Saskatchewan Geological Survey 1 Summary of Investigations 2016, Volume 2

The Search for Kimberlites: Airborne Magnetic Data Processing in the Northwest Athabasca Basin

Omid Mahmoodi 1 Information from this publication may be used if credit is given. It is recommended that reference to this publication be made in the following form:

Mahmoodi, O. (2016): The search for kimberlites: airborne magnetic data processing in the northwest Athabasca Basin; in Summary of Investigations 2016, Volume 2, Saskatchewan Geological Survey, Saskatchewan Ministry of the Economy, Miscellaneous Report 2016-4.2, Paper A-6, 14p.

Abstract Magnetic susceptibility surveys have been widely used as a standard geophysical method for diamond exploration due to previous success in differentiating the magnetic signatures of the kimberlitic host rocks from surrounding country rocks. In this study, regional airborne magnetic data with 400 m line spacing collected from the northwest Athabasca Basin were processed to find magnetic signatures reflecting potential kimberlite occurrences. If not eroded, kimberlites form circular or elliptical anomalies in closed clusters. High-pass filters and analytic signal calculations were applied to the data to enhance the boundaries and centres of magnetic anomalies. Plots of the resultant filtered data were used to manually locate anomalies. Magnetic anomalies occurring in clusters and spatially associated with dykes and structural discontinuities were proposed as potential targets. A matched-filter, grid-based approach was also used to identify roughly circular anomalies based on their correlation with an anomaly generated by a vertical magnetic cylinder located at 30 m depth. Although some of the targets detected by this method had already been manually selected, more anomalies potentially associated with intrusions were added. The located 3D Euler deconvolution was also implemented, to estimate depth to the magnetic source for selected anomalies. The product was a map indicating the locations and estimated depth of selected anomalies, which could be of interest for further exploration in the area.

Keywords: kimberlite, airborne magnetic survey, Athabasca Basin, target selection, depth estimation, Keating anomaly, Euler deconvolution

1. Introduction Kimberlites are important targets for exploration due to the possibility that they may contain diamonds in economic concentrations. Kimberlite is an ultrabasic igneous rock derived from the mantle. As shown in Figure 1, the intrusion is usually fed by one or several deep-seated dykes and cuts all pre-existing rocks and structures as it rises. Kimberlite emplacement is mainly controlled by structural discontinuities, which are suitable conduits for these volatile-rich intrusions. They are typically steep, pipe-shaped bodies (75 to 85° dips), up to 2 km in diameter, and commonly occur in clusters of 3 to 10 pipes. In terms of the regional tectonic setting, stable continental areas (shield) are suitable areas to host diamondiferous kimberlites (Kamara, 1981).

The province of Saskatchewan has become an interesting area for kimberlite exploration since the discovery of kimberlite near Sturgeon Lake, about 30 km northwest of Prince Albert, in 1988. Airborne magnetic surveys identified approximately 70 anomalies in the Fort à la Corne area, east of the city of Prince Albert in central Saskatchewan, in the 1980s (Saskatchewan Geological Survey, 2003). Exploration in that area led to the development of several projects, including those focused on the Star and Orion South kimberlites (Shore Gold Inc., 2016). The Pikoo kimberlite field in the southeastern Glennie Domain, in the Deschambault Lake area of northeastern Saskatchewan, has also been developed over the past years (North Arrow Minerals Inc., 2016).

Geophysical surveys, particularly those that measure magnetic susceptibility and resistivity, have been capable tools in kimberlite exploration conducted in certain parts of the province because of the significant contrasting physical properties between kimberlites and sedimentary rocks. Generally, kimberlites have higher magnetic susceptibility than surrounding country rocks. Usually the crater facies (Figure 1) contains non-magnetically susceptible material

1 Saskatchewan Ministry of the Economy, Saskatchewan Geological Survey, 1000-2103 11th Avenue, Regina, SK S4P 3Z8 Although the Saskatchewan Ministry of the Economy has exercised all reasonable care in the compilation, interpretation and production of this product, it is not possible to ensure total accuracy, and all persons who rely on the information contained herein do so at their own risk. The Saskatchewan Ministry of the Economy and the Government of Saskatchewan do not accept liability for any errors, omissions or inaccuracies that may be included in, or derived from, this product.

