identification and mapping of minerals in drill core using

IDENTIFICATION AND MAPPING OF MINERALS IN DRILL CORE USING HYPERSPECTRAL IMAGE ANALYSIS OF INFRARED REFLECTANCE SPECTRA F. A. Kruse Center for the Study of Earth from Space (CSES) Cooperative Institute for Research in Environmental Sciences (CIRES) University of Colorado, Campus Box 216, Boulder, CO, USA 80309 and Department of Geological Sciences, University of Colorado** ABSTRACT A Portable Infrared Mineral Analyzer II (PIMA II) field spectrometer was used to measure infrared reflectance spectra (1.3 - 2.5 µm) of split drill core at 1 cm intervals in both the along-core and cross-core directions. These data were formatted into an image cube similar to that acquired by an imaging spectrometer with 600 spectral channels and multispectral and hyperspectral analysis techniques were used for analysis. Color images and enhancements provided visual displays of the spectral information, while real-time digital extraction of individual spectra allowed identification of minerals. Absorption band-depth mapping and spectral classification were used to map the spatial distribution of specific minerals in the core. Linear spectral unmixing provided estimated mineral abundances. Analysis results demonstrate that multispectral and hyperspectral image analysis methods can be used to produce detailed mineralogical maps of drill core. They suggest that the concepts and analytical techniques developed for analysis of hyperspectral image data can be applied to field and laboratory spectra in a variety of disciplines, and raise the question of the use of hyperspectral scanners in the laboratory. 1. INTRODUCTION Lithologic and mineralogic logging of drill core (or chips) are used by both the mining and petroleum industries to help manage drill operations and as one of several first indicators of mineralization or petroleum potential. Unfortunately, descriptions and interpretations of core materials can vary widely among geologists, and mineralogical analysis using techniques such as X-Ray diffraction, while widely used, require expensive equipment (or outside analysis), and significant turnaround time. This research explores an alternate method for describing drill core using infrared spectroscopy and a variation of analysis methods used for imaging spectrometer data analysis. Imaging Spectrometers (also known as "hyperspectral" sensors) measure the reflectance of the Earth's surface in hundreds of spectral bands (Goetz et al., 1985). Over the last 10 years, as the result of maturing hardware technology and available specialized software, the use of these data has expanded from only a few expert users **

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primarily for mapping alteration mineralogy, to use by a broader portion of the remote sensing community in a variety of disciplines. Unfortunately, the supporting area of field spectrometry has not kept pace with the development of the imaging sensors and analysis capabilities. Only recently have truly portable high-spectral-resolution field spectrometers become available and even now, little analytical software exists for qualitative or quantitative analysis of the field data. This research describes an approach to and results from analysis of groups of field spectra using "hyperspectral" image processing techniques that provides a cost effective, quantitative measure of core composition. This methodology is an obvious extension of previous work done using multispectral scanner data to provide images for improved digital and photointerpretive extraction of geologic information from slabs of oil well cores (Kowalik et al., 1991). 2. METHODS 2.1 DATA COLLECTION A Portable Infrared Mineral Analyzer (PIMA II) field spectrometer was used to measure reflectance spectra of split drill core from mineral prospects in the Dolly Varden Mountains, Nevada. The PIMA, manufactured by Integrated Spectronics, Sydney, Australia, measures reflected light in the 1.3 to 2.5 µm region in approximately 600 spectral channels. It is a contact instrument, that is, the head of the spectrometer is placed in direct contact with the rock (Figure 1). An internal light source is used to illuminate the sample. The data is automatically reduced to reflectance relative to an internal standard. Additional pre-processing using a dark target and a halon standard measured at the beginning and end of each data run and an NBS (National Bureau of Standards) halon absolute reflectance measurement converts the data to absolute reflectance relative to halon.

Figure 1.

Photograph showing measurements being made with the PIMA II field spectrometer.

