NOTE

Characterizing clay mineralogy in Lake Towuti, Indonesia,with reflectance spectroscopy

Andrea K. Weber . James M. Russell . Timothy A. Goudge .

Mark R. Salvatore . John F. Mustard . Satria Bijaksana

Received: 11 June 2014 / Accepted: 16 May 2015 / Published online: 26 May 2015

� Springer Science+Business Media Dordrecht 2015

Abstract We tested the use of visible to near-infrared

(VNIR) reflectance spectroscopy to characterize the

relative abundances of clay minerals in sediments from

Lake Towuti, a large tectonic lake in Sulawesi,

Indonesia. We measured VNIR spectra of lake and

river sediments from Lake Towuti and its catchment to

identify clay minerals, fit major VNIR absorption

features with a modified Gaussian model to estimate

relative abundances of these minerals, and compared

these absorptions to the samples’ chemistry to test the

utility of VNIR spectroscopy to characterize sediment

compositional variations. We found that major absorp-

tions are caused by vibrations of Al–OH in kaolinite

(2.21 lm), Fe–OH in nontronite (2.29 lm), Mg–OH in

saponite and serpentine (2.31 lm), and Mg–OH in

serpentine (2.34 lm). This was confirmed with X-ray

diffraction data. The correlations between absorption

band areas for Fe–OH, Al–OH, and Mg–OH vibrations

and Fe, Al and Mg concentrations, respectively, are

statistically significant, varying between r = 0.51 and

r = 0.90, and spatial variations in inferred clay min-

eralogy within the lake are consistent with variations in

the geology of the catchment. We conclude that VNIR

spectroscopy is an effective way to characterize the clay

mineralogy of lake sediments, and can be used to

investigate changes in mineral inputs to lake deposits.

Keywords Clay mineralogy � Lake sedimentology �Paleolimnology � Spectroscopy � Modified Gaussian

modeling

Electronic supplementary material The online version ofthis article (doi:10.1007/s10933-015-9844-4) contains supple-mentary material, which is available to authorized users.

A. K. Weber (&) � J. M. Russell � T. A. Goudge �M. R. Salvatore � J. F. Mustard

Department of Earth, Environmental, and Planetary

Sciences, Brown University, 324 Brook St., Providence,

RI 02912, USA

e-mail: Andrea_Weber@alumni.brown.edu

J. M. Russell

e-mail: James_Russell@brown.edu

T. A. Goudge

e-mail: Tim_Goudge@brown.edu

M. R. Salvatore

e-mail: Mark.Salvatore@asu.edu

J. F. Mustard

e-mail: John_Mustard@brown.edu

Present Address:

A. K. Weber

School of Engineering and Applied Sciences, Harvard

University, 58 Oxford Street, Cambridge, MA 02138,

USA

Present Address:

M. R. Salvatore

School of Earth and Space Exploration, Arizona State

University, Tempe, AZ 85287-1404, USA

S. Bijaksana

Global Geophysics Research Group, Faculty of Mining

and Petroleum Engineering, Institut Teknologi Bandung,

Jalan Ganesa 10, Bandung 40132, Indonesia

e-mail: satria@fi.itb.ac.id

123

J Paleolimnol (2015) 54:253–261

DOI 10.1007/s10933-015-9844-4

Introduction

The clay mineralogy of lake sediments can provide

valuable insight into the history of sediment prove-

nance (Mitchell 1955; Johnson 1970), weathering

processes (Asikainen et al. 2006), and depositional

environments. Clay mineralogy is commonly mea-

sured using X-ray diffraction (XRD), although this can

be a relatively difficult process involving numerous

and often destructive pretreatments of large sediment

samples (Yuretich et al. 1999). In contrast, visible to

near-infrared (VNIR) reflectance spectroscopy is a

rapid, non-destructive method that can provide infor-

mation on the mineral composition of sediments

through the position and shape of the absorption

features, which are controlled by crystal structure and

mineral chemistry. Infrared spectroscopy has been

used to measure organic carbon, biogenic opal, and

carbonate mineral concentrations in lake sediment

samples to understand long-term paleoenvironmental

variations (Rosen and Persson 2006; Vogel et al. 2008;

Rosen et al. 2010), to identify and map clay minerals

in paleolake deposits on Mars (Ehlmann et al. 2008;

Milliken and Bish 2010), and to determine the compo-

sition of terrestrial soils (Viscarra Rossel et al. 2006,

2009). Despite the sensitivity of VNIR reflectance data

to variations in clay mineralogy, this technique has not

been widely employed to measure clay mineralogical

variations in lake sediment samples.

