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and Haskin
1991
). The magma ocean hypothesis was developed following the first
sample return from the Moon. The crystallization of the magma ocean would result
in FeO poor anorthosite rocks concentrating in the crust.
As was mentioned by Lucey et al., the key test of the magma ocean hypothesis
is the abundance of anorthosite (Lucey et al.
1995
). Usually, anorthosite assembles
in lunar highlands, so iron abundances in these regions could represent the global
anorthosite concentration. In remote detection, the global mode of FeO concentra-
tion represents iron abundance of lunar highland regions. The global mode of iron
abundance derived by Lucey et al. in 1995 is about 3 wt% (in Fe, and 3.96 wt% in
FeO), and he improved the algorithm in year 1998, and the new global mode of FeO
is 4.8 wt% (Lucey et al.
1998
). These results are consistent with our result (5.1 wt%).
As far as lunar meteorites study are concerned, Korotev et al. studied eight best
characterized feldspathic lunar meteorites and showed the average concentration of
FeO is 4.4
˙
0.5 wt%, and the FeO range is 3-6 wt% (Korotev et al.
1996
;
2003
;
Korotev
2005
). The global mode of PLS-derived FeO map is 5.1 wt%, which also
agrees well with meteorite studies and remote sensing results (Lucey et al.
1995
,
1998
,
2000
;Lawrenceetal.
2002
) and thus could also support the magma ocean
hypothesis of lunar crust.
1.7
Conclusions
We derived a new iron model with PLS method, which has been verified to be able
to derive robust iron abundances for the Moon. We apply this model to Clementine
DIM and obtain global distribution of iron. Our results show that peak distribution
of iron abundance in highlands and mare regions are 5.1 wt% and 16.9 wt%,
respectively. Comparing our iron map to Lucey's algorithm as well as that detected
by Lunar Prospector gamma-ray spectrometer, we find the three results agree well
in mare regions, while PLS model and LP iron maps show higher iron content in
highlands. Local comparisons (e.g., Tycho crater and SPA basin) also suggest our
PLS model is reliable and more consistent with the LP results. Besides, the PLS
model-derived iron abundance peak of lunar farside is 5.1 wt%, which agrees well
with the lunar meteorites that are assumed from lunar highland. Our global FeO
distributions are also consistent with the lunar magma ocean hypothesis as has been
presented by previous work (Lucey et al.
1995
,
1998
; Wood et al.
1970
; Warren and
Haskin
1991
).
Although our PLS algorithms have already shown its potential for extraction
of lunar iron abundance, it should be kept in mind that there are limitations, i.e.,
the exact physical significance of PLS is not as evident as experience algorithms,
and PLS regression highly depends on the type of the modeling data inputs. More
lunar samples and precise geographic location of them would definitely contribute
to the improvement of PLS modeling for iron. Interference Imaging Spectrometer
(IIM) onboard Chang'E-1 has achieved the abundance of some key elements of the
Moon (Ling et al.
2011
;Wuetal.
2012
; Jin et al.
2013
). As is known, China's new
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