Geophysics - More Than NumbersProcessing and Presentation of Geophysical Data

Ian N. MacLeod and Tim M. Dobush

AbstractGeophysical techniques are now routinely applied to groundwater and environmental studies andgeologists are forced to deal with the ever increasing volumes of numbers that are generated.Geophysical data can and should be processed to extract the maximum amount of useful informationand the results presented to effectively convey the meaning of the data to others, who are often lessspecialized. With common low-cost computers we are able to process and present geophysical data inmany more useful, creative and revealing ways than the traditional contour map.

Geophysical surveys are sensitive to instrument accuracy, survey methodology, cultural noise, geologicnoise (the geology that we are NOT interested in) and the geologic model that we are studying. Theintelligent application of filters can be used to both remove the effects of noise and to enhance thosecomponents of the data that are of interest. It is important to understand the relationship of filters,gridding methods, sample density and noise characteristics in order to use processed data effectively.

This paper will use a number of practical examples to illustrate the use of filters in processing, as well as the useof colour, shading and perspective to help visualize and enhance the results of geophysical studies. Theexamples include conductivity data from a waste disposal site in Novo Horizontal in Brazil, VLF data from aland-fill site near North Bay, Ontario, radar data from an overburden study at Chalk River, Ontario andmagnetometer data from a study to locate abandoned water wells at the Rocky Mountain Arsenal in Colorado.

IntroductionApplying geophysics requires more than just taking readings. Effective processing of the data canextract more useful information than is immediately apparent. Equally important, the method of datapresentation can significantly change the appreciation of the data. The ultimate aim of both processingand presentation is an improved interpretation of what the data means.

Survey DesignOnce a geophysical method is deemed applicable to a specific problem, perhaps the use of magneticsto locate buried barrels, or an electromagnetic survey to detect contamination plumes, a survey must bedesigned to collect the required data. Aside from choice of instrumentation and survey procedures, oneof the most important considerations is the locations at which to take the readings. In particular, thedensity of readings should be sufficient to properly define the features that are being studied. However,survey density must also be balanced with time and cost considerations.

To establish a reading density, we must first determine the anticipated size of the features or anomaliesthat will be of interest. There should be at least four readings on the anomaly that are greater than thedetection limit of the instrumentation. This represents a minimum sampling density that should beincreased if finer details of an anomaly are important, as would be the case if modeling of anomaliesis required. Also, when high amplitude, but smaller width noise in the data is anticipated, theseparation between reading should be even further reduced in order to be able to properly remove the

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noise from the data in later processing. This last point is quite important because undersampled noiseoften cannot be removed and can be interpreted incorrectly.

One characteristic of geological data and many cultural anomalies that can be used in survey designs isthe presence of a consistent lineation. For example, geologic strike produces long and narrowgeophysical anomalies, as will a buried pipe or ditch. In these and similar situations, surveys aredesigned to sample at a high density along ‘lines’ that cross the features, and at a lower density in thelinear direction of the feature, as illustrated in Figure 1. In this way we can reduce the time and cost of asurvey yet still obtain sufficient information to resolve the subject of the survey. This method also lendsitself to continuous profiling instruments that can collect very high densities of information down lines. Forthese reasons, ‘line’ oriented surveys are the most common method used to collect geophysical data.

ProcessingData produced by a geophysical survey can be described by a position (X and Y in plan, or X anddepth in section) and one or more geophysical readings referred to as Z values. This basic X,Y,Zstructure is described as Point data. If a survey has been conducted along lines, a series of X, Y, Z pointscan be collected together as a group and referred to as a line of data. Point data and line data arehandled differently in processing in order to take advantage of the characteristics of the survey layout.

Once survey data has been verified for position and data integrity, the first process normally applied isto grid the data. Gridding is the process of interpolating data values at the nodes of a two dimensionalgrid from either point or line data that is less regularly distributed. Such grid representations of data areused for a number of two dimensional procedures such as contouring, imaging and two-dimensionalfiltering. There are many gridding methods available, each with different characteristics that canproduce quite different results, and it is important to choose the method that best matches the data.

Point data can only be gridded using ‘random’ gridding techniques. The most common of these includeinverse distance, minimum curvature, triangulation and Kriging. These methods are suitable wheneverthe data is well sampled, although they can be very slow, particularly with large data sets, becausethey make few assumptions about the data.

Line data has the characteristic of high density in one direction and low density in another and containsan implied assumption that the features being sampled are roughly linear in the across-line direction.The common random gridding techniques fail to properly account for this and result in grids that havepoor line-to-line correlation and exhibit the common problem of ‘bulls eyes’ as shown in Figure 2a.

