Remote Sensing for Mineral Exploration

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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 157183
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Outline

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

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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).

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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

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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.

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• 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.

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

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Mineral exploration overview

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

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• 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.

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• 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.

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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)

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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.

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Fig. 3. Map showing geology and hydrothermal
alteration of Goldfield mining district, NV. From
Ashley (1979, Figs. 1 and 8)
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2. Recognizing hydrothermal alteration on Landsat
images
Fig. 4A, an enhanced normal color image of TM
bands 123 shown in blue, green, and red,
respectively.
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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.
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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.
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Fig. 5C is a histogram of the 5/7 ratio image
that shows the higher ratio values (DNs gt145) of
the altered rocks. Low ratio values represent
unaltered rocks.
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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 DNgt145 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.
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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.
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Fig. 7B is a 3/1 ratio image with high DN values
shown in bright tones.
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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 DNgt150
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.
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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.
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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).

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Fig. 3E TM unsupervised classification map.
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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.

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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,
  20762099.