This paper evaluates VNIR reflectance spectra of

sediment samples from Lake Towuti, Indonesia, which

is located in the East Sulawesi Ophiolite. This ophiolite

consists largely of serpentinized and unserpentinized

peridotites, lehrzolites, and mafic–ultramafic rocks

including gabbros, dolerites and basalts, as well as

recent sediments derived from those rocks (Fig. 1;

Kadarusman et al. 2004). Nickeliferrous oxisol soils

formed on these ultramafic rocks dominate the region

and contain up to 60 % iron oxide (Golightly 1981).

VNIR reflectance data of surface sediment samples

from Lake Towuti were compared to their bulk

elemental composition to test whether VNIR re-

flectance spectra can be correlated to spatial variations

in sediment composition and clay mineralogy.

Materials and methods

Sample preparation and analysis

River sediment samples (9) and lake surface sediment

samples (33) were collected in 2011 and 2012.

Offshore lake sediment samples were characterized

visually and microscopically and consist almost

Fig. 1 Location and

geology surrounding Lake

Towuti in Indonesia. Map

modified from Costa et al.

(2015). (Color figure online)

254 J Paleolimnol (2015) 54:253–261

123

entirely of very fine silt and clay, but river samples had

widely variable grain sizes, ranging up to coarse

gravel. River sediment samples were therefore sieved

to\125 lm before being powdered to prevent spec-

troscopic and geochemical measurements from being

biased by large grains (coarse sands and gravels).

Samples were freeze-dried, powdered, and ho-

mogenized, and aliquots were prepared via flux fusion

to analyze their bulk elemental chemistry following

the procedure outlined by Murray et al. (2000).

Inductively Coupled Plasma-Atomic Emission Spec-

troscopy (ICP-AES) was used to measure Al, Ca, K,

Fe, Si, Ti, Mg, Mn, and Cr concentrations. The VNIR

spectra of separate aliquots were measured using an

Analytical Spectral Devices (ASD Inc., CO, USA)

FieldSpec 3 portable spectrometer (subsequently

referred to as an ASD Spectrometer), a VNIR

reflectance spectrometer that covers the wavelength

range from 350 to 2500 nm [see Electronic Supple-

mentary Material (ESM) for details on measure-

ments]. High-resolution spectral data were also

acquired for three lake sediment samples at the Brown

University Keck/NASA Reflectance Experiment Lab

(RELAB; Pieters 1983) to evaluate the lower-resolu-

tion VNIR data from the ASD Spectrometer (see ESM

for discussion). Powder XRD data on select samples

were also collected using a Bruker D2 PHASER X-ray

diffractometer to provide complementary mineralogy

to VNIR reflectance measurements (see ESM for

details on measurements).

VNIR data analysis and modified Gaussian

modeling

A modified Gaussian model (MGM) was used to

quantify the strengths of clay mineral absorption

features in the VNIR spectra, which are, to first order,

related to the relative abundances of clay minerals in

samples (Clark 1999). The MGM, first presented by

Sunshine et al. (1990), was initially developed and

validated for crystal field absorptions in pyroxene (see

ESM for further details). Subsequently, it has been

shown that the MGM can also be used to model

vibrational absorption bands caused by OH within

actinolite (Mustard 1992). Modified Gaussian fits to

each absorption band of interest in our samples were

used to determine the band area, which is controlled by

the absorption strength and can be used as a proxy for

the relative abundances of different minerals, in

contrast to absolute abundances. Whereas band depth

is also a useful proxy for mineral abundance, the

overlapping absorption features of many clay minerals

necessitates the use of absorption band area to

deconvolve complex spectral data into a series of

individual absorption features.

Results

Chemistry

The ICP-AES data indicate large chemical variations

in sediment within the lake (Fig. 2a–c). Iron concen-

trations in the lake sediments vary between 8.2 and

18.9 wt%, and generally increase from west to east

and north to south across the lake. Lake surface

sediment samples show the opposite trend in alu-

minum, with concentrations increasing from 2.6 wt%

in the east to 6.8 wt% in the west. This E–W gradient

is also apparent in the rivers, with a maximum Al

concentration of 7.09 wt% in the Loeha River that

flows into the northwest corner of the lake, and

maximum Fe concentrations of *26 wt% measured

in the Lemo-lemo River that flows in from the east. Mg

concentrations are high in rivers on Towuti’s eastern

shoreline, but Mg concentrations are highest in the

central part of the lake, south of the Mahalona River.

