THE METER Cost effectiveness of geophysical inversions in ... · in the exploration process and to test the effectiveness of dif-ferent methods over such a deposit. While answers

Effective mineral exploration programs maximize the ben-efit of appropriate technologies in order to increase costeffectiveness by optimizing the use of drilling, reducingrisks, and increasing the speed of discovery. In other words,correct use of available tools can allow exploration programsto find more ore, faster, with less expense.

It is well documented that geophysical data can beextremely useful in mineral exploration programs. This hasbecome more evident in recent years due to the increasedability to invert geophysical data to produce 3-D subsurfacemodels of physical properties. These models, along withphysical property values of local rock types, are used tointerpret geology and structure, and ultimately help thegeologist spot drill holes. Often the most effective use of geo-physics comes when it is employed in an iterative manner.It can build on geologic information already obtained andhelp guide further exploration as the project proceeds fromreconnaissance, to anomaly follow-up, to the delineation ofknown occurrences and mine development.

The purpose of this paper is to illustrate potential costeffectiveness by showing how much information can beobtained about a deposit by inverting surface geophysicaldata alone. The only other information needed is estimatesof the physical properties of the various rock types in thearea. Geologic information is only used as a measure of suc-cess of the inversions performed. As an example we presentthe results of the inversion of different geophysical data setsover the San Nicolas copper and zinc deposit. The high qual-ity 3-D images delineate the major aspects of the deposit andthus, in addition to finding the deposit, they have the poten-tial for substantially reducing costs in any drilling program.

San Nicolas, owned by Teck Corporation and WesternCopper Holdings, is an unmined, volcanic hosted, massivesulphide deposit in Zacatecas State, Mexico. In 1997, a gra-dient array induced polarization survey indicated a charge-ability anomaly which, when drill tested, proved to be causedby a significant volcanogenic massive sulfide deposit. Thatdeposit is now known as San Nicolas.

The huge volume of sulphides, and resulting large reserveestimates (72 million tons grading 1.35% copper and 2.27%zinc), has prompted the acquisition of many different geo-physical data sets at San Nicolas. Data were collected to helpin the exploration process and to test the effectiveness of dif-ferent methods over such a deposit. While answers were alsosought to more specific geologic questions, such as detect-ing high-grade zones at depth and finding new explorationtargets, this paper focuses on the success of geophysicalinversion in detecting the deposit itself. We show the resultsfrom inversion of gravity, ground magnetic, controlled sourceaudio magnetotelluric (CSAMT), and induced polarization(IP) data.

Geology and physical properties. The San Nicolas depositis a volcanogenic massive sulphide deposit containing ore-

grade copper and zinc with associated gold and silver. Thedeposit is hosted in a series of interwoven mafic and felsicvolcanic rocks which lie unconformably over graphitic mud-stones (Figure 1). The deposit is almost entirely bounded tothe east by a southwest-dipping fault, which could have beena feeder structure. Mineralization continues to follow the faultat depth in an unconstrained part of the deposit referred toas the “keel.” The deposit is flanked by a thick succession ofrhyolites to the west. The volcanic succession that envelopsthe deposit is overlain by a Tertiary-aged breccia overbur-den, which varies in thickness from 50 to 150 m. The brec-cia includes tuffs and clasts derived from the underlyingvolcanics. Outcrops of the breccia have been mapped to thenorthwest, but an overlying thin veneer of Quaternary allu-vium is present in the vicinity of the deposit. Hydrothermalalteration, which may cause a change in physical propertyvalues, is prevalent throughout the deposit and surround-ing geology.

Laboratory measurements on core samples, inferencesfrom simple modeling, and published data are used to assignappropriate estimates of physical property values to the rocktypes found at San Nicolas. Table 1 shows that the massive

0000 THE LEADING EDGE DECEMBER 2001 DECEMBER 2001 THE LEADING EDGE 1351

Cost effectiveness of geophysical inversions in mineral exploration: Applications at SanNicolas

NIGEL PHILLIPS, DOUG OLDENBURG, and JIUPING CHEN, University of British Columbia, CanadaYAOGUO LI, Colorado School of Mines, U.S.PARTHA ROUTH, Conoco, Ponca City, Oklahoma, U.S.

