Lunar and Planetary Science XXX
1825.pdf
LINEAR DECONVOLUTION OF MAFIC IGNEOUS ROCK SPECTRA AND IMPLICATIONS FOR
INTERPRETATION OF TES DATA. Victoria E. Hamilton, Department of Geology, Arizona State University,
Box 871404, Tempe, AZ 85287-1404; hamilton@tes.la.asu.edu.
Introduction: Virtually every mineral exhibits a
unique thermal IR spectrum resulting from the vibrations of atoms within the crystal lattice. In the case of
rocks and particulate mixtures, the spectra of the individual mineral components add linearly (in proportion
to the abundance observed) to produce the spectrum of
the rock. Linear least-squares modeling provides a
method for deconvolving the spectrum of an unknown
rock into its constituent minerals and presenting the
results in terms of modal mineral abundances that may
be applied to the general lithologic classification of
unknown samples [1 – 3]. The objective of this work
is to determine the accuracy of linear deconvolution in
fitting not only the proper minerals to mafic rock spectra, but also in modeling solid solution compositions
(pyroxene and feldspar) and effective bulk chemistry in
the rock sample. These results are critically important
to understanding the limitations of this technique as it
will be applied to spectra of the martian surface returned by the Thermal Emission Spectrometer (TES)
aboard the Mars Global Surveyor (MGS) [4, 5], primarily due to the decrease in the spectral resolution of
the TES (10 cm-1 for TES vs. 2 cm–1 in the lab).
Samples: The 20 igneous rock samples studied in
this work were selected for their petrologic and compositional diversity, consisting of intrusive and extrusive
textures and having mafic and ultramafic compositions.
Samples include: subalkali basalts, Deccan basalt, basaltic andesites, gabbronorites, norites, diabase, augite
and olivine basalt porphyrys. All samples have predetermined compositional data of one or more of the
following kinds: optical modal analysis, bulk chemistry, and/or electron microprobe analysis. The ~80
minerals used in deconvolution modeling are pure particulate samples from the Arizona State University
(ASU) Thermal Emission Spectrometer Library [6].
Data Acquisition: Thermal infrared emission
spectra of all samples were acquired using the Mattson
Cygnus 100 interferometric spectrometer at ASU and
calibrated according to the techniques described in [7].
Linear Deconvolution: Deconvolution of thermal
IR spectra of rocks is based on the principal that the
energy emitted from a rock surface is equivalent to the
linear addition of the emitted enertgy of each component mineral in proportion to its observed areal percentage. Thus, a linear least-squares algorithm may be
applied to the spectra of rocks to derive the percentages
of the constituent minerals and their compositions [8,
9]. The algorithm, including model inputs, procedure,
and error analysis, is described in detail by [9]. Additional user techniques are described by [2 and 10].
Spectral Characteristics of Mafic Rocks: Not all
mafic and ultramafic rocks look alike in the thermal IR.
Distinct differences in texture and composition result in
readily discernable spectral characteristics.
Intrusive vs. extrusive. Almost all types of igneous
rocks have intrusive and extrusive equivalents. The
physical textures of intrusive and extrusive igneous
rocks are substantially different, and result in differing
spectral character, making it easy to distinguish between the two types even when the rocks are similar
compositionally (Figure 1). Spectra of basaltic samples are characterized by broad silicate absorptions that
are generally higher in overall emissivity and have
fewer high frequency bands than spectra of coarsegrained plutonic samples, which have deeper, narrower
band minima.
Spectral variability within rock type. Although as
a class basalts share great similarities in bulk composition (e.g., as compared to granites), within the basaltic
classification, there are many subordinate classes representing more subtle variations in the relative abundances of common minerals or the presence or absence
of certain phases. Thermal infrared spectra are sensitive
to these more subtle variances in composition – not all
basalts look the same. The basaltic spectra in Figure 2
are all representative of subalkali basalts from Arizona
[3, 10]. These rocks all contain between 65-75% feldspar, 15-25% pyroxene, and 5-10% olivine, yet their
spectra are still distinguishable based on the spectral
effect of subtle differences in the relative abundances of
these minerals.
Results and Discussion:
Compositional modeling. Overall, the spectral fits
generated by the linear deconvolution algorithm are
extremely good, with residual errors much less than
those of the martian meteorite samples studied by [2].