The Mahalona River has Mg concentrations that

exceed 10 wt% compared to an average concentration

of 5.3 wt% in all rivers.

VNIR spectroscopy and XRD mineralogy

VNIR spectroscopy is based upon the absorption of

electromagnetic radiation at specific wavelengths

through excitation of electronic transitions or mole-

cular vibrations within mineral structures. The precise

position and shape of VNIR absorption features, or

absorption bands, are related to the optical constants of

the material being investigated. In minerals, the

position and shape of the absorption features are

controlled by the crystal structure and mineral chem-

istry. Whereas each individual absorption feature is

related to a specific cation’s coordination state and

electrons (electronic transition absorptions) or bond

(vibrational absorption), taken together, multiple ab-

sorption features can be diagnostic of specific miner-

als. Different minerals have unique VNIR spectral

J Paleolimnol (2015) 54:253–261 255

123

properties, and thus sample mineralogy can be

uniquely identified on the basis of infrared reflectance

data (Burns 1993; Farmer 1974; Hunt 1977; Hunt and

Salisbury 1970).

VNIR spectra of both the lake and river sediment

samples have distinct absorption features at *1.4,

*1.9, 2.21 lm and a complex set of features from

2.29 to 2.4 lm (Fig. 3a, b). Spectra from the high-

resolution BDR spectrometer and the lower-resolution

ASD Spectrometer have the same spectral features in

these areas, indicating that the detection of these

features in the ASD Spectrometer data is robust (see

the ESM for discussion).

Many of the absorption bands present in the Lake

Towuti samples are common to multiple minerals. The

sharp absorption at*1.9 lm indicates the presence of

structural water common to many phyllosilicates and

opal (Clark et al. 1990; Bishop et al. 1994), whereas

the absorptions at *1.4 lm are caused by the first

overtone of structural OH, as well as combination

tones of structural H2O (Clark et al. 1990; Gaffey et al.

1993). In contrast, absorption bands in the *2.2 to

Fig. 2 Map of sample locations in Lake Towuti. Squares are

lake sediment samples and triangles are river sediment samples.

The highest values are in red and the lowest are in green. The

color bins vary between maps. a Al wt%, b Fe wt% and c Mg

wt%. Figures d, e, and f show the MGM areas calculated for

each absorption band of interest. These areas were normalized to

the sum of absorption band areas for that spectrum. d Al–OH

(kaolinite) absorption band areas, e Fe–OH (nontronite)

absorption band areas, f Mg–OH (saponite ? serpentine)

absorption band areas. (Color figure online)

256 J Paleolimnol (2015) 54:253–261

123

2.4 lm region are caused by combinations of the

stretching and bending modes of OH and metal-OH,

respectively, and are diagnostic for many clay miner-

als in weathered oxisol soils (Fig. 3c, d; Clark et al.

1990). River and lake sediment samples from the Lake

Towuti basin show several absorption features in this

wavelength region. Based on the analysis of the

locations and shapes of the identified absorption

features in the measured spectra, in comparison to

those in pure minerals (Clark et al. 1990), we attribute

absorptions at *2.21 lm to Al–OH vibrations in

kaolinite, absorptions at *2.29 lm to Fe–OH

vibrations in an Fe-bearing smectite (nontronite),

and absorptions at *2.31 and 2.34 lm to Mg–OH

vibrations in a combination of an Mg-bearing smectite

(saponite) and serpentine.

The absorption band at *2.21 lm is interpreted to

be caused by Al–OH vibrations in kaolinite, as

opposed to some other Al-bearing phyllosilicate such

as montmorillonite or illite, due to the precise position

of the 2.21 lm absorption coupled with an asymmetric

shoulder in the band near *2.16 lm (Clark et al.

1990; Bishop et al. 2008). The complex spectral signal

near *2.3 lm is a region often characteristic of Fe/

Fig. 3 a, b Are averages of the grab, river, and core top

sediments. Spectra in part (a) are continuum removed. These

averages clearly define the area of interest to be from *2.20 to

2.40 lm. c, d Are library spectra of kaolinite, nontronite,

saponite, and an example of the Mg-rich serpentine chrysotile.