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AD

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THE METER

Table 1. Estimated physical properties for the five majorrock units shown in Figure 1

Rock Type

Tertiary brecciaMafic volcanicsSulphide Quartz rhyoliteGraphitic mud-

stone

Density

(g/cm3)2.32.73.52.4

2.4

Magneticsusceptibility(S.I. x10-3)

0-5510

0-10

0

Resistivity

(Ohm-m)2080

20-30100

100+

Chargeability

(msec)10-3030-50200

10-20

30-70

Figure 1. North-facing simplified geologic cross-sectionof the San Nicolas deposit (line 400 South) as interpretedfrom drill holes.

sulphide deposit has been assigned high density, magneticsusceptibility, chargeability, and low resistivity. Based on theabove information, the deposit can be delineated by usinggravity, magnetic, electric/electromagnetic, and IP methods.It is noted that the low resistivity values assigned to boththe sulphide and overlying Tertiary breccia can make it dif-ficult to distinguish the deposit from the Tertiary overbur-den.

Inversion. The goal of an inversion process is to find the dis-tribution of a physical property (which we generically referto as the model) that produced the observations. The pri-mary difficulty is nonuniqueness. The data supply only afinite number of constraints upon the model and thus thereare infinitely many solutions. To find a specific answer thatis geologically interpretable we proceed in the followingmanner. We first define a model objective function that mea-sures the amount of horizontal or vertical roughness of themodel or distance from a reference model. Then, from allthe models that acceptably fit the data, we choose the onethat minimizes this objective function. If the objective func-tion is suitably chosen, then at least the larger scale featuresof the constructed model should reflect the major featuresof the earth.

In addition to designing the model objective function, itis also necessary to be specific about what it means to “fit”the data. Unfortunately, we usually don’t know what the dataerrors are so we assume that they have a Gaussian distrib-ution and we supply an initial guess regarding their stan-dard deviation. We hope our initial guess is good enoughthat it provides the correct relative errors between the data.

If so, then the absolute noise level can be estimated throughL-curve or Generalized Cross-Validation techniques, or bycarrying out a couple of inversions using different toler-ances for the misfit. The inverse problem is therefore statedas the following optimization problem: Find a model thatminimizes the model objective function subject to fitting thedata to a specified tolerance. It is important to understandthat the generated computer model depends upon the modelobjective function, the assigned errors, and the achievedmisfit tolerance. The latter is important. If we reproduce the


Table 2. Survey and inversion parameters for each of the four data setsSurvey typeInversion time(CPU time only)Configuration

Number of observationsNumber of linesLine spacingStation spacingPreprocessing

Other survey specifications

Recovered modelNoise estimationNumber of cellsCell sizeStarting modelReference modelWeighting

Method of regularizationAchieved misfitOther inversion

parameters

GravityRegional: 11.42 hrLocal: 14.75 hrEast-West lines

81315100 m – 200 m25 m – 100 mInstrument/tidal driftFree-airBouguer anomalyRegional removalLocation determined usingGPS

3-D density contrast0.05 mGal446 68425 m x 25 m x 25 m 0.0 g/cm3

0.0 g/ccLe=Ln=Lz=50 (�s= 0.001,�x=�y=�z=2.5) and depthweighting: (z+z0)-2

GCV106Topography included

Magnetics1.3 hr

East-West lines

6147100 m12.5 mDiurnal corrections 44 000 base

level removedContaminated data discarded

Inc 50.638 Dec -13.43 (w/ ref. tolocal grid) Inducing field 44 000 nT3-D magnetic susceptibility2nT115 20025 m x 50 m x 25 m0.001 S.I.0.0 S.I.�s= 0.001, �x=�y=�z=1, and

depth weighting: (z+z0)-3

Chifactor=1662Positivity enforced Topography not included

CSAMT10 hr

T.M. mode East-West lines w/Tx electrode 3.5 km north

5400 (30/station)3200 m25 mApparent resistivity and phase

calculated from Ex and Hyfield measurements.