Bi-directional gridding (Bhattacharyya, 1969; Akima, 1970) is a preferred method for handling linedata because it has the ability to enhance trends that cross the line direction, as illustrated in Figure 2b.In this method, each line of data is first interpolated to determine the data values at the intersections ofgrid lines, and then each grid line is interpolated to the final nodes of the grid. The result is that evennarrow features that continue from line to line are interpolated correctly. Also, by taking advantage ofthe fact that line data is already ordered, bi-directional gridding is extremely fast - typically 10 to 100times faster than random gridding techniques. Bi-directional gridding can also handle very large datasets (typically more than one million points), which is increasingly important as we move to highersampling rates, particularly from profiling methods.

Figure 1. LINE BASED

SURVEYS SAMPLE AT A HIGH

DENSITY ACROSS LINEAR

FEATURES OF INTEREST.

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FiltersGeophysical data contains broad-band information. In other words, each reading includes the effect ofall physical sources, geological and cultural, which produce a response at the point of measurement.Through the intelligent application of filters, we are able to remove or minimize unwanted componentsin the data and enhance those parts of the data that are of interest.

The most common filters for geophysical applications operate on the wavelength, or spatial size ofanomalies, and can be considered as either high-pass, which allow short wavelength features toremain in the data; or low-pass, which remove short wavelength information from the data.Combinations of high-pass and low-pass filters are known as band-pass filters. These can be defined bya set of convolution coefficients that are applied to an evenly sampled data set, either in one dimensionto be used on line data, or in two dimensions to be used with gridded data.

A simple example of a low-pass convolution filter is the three point running average, in which a datapoint is replaced by the average of itself and its two neighbors. The coefficients of this filter are(0.333,0.333,0.333). In practice, it is most convenient to use a simple program to design a linearfilter based on the size of the features that we wish to either remove or leave in the data. The size orwavelength that separates what we want from what we don’t can be used to design the best possibleconvolution filter to be applied to the data (Fraser, 1966).

Figure 2 TRADITIONAL

RANDOM GRIDDING (A.) OF

LINE BASED DATA OFTEN FAILS

TO HONOUR CORRELATIONS

FROM LINE TO LINE, WHILE BI-DIRECTIONAL GRIDDING (B.) ISDESIGNED TO ENHANCE SUCH

TRENDS.

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High-pass filters are useful for extracting detailed information that is ‘hidden’ in the gradients ofbroader anomalies. Figure 3 shows the use of a simple curvature residual filter to help resolve theexact location of old wells from a ground magnetic survey. This is a two-dimensional filter with thefollowing coefficients:

-.06 -.10 -.06

-.10 0.76 -.10

-.06 -.10 -.06

It is applied to gridded data and produces positive values wherever there is a positive curvature inthe data, negative values for negative curvature, and zero values at inflection points. The residualis particularly effective with magnetic data where inflection points mark the edges of magnetic features.The filter is also most sensitive to the smallest features in the data, and as a result also tends toemphasize noise.

The most troublesome noise components in many geophysical surveys result from the geophysicalresponse of very shallow or surface features. For example, the electromagnetic response fromconductive features (rails, pipes, buildings and litter) can mask anomalies that would beassociated with a contamination plume, which may be the subject of a survey. Figure 4a.illustrates a similar situation in which soil salinity is being mapped by measuring groundconductivity with the Geonics EM-31. Saline areas have high conductivity, and in this case ananomalous area extends up from the bottom edge of the map. The expression of this area isalmost completely hidden in the noise in the unfiltered data in Figure 4a. The shorter-wavelengthnoise features are stretched across lines because they have been under-sampled in the across-linedirection (a phenomena referred to as aliasing).

In these situations, we can apply a low-pass filter to remove the effects of smaller features inthe data in order to more clearly define the anomaly (Figure 4b.). Because the noise has beentoo poorly sampled across the survey lines to be gridded properly, the original line data hasbeen smoothed with the application of a 100 metre low-pass filter. This size was chosen bymultiplying the line separation (50 metres) by 2, which represents the shortest wavelength (theNyquist wavelength) that can be sampled with confidence in the across line direction (Reid,1980). This application again illustrates the importance of being able to manipulate line baseddata as separate lines rather than simply a collection points. The resulting filtered data moreclearly shows the limits of the plume, although the effects of some very strong noise anomaliescan still be seen.

When ordinary linear low-pass filters are applied to noise spikes, they simply get smoothedinto much lower amplitude and broader anomalies. If the amplitude of the noise is very muchgreater than the amplitude of the anomalies of interest, the resulting smoothed noise can beconfused with valid anomalies. This situation is common in geophysical data and the use ofnon-linear filters (Naudy and Dreyer, 1968) can be an effective solution, as illustrated in Figure4c. Non-linear filters differ from linear filters in that they are sensitive to wavelength only, andnot amplitude.

