Remote Sensing for Mineral Exploration Floyd F. Sabins * Remote Sensing Enterprises, 1724 Celeste Lane, Fullerton, CA 92833, USA Received 13 November 1998; accepted 20 April 1999 From : Ore Geology Reviews 14 1999 157–183

Outline Introduction Remote sensing technology Landsat images Digital image processing Mineral exploration overview Mapping hydrothermal alteration at epithermal Vein deposits — Goldfield, Nevada Summary References

Introduction Remote sensing is the science of acquiring, processing, and interpreting images and related data, acquired from aircraft and satellites, that record the interaction between matter and electromagnetic energy (Sabins, 1997, p. 1).

Remote sensing technology Advantages: archives of worldwide data are readily available images cover large areas on the ground prices per square kilometer are generally lower Disadvantages : the latest hyperspectral technology is currently available only from aircraft aircraft missions can be configured to match the requirements of a project

Landsat images Landsat satellites that have acquired valuable remote sensing data for mineral exploration and other applications. The first generation Landsats 1, 2, and operated from 1972 to 1985. The second generation Landsats 4, 5 and 7, which began in 1982 and continues to the present. Landsat 6 of the second generation was launched in 1993, but failed to reach orbit.

The TM system records three wavelengths of visible energy blue, green, and red (Band 1, 2 and 3) and three bands of reflected IR energy (Band 4, 5 and 7). These visible and reflected IR have a spatial resolution of 30 m. Band 6 records thermal IR energy 10.5 to 12.5 mm with a spatial resolution of 120 m. Each TM scene records 170 by 185 km of terrain. The image data are telemetered to earth receiving stations. The second generation of Landsat continued with Landsat 7, launched in April, 1999, with an enhanced TM system. A panchromatic band 8 0.52 to 0.90 mm with spatial resolution of 15 m is added.

Fig. 1. Landsat TM visible and reflected IR images of Goldfield mining district, NV. (A) Band 1, blue 0.45 to 0.52 mm. (B) Band 2, green 0.52 to 0.60 mm. (C) Band 3, red 0.63 to 0.69 mm. (D) Band 4 reflected IR 0.76 to 0.90 mm. (E) Band 5, reflected IR 1.55 to 1.75 mm. (F) Band 7, reflected IR 2.08 to 2.35 mm.

Fig. 2. Spectral bands recorded by remote sensing systems Fig. 2. Spectral bands recorded by remote sensing systems. Spectral reflectance curves are for vegetation and sedimentary rocks. From Sabins (1997, Fig. 4-1)

Digital image processing Sabins, 1997 groups image-processing methods into three functional categories : Image restoration compensates for image errors, noise, and geometric distortions introduced during the scanning, recording, and playback operations. The objective is to make the restored image resemble the scene on the terrain. Image enhancement alters the visual impact that the image has on the interpreter. The objective is to improve the information content of the image. Information extraction utilizes the computer to combine and interact between different aspects of a data set. The objective is to display spectral and other characteristics of the scene that are not apparent on restored and enhanced images.

Mineral exploration overview Table 1 Representative mineral exploration investigations using remote sensing. From Sabins 1997, Table 11-3

These studies describe two different approaches to mineral exploration. Mapping of geology and fracture patterns at regional and local scales. Rowan and Wetlaufer 1975 used a Landsat mosaic of Nevada to interpret regional lineaments.Comparing the lineament patterns with ore occurrences showed that mining districts tend to occur along lineaments and are concentrated at the intersections of lineaments. Nicolais 1974 interpreted local fracture patterns from a Landsat image in Colorado. The mines tend to occur in areas with a high density of fractures and a concentration of fracture intersections. Rowan and Bowers 1995 used TM and aircraft radar images to interpret linear features in western Nevada. They concluded that the linear features correlate with the geologic structures that controlled mineralization.

Recognition of hydrothermally altered rocks that may be associated with mineral deposits. The spectral bands of Landsat TM are well-suited for recognizing assemblages of alteration minerals iron oxides, clay, and alunite that occur in hydrothermally altered rocks. In my experience the best exploration results are obtained by combining geologic and fracture mapping with the recognition of hydrothermally altered rocks.