15 frequencies: (0.5 Hz–8192 Hz) Transmitter dipole length: 1.7 kmMean Tx-Rx separation: 3.7 km1-D electrical resistivityResistivity: 5% Phase: 35 mrads60 layers/station1 m to 28 kmbest fitting half space100 Ohm-m�s= 0.01, �z=1

Chifactor = 1Mean of 31

IPResistivity: 43.1 hr

Chargeability: 4.6 hrCombined gradient array

and Realsection array11829 + 3100 m and 200 m20-25 mElectrodes moved to fit

nodes on 3-D mesh.

5 transmitter spacings: 500 m to 2500 m

3-D chargeability5% + 1 msec117 60025 m x 25 m x 25 m0 msec0 msecLe=Ln=Lz=50 (�s= 0.001,�x=�y=�z=2.5) and dis-tance from Tx electrode:(1/r2)

Chifactor = 0.25293

Figure 2. Survey layout. Lateral extents of the data setsused for the inversion are shown relative to the deposit.

data too well, then we are fitting the noise in the observa-tions and the model will have artificial and erroneous struc-ture. If we fit the data too poorly, then we are not extractingall the information that the data contain about the earth.

Because the information that we are able to recover aboutthe physical property depends on the degree to which weare able to fit the data, it follows that field data should be asaccurate as possible. This involves two aspects:

1) The datum should be measured as accurately as possible.This generally requires repeat observations. Estimates ofuncertainty, perhaps obtained from the repetitive obser-vations, should also be provided, because a datum sup-plied without an uncertainty estimate is incomplete.

2) In order to work with the datum it is required to knowprecisely what the datum is. Thus, locations of transmit-ter and receiver electrodes or coils, orientations of instru-ments, data normalizations or changes in units, anddetailed knowledge of any processing that is applied tothe data, are crucial elements in defining what the datumis and how it is connected to the physical property dis-tribution.

These “details” quantify the datum, and without their knowl-edge the inversion cannot proceed! This is a major differ-ence compared with older-style use of geophysicalinformation where anomaly detection, generally in the formof “bump-finding,” was needed. There, field data undergo-ing numerous normalizations and smoothing could be plot-ted to reveal interesting areas. Although those proceduresremain valid today, such data cannot be rigorously inverted.

From the above, where we showed that the inversiondepends on choice of model objective function, defining amisfit criterion, and deciding how well the data should be

fit, it follows that the inversion of any geophysical data isnot a turn-key operation. Most data require a couple of inver-sion runs to provide insight about the data errors and howwell the data can, or should, be fit. Additional inversions,using different model objective functions, may also be runto provide insight about the nonuniqueness or to generatea model that has different geologic structure. As a conse-quence, any geophysical data set is likely to be inverted atleast a few times. This requires time on the part of a skilledprocessor. It also requires computational resources.

The inverse problem is solved computationally, first bydividing the earth into cells whose physical property valuesare constant. The size of the cells should be small enough sothat they don’t act as an additional regularization for theinverse problem. In other words, the cells should be smallerthan the resolving power of the experiment at any depth.Forward modeling consists of solving a system of equationsto predict the responses at each observation location. For 1-D problems, the cells are layers, and the size of the problemis small. However, for 3-D models (as used in the gravity,magnetic, and IP studies), the number of cells is large (100000 or greater). Thus large matrix systems need to be solved.In addition, when the problem is nonlinear, the inverse prob-lem is iterative and the large matrix systems need to besolved many times.

