Comparison of two classification methods to identi

Citation: Wang YZ, Wu YQ, Pan MH, et al., 2018. Comparison of two classification methods to identify grain size fractions of aeolian sediment. Sciences in Cold and Arid Regions, 10(5): 0413–0420. DOI: 10.3724/SP.J.1226.2018.00413.
Comparison of two classification methods to identify grain size
fractions of aeolian sediment
YanZai Wang 1,2,3*, YongQiu Wu 2,3, MeiHui Pan 2,4, RuiJie Lu 2,3
1. College of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China
2. State Key Laboratory of Earth Surface Process and Resource Ecology, Beijing Normal University, Beijing 100875,
China
3. Engineering Center of Desertification and Blown-Sand Control, Ministry of Education, Beijing Normal University,
Beijing 100875, China
4. College of Geography and Environment Science, Northwest Normal University, Lanzhou, Gansu 730070, China
*Correspondence to: YanZai Wang, College of Geography and Tourism, Chongqing Normal University, University Town, Shapaingba District, Chongqing 401331, China. E-mail: wyz2003qu@
Received: February 17, 2018 Accepted: June 28, 2018
ABSTRACT
Grain-size class-Std (GSCStd) and Grain-size class-dD (GSCdD) methods are simple statistical approaches for classifying bulk grain-size distributions (GSDs) into grain-size fractions. Although these two methods were developed based on simil-ar statistical principles, the classification difference between these two methods has not been analyzed. In this study, GSC-Std and GSCdD methods are conducted in thirteen grain-size data sequences to examine the applicability for identifying grain size fractions. Results show that, application of the GSCStd method is equivalent to that of the GSCdD method in identifying finer grain-size fractions, and the difference between the two methods mainly comes from the identification of coarse grain-size fractions. Thus, finer grain-size fractions are recommended for use in research of surface aeolian and pa-leo-aeolian sediments. In addition, our results do not completely agree with previous studies, coarser grain-size fractions in our case suggest that the GSCdD method may not be more applicable than the GSCStd method.
Keywords: Grain-size class-Std method; Grain-size class-dD method; grain-size fractions
1 Introduction
The change of grain-size distribution of aeolian sediment can be considered a proxy of depositional modification, which can help us investigate sediment provenance and depositional processes (Du et al., 2009; He et al., 2009; Liao et al., 2010; Roskin et al., 2014; Liu et al., 2016). Therefore, the classification of aeolian sediment is crucial for aeolian research to ex-amine the change of grain-size distribution.
Two simple approaches were introduced to classi-fy bulk grain size distributions into different grain size fractions, of which the content can be adopted to investigate the change of grain size distribution along a grain size data sequence. The GSCStd method is a typical approach (Guan et al., 2010; Liu et al., 2016) where grain size fractions can be identified by analyz-ing the standard deviation (Std) of each grain-size class for a grain size data sequence. In recent years, the GSCStd method was widely used in research of paleo-climate and surface aeolian sediment in North-west China (Xu et al., 2006, 2007; Long et al., 2007; Xue and Zhang, 2008; Huang et al., 2009; Guan et al., 2010, 2013; Niu et al., 2010; Ma et al., 2012; Liu et al., 2013, 2016). The GSCdD method is another clas-sification approach, recently introduced by Notte-baum et al. (2014), where deciles difference (dD:
Sciences in Cold and Arid Regions Volume 10, Issue 5, October, 2018
D 90–D 10) instead of standard deviation (Std) was re-commended for identifying grain size fractions.Moreover, Nottebaum et al . (2014) noted that the ap-plication of dD is better than that of Std in identifying grain-size fractions. However, scarce research has been done to compare the differences between GSC-Std and GSCdD, and it is uncertain which method is more applicable in identification of grain size frac-tions. In this study, both GSCStd and GSCdD meth-ods are applied to the same grain size data set. The purpose of this study is to compare the difference between GSCStd and GSCdD, so as to examine the applicability of the two methods for identification of grain size fractions.2 Material and methods 2.1 Material
2.1.1 Grain-size data sequences of surface sediment Ten grain-size data sequences were collected from
ground surface of ten different transects (Figure 1a).These transects are located in the shifting dune field of Taklimakan Desert, Northwestern China. Each transect is perpendicular to the desert highway and over the shelter system of the desert highway.
