquiring service right up tothe antenna.

The cell phone is operating at a frequencynear 900 MHz and the radar antenna coupled tothe concrete has a y3-dB bandwidth of roughly300 to 700 MHz. With the radar operating andthe antenna stationary, the data in Fig. 11 are

Fig. 11. The right half of this image is in the presence of a cell phone that is turned off. The left half, the cell phone is onand acquiring service, but there is no communication. In the middle, the cell phone is being used for voice communication.The plotted line across the top of the image represents the data under the horizontal black line through the image, showingthe change in noise level at 61.6 ns two-way travel time. The plotted lines in the boxes to the right are single wavelet wiggle

Ž .traces at 806 and 810 scans across the images. At 806 left box the cell phone is on and being used to communicate, and atŽ .810 right box and current vertical cursor position , the cell phone is off.


obtained. In the image portion of Fig. 11 on theleft, the cell phone is on, obtaining service, but

Ž .not being used in standby . On the right, thecell phone is turned off. In the middle, the cellphone is being used to communicate a voiceconversation. The data at the position of thehorizontal line across the bottom of the imageare plotted across the top of Fig. 11, clearlyshowing the change in noise amplitude levelswith cell phone operation. The change in noisecharacter from top to bottom in the image re-flect the modulation of the range gain, plotted inthe right side of Fig. 11. The right two boxescontaining wiggle trace plots of single scans are

Ž .with the cell phone on and in use left and offŽ .right at vertical cursor positions 806 and 810scans, respectively.

Under the conditions on the right with thecell phone off, the radar can map the thicknessof the concrete, see the rebar in the concrete,and see the sewer pipe beneath the floor. On theleft with the cell phone in standby, the radiofrequency noise masks the sewer pipe so itcannot be found. In the middle, the active cellphone conversation makes rebar detectionmarginal and concrete thickness determinationis not possible. See http:rrwww.fcc.gov andNOI 98-153 for more information about theissues.

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