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and, therefore, shows the lowest contrast with the generally less magnetic sedimentary rocks. However, diatreme facies and hypabyssal facies (Figure 1) have greater magnetic susceptibility contrast with surrounding country rocks. Weathering processes can alter the magnetite to non-magnetic hematite, decreasing or removing the contrast in magnetic susceptibility between the kimberlite and its host rocks. In addition to weathering and primary magnetite content, remnant magnetism, thickness and physical characteristics of the overburden, and size of the pipe can dramatically vary the measured physical response of kimberlites (Power et al., 2004; Reed and Witherly, 2007). Drumlins can generate false weak anomalies, which can be mistaken for kimberlite signatures. Therefore, anomalies should be screened based on their location in relation to structures and known kimberlite intrusions. Regional faults, geological contacts, and particularly the intersections of these features can provide conduits for kimberlite intrusions (Cowan et al., 2000).

The early stages of exploration for kimberlites routinely include a regional geophysical search, using airborne magnetic and electromagnetic surveys at relatively wide flight line spacing. If promising, survey resolution can be enhanced over selected areas for detailed exploration (Power et al., 2004; Reed and Witherly, 2007). In this study, airborne magnetic data with 400 m line spacing were processed to search for potential kimberlite targets.

2. Methods High-pass and band-pass filters are useful tools for separating the high-frequency portions of magnetic field data related to shallow sources from the low-frequency data generated by deep sources (Cowan and Cowan, 1993; Milligan and Gunn, 1997). Filters should be finely tuned to avoid removing signal and/or enhancing noise. To choose proper filter parameters and also investigate high-frequency leveling errors or gridding effects, a radially averaged power spectrum and a two-dimensional (2D) power spectrum can be used (Syberg, 1972; Cowan et al., 2000). The 2D power spectrum displays the amplitude of the Fourier-transformed signal in all directions for spatial frequencies between zero and the Nyquiest limit imposed by the grid size. The rate of decay of map spectra is largely determined by the mean depth of the bodies (Spector and Grant, 1970). A graph of the radially averaged power spectrum represents the average of the power of each wavenumber. As airborne magnetic data are mostly dominated by the low frequencies, the radially averaged power spectrum is displayed in logarithmic scale. Changes of the slope on this logarithmic-scale graph can be used as guides to estimate thresholds between regional and residual field, depth to source, and noise level (Stefan and Vijay, 1996; Billings and Richards, 2000).

Calculation of the analytic signal, which is defined as the square root of the squared sum of the vertical and horizontal derivatives of the magnetic field, enhances the boundaries and centres of magnetic sources. As defined by the following equation:

|𝐴𝐴(𝑥𝑥,𝑦𝑦)| = ��𝑑𝑑𝑑𝑑𝑑𝑑𝑥𝑥�2

+ �𝑑𝑑𝑑𝑑𝑑𝑑𝑦𝑦�2

+ �𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑�2

�1 2⁄

the calculated analytic signal A is always positive, so interpreting an analytic signal grid is easier than a total magnetic field (T) grid because there is always a positive anomaly above a magnetic source irrespective of the direction of magnetization (Roest et al., 1992).

Figure 1 – Typical model of a kimberlite pipe with old and revised facies terminology (after Kjarsgaard, 2007).

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A match filtering approach was introduced by Keating (1995) for kimberlite signature detection. It calculates correlation of the observed magnetic anomalies with the response from a magnetic cylinder with an assigned magnetization factor, size and position. The assigned size and position of the magnetic cylinder is chosen by the user. The method is capable of identifying circular anomalies.