To put this into perspective, a typical 3-D inversion ofgravity or magnetic data may require a few hours or a dayto complete, and a 3-D dc resistivity and IP inversion maytake a couple of days. Inversion times for a Pentium III, 600-MHz processor with 1Gbyte of RAM are given in Table 2 forthe results presented here. Man-hours to prepare and invertthe data are variable but if computing resources and back-ground information about physical property values wereavailable, the following work is estimated to require three


to four weeks to complete.Despite the computational time and manpower costs, the

expenditures to invert the data can be worthwhile. This isillustrated by the following inversions. Gravity, ground mag-netic, CSAMT, and IP data are inverted to produce densitycontrast, magnetic susceptibility, resistivity, and chargeabil-ity models respectively. Figure 2 shows a plan view of thelocations for each survey along with the approximate extentsof the deposit projected to surface. All inversions were three-dimensional except for the CSAMT. Those data were inverted

to recover a vertical 1-D resistivity distribution beneath eachstation and the results were concatenated or “stitched”together to produce a 3-D resistivity model.

Inversion of gravity observations. Gravity surveys involvemeasuring local irregularities in the earth’s gravitationalfield with the aim of using these measurements to determinesubsurface density variations. Quantec Geofisica de Mexicoand Geociencias Consultores collected gravity data at SanNicolas in 1998. Traditional corrections were applied to thedata; however, no terrain corrections were applied becauseof low topographic relief. Table 2 summarizes survey spec-ifications and inversion parameters.

Inverting the gravity data required a two-pass procedure.First, 3934 data, over an area of 7 � 7 km centered on thedeposit, were inverted to produce a regional density model.This large-scale density model was used to generate a


Figure 4. Inversion of ground magnetic data. (a)Observed total field magnetic data. (b) Predicted mag-netic data. (c) Perspective view of magnetic susceptibil-ity model, volume-rendered with a cutoff at 5 � 10-3 SI.

a)

b)

c)

a)

b)

c)

Figure 3. Inversion of gravity data. (a) Observed gravitydata with regional trend removed. (b) Predicted gravitydata. (c) Perspective view of density contrast model, vol-ume-rendered with a cutoff at 0.17 g/cm3. The SanNicolas deposit is represented by the center anomaly.

“regional” field over an area of 1.8 � 2.4 km centered on thedeposit. The regional response was subtracted from the orig-inal data to produce a local data set to be inverted (Figure3a). The local data clearly show the gravitational anomalydue to the San Nicolas deposit (labeled), as well as a simi-lar sized anomaly to the northeast and a smaller anomaly to

the west.The model domain was divided into cells and the 3-D

inversion carried out with the parameters in Table 2. Surfacegravity data contain relatively strong information about hor-izontal variations in density but they don’t have any inher-ent depth resolution. The depth distribution comes fromincorporating an additional weighting into the inversion. Theweighting procedure has been developed through the useof synthetic modeling and inversion. Figure 3b shows thepredicted gravity data generated from inversion of the localdata. The observed data have been reproduced very well.

The computed density contrast model is shown as a vol-ume-rendered, isosurface plot in Figure 3c using a cutoff of0.17 g/cm3. Cells with density contrasts less than this valueare invisible. Three bodies exhibiting high-density contrastsare well defined by the model. The center density contrastanomaly is coincident with the San Nicolas deposit and cor-responds well with the dense massive sulphides. Figure 7ashows a north-facing cross-section of the density model over-laid with geologic boundaries.

Inversion of magnetic observations. In a similar manner togravity surveys, variations in the earth’s magnetic field aremeasured at the surface in order to gain information aboutsubsurface magnetic susceptibility distributions.

Airborne and ground magnetic data were acquired at SanNicolas; here we consider inversion of total field ground datacollected by Quantec in 1998. A base level of 44 000 nT wasremoved from the 614 diurnally corrected data as a pro-cessing step prior to inverting. In addition, several data inthe center of the survey that were contaminated with cul-tural noise, such as a fence or steel-cased drill hole, were dis-carded.

The plot of observed total-field ground magnetic data(Figure 4a) shows a large response from the deposit and asmall magnetic anomaly to the north. The observation loca-tions are also displayed and the gaps where data have beendiscarded are clearly visible. The 3-D inversion was carriedout with the parameters listed in Table 2. Magnetic data alsohave no inherent depth resolution and so, as with the grav-ity inversion, depth weighting is needed. The predicteddata, shown in Figure 4b, are in good agreement with theobservations.