The best model fit (Figure 3) yielded modal abundances that were within 2 vol.% of optical modal
analysis results, well within the 5-15% error associated
with the optical technique [10]. The highest errors in
modal abundance modeling are associated with minor
components (<10 vol.%) [9,10] because of their lesser
contribution to the overall spectral shape. Based on
the dominant mineralogy of mafic rocks, the following
minerals were targeted for examination of modeled modal accuracy: feldspar, clino- and orthopyroxene, and
olivine. In 60 of 63 modeled modes for these minerals, the modeled vol.% was within 5-15% of the optically determined mode. Thus, linear deconvolution of
thermal IR spectra can be used to determine gross modal mineralogy of rocks with a level of accuracy similar
to that obtained by traditional optical analysis. The
accuracy of these models also demonstrates that the
deconvolution technique is sensitive to the spectral
variations among a single rock type (Figure 2).
Lunar and Planetary Science XXX
1825.pdf
LINEAR DECONVOLUTION OF MAFIC ROCK SPECTRA: V. E. Hamilton
Modeling solid solution compositions. In many
cases, the model fit to a spectrum includes several
endmembers from within a solid solution series (e.g.,
plagioclase). Because it is probable that none of the
spectral endmembers represents exactly the same composition as a mineral in a given rock, it seems logical
that several similar endmembers may be used in the fit
to reproduce the spectral contribution (composition) of
the actual mineral in the rock. This idea was tested by
comparing the weighted average composition of modeled orthopyroxene (opx), clinopyroxene (cpx), and
plagioclase feldspar endmembers to the actual composition of those minerals as measured by either electron
microprobe or optical methods. An#’s resulting from
averaging of modeled plagioclase compositions were
within 1-13 An# of the known for all cases. In 7 of 8
rocks, pyroxenes were correctly identified as opx or
cpx. In 9 of 12 samples, averaged model pyroxene
compositions fall within 2-14 Mg# of the measured
value. Three others were within 11-25 Mg#, and these
higher errors are attributed to the small modal abundances (~5-10%) of cpx or opx in the rocks (as above).
Reproduction of wt.% oxides. Because basaltic
rocks are typically classified on the basis of their bulk
chemistry rather than their modal mineralogy, it may
simplify the classification of unknown samples if deconvolution results can be converted to bulk oxides.
This is accomplished by combining the known chemistries of the endmembers used in the model fit to produce a bulk chemistry that may be compared here to
the bulk chemistry of the rocks as measured by traditional mass spectroscopy methods. Average errors for
major oxides range from 0.4 to 8.2 wt.%, with the
highest errors occurring in Al2O3 (8.2%), FeO (4.2%),
and MgO (2.7%). SiO wt.% is commonly used in the
classification of basalts, and the average error in SiO
determination is 2.5%. This percent error is significant enough to result in misclassification of most basalts when plotting wt.% SiO vs. wt.%Na2O+K2O.
Based on these results, [11] are making an effort to
establish new methods for classifying these rock types
on the basis of modal mineralogy.
Implications for analysis of TES data. Because
the results presented here reflect the success of this
technique with high SNR, high spectral resolution
laboratory data, equally accurate results should not be
expected with TES data. However, the linear deconvolution technique will nonetheless provide significant
insight into the major component mineralogy of
atmospherically-corrected TES spectra [4, 5]. At the
time of this writing, studies examining the impact of
lower spectral resolution on these deconvolution results are incomplete, but when finished will provide a
sense of what accuracy may be expected with the application of this technique to TES data.
References: [1] Ramsey, M. S. (1996) Ph.D. Dissertation, ASU. [2] Hamilton, V. E. et al. (1997),
JGR, 102, 25,593-25,603. [3] Feely, K. C. (1997)
M.S. Thesis, ASU. [4] Christensen, P. R. et al.
(1999) LPS, XXX. [5] Bandfield, J. L. et al. (1999)
LPS, XXX. [6] Christensen, P. R., submitted. [7]
Ruff, S. W. et al. (1997) JGR, 102, 14,899-14,913. [8]
Thomson, J. L. and Salisbury, J. W. (1993) Remote
Sens. Env., 45, 1-13. [9] Ramsey, M. S. and Christensen, P. R. (1998) JGR, 103, 577-596. [10] Hamilton,
V. E. (1998) Ph.D. Dissertation, ASU. [11] Wyatt,
M. B. et al. (1999) LPS, XXX.
Figure 1. Spectra of five basaltic compositions.
Figure 2. Comparison of intrusive and extrusive
equivalent compositions, basalt and gabbronorite.
Figure 3. Example of linear deconvolution model.