Two cores totaling approximately 10 meters of discontinuous split drill core covering a vertical range of approximately 800 meters were measured using the PIMA. The analysis results for approximately 3 meters (150 meters vertical) are described here to validate the concept. Drill core to be measured was laid out according to depth and measured sequentially (Figure 2). Each drill core segment was measured at 1 cm intervals in both the along-core and cross-core directions (Figure 3), resulting in a data cube similar to that acquired by an imaging spectrometer (Goetz et al., 1985)(Figure 4). The cross-core dimension is equivalent to the scanline direction, the along-core dimension to the flightline direction, and each spectrum is equivalent to the spectrum acquired at a single image pixel. The resulting image cube has coarse spatial resolution (in terms of adequately imaging the core) but extremely high spectral resolution (600 spectral channels at ~3 nm resolution. Figure 5 shows examples of extracted spectra (A), and images (B and C).

Figure 2.

Photograph showing portion of the drill core.

Figure 3.

Schematic diagram showing core spectral measurement layout. Numbers label individual spectra at 1 cm spacing.

Figure 4.

Core image cube specifications. Numbers refer to individual spectral measurements made as shown in Figure 3.

2.2 IMAGE DISPLAY AND ANALYSIS Techniques developed for display and analysis of multispectral image data were used to analyze the field spectrometer data in the image domain. Gray scale images of individual bands and standard color composite images were used to compare the spectral response at specific wavelengths. Common image enhancements such as linear contrast stretching were used to improve the visual display of the spectral information (Figure 5 B). Because high spectral resolution data are typically highly correlated from band to band, additional color enhancements were required to produce usable color composites. Various stretches, color combinations, band ratioing, and decorelation stretching were evaluated. Decorrelation stretching (Gillespie et al., 1986) produced the best images with enhanced color separation of spectral features (Figure 5 C).

Figure 5.

(A) PIMA core spectra, (B) 1.418, 1.916, 2.214 µm (RGB) color composite, (C) Decorrelation stretch using 1.418, 1.916, 2.214 µm (RGB), (D) Linear spectral unmixing image with kaolinite, illite, and montmorillonite (RGB), and (E) interpreted mineral assemblages. Numbers in the interpretation refer to specific core depths (originally measured in feet). Pixels are approximately 1 cm.

2.3 INTERACTIVE MINERAL IDENTIFICATION The core was next physically examined in conjunction with real-time digital extraction of individual spectra from areas highlighted on the color images to identify specific minerals and to determine their locations on the core. A spectral library of 25 common minerals (amphiboles, clays, carbonates, sulfates, iron minerals, etc.) compiled using the PIMA instrument (Kruse et al., 1993a) was used for comparison. Visual comparison of observed spectra to the spectral library confirmed the presence of the minerals kaolinite, illite, chlorite, montmorillonite, and mixtures (Figure 5A, Figure 6). A better understanding of the color composites and the relation of color to the individual spectral features was obtained by comparing gray scale images showing the depth of the absorption features (Figure 7) to the spectra and color images shown in Figure 5A and B. Brighter pixels represent portions of the core with deeper mineral absorption features at each image's corresponding wavelength. For additional information on calculation of band depth images see Kruse et al (1993b).

Figure 6.

Plots comparing spectra of reference minerals to core spectra. The labels on the right side of the plots correspond to the position of the spectra in the plots. The top label corresponds to the top spectrum; the bottom spectrum to the bottom spectrum. Core spectra are labeled with the prefix “scb”, while reference spectra labels begin with the mineral name.

Figure 7.

Band depth images for 1.418, 1.916, and 2.214 µm. Brighter pixels represent deeper absorption bands. Pixels are approximately 1 cm.

2.4 SPATIAL MAPPING OF MINERAL DISTRIBUTION Spectral Angle Mapping (SAM, CSES, 1992; Kruse et al., 1993a) was used to produce image maps of the core showing the spatial occurrence of specific minerals. Image spectra are analyzed as vectors of length "nb", where nb is the number of bands (in this case having a length of 600). The SAM algorithm compares the angle in radians between a reference spectrum vector and each image spectrum. Smaller spectral angles represent closer matches to the reference spectra. The SAM images are then inverted to show closer matches as bright pixels and less similar image spectra as dark pixels. Figure 8 shows SAM images for the four minerals identified in the core; kaolinite, illite, montmorillonite, and chlorite.