Figure 4c is an isosurface representation of the 3-D sus-ceptibility structure. The cutoff value is 5 � 10-3 SI. A dis-tinct body of higher susceptibility is modeled. The majorityof the susceptibility is coincident with the deposit, however,high values continue to the north. The correlation betweenmagnetic susceptibility values and geology in the vicinity ofthe deposit can be seen in the north-facing cross-section(Figure 7b). The high magnetic susceptibilities associated withthe deposit align well with the boundaries of the sulphidebody.

Inversion of CSAMT observations. The controlled sourceaudio magnetotelluric method is an electromagnetic tech-nique that uses a grounded dipole source and measurescomponents of the electric and magnetic field at a numberof frequencies in the audio range (0.1 Hz-10 kHz).Perpendicular, horizontal electric and magnetic field valuesare used to calculate apparent resistivity and phase at dif-ferent frequencies. It is assumed that the fields are measuredfar from the source. At San Nicolas, data were collected withreceiver lines perpendicular to perceived geologic strike. Itis not obvious from the observed apparent resistivity orphase data (on the left in Figures 5a and 5b) that a conduc-tive ore deposit is present.


Figure 5. Inversion of CSAMT data. (a) Apparent resis-tivity data. Each data panel is displayed with increasingfrequency in the vertical direction (from 0.5 Hz at thebottom to 8192 Hz at the top) and increasing station loca-tion in the horizontal direction (-2200 east at the left to–800 east to the right). Observed data are in the left pan-els and predicted data in the right panels. (b) Phase data(viewed in the same manner as apparent resistivitydata). (c) Perspective view of resistivity model, volume-rendered with a cutoff at 30 Ohm-m. The 3-D resistivitymodel was constructed by “stitching” together the 1-Dmodels created at each data station and interpolating tofill in volumes between the survey lines.

a)

b)

c)

The apparent resistivity and phase data are inverted torecover a one-dimensional, resistivity model beneath eachstation. The predicted apparent resistivity and phase data(on the right in Figures 5a and 5b) match the observed dataquite well. The 180 1-D models were stitched together to forma 2-D model along each of the three lines of data. These wereinterpolated to fill in the volumes between the lines and pro-duce a 3-D resistivity model. A volume-rendered image ofthe model, with an isosurface cutoff of 30 Ohm-m, is shownin Figure 5c. The large conductive feature at depth is thedeposit. It is separated from a conductive overburden by anintervening layer of higher resistivity. The cross-section ofthe resistivity model (Figure 7c) shows that the depositboundaries inferred from the inversion agree reasonablywell with those inferred from drilling.

In order to test the validity of the 1-D models, the“stitched” 3-D model was forward modeled and apparentresistivity and phase data were calculated from the electricand magnetic field measurements. The forward-modeleddata replicated the original data quite well at higher fre-quencies. Because the high-frequency data are sensitive toshallow features, this suggests that structure in the upperregion of the stitched 1-D models is valid.

Inversion of IP observations. Induced polarization occurswhen a current is applied to the earth and there is an accu-mulation of positive or negative ions in the pore fluid dueto either the presence of metallic minerals, clay minerals, orgraphite, or restrictions in the pore itself. The ability for amaterial to accumulate these charges is summarized by itschargeability. Voltages associated with induced charges canbe measured in dc resistivity surveys.

At San Nicolas, gradient array and “Realsection” arrayconfiguration data were acquired by Quantec. In the gradi-ent array configuration, current electrodes are outside a rec-tangular area to be surveyed. The dc/IP potential data arecollected using a roving receiver dipole within the rectan-gular area. The data are generally used only as a mappingtool because detailed information about conductivity atdepth cannot be obtained without having multiple trans-mitter locations. However, the gradient data do providesome constraints on the resistivity, and they can be invertedalong with the Realsection data. The gradient array IP datafor San Nicolas are in Figure 6a. The large chargeabilityanomaly is associated with the deposit. There is no doubtthat this is extremely valuable information regarding possi-ble existence of an ore body, but the data provide no infor-mation about what is happening at depth. That requiresdata from other locations of the current electrodes.

