International Journal of Geosciences, 2012, 3, 206-210 Published Online February 2012 (
A Semi Automated Method for Laminated Sediments
Mapathe Ndiaye1, Eric Davaud2, Daniel Ariztegui2, Meissa Fall1
1Laboratoire de Mécanique et Modélisation, Université de Thiès, Thiès, Senegal
2Section of Earth and Environmental Sciences, University of Geneva, Geneva, Switzerland
Received November 8, 2011; revised December 23, 2011; accepted January 24, 2012
We developed a software performing laminae counting, thickness measurements, spectral and wavelet analysis of lami-
nated sediments embedded signal. We validated the software on varved sediments. Varved laminae are automatically
counted using an image analysis classification method based on K-Nearest Neighbors (KNN) algorithm. In a next step,
the signal corresponding to varv ed black laminae thickness variatio n is retrieved. The obtained sign al is a good proxy to
study the paleoclimatic constraints controlling sedimentation. Finally, the use of spectral and wavelet analysis methods
on the variation of black laminae thickness revealed the existence of frequencies and periods which can be linked to
known paleoclimatic events.
Keywords: Varve; Laminated Sediment; K-Nearest Neighbor; Signal; Time-Series; Spectral Analysis;
Wavelet Analysis
1. Introduction
Laminated sediments often present alternating lamina
that can be attribu ted to seasonally driven oscillation bet-
ween two or more sedimentary phases [1]. Laminae de-
position is linked to sedimentary process, which can be
periodic or not. Existing period ranges through magni-
tudes varying from some hours for a tidal channel de-
posit [2] to millions years for some laminated sediments
Laminated sediments in geosciences provide high-re-
solution data and therefore, a good proxy for depositi-
onal/climatic chang es studies [5].
The facies change and the associated thickness varia-
tion can be used to generate high-resolution time-series
revealing the rhythm of climatic variation [6] or sedi-
mentary inflow rates [7].
When the depositional period of a sequence of laminae
is known, the duration of the stratigraphic log can be
deduced by cou nting the numb er of sequences. Moreov er,
when the thickness or compositional variation of the
laminated sediment in time is availab le, it can be studied
using time-series analysis to highlight existing cyclic
Laminated sediments can be counted manually when
the number of lamina is low, but counting becomes tedi-
ous as the number of lamina increases. This motivated
the implementation of semi automated analysis methods
based on color properties observed on core photographs,
x-ray radiographs, etc. [8-12]. In the particular case of
varved sediments, it is often easy to differentiate black
from white laminae. Nevertheless, difficulties can arise
from the existence of trend or color change among the
laminae of the same facies induced by the acquisition
material itself or by uneven lightening during acquisition.
These problems motivated the implementation in this
work of complementary semi-automated analysis meth-
ods combing image analysis and signal processing me-
2. Material and Method
We developed a software named Strati-signal using the
Java language. Strati-signal is dedicated to stratigraphic
signal analysis. Indeed, in addition to laminated sedi-
ments analysis methods presented here, Strati-signal con-
tains many other stratigraphic signal analysis methods but
they are out of the scope of this paper.
Strati-signal and its user guide are available for dow-
nload as freeware at:
We carried out a case study on the varves of CAR
99-10P Section Number 5 of Lago Cardiel sedimentary
core ([13,14]) (Figure 1). The cores Number 1 to 4 were
not used because of the particularity induced by high pre-
sence of turbidite deposits. The Lago Cardiel is located
opyright © 2012 SciRes. IJG
Figure 1. Section 5 of Lago Cardiel Core CAR 99-10P. For easy handling of the image, the section is subdivided into 5 pieces
of about 20 cm length. Note that each two consecutive pieces overlap perfectly.
49˚S on the Patagonian plateau of Argentina. The varved
sediments present white laminae alternating with iron
oxyhydroxyde rich black laminae. The oxyhydroxyde
materials input is mostly linked to the wind system. Each
couplet of black/white laminae corresponds to one-year
deposit ([13,15]). These varved sediments are thus good
proxy for past changes in wind intensity of the region.
The analysis of the laminated sediment is performed
through three steps:
The first step consists in raw signal extraction. In this
step, data corresponding to CIE Lab color variation thr-
ough the core image is retrieved. The signal is obtained
combining data from one or more scan lines with user
defined size and position (Figure 2).
During the raw signal extraction, multiplying scan
lines avoid artifacts on the source image. After the setup
of the scan lines, the user hits the “get” button in the
toolbar to extract the signal (Figure 3).
In the second step of the analysis, the varved laminae
are counted. Laminae counting is a classification process
based on the K-nearest neighbor (K-NN) algorithm [16].