Spectra in part (c) are continuum removed. Spectra are from the

USGS spectral library (Clark et al. 2007). (Color figure online)

J Paleolimnol (2015) 54:253–261 257

123

Mg-bearing phyllosilicates. Nontronite is a dioctahe-

dral ferric iron-bearing smectite and has an absorption

centered at *2.29 lm, caused by the Fe3?–OH bond

(Clark et al. 1990; Bishop et al. 2002, 2008). Saponite,

a trioctahedral magnesium-bearing smectite, has an

absorption centered at *2.31 to 2.32 lm caused by

Mg–OH (Clark et al. 1990). Although some samples

show strong absorptions at *2.29 or *2.32 lm,

indicating nontronite or saponite, respectively (Clark

et al. 1990; Bishop et al. 2002, 2008), many samples

have absorption features between 2.29 and 2.32 lm,

likely from either Fe/Mg substitution between non-

tronite and saponite or physical mixtures of the two

mineral species. Absorption bands that fall between

the endmember wavelengths are interpreted as a

mixture of clays with varying Mg/Fe ratios (Grauby

et al. 1994).

The absorption band at *2.33 lm and a more

subtle absorption feature at *2.1 lm in several

samples indicates the presence of serpentine. Serpen-

tine has a prominent absorption feature caused byMg–

OH that overlaps with the absorption features of the

Fe/Mg-bearing smectites (Fig. 3c, d), but with a band

center at slightly longer wavelengths, near *2.33 to

2.34 lm (King and Clark 1989; Bishop et al. 2008).

Serpentines also have a shallow, but broad diagnostic

absorption feature centered near*2.1 lm (Fig. 3c, d;

King and Clark 1989; Bishop et al. 2008) that may

relate to the presence of Mg–OH (Clark et al. 1990).

Our XRD results indicate the presence of several

mineral phases in these samples, including serpentine,

kaolinite, smectite and quartz (ESM Fig. 3). Serpen-

tine is identified based on prominent 001 and 002

reflections, as well as a 100 reflection peak. Kaolinite

is identified based on 001 and 002 reflections, and a

prominent 020 reflection. Smectite is identified based

on a small 001 basal reflection peak. These XRD

results confirm the major clay mineral phases inferred

from our VNIR reflectance spectroscopy results.

Modified Gaussian modeling

The modified Gaussian model fits the clay mineral

absorption features well (ESM Fig. 2). A single

modified Gaussian absorption band fits the kaolinite

Al–OH absorption, with root-mean-square error

(RMSE) values ranging from 0.10 to 0.28 %. The

complex absorption band from *2.3 to 2.35 lmshows several absorption features that are optimally fit

by three modified Gaussians, resulting in fits with

RMSE values ranging from 0.08 to 0.31 %. Based

upon these results, we modeled sample spectra with

modified Gaussians centered at 2.205 lm(r = 0.0127 lm), 2.295 lm (r = 0.0115 lm),

2.315 lm (r = 0.0106 lm), and 2.345 lm(r = 0.0106 lm), which represent contributions from

Al–OH (kaolinite), Fe–OH (nontronite), Mg–OH

(saponite ? serpentine), and Mg–OH (serpentine),

respectively (ESM Table 1).

The absorption band areas calculated from the

MGM indicate substantial variations in the relative

absorption areas (or relative abundances) of Al–OH

(kaolinite), Fe–OH (nontronite), and Mg–OH

(saponite ? serpentine) among samples (Fig. 2d–f).

Samples with the strongest Al–OH (kaolinite) absorp-

tions are located in the western part of the lake

(Fig. 2d), whereas samples with the strongest Fe-

smectite absorptions are located in the east (Fig. 2e).

Samples with the strongest Mg-smectite/serpentine

absorptions are located in the central part of the lake

(Fig. 2f). Although we did not perform quantitative

XRD analysis, our XRD results qualitatively confirm

the variation in mineralogy inferred from our MGM

modelling results. The sample with a strong Mg–OH

(serpentine) absorption has an XRD pattern that is

more dominated by serpentine than the sample with a

strong Al–OH absorption, which appears to have a

higher proportion of kaolinite (ESM Fig. 3).

Chemistry and absorption area correlations

The modeled Al–OH (kaolinite) absorption area is

strongly correlated to Al concentration (Fig. 4a;

r = 0.81, p\ 0.01), and the sum of the modeled

absorption areas for Mg–OH (saponite ? serpentine)

is even more strongly correlated to Mg concentration

(Fig. 4b) with r = 0.90 (p\ 0.01) (ESM Table 2).