In a Realsection survey, the voltages from consecutivedipoles are measured and plotted in a pseudosection format.The current transmitter electrodes straddle the potential elec-trode array and the transmitter electrode spacing is contin-ually reduced to change current flow in the subsurface. Dataare recorded only at those potential electrodes lying interiorto the transmitters. The calculated chargeability is plottedbeneath the receiver dipole and each row of the resultantpseudosection-type plot corresponds to a different locationof the current electrodes. The final plot has an invertedappearance compared to pseudosections obtained with moretraditional pole-dipole or dipole-dipole plots. Figure 6bshows the Realsection plots for San Nicolas. It is apparentthat a chargeable body is present, but these pseudosectionsprovide no tangible information about the depth of the tar-get. That can only be obtained through inversion.

The inversion procedure for chargeability is a three-stepprocess. First we need to estimate the resistivity for the model


a)

b)

c)

Figure 6. Inversion of IP data. (a) Observed (top panel)and predicted (bottom panel) gradient array data in planview. (b) Observed (left panels) and predicted (rightpanels) Realsection array data for each line. The datapanels are plotted with each row of data correspondingto a different transmitter electrode separation. The trans-mitter separation for the top row is 500 m and uniformlyincreases to a separation of 2500 m for the bottom row.(c) Perspective view of the chargeability model, volume-rendered with a cutoff at 45 ms.

volume; we do that by inverting the dc potentials. The sen-sitivities needed for the IP inversion are then calculated.

Lastly the IP data are inverted to recover a 3-D charge-ability model. The details for the dc resistivity and IP inver-sion are in Table 2. Both the gradient data and Realsectiondata were inverted simultaneously. Figures 6a and 6b show

the gradient and Realsection data predicted from the calcu-late model. These are in good agreement with the observa-tions.

Figure 6c shows a northwest facing, perspective view ofthe volume-rendered chargeability model with a cutoff valueof 40 ms. The spatial distribution of high chargeability val-


Figure 7. North-facing cross-section of physical property models at line 400 south with geology overlaid. (a) Densitycontrast model. (b) Magnetic susceptibility model. (c) Resistivity model. (d) Chargeability model.

a) b)

c)d)

ues agrees quite well with the location of the deposit, as seenby viewing the cross-section in Figure 7d. That image alsoshows that areas of local, high chargeability, within the anom-aly, are centered about the southwest-dipping fault.

Summary of inversion results. The physical property mod-els recovered from the inversion are valuable in a numberof ways. First, the volume-rendered images show a regionat depth that has high density, high magnetic susceptibility,low resistivity, and high chargeability. From the physicalproperty table this volume is thus a good candidate for beingthe sulfide. The volumetric images will change dependingon the cutoff with which they are viewed but a drill holespotted to intersect a zone of high density, susceptibility,chargeability, and low resistivity would have hit the orezone. The massive size of San Nicolas perhaps makes thisseem easy. However, the same procedure of carrying out theinversion and looking for co-locations of desired physicalproperty contrasts can be used for finding smaller depositswhose data signatures are much more subtly encoded in thedata.

On a more detailed level, the physical property modelscan be looked at in plan view or in cross-section. It is impor-tant to remember that the inversions are constructed to besmooth in the three spatial directions and that geophysicaldata acquired with surface sources and receivers havedecreasing resolution with depth. Thus we do not expect tosee fine scale structure in the recovered models, but struc-ture that we do see is hopefully indicative of subsurfacevariation. Also, sharp boundaries will manifest themselvesas smooth transition zones. These statements seem to besubstantiated by Figure 7 where inversion results along aneast-west section through the deposit are overlaid with geo-logic information obtained from the drilling program.

The density cross-section (Figure 7a) shows that theinversion has provided first order information about the sul-fide location. The lateral dimensions are reasonably welldefined and the centroid of the anomalous density coincideswith the ore body. This is a useful result when one consid-ers the lack of depth information contained in the data. Therecovered density contrast is a smoothed version of the truedensity contrast, and it does not contain highly detailedstructural information.