Figure 8. SAM images showing the match to the minerals kaolinite, illite, montmorillonite, and chlorite. Brighter pixels are more similar to the reference spectrum than darker pixels. Pixels are approximately 1 cm. 2.5 MINERAL ABUNDANCES The minerals identified by comparison to the library (Figure 6) and mapped by the SAM algorithm were also used as endmembers for linear spectral unmixing. Unmixing assumes that the spectral reflectance of a pixel is caused by contributions from aerial mixtures of various components within the resolution element (Boardman, 1989, 1991). In this case, on the cm-scale, there is without doubt intimate (non-linear) mixing taking place (Nash and Conel, 1974), however, our experience in working with spectra of mixtures indicates that to the first order, a linear approach provides adequate information for most geologic problems (Boardman and Kruse, 1994).

The spectral unmixing produced mineral abundance images, one for each endmember (in this case 4). These are typically presented as images with brighter pixels representing higher abundances (Figure 9) (Boardman, 1991). Examination of a calculated root-mean-square (RMS) error image was used to determine that the selected endmembers accounted for most of the variability. Figure 5D shows a color composite of the unmixing results, with kaolinite abundances in red, alunite abundances in green, and montmorillonite abundances in blue.

Figure 9. Linear spectral unmixing images showing the abundance of the endmember minerals and an interpretation of the mineralogy. Values are stretched between 0 and 0.5 (50%). Brighter pixels correspond to higher abundances. The RMS image is stretched between 0 and 0.01 (1% error). Pixels are approximately 1 cm. Numbers in the interpretation refer to specific core depths (originally measured in feet).

3. DISCUSSION AND CONCLUSIONS The interpretation shown in Figure 5E and Figure 9 is the result of using the color composites, the band depth images, the SAM images, the unmixing images, and the individual spectra to generalize the mineral assemblages present in the core. Slight color differences observed in a color composite of bands at 1.418, 1.916, and 2.214 µm (RGB) (Figure 5B) are caused by differences in the relative strengths of these bands in the different clay minerals. These color differences are enhanced in the decorrelation stretched image (Figure 5C) and can be easily interpreted based on the characteristic absorption bands. For example, illite, which has strong 1.4 and 2.2 µm absorption bands and a relatively weak 1.9 µm absorption band, appears green on the image. Montmorillonite, which has a very strong 1.9 µm band and relatively weaker 1.4 and 2.2 µm bands appears blue to magenta on the decorrelated image. Kaolinite, which has a strong 2.2 µm band with nearly equal 1.4 and 1.9 µm bands appears yellow on the image. The band depth images (Figure 6) verify these observations. Areas interpreted as illite show bright pixels (large band depth) at both 1.4 and 2.2 µm and darker pixels (small band depth) at 1.9 µm. Areas of montmorillonite show bright pixels at 1.9 µm and mid-level gray pixels at 1.4 and 2.2 µm. Areas of kaolinite which were observed to generally have overall shallower band-depths are not easily interpreted on the band depth images. All of the visual image interpretations were verified by the extraction of individual spectra and matching to reference spectra as shown in Figure 6. The SAM results shown in Figure 7 provide additional support for the color image interpretations. Bright pixels in these images correspond to high matches between the reference spectra and the core spectra. Many of the extracted spectra show characteristics of several minerals. Comparison of these to the endmember spectra generally verifies the mineral assemblages derived by the unmixing results shown in Figure 8. No supporting analytical measurements (eg: X-Ray Diffraction [XRD]) have been made to verify these mapped mineral distributions, however, infrared spectroscopy is equally diagnostic (and in some case does a better job of identifying minerals than XRD) (See Clark et al., 1990). Visual examination of the core itself shows good correspondence between the interpretations and the actual mineralogy. For example, segment "054" contains typical quartz-sericite-pyrite alteration which corresponds to the mapped illite distribution. Segment "096" exhibits alteration of plagioclase to kaolinite. Visual identification of montmorillonite is problematic, however, for those segments identified as containing montmorillonite, they are altered to a white clay; not kaolinite. Segments "385" and "428" contain visible chlorite, while segments "438" and "455" contain biotite altering to chlorite. Based on these observations and the comparison of individual spectra to the PIMA reference spectral library, it is clear that the image analysis of field reflectance spectra is producing reasonable and accurate results. These analysis results demonstrate that multispectral and hyperspectral image analysis methods can be used to produce mineralogical maps of drill core. They suggest that the