Provided with a little prior knowledge about the source geometry, the depth to the magnetic source can be estimated by Euler deconvolution. The location of a magnetic source is estimated through solving Euler’s homogeneity equation:

(𝑥𝑥 − 𝑥𝑥0)𝑑𝑑𝑑𝑑/𝑑𝑑𝑥𝑥 + (𝑦𝑦 − 𝑦𝑦0)𝑑𝑑𝑑𝑑/𝑑𝑑𝑦𝑦 + (𝑑𝑑 − 𝑑𝑑0)𝑑𝑑𝑑𝑑/𝑑𝑑𝑑𝑑 = 𝑁𝑁(𝐵𝐵 − 𝑑𝑑)

where B is the regional magnetic field, T is the measured magnetic field at x, y, z of a magnetic source located at x0, y0, z0, and N is the structural index through which the magnetic field and its gradient components are related to the location of the source. Proper selection of the structural index is critical to obtaining accurate solutions for location and depth of source. A properly selected index for specific geological features of interest results in a tight cluster of Euler depth and location solutions around it. A physically plausible N value for magnetic data is zero for contacts, 1 for thin dykes and sills, 2 for pipes and 3 for spherical bodies (Reid et al., 1990; Keating and Pilkington, 2004; Ghosh et al., 2012). Geosoft Oasis montaj software was used in this study to implement the data processing methods.

3. Dataset An airborne magnetic survey, commissioned by the Saskatchewan Geological Survey, Saskatchewan Ministry of the Economy, in partnership with the Geological Survey of Canada, was conducted by Goldak Airborne Surveys over the northwest Athabasca Basin (Figure 2A) in 2010 as a part of a regional radiometric and magnetic survey (Fortin et al., 2011). Traverse lines were 400 m apart and aligned northwest-southeast to cross the mainly northeast-southwest strike of magnetic anomalies on pre-existing surveys, and tie line spacing was 2400 m. The target ground speed of the survey aircraft was 200 to 270 km/h with measurements every 0.1 seconds and a nominal terrain clearance of 125 m. The residual magnetic map of the survey is shown in Figure 2B. The magnetic grid of the study area (Figure 2C) was cropped from the regional grid (Figure 2B). The study’s eastern boundary was chosen to exclude as much as possible an area that is dominated by drumlins, which commonly have magnetic signatures similar to kimberlites.

All plots of the magnetic data in this study are sun-shaded for enhancement of features, using a sun angle of 45°.

4. Results and Discussion The gridded residual magnetic data with 100 m cell size was used for data processing. To better understand the frequency content information, a radially averaged power spectrum (red line in Figure 3A) and a 2D power spectrum (Figure 3B) were created. The 2D power spectrum indicates that the grid is dominated by the energy of low-frequency data, which is typical for geophysical data. There is no dominant linear trend in this power spectrum, indicating there was no leveling error, and the energy gradually decreases from the low wavenumbers toward the high wavenumbers. Because trends of features on the grid are displayed perpendicular on the 2D power spectrum, a visible northwest trend (dotted line on Figure 3B) at larger wavenumbers can be related to the magnetic mafic dykes labeled on Figure 2C.

The radially averaged spectrum represents the three main slopes, indicated by the blue dashed lines, that can be related to the deep sources, intermediate sources, and shallow sources (also containing high-frequency surface noise) in the data. The green dashed lines in Figure 3A demonstrate that 2500 m and 330 m are the approximate wavelengths indicating the change in slope. These values are good estimates for the starting parameters for high-pass filtering. During the filtering process, the averaged power spectrum of the filtered data can be compared to the original spectrum, to see which parts of the data were suppressed or enhanced.