Figure 1a is the location of sampling transect.Samples were collected from ground surface, and the data has been published in Wang et al . (2009) and Wang (2013). Figure 1b is the location of sampling section. Samples were collected from paleo-aeolian section, and data of sections XL and JJ are published in Pan et al . (2014), data of section SDG is published in Lu et al . (2010).
In this study, each transect is named after its loca-tion milestone of the desert highway, e.g., TK-K134is located at the milestone of 134 km. For most tran-sects, due to the long distance between each other,sediment provenance is also different from each other (Zhu et al ., 2013). Also, for different transects there are different amounts of sand samples, ranging from 15 to 49 samples.
CHINA
Taklimakan Desert
Legend
River Highway Desert
Sampling transect Residential area
Tarim
River
Tk-K286
Tk-K290
Tk-K299Xiaotang TK-K134
H i g h
w a y
Tazhong
Taklimakan Desert
(a)
N
0160
320
Minfeng km Kunlun
Tk-K534Mountains
D e s e r
t
The Profilc of Mcga-Duncs Topography
TK-K352-3TK-K352-4
Tk-K352-2
Tk-K352-1
S
TK-K352-5Distance (m)
12002400
H i g h (m )
2010
XL
JJ
Tibet
500
1000 km
D e p t h (c m )
(b)XL JJ SDG
100
200
300
Sandy paleosol
Sand
The stratigraph of sampling section
SDG
CHINA
Legend
River Desert
Sampling section
N
Figure 1 Study area and sampling locations
414
YanZai Wang et al., 2018 / Sciences in Cold and Arid Regions, 10(5): 0413–0420
2.1.2 Grain-size data sequences of paleo-aeolian
sediment
Three grain-size data sequences were collected from three different aeolian sediment sections (Figure 1b). These sections are typical paleo-aeolian, and re-spectively named after their sampling location, corres-pondingly abbreviated XL, JJ, and SDG. Of the three sections, sections XL and JJ are located in Qinghai-Ti-bet Plateau, China, and section SDG is located in the margin of Mu Us Desert, China. The thickness of each section is about 2–3 m. The sample amount is 134, 138 and 41 for sections XL, JJ and SDG, respect-ively (Lu et al., 2010; Pan et al., 2014). In this study, according to the field description of sedimentary char-acteristics in previous studies, each section was di-vided into two sedimentary types namely sandy pa-leosoil and sand layers.
2.2 Methods
Grain-size analysis of each sample was per-formed by using a Malvern particle size laser analyz-er. After grain-size analysis, grain-size frequency data was shown up with 100 grain-size classes in the range of 0.01–2,000 μm.
2.2.1 Calculation of Std and dD
For one grain-size class, the standard deviation (Std) of a grain-size data sequence can be written as follows,
where j is the number of grain-size class, 1≤ j ≤100,
and Std
j
is the standard deviation of j th grain-size
class' proportion for the grain-size data sequence. P
i
is the proportion of j th grain-size class for ith sample,
P
m
is the mean proportion of j th grain-size class for the grain-size data sequence, and n is the sampling amount of the grain-size data sequence.
Correspondingly, for one grain-size class, after ar-ranging the grain-size class' proportion of all samples in a deciles order, the proportion difference between two samples with
D90th deciles order and D10th deciles order can be written as follows,
where d D
j
is the proportion difference of j th grain-
size class, P
D90
is j th grain-size class' proportion for
D90th sample, and P
D10
is j th grain-size class' proportion for D10th sample.
2.2.2 Grain-size fractions' identification and its
application
After calculating Stds and dDs, the data can be plotted on a graph against corresponding grain-size classes (Figure 2). Grain-size fractions are typically
classified at cutoffs of valley grain-size class (u
s
), or
fine peak grain-size (u
f
) and coarse peak grain-size
(u
c
) in the graph. Finally, the proportion of grain-size
fractions (class) u
f
and u
c
, as well as grain-size frac-
tion <u
s
are exported by Malvern particle size laser analyzer, and these grain-size fractions and their pro-portion can be considered as proxies for analysis of aeolian process or paleo-climate change.