The highest concentration of magnetic susceptibility(Figure 7b) also coincides with the sulfide unit, although thecentroid is displaced slightly to the left. Previous case his-tories have shown that the inversion is generally quite goodat defining the horizontal limits of the body and also thedepth to the top. This seems to be the case in Figure 7b. Theexplanation for the relatively high magnetic susceptibilityextending downward from the sulfide unit is not known. Itappears, however, that understanding the complete natureof magnetic susceptibility is not a straightforward exercise.We expect high susceptibilities to be associated with the sul-fides. However, there can be magnetic minerals in host rocksand also magnetic minerals might be deposited by hydrother-mal events that are not associated with San Nicolas deposititself. This might explain the high susceptibility values thatpersist to the north of the deposit.

Figure 7c is a cross-section of the resistivity recoveredfrom the CSAMT data. Electromagnetic data have depthresolution because data are acquired at different frequencies.The resistivity model locates San Nicolas and would havebeen very useful for exploration purposes prior to drilling.Identification of a resistive layer between the Tertiary over-burden and the deposit suggests the survey has adequateresolution. This is confirmed by drill-hole information, which

indicates only a thin (40 m) layer of mafic volcanics sepa-rating the two at a depth of about 150 m.

The chargeability cross-section (Figure 7d) identifies theSan Nicolas deposit and allows interpretation of lateral anddepth extents not apparent in the raw data. Along with theanomalous values that reflect the sulphide, the highestchargeability values are coincident with the southwest-dip-ping fault that is known to contain semimassive sulphides.

Summary. Thorough analysis of geophysical data by inver-sion provide the earth scientist with clear, practical infor-mation that can be used at different stages of the explorationprocess, either to increase the success of the first drill hole,or to aid in cost-effective delineation and in-fill drilling.

As a first stage, the models from individual surveys canbe used in combination to select particular targets that havethe physical property contrasts expected for the deposit.First pass inversions can generally be completed within oneto a couple of days. The time depends on the survey typeand the number of data. The next stage involves more analy-sis. Before the first hole is spotted, it is often prudent to carryout a few more inversions to look at the effects on the modelof:

• fitting the data to a greater or lesser degree• making changes to the model objective function (perhaps

by altering the reference model)• subtracting a different regional from gravity and magnetic

data.

Ideally this is also a stage at which the model objectivefunction is modified to incorporate a priori geologic infor-mation about the deposit, if such information is available.

The net result from a well-performed inversion is amodel, or set of images, from which geologic informationcan be extracted and drill holes spotted. Good images canalso impact on further acquisition of data needed to providemore information about possible targets. Finally, forwardmodeling and inversion can help design the most effectivearrays needed to illuminate targets.

The increased information extracted from geophysicaldata must be traded off against manpower, computational,and time costs. What we have attempted to show here is thatthe superior information obtained by inverting data, com-pared to simply viewing the data themselves, is worth thiscost. That is, the final product demonstrates a valuable returnthat even the fast-paced exploration program should findcost effective.

Suggested reading. Information on the 3-D inversion tech-niques used in this study can be found in “3-D inversion of grav-ity data” by Li and Oldenburg (GEOPHYSICS, 1998), “3-D inversionof magnetic data” by Li and Oldenburg (GEOPHYSICS, 1996),“Inversion of CSAMT data for a horizontally layered earth” byRouth and Oldenburg (GEOPHYSICS, 1999), and “3-D inversionof induced polarization data” by Li and Oldenburg (GEOPHYSICS,2000). Oldenburg et al. overview inversion applied to mineralexploration in “Applications of geophysical inversions in min-eral exploration” (TLE, 1998). LE

Acknowledgments: We thank Teck Corporation for access to the data,Quantec for the data collection and initial processing, and members of theGeophysical Inversion Facility at the University of British Columbia:Colin Farquharson, Eldad Haber, Chad Hewson, Christophe Hyde, FrancisJones, and Roman Shekhtman.

Corresponding author: [email protected]


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THE METER Cost effectiveness of geophysical inversions in ... · in the exploration process and to test the effectiveness of dif-ferent methods over such a deposit. While answers