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Figure 2 – A) The location of the airborne magnetic survey (red outline; mainly within the northwest Athabasca Basin) is shown overlain on the bedrock geological map from the Saskatchewan Geological Atlas (http://www.infomaps.gov.sk.ca/website/ SIR_Geological_Atlas/viewer.htm.) B) Plotted results of the magnetic survey by Goldak Airborne Surveys. The area outlined in black represents the area cropped from the survey and used in this study. C) Detail of the cropped area from B, showing the residual magnetic data for the study area. Northwest-trending magnetic mafic dykes are labeled. All grids in this report are labeled with the UTM coordinate system (NAD83, Zone 13).

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Figure 3 – A) Red line shows radially averaged power spectrum; blue dashed lines indicate three slopes representing the power of frequencies for deep sources, intermediate sources, and shallow sources of the data; green dashed lines demonstrate that 2500 m and 330 m are the approximate wavelengths indicating the change in slope. B) 2D power spectrum of the residual magnetic data grid. The dotted line represents a northwest trend at larger wavenumber, which can be related to the magnetic mafic dykes.

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For a better visualization of the data processing results, a rectangular area containing anomalies that could represent kimberlites was cropped from the main residual magnetic grid (Figure 4A) and enlarged (the results for the entire area are presented in Appendices 1 to 3). The residual magnetic grid was high-pass filtered using the Butterworth filter to enhance the shallow and intermediate magnetic sources, which are of interest. The Butterworth filter value L for the wavenumber k is described by the equation:

𝐿𝐿(𝑘𝑘) =1

1 + � 𝑘𝑘𝑘𝑘𝑐𝑐�𝑛𝑛

where 𝑘𝑘𝑐𝑐 is the central wavelength of the filter, and n is the degree of the filter. A higher degree of the filter results in a sharper cut-off, and increases the ringing effect (Hinze et al., 2013). The degree of filter roll-off can be adjusted to control the ringing effect. The filter parameters for this study were initially assigned as suggested by the radially averaged power spectrum and then adjusted for better anomaly separation from the background. Figures 4D and 4E represent filtered data using different parameters for a Butterworth filter. The ringing effect is obvious in Figure 4E, where a filter with 1000 m central cut-off wavelength and degree of 10 was used. The cut-off wavelength was increased to 2220 m and the filter degree reduced to 1.5 to produce the filtered grid shown in Figure 4D. The profile along line A-B intersecting an anomaly (Figure 4F) clearly shows enhanced anomalies and also the ringing effect due to filtering. Although filters significantly enhance the contrast between the anomaly and background, the ringing effect might confuse the interpretation and visualization of anomalies. The noise content in the data is also problematic, as it is intensified within the high-pass filtered data. A grid of the calculated analytic signal of the residual magnetic data was also produced. As shown in Figures 4C and 4F, calculation of the analytic signal enhances the anomalies and results in a positive anomaly above the magnetic sources.

High-pass filtered data (Appendices 1 and 2) and analytic signal (Appendix 3) maps were used to manually select circular anomalies. The main criteria for choosing potential anomalies were the shape of anomalies; the location of anomalies in relation to other similar signatures; and the proximity of the anomalies to structures such as faults and dykes. Manually selected targets are shown in Figure 5. Only a few targets, which are spatially close to structures, were selected on the east side of the study area, because this area is dominated by magnetic features caused by drumlins (i.e., they add noise).

Manual target selection might be affected by data visualization and subjective decisions by the interpreter, so, in addition to manual target selection, the Keating method was also used in this study to objectively find magnetic signatures that are similar to anomalies generated by a synthetic magnetic pipe. For processing the magnetic data using the Keating method, a pipe with 200 m diameter located at a depth of 30 m was used as a synthetic model. The depth was chosen based on the average thickness of Quaternary sediment recorded in drillholes in the area, and the diameter was selected by both trial and error and assessing the ability of the method to detect significant anomalies. A square window of nine grid cells was used to calculate the correlation between the analytic signal of the modeled pipe and the analytic signal of the residual magnetic map. Anomalies on the analytic signal map of the residual magnetic data with correlations2 higher than 0.75 with the analytic signal of the modeled pipe were selected as targets. They were mostly co-located with the manually chosen targets, although analysis of the data using the Keating method also suggested a few new targets. As shown in Figure 5, identified targets mostly occurred in clusters and in close proximity to the faults and dykes.