Mapping hydrothermal alteration at epithermal vein deposits -- Goldfield, Nevada Many mines were discovered by recognizing outcrops of altered rocks, followed by assays of rock samples. Today remote sensing and digital image processing enable us to use additional spectral bands for mineral exploration. In regions where bedrock is exposed, multispectral remote sensing can be used to recognize altered rocks because their reflectance spectra differ from those of the unaltered country rock. The Goldfield Mining District in south-central Nevada is the test site where remote sensing methods were first developed to recognize hydrothermally altered rocks (Rowan et al., 1974)

1. Geology, ore deposits, and hydrothermal alteration The Goldfield district was noted for the richness of its ore. Volcanism began in the Oligocene epoch with eruption of rhyolite and quartz latite flows and the formation of a small caldera and ring-fracture system. Hydrothermal alteration and ore deposition occurred during a second period of volcanism in the early Miocene epoch when the dacite and andesite flows that host the ore deposits were extruded. Following ore deposition, the area was covered by younger volcanic flows. Later doming and erosion have exposed the older volcanic center with altered rocks and ore deposits.

Fig. 3. Map showing geology and hydrothermal alteration of Goldfield mining district, NV. From Ashley (1979, Figs. 1 and 8)

2. Recognizing hydrothermal alteration on Landsat images Fig. 4A, an enhanced normal color image of TM bands 1–2–3 shown in blue, green, and red, respectively.

2.1. Alunite and clay minerals on 5/7 ratio images Fig. 5A shows reflectance spectra of alunite and the three common hydrothermal clay minerals illite, kaolinite, and montmorillonite. These minerals have distinctive absorption features reflectance minima at wavelengths within the bandpass of TM band 7 which is shown with a stippled pattern in Fig. 5A.

Table 2

Fig. 5B is a 5/7 ratio image of Goldfield with higher ratio values shown in brighter tones. Comparing the image with the map Fig. 4 shows that the high ratio values correlate with hydrothermally altered rocks.

Fig. 5C is a histogram of the 5/7 ratio image that shows the higher ratio values (DNs >145) of the altered rocks. Low ratio values represent unaltered rocks.

Fig. 6C is a color density slice version of the 5/7 image in which the gray scale is replaced by the colors shown in the histogram (Fig. 5C) . Highest ratio values DN>145 are shown in red, with the next highest values DN 125 to 145 shown in yellow. The red and yellow colors on the ratio image (Fig. 3C) therefore correlate with the altered rocks.

2.2. Iron minerals on 3/1 ratio images Fig. 7A shows spectra of the iron minerals which have low blue reflectance TM band 1 and high red reflectance TM band 3.

Fig. 7B is a 3/1 ratio image with high DN values shown in bright tones.

Fig. 3D is a color density slice version of the 3/1 image, with color assignments shown in the histogram of Fig. 7C. Highest ratio values DN>150 are shown in red, with the next highest values DN 135 to 150 shown in yellow. The red and yellow colors therefore correlate with the altered rocks.

2.3. Color composite ratio images Fig. 3B shows ratios 3/5, 3/1, and 5/7 in red, green, and blue, respectively. The orange and yellow hues delineate the outer and inner areas of altered rocks in a pattern similar to that of the density sliced ratio images.

2.4. Classification images Multispectral classification is a computer routine for information extraction that assigns pixels into classes based on similar spectral properties. supervised multispectral classification, the operator specifies the classes that will be used (Sabins, 1997. Chap.8). unsupervised multispectral classification, the computer specifies the classes that will be used (Sabins, 1997. Chap. 8).

Fig. 3E TM unsupervised classification map.

3. Summary Many ore deposits are localized along regional and local fracture patterns that provided conduits along which ore-forming solutions penetrated host rocks. Hydrothermally altered rocks associated with many ore deposits have distinctive spectral features that are recognizable on digitally processed TM images. Detection of hydrothermally altered rocks is not possible in vegetated areas, so this environment requires other remote sensing methods. Reflectance spectra of foliage growing over mineralized areas may differ from spectra of foliage in adjacent nonmineralized areas. The image interpretation will produce a map of localities, or prospects, with favorable conditions for mineral deposits. The image can also be used to plan the best ground access to the intersting prospects.

References Rowan, L.C., Wetlaufer, P.H., Goetz, A.F.H., Billingsley, F.C., Stewart, J.H., 1974. Discrimination of rock types and detection of hydrothermally altered areas in south central Nevada by the use of computer-enhanced ERTS images. U.S. Geol.Surv. Prof. Pap. 883, 35. Sabins, F.F., 1983. Geologic interpretation of Space Shuttle images of Indonesia. Am. Assoc. Pet. Geol. Bull. 67, 2076–2099.