concepts and analytical techniques developed for analysis of image data can be applied to field (or laboratory) reflectance spectra for geologic interpretation. This type approach is much more practical than analysis of large numbers of field or laboratory spectra using a single-spectrum analysis methodology. It allows presentation of the information in image format while taking advantage of the full spectral resolution of the instrument. 4. RECOMMENDATIONS The success of this effort implies that a hyperspectral scanner could be designed specifically to address laboratory analytical requirements. Such a scanner would ideally combine high spatial resolution (on the order of millimeters rather than the cm scale used here) with high spectral resolution similar to the PIMA instrument. The proof of concept for such a scanner could be economically accomplished by measuring core using a higher spatial resolution field or laboratory spectrometer and using the methodology described here to analyze the spectra. Such an effort should concentrate on automating the image analysis to permit analysis of large volumes of data. This should be followed by construction of an imaging prototype aimed at scanning more extensive core acquisitions. When implemented, the imaging spectrometer approach could be used to supplement (and in some cases possibly even replace) X-Ray analyses of core, and potentially could bring new information to core logging used in a variety of geologic subdisiplines. 5. ACKNOWLEDGMENTS The drill core used in this study was provided by W. W. Atkinson Jr, Department of Geological Sciences, University of Colorado. William M. Baugh collected the field spectrometer measurements. This research was partially supported by the CSES "Program for Industrial Excellence in Remote Sensing (PIERS)" program, a consortium of industry sponsors. 6. REFERENCES CITED Boardman, J. W., 1989, Inversion of imaging spectrometry data using singular value decomposition: in Proceedings IGARSS '89, 12th Canadian Symposium on Remote Sensing, v. 4, IGARSS’89, Canada, p. 2069 - 2072. Boardman, J. W., 1991, Sedimentary facies analysis using imaging spectrometry: A geophysical inverse problem: Unpublished Ph. D. Thesis, University of Colorado, Boulder, 212 p. Boardman, J. W., and Kruse, F. A., 1994, Automated spectral analysis: A geological example using AVIRIS data, northern Grapevine Mountains, Nevada: in Proceedings, Tenth Thematic Conference, Geologic Remote Sensing, 9-12 May 1994, San Antonio, Texas, Environmental Research Institute of Michigan (ERIM), Ann Arbor, MI, p. I-407 - I-418.

Center for the Study of Earth from Space (CSES), SIPS User’s Guide, Spectral Image Processing System, Version 1.2, Center for the Study of Earth from Space, Boulder, CO, 88 p. Clark, R. N., King, T. V. V., Klejwa, M., and Swayze, G. A., 1990, High spectral resolution spectroscopy of minerals: Journal of Geophysical Research, v. 95, no. B8, p. 12,53 - 12,680. Goetz, A. F. H., Vane, G., Solomon, J. E., and Rock , B. N., 1985, Imaging spectrometry for earth remote sensing: Science, v. 228, p. 1147 - 1153. Gillespie, A. R., Kahle, A. B., and Walker, R. E., 1986, Color enhancement of highly correlated images I: Remote Sensing of Environment, v. 20, p. 209-235. Kowalik, W. S., Sabins, F. F., Corea, W. C., and Alameda, G. K., 1991, Multispectral scanning and digital processing of well cores: in Proceedings, Eighth Thematic Conference on Geologic Remote Sensing, Environmental Research Institute of Michigan (ERIM), Ann Arbor, MI., p. 27 - 29. Kruse, F. A., Lefkoff, A. B., Boardman, J. B., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J., and Goetz, A. F. H., 1993a, The Spectral Image Processing System (SIPS) Interactive Visualization and Analysis of Imaging Spectrometer Data: Remote Sensing of Environment, Special issue on AVIRIS, May-June 1993, v. 44, p. 145 163. Kruse, F. A., Lefkoff, A. B., and Dietz, J. B., 1993b, Expert System-Based Mineral Mapping in northern Death Valley, California/Nevada using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): Remote Sensing of Environment, Special issue on AVIRIS, May-June 1993, v. 44, p. 309 - 336. Nash, E. B., and Conel, J. E., 1974, Spectral reflectance systematics for mixtures of powdered hypersthene, labradorite, and ilmenite, Journal of Geophysical Research, Vol 79, p. 1615 - 1621.