The principle is to classify laminae, based on the closest
training samples in a user-predefined feature space.
The three dimensions of the feature space correspond
to three signals retrieved from each one of the three CIE
Lab (or RGB) channels of the crop image. The learning
samples are plotted in the feature space. The quality of
learning step can be controlled visualizing each plane of
the feature space (Figure 4(E)). The operator can use a
list to show each one of the three planes forming the fea-
ture space. If learning step is well performed, two groups
of samples will be formed in the feature space corre-
sponding respectively to white and black laminae.
The training samples are got by successive mouse cli-
cks on a user-desired part of the synthetic image (Figure
4(B)) to pick significative samples of white or black
la min a e . I n oth e r wo r d s, t h e user indicates su ccess ively to
the classifier what a black lamina and a white lamina is.
When the user hits the “Apply” button (Figure 4(F)),
the classification is performed. Classification gives the
number of lamina for each type (black or white) and the
thickness variation. The signal corresponding to black
lamina thickness which is linked to the past wind inten-
sity, can be explored using spectral or wavelet analysis.
3. Results
3.1. Varved Laminae Counting
The number of lamina is shown in a report and the
thickness variation in a tab le.
According to the nature of the input image, some
varved laminae should be misclassified. Misclassification
occurs when a white lamina is classified as a black one or
vice versa. Misclassification can be visually appreciated
from the classification results windows (Figure 5(A1)
and (A2)) comparing the source image (left) with the
synthetic image made from classification results (right).
Figure 2. Example of raw signal extraction from two scan
lines (S1 and S2). Scan lines can be managed (add, remove,
modify) using tools on the right panel.
Copyright © 2012 SciRes. IJG
Figure 3. Screenshot showing the extracted raw signal (C), a
synthetic image from the signal (B) and a crop image cor-
responding to the region of interest in the bulk image (A).
Figure 4. Example of classification window showing the
our example (Figure 5), a white laminae in the source
e two sources of misclassification. insignifi-
tion is linked to the
extracted signals (A), a synthetic image from extracted sig-
nal (B), a crop image (C), indications on the learning step
(D) and the graphic for one plane of the feature space (E).
image may appear in light green color in the synthetic
image, otherwise it is misclassified and must be corrected
There ar
The first type of misclassification, normally
nt, is linked to the classifier. It depends on the error
made by the operator during the learning step. It is
evaluated by the software and indicated by the accuracy
number in the classification report.
The second type of misclassifica
Figure 5. Results of laminae counting using K-NN. Notice
uality of the input data (e.g. absence of tren d, stationar-
r-friendly tools for manual correction, when auto-
3.2. Varves Spectral and Wavelet Analysis
a thic-
sis methods are included in Strati-
the comparison between source image (A1) and synthetic
image made from the classification results (A2), the report
of the classification (C) and a graph corresponding to
thickness variation for each type of lamina (B).
ity of the input signal) and is more difficu lt to quantify. It
can be appreciated when a high number of lamina is mis-
classified despite a high accuracy number in the classifi-
cation report. In this case, manual correction must be
atic classification fails, are included to overcome such
difficulties. To use manual correction, one must select
the type of lamina and draw it directly on the synthetic
image (Figure 5(A2)).
The existence of cyclic events on the black lamin
kness signal is searched using successively spectral and
wavelet analysis. Spectral analysis allows detection of
existing periodicity in the signal while wavelet analysis
shows, in addition to the existence of a given period, the
instant it occurs [17 ].
Many spectral analy
gnal, but we used the classical Fou rier transform in this
study. For wavelet analysis, we used the Mortlet wavelet.
Spectral and wavelet analysis show periods varying
ightly between 2 and 8 years. Spectral analysis (Figure
6) shows two peaks corresponding to 2 and 6 years peri-
ods. Wavelet analysis (Figure 7) confirms the existence
of these two periods and shows their presence in the
starting and the end of the signal.
The observed events can be linked to a forcing having
period of the same length. Torrence and Compo [18],
Wang [19,20] and others allocate these periods to ENSO
(El Niño) phenomena. Although, it has been shown that
ENSO has been active for this time interval in this area
[13]. These data show by the first time that ENSO influ-
ence seems to be not continuous but punctuated. These
Copyright © 2012 SciRes. IJG
Figure 6. Fourier spectrum of black laminae signal. The
periods corresponding to the main peaks are indicated in
Figure 7. Wavelet analysis of black laminae signal.
tervals displaying increasing ENSO frequencies a
4. Conclusions
bine image analysis and signal proc
tion step is the critical part
ed sediments
5. Acknowledgements
f Strati-signal software fo
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We noted that the classifica
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