After choosing possible targets, standard three-dimensional (3D) Euler deconvolution was implemented to estimate the depth to magnetic sources that were not within the range of depths and diameters used in the Keating method. A square window of nine grid cells was used to calculate the Euler solutions. Based on the usual geometry of magnetic source targets, a structural index of 2 is used for pipes. To limit the number of solutions to those corresponding to the location of selected anomalies shown in Figure 5, ‘located Euler’ was applied. The results of the depth estimation are represented by symbol size in Figure 6, where the large symbols represent deeper sources and the small symbols corresponding with strong anomalies are related to the shallow pipes that form the main targets in the area.

2 Correlation coefficients are typically given between -1.0 and 1.0 with 0 being neutral.

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Figure 4 – A) Plot of the residual magnetic data for the entire study area. B) Enlarged area outlined in black in A, containing circular magnetic anomalies. C) Same enlarged area as in B, showing the analytic signal map. D) High-pass filtered map generated by a Butterworth filter with 2220 m central cut-off wavelength and degree of 1.5. E) High-pass filtered map produced by a Butterworth filter with 1000 m central cut-off wavelength and degree of 10. Notice that the filtered data using the higher filter degree results in a significant ringing effect; however, this type of filtering is still useful for locating anomalies when interpreted along with other maps. F) The values derived from a section along line A-B shown in B. Anomaly enhancement and ringing effect are well illustrated in this graph, particularly by the purple line representing the processed data by the Butterworth filter with 1000 m central cut-off wavelength and degree of 10. Abbreviations: Res_hpass_filter = residual magnetic field data processed by the Butterworth filter with 1000 m central cut-off wavelength and degree of 10; Res_bw_filter = residual magnetic field data processed by the Butterworth filter with 2220 m central cut-off wavelength and degree of 1.5; AS = calculated analytic signal for the residual magnetic field data; Res_Mag = unfiltered, unprocessed residual magnetic field data.

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Figure 5 – Location of manually selected targets (black dots) and targets selected by Keating method (green dots) overlain on the transparent residual magnetic map. Targets are spatially related to the major faults in the area and their intersection.

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Figure 6 – 3D Euler solutions at the locations of selected targets are represented by symbols with different sizes. The estimated depth determines the size of the symbols, with the larger symbols representing deeper depths.

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5. Conclusion This study shows that airborne magnetic data are a useful tool when searching for geophysical signatures of potential kimberlite targets in the northwest part of the Athabasca Basin. Data processing is a necessary step to enhance the high-frequency data, which contain a significant portion of the magnetic anomalies with shallow sources. Drumlins are a significant hindrance to the interpretation of magnetic maps of the northwest Athabasca Basin because they can generate anomalies similar to those that are typically produced by kimberlites, that is, circular anomalies. The remarkable number of possible targets selected in the area suggests that a survey with tighter line spacing would be useful to find more targets that were possibly missed due to the loose line spacing of the regional survey.

6. References Billings, S. and Richards, D. (2000): Quality control of gridded aeromagnetic data; Exploration Geophysics, v.31, issue 4, p.611-

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Cowan, D.R. and Cowan, S. (1993): Separation filtering applied to aeromagnetic data; Exploration Geophysics, v.24, issue 4, p.429-436.

Cowan, D.R., Tompkins, L.A. and Cowan, S. (2000): Screening kimberlite magnetic anomalies in magnetically active areas; Exploration Geophysics, v.31, issue 2, p.66-72, http://dx.doi.org/10.1071/EG00066

Fortin, R., Coyle, M., Buckle, J., Hefford, S. and Delaney, G. (2011): Northwestern Athabasca Basin geophysical survey, Saskatchewan; Geological Survey of Canada, Open Files 6816 to 6820.