The Fe–OH (nontronite) absorption band is less well

correlated to sedimentary iron concentrations, with

r = 0.51 (Fig. 4c). Iron is present in the surrounding

soils in many different minerals, including highly

abundant iron oxides and oxyhydroxides (Golightly

1981). We attribute the poor correlation between Fe

concentration and Fe–OH (nontronite) absorption area

to the presence of iron oxyhydroxides in the

sediments, in addition to nontronite. Despite this

complication, the correlation between Fe and OH

(nontronite) absorption area and Fe concentration is

258 J Paleolimnol (2015) 54:253–261

123

statistically significant (p\ 0.01), and the strongest

Fe–OH (nontronite) absorption features are observed

on the eastern shoreline where the river inputs are most

Fe-rich. The spatial variations in the relative abun-

dances of these clay minerals within the lake (Fig. 2d–

f) correspond strongly to gradients in lake and river

sediment chemistry (Fig. 2a–c). Aluminum phyl-

losilicates increase from east to west, Fe-smectites

increase from west to east, and the Mg-rich phyl-

losilicates saponite and serpentine increase towards

the middle of the lake (Fig. 2d–f).

Discussion

We found a strong correlation between Fe, Mg, and Al

elemental concentration and VNIR spectral re-

flectance features that distinguish Fe, Mg, and Al-rich

clay minerals, in lake and river sediments from the

Towuti basin. Elevated concentrations of aluminum

and inferred high relative kaolinite abundance on the

western shore, elevated concentrations of iron with the

inferred high relative nontronite abundance on the

eastern shore, and elevated concentrations of magne-

sium with the inferred high relative saponite/serpen-

tine abundances in the central part of the lake

correspond to variations in the geology of Lake

Towuti’s catchment, namely, the presence of felsic

melanges to the west, highly serpentenized peridotites

to the north, and ultramafic lithologies to the east.

These results show that VNIR reflectance spec-

troscopy is an effective, statistically significant way

to characterize the clay mineralogy of Lake Towuti

sediment. Despite the fact that samples must be freeze-

dried to remove the effects of interstitial water on the

VNIR spectra, our work suggests that VNIR spec-

troscopy is a very practical, time- and cost-effective

tool for clay mineral characterization relative to other

procedures such as XRD. Although this study demon-

strates the utility of VNIR spectroscopy using select

absorption features, many other wavelengths could be

investigated using VNIR spectra to characterize

mineralogy. For example, absorption bands under

1 lm can be diagnostic of iron oxides and absorption

bands up to 4 lm are diagnostic of carbonate features

(Clark et al. 1990; Bishop et al. 2008). Moreover, as

VNIR spectrometers may now be employed on

automated core logging devices, this work suggests

there is potential to characterize the relative abun-

dances of clays in sediment cores at relatively high

resolution, which can be used for paleoenvironmental

studies.

The ability to correlate the chemical composition of

lake sediment samples to their mineralogy deduced

from VNIR reflectance spectra has important impli-

cations for characterizing clay mineralogy and sedi-

ment sources not only on Earth, but also onMars. Lake

Towuti and many martian paleolake deposits have

strong spectral signals of weathered clay minerals and

contain many of the same mineralogic constituents

(Ehlmann et al. 2008; Milliken and Bish 2010).

Previous authors have explored the use of clay

mineralogy to determine sediment source regions for

martian paleolakes (Ehlmann et al. 2008), and to infer

martian hydrologic history (Milliken and Bish 2010),

yet these mineral identifications have rarely been

validated using natural lake sediment samples. Our

findings thus provide important ‘groundtruthing’ for

clay mineralogies inferred from spectroscopic data.

This study also suggests that clays in this dilute,

Fig. 4 Plots of area versus concentration for a Al–OH

(kaolinite), b Mg–OH (saponite ? serpentine) and c Fe–OH

(nontronite) with a linear regression shown in red. The minerals

assigned to the Al–OH, Fe–OH and Mg–OH bands are based on

analysis of the full VNIR spectrum. (Color figure online)

J Paleolimnol (2015) 54:253–261 259

123

ultramafic basin primarily represent allochthonous

material that records catchment weathering and trans-

port processes, with important implications for the

interpretation of long-term climate records. Overall,

this work shows that VNIR reflectance spectroscopy is

a powerful tool when combined with chemical

analysis. With proper caution, it can be used to

characterize the primary clay mineral constituents of

natural sediment samples.

Acknowledgments The authors would like to thank Dave

Murray, Joe Orchardo and Dr. Takahiro Hiroi for technical

support and assistance and SatrioWicaksono, Sinyo Rio, and PT

Vale for field assistance in Indonesia. The authors would also

like to thank Dr. Kevin Robertson for assistance with XRD data

interpretation and two anonymous reviewers who provided

excellent feedback to strengthen this paper. Research permits

for this work were granted by the Indonesian Ministry of

Research and Technology (RISTEK). This material is based

upon work supported by the National Science Foundation under

Grant Number EAR-1144623 to J. Russell.

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