In left graph, Std data from Xu et al. (2006). In right graph, dD data from Nottebaum et al. (2013).
3 Results
3.1 Classification results of surface sediment
The graph of Stds is similar to that of dDs for each grain-size data sequence. The grain size class against peaks and valleys in the Stds graph is nearly equal to that in the dDs graph (Figure 3).
Regression analysis shows that Stds correlates with dDs at the 0.01 significant level for each grain-size data sequence, most determination coefficient R2 of ten regression equations mainly reaches a level lar-ger than 0.95 (Figure 3). However, in some grain-size
Figure 2 Application of GSCStd and GSCdD methods in previous studies
YanZai Wang et al., 2018 / Sciences in Cold and Arid Regions, 10(5): 0413–0420415
data sequences, such as TK-K286, TK-K290, TK-K299, TK-K352-1, TK-K352-4 and TK-K534, the Stds graph differs from the dDs graph at coarse grain-size classes (Figure 3, see black arrow), and these dif-ferences between Stds and dDs graphs lead to lower determination coefficient R 2 of regression equation.Further, grain-size classes u f and u c , and u s are extrac-ted from Stds and dDs graphs, then the proportion of grain-size fractions u f and u c , and <u s are exported
from Malvern particle size laser analyzer for each sample. The proportion results of grain-size fractions along each transect show that (Figure 4), the chan-ging tendency of grain-size fractions u f and <u s of GSCStd nearly mask that of GSCdD. However, in transect TK-K299, TK-K352-2, TK-K352-3, TK-K352-4 and TK-K352-5, the proportion changing tendency of grain-size fraction u c -GSCStd is different from that of GSCdD.
Because smaller wind speed is located in the shel-ter belt compared to that outside the shelter belt (Cheng et al ., 2015), the proportion of grain size frac-tion u c would go down correspondingly in the shelter belt, while the proportion of grain size fractions u f and <u s would increase in the shelter belt. Generally, the more the proportion decreases or increases, the better the grain-size fraction. The average proportion ratio between samples outside and inside the shelter belt was calculated for each grain-size fraction of all grain
size data sequences (Table 1). The difference of pro-portion ratio between grain-size fractions u c -GSCStd and u c -GSCdD exist in 6 grain size data pared to grain-size fraction u c -GSCdD, the pro-portion ratio of u c -GSCStd shows larger value in tran-sects TK-K352-2, TK-K352-3, TK-K352-5 and TK-K534, but smaller value in transects TK-K299 and TK-K352-4 (the gray shade in Table 1). However, the difference of u f -GSCStd vs. u f -GSCdD, and u s -GSC-Std vs. u s -GSCdD respectively exist in 2 and 3 grain
2
Std dD
TK-K290
4
d D
24
0510d D
Std
y = 2.56x −0.12
R 2 = 0.98P <0.001TK-K2990
24
0510d D Std
y = 2.49x −0.12
R 2 = 0.94P<0.0010
4
d D
TK-K352-20
24
0510d D
Std
y = 2.51x −0.04
R 2 = 0.99P <0.001TK-K352-30
24
0510d D
Std
y = 2.65x −0.06
R 2 = 0.99P <0.001100
101
102
10
3
TK-K352-4
Grain size (μm)
24
0510d D
Std
y = 2.05x −0.06
R 2 = 0.80
P <0.0014
d D 10
101
102
10
3
Grain size (μm)
S t d
2
S t d
2
S t d
2
S t d
100
101
102
10
3
TK-K352-5
Grain size (μm)
4
d D
0240510d D
Std
y = 2.49x −0.06
R 2 = 0.97P <0.001(d)
(g)
(h)
(i)
(f)
(c)
Figure 3 Classification results of GSCStd and GSCdD methods for grain-size data sequence of surface sediment. The scatter plot
refers to regression analysis between Stds and dD for each grain-size data sequence. The classification
difference between Stds and dD graphs occurs over coarse grain-size classes
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size data sequences. It seems that the classification difference between GSCStd and GSCdD methods mainly comes from coarse grain-size fractions u c .ANOVA was then conducted for each of the three
grain size fraction pair of u f , u c and u s . The results show that, the proportion ratio produced by the GSC-Std method does not significantly differ from that of the GSCdD method (at the 0.05 significant level).