Ghosh, G.K., Gupta, R.D., Khanna, A.K. and Singh, S.N. (2012): Application of Euler deconvolution of gravity and magnetic data for basement depth estimation in Mizoram area; in 5th EAGE International Conference and Exhibition on Geosciences, St. Petersburg, doi: 10.3997/2214-4609.20143715

Hinze, W., von Frese, R. and Saad, A.H. (2013): Oasis Montaj Tutorial for Gravity and Magnetic Exploration: Principles, Practices, and Applications; Cambridge University Press, Cambridge, UK, 512p.

Kamara, A. (1981): Review: Geophysical methods for kimberlite prospecting; Bulletin of the Australian Society of Exploration Geophysicists, v.12, issue 3, p.43-51.

Keating, P. (1995): A simple technique to identify magnetic anomalies due to kimberlite pipes; Exploration and Mining Geology, v.4, no.2, p.121-125.

Keating, P. and Pilkington, M. (2004): Euler deconvolution of the analytic signal and its application to magnetic interpretation; Geophysical Prospecting, v.52, p.165-182, doi:10.1111/j.1365-2478.2004.00408.x

Kjarsgaard, B.A. (2007): Kimberlite pipe models: significance for exploration; in Exploration in the New Millennium, Proceedings of the Fifth Decennial International Conference on Mineral Exploration, Milkereit, B. (ed.), Decennial Mineral Exploration Conferences, Toronto, Canada, p.667-677.

Milligan, P.R. and Gunn, P.J. (1997): Enhancement and presentation of airborne geophysical data; ASGO Journal of Australian Geology and Geophysics, v.17, issue 2, p.63-75.

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Power, M., Belcourt, G. and Rockel, E. (2004): Geophysical methods for kimberlite exploration in northern Canada; Society of Exploration Geophysicists, The Leading Edge, v.23, issue 11, doi: 10.1190/1.1825939

Reed, L.E. and Witherly, K.E. (2007): 50 years of kimberlite geophysics: a review; in Exploration in the New Millennium, Proceedings of the Fifth Decennial International Conference on Mineral Exploration, Milkereit, B. (ed.), Decennial Mineral Exploration Conferences, Toronto, Canada, p.679-689.

Reid, A.B., Allsop, J.M., Granser, H., Millett, A.J. and Somerton, I.W. (1990): Magnetic interpretation in three dimensions using Euler deconvolution; Geophysics, v.55, p.80-90, doi: 10.1190/1.1442774

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Roest, W., Verhoef, J. and Pilkington, M. (1992): Magnetic interpretation using the 3-D analytic signal; Geophysics, v.57, p.116-125.

Saskatchewan Geological Survey (2003): section on Diamond; in Geology, and Mineral and Petroleum Resources of Saskatchewan, Saskatchewan Industry and Resources, Miscellaneous Report 2003-7, p.117-122.

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Spector, A. and Grant, F.S. (1970): Statistical models for interpreting aeromagnetic data; Geophysics, v.35, issue 2, p.293-302.

Stefan, M. and Vijay, D. (1996): Depth estimation from the scaling power spectrum of potential fields; Geophysical Journal International, v.124, p.113-120, doi:10.1111/j.1365-246X.1996.tb06356.x

Syberg, F.J.R. (1972): A Fourier method for the regional-residual problem of potential fields; Geophysical Prospecting, v.20, p.47-75, doi:10.1111/j.1365-2478.1972.tb00619.x

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Appendix 1 – High-pass Filtered Data with 2220 m Cut-off Wavelength and degree of 1.5

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Appendix 2 – High-pass Filtered Data with 1000 m Cut-off Wavelength and Degree of 10

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Appendix 3 – Analytic Signal Map