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Figure 4. FIGURE A. SHOWS A

CONDUCTIVITY SURVEY CONDUCTED IN

AN AREA WITH STRONG SHORT

WAVELENGTH NOISE THAT HIDES THE

ANOMALIES OF INTEREST, IN THIS CASE

HIGH SALINITY ANOMALIES. FIGURE B.IMPROVES THE SITUATION WITH THE

APPLICATION OF A 100 METRE LOW-PASS

FILTER. FIGURE C. SHOWS THE SAME DATA

WITH THE APPLICATION OF A 25 METRE

NON-LINEAR FILTER BEFORE THE 100METRE LOW-PASS.

Figure 3. A SIMPLE HIGH PASS

RESIDUAL FILTER IS USEFUL FOR EXTRACTING

THE MOST DETAILED INFORMATION FROM

DATA. IN THIS CASE, THE LOCATION OF AN

ABANDONED WELL IS MORE CLEARLY

DEFINED BY THE RESIDUAL MAP (B.).

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If a feature is narrower that a specified limit, it is simply removed and replaced by a valueinterpolated from the surrounding data. An amplitude tolerance can also be imposed whichprevents the filter from altering any data with an amplitude less than that expected from noisespikes. In Figure 4c., a 25 metre non-linear filter was applied to remove single point spikesgreater than 20 units in amplitude. This was followed by the same 100 metre filter as wasapplied for Figure 4b. The definition of the contamination plume extending up from the bottomof the plot is now quite clear. It should be noted that filters are not perfect, and decisions onfilter characteristics are usually compromised in which we are satisfied that what we leave inthe data is worth the loss of what we have removed.

Data PresentationGeophysical methods have a reputation for being highly technical and difficult for anyone but thespecialist to understand. One of the main tasks of geophysical practitioners has been to convey theresults of geophysical surveys in ways that can be comprehended by others. The key to this is effectivegraphical presentation of the data. When a map can illustrate a conclusion visually and clearly, thetask of explaining that conclusion is much easier. With lowcost computers and advanced software,there are now many options available for the final presentation of geophysical data.

The two most common methods used to present geophysical results are profile maps and contour mapsas shown in Figure 5. This conductivity data calculated from Geonics EM-16-R VLF data at municipallandfill site near North Bay, Ontario, Canada, in which the migration of a contamination plume ismapped by higher conductivities in the ground. The type of profile map shown here is termed an offsetprofile map in which each data profile is drawn relative to the plan line location. To produce this typeof profile map the data must be stored and manipulated as lines.

Contour maps have become a standard for presenting geophysical data in plan. However, many typesof geophysical data have a very high dynamic range that must be represented faithfully in a contourmap. For example, a magnetic survey may contain anomalies of many thousand nanotesla togetherwith the anomalies of interest that are only a few hundred nanotesla.

Standard contouring programs are often limited to contour intervals that will only show thehighest amplitude features. For this reason, specially designed geophysical contouring programshave been developed to handle high dynamic range by allowing the definition of manysuccessively greater contour intervals that are feathered in and out as required. Figure 6 showsa typical example in which the feathering of 50 nanotesla contours permits a much moredetailed contour map.

Effective as contour maps are, it is often difficult to appreciate relative amplitudes of different featureswithout determining the actual contour intervals. This is helped somewhat by clear annotations and themarking of depressions by hachure marks. However, high dynamic range data and contoursuppression can still make contour maps relatively ‘flat’ in appearance.

An effective way to overcome this is with the addition of colour. Using deep blues to represent lowsand working through the colour spectrum to deep reds for highs provides an immediate appreciation ofthe data by the viewer. Low cost colour printers now offer a practical way to generate high qualitycolour maps as standard products. Colour can also be used creatively to strengthen conclusions. Forexample, showing a contamination plume in a deep red rising out of neutral yellow background is verystrong visually.

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Figure 6. THE HIGH DYNAMIC

RANGE IN GEOPHYSICAL DATA REQUIRES

A FINER CONTOUR INTERVAL THAN CAN

BE ACHIEVED WITH CONVENTIONAL

CONTOURING SYSTEMS (A.).GEOPHYSICAL CONTOURING PROGRAMS

ARE ABLE TO FEATHER OUT LOWER-LEVEL

CONTOURS IN HIGH GRADIENT AREAS (B)

Figure 5. AN OFFSET PROFILE MAP

(A.) AND A CONTOUR MAP (B.) OF THE

SAME VLF CONDUCTIVITY DATA.