3.2 Classification results of paleo-aeolian sediment The graph of Stds is similar to that of dDs for each paleo-aeolian sediment section. The grain-size class against peaks and valleys in the Stds graph nearly equal to that in the dDs graph (Figure 5). Regression
Figure 4 The proportion changing tendency of grain-size fractions extracted from Std and dD graphs of surface sediment
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analysis shows that Stds correlates with dDs at the 0.01 significant level for each grain-size data se-quence, the determination coefficient R2 of regression equation mainly reaches 0.98.
The proportion results of grain-size fraction along the depth of each section shows that (Figure 6), the
changing tendency of grain-size fractions u
f -GSCStd
and <u
s -GSCStd nearly mask that of GSCdD.
However, in section SDG, the proportion changing
tendency of grain-size fraction u
c -GSCSt
d is different
from that of GSCdD. Typically, due to the influence
of climate; sediment in sandy paleosol layers of sedi-
ment section would be finer than that of sand layers.
Thus, the larger the proportion differs between sandy
paleosol layers and sand layers, the better the grain-
size fraction.
Figure 5 Classification results of GSCStd and GSCdD methods for grain-size data sequence of paleo-aeolian sediment. The scatter plot refers to regression analysis between Stds and dD for each grain-size data sequence. The classification difference
between Stds and dD graphs occurs over coarse grain-size classes
Figure 6 The proportion changing tendency of grain-size fractions extracted from Std and dD graphs of paleo-aeolian sediment 418YanZai Wang et al., 2018 / Sciences in Cold and Arid Regions, 10(5): 0413–0420
The average proportion ratio between samples of sandy paleosol and of sand was calculated for each grain-size fraction of all grain size data sequences (Table 2). Compared to the grain size fraction u c -GSC-dD, the proportion ratio of u c -GSCStd shows lower value in all three sections (the gray shade in Table 2).Similarly, the differences of u s -GSCStd vs. u s -GSCdD also display in all three sections, larger proportion ra-tios in JJ and SDG correspond to u s -GSCStd, while the larger proportion ratio in XL corresponds to u s -GSC-dD (the gray shade in Table 2). However, the differ-ence of u f -GSCStd vs. u f -GSCdD only exist in XL (the gray shade in Table 2). It seems that the classific-ation differences between GSCStd and GSCdD meth-ods come from grain-size fractions u c and u s . AN-OVA was also conducted for each of three grain size fraction pair of u f , u c and u s . The results show that, the proportion ratio produced by the GSCStd method does not significantly differ from that of the GSCdD method (at the 0.05 significant level).
4 Discussion and conclusion
In this study, we investigated the classification difference between GSCStd and GSCdD methods. We found that, the proportion changing tendency of grain-size fractions u f -GSCStd and < u s -GSCStd nearly masks that of GSCdD on the graph of each grain-size sequence. The proportion results of three grain size fractions from the GSCStd method do not signific-antly differ from that of the GSCdD method for tran-sects and sections (at the 0.05 significant level). The aforementioned results show that, application of the GSCStd method is equivalent to that of the GSCdD method in identification of finer grain-size fractions.In addition, the classification difference between GSCStd and GSCdD methods mainly comes from coarse grain-size fractions. The proportion ratio of coarse grain-size fractions show that, the GSCStd method seems more applicable than the GSCdD meth-od in grain-size data sequences such as TK-K352-2,TK-K352-3, TK-K352-5, TK-K534 and all three sec-tions (Figures 1, 2). Nottebaum et al . (2014) noted that the GSCdD method is more applicable than the GSCStd method, however, from our results, we can note that the statements of Nottebaum et al . (2014)may not be correct. Overall, our results suggest that finer grain-size fractions that were identified by GSC-Std and GSCdD methods can be considered as a suit-able indicator in aeolian sediment research.
Acknowledgments:
This study is supported by project funding from Chongqing Normal University (No. 12XLB009) and Key Projects in the National Science & Technology
Program (No. 2006BAD26B0302). Map data of the desert used in this study was provided by "Environmental & Ecological Science Data Center for West China, National Natural Science Foundation of China".References:
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