6.2 Unmixing Sources
In order to further characterize the evolving source of sediments to the
Indus Fan we employ the unmixing software of Sundell and Saylor
[2017], which analyzes the U-Pb age spectra from each of the samples
and compares them with the defined end-member compositions of the
different source ranges compiled from the published literature. This
approach works particularly well in the western Himalaya where the
sources are well defined and often unique. Data from the Tethyan,
Greater and Lesser Himalaya were compiled from DeCelles et al.[2004]. Data from the Karakoram are from Le Fort et al.[1983], Parrish and Tirrul [1989], Schärer et al.[1990], Fraser et al. [2001] and Ravikant et al.[2009]. Data from Nanga Parbat are from Zeitler and Chamberlain
[1991] and Zeitler et al. [1993]. Data from the
Transhimalayan are from Honegger et al. [1982], Schäreret al. [1984], Krol et al. [1996], Weinberg and
Dunlap [2000], Zeilinger et al. [2001], Dunlap and
Wysoczanski [2002], Singh et al. [2007], and Ravikantet al. [2009].
This unmixing method uses a Monte Carlo approach to estimate the
contributions from the different sources that would be required to
generate the modes and modal abundances of U-Pb ages seen in the
sediment samples. Because this is relatively objective the method is
considered robust for analyzing potential source contributions, assuming
that the sources themselves have been well characterized. The bedrock
sources of the Indus catchment have significant differences between many
of them and are some of the best characterized worldwide. Results of the
Monte Carlo simulation are provided in Table 4, showing the output using
all three statistical comparison methods, cross-correlation, the best V
value in the Kuiper test. as well as the best D value in the K-S test.
The method involves creating 10,000 model mixed sediments using the
defined bedrock source end members. The DZMix software then compares the
model with the measured spectra and retains the best 1% of these models
in order to estimate which sources were contributing the sampled
material. We favor the unmixing models derived from the
cross-correlation approach as being geological reasonable and favored by
Sundell and Saylor [2017].
The results of our unmixing calculations show a progressive provenance
evolution that is consistent with that seen in the MDS diagram (Figs. 11
and 12). The very oldest sample deposited at 15.5 Ma shows a dominance
of sediment eroded from the Karakoram and from the Tethyan and Greater
Himalaya. Most of the Miocene samples dated between 8.2 and 7.0 Ma are
more dominated by material from the Karakoram but also usually show
significant Tethyan and Greater Himalayan contributions. This Himalayan
component is particularly noteworthy at 7.99, 7.84, 7.78, 7.66, and 7.0
Ma during this interval. The proportion of Karakoram zircons shows a
significant decrease starting no later than 5.72 Ma and again at 3.02
Ma. The sediment deposited at 3.17 Ma shows the greatest amount of
modeled erosion from Karakoram sources of any sample.
From 3.02 Ma onwards the Himalaya dominate as sources to the submarine
fan, with significant amounts of material from the Lesser Himalaya first
appearing at 1.56 Ma. The sample dated as being deposited at 0.93 Ma is
anomalous for being very similar in source signature to Tethyan and
Greater Himalayan bed rocks sources. However, we note that fission track
data indicate that this sample was derived from peninsular India
[Zhou et al. , 2019]. The unmixing analysis largely mirrors
the pattern shown by the MDS diagram, in showing a progressive long-term
increase in erosion from the Himalaya relative to the Karakoram,
although with significant steps making the evolution nonlinear. All of
the samples contain a small amount of very young <25 Ma
zircons. None of the samples analyzed show a close similarity with
post-LGM river compositions. Delta samples deposited at 6.6 and 15 ka
are strongly enriched in Karakoram-derived grains compared to fan
sediments deposited at and after 3.02 Ma. This short term variability is
interpreted to reflect the short-term changes in erosion patterns linked
to monsoon strength, modulated by glacial cycles since the onset of the
NHG.