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Another method that further adds to the appreciation of depth in a map is the addition of shading tothe colour. Shading is calculated by interpreting the geophysical values as though they representedtopography and then illuminating this topography from a desired direction and inclination. Gray is thenadded to the colours depending on how much ‘light’ would fall on a surface at any grid point.Shading alone without colour can also be used for showing quite detailed results at small scales wherecontour maps become too detailed. This format is very effective for detecting subtle textural and linearfeatures. Figure 7 shows an example of Geonics EM-38 data over an historic site at Fort Niagara nearNiagara Falls, Canada. The EM-38 was used to collect data at a very high density, which allowed thedefinition of very small features in the fort.

With the capability to conveniently add colour and shading to geophysical data, we find that the sametechniques can be applied to all types of geophysical data. Figure 8 shows ground penetrating radardata collected with a Pulse EKO IV radar system at Chalk River in Canada. The data is presented as agray scale section that reveals many more subtle features than could be seen in a more conventionalwiggle-trace presentation.

A three dimensional perspective view of data through the use of a fish-net presentation as shown inFigure 9 is also a dramatic way to show relative amplitudes and shapes of anomalies. However, it isdifficult to determine actual ground locations from these types of maps, and they can also hide smalldetails behind larger foreground features. As a result, 3-D perspective maps tend to be used to showoff a specific feature or to compliment other 2-D presentations.

ConclusionsIn the past, the main emphasis in geophysical investigations has been collecting the raw data, afterwhich simple contour maps were often used to present results. With the availability of good processingand presentation software, we have the capability to extract more useful information from our data andpresent it in many more effective ways. The use of colour and imaging techniques combined with thegeneral movement towards increased survey reading densities is providing new standards forprocessing and presenting our results. Although the examples presented here use geophysical data, thesame concepts are also being applied to may other map or image based disciplines, includinggeochemical studies.

ReferencesAKIMA, H., 1970: A new method of interpolation and smooth curve fitting based on local procedures.Journal of Association for Computing Machinery, v.17, no. 4 pp. 589-602.

BHATTACHARYYA, B.K., 1969: Bicubic spline interpolation as a method for treatment of potential fielddata. Geophysics, v.34, no. 4 pp. 402-423.

FRASER, D.C., FULLER, B.D., WARD S.H., 1966: Some numerical techniques for application in miningexploration. Geophysics, v.31, no. 6, pp. 1066-1077

NAUDY H., DREYER H., 1968: Essai de filtrage nonlineare applique aux profils aeromagnetiques.Geophysical Prospecting, v.16, no. 2, p. 171.

REID, A.B., 1980: Aeromagnetic survey design. Geophysics Short Note, v.45, no.5, pp. 973-976.

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Figure 8. GROUND PENETRATING RADAR DATA PRESENTED AS A

GRAY-SCALE SHADED SECTION RATHER THAN THE TRADITIONAL

WIGGLE TRACE. THE PURPOSE OF THIS SURVEY WAS TO DETERMINE

THE LOCATION AND SHAPE OF A BURIED RIVER CHANNEL THAT

COULD ALSO CHANNEL RADIOACTIVE CONTAMINANTS.

Figure 7. HIGHLY DETAILED

DATA IS NOT PRESENTED WELL

WITH SIMPLE CONTOURS (A.).AN ALTERNATIVE IS TO USE

SHADED RELIEF (B.).

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For more information on the software used in this paper, contact software@geosoft.com. Visit www.geosoft.com.

AcknoledgmentsThe following sources of data for the examples in this paper are gratefully acknowledged:

The magnetic data from the Rocky Mountain Arsenal was provided by Brian Martinek at the Denveroffice of Geraghty and Miller Inc.

The Brazilian EM data is from the Masters Thesis of Mark Monier-Williams, University of Waterloo inOntario.

The radar data was provided by MultiView Geoservices Inc. and Sensors and Software Inc.

The EM-38 data over Fort Niagara and the EM-31 data was provided by Geonics Limited, Toronto,Canada.

The North Bay VLF data is from the Masters Thesis of Khaled Ben-Miloud, University of Waterloo.

The processing and presentation software used in this paper was part of the Geosoft 2-D Mappingsystem, a PC based software package designed for earth science applications.

Figure 9. THREE-DIMENSIONAL

PERSPECTIVE VIEW OF GEONICS EM-34CONDUCTIVITY DATA (10 METRE VERTICAL

CONFIGURATION) FROM A LANDFILL SITE

IN NOVO HORIZONTE, BRAZIL. THE HIGH

PEAKS REPRESENT CONTAMINATION

PLUMES.