Applicability of chemical indices to characterize weathering degrees in decomposed volcanic rocks

Catena 189 (2020) 104475

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Applicability of chemical indices to characterize weathering degrees in decomposed volcanic rocks

T

Ismail Adeniyi Okewale1 City University of Hong Kong, Kowloon, Hong Kong

ARTICLE INFO

ABSTRACT

Keywords: Weathering Chemical indices Geochemical analysis Statistical analysis Variability Decomposed volcanic rocks

Among the useful tools in characterizing weathering degrees are chemical indices, which combines bulk major elemental oxides into a single metric value and commonly presented along a vertical profile. However, characterizing geomaterials of diverse nature using chemical indices may be problematic. In this study, chemical indices were evaluated to characterize weathering degrees in decomposed volcanic rocks from different depths, locations and formations. This was achieved by conducting comprehensive geochemical tests and statistical analyses. A good index must give appropriate trends, provide meaningful statistics and varies greatly with weathering degrees irrespective of the approach employed. Correlations were made between chemical indices and specific volume, depth, weathering degree as well as geological formation using tentative numerical value. The extent of weathering is best characterized by relating chemical indices with in-situ specific volume rather than depth, which is commonly used. The weathering potential index (WPI), hydration coefficient (Hc), leaching coefficient (Lc) and weathering index of Parker (WIP) are the most appropriate indices that could be used to characterize weathering in decomposed volcanic rocks. The chemical indices that have many mobile elements or cations and water content in their formulations are most useful. The chemical indices that are relating few oxides to each other should be avoided. The influence of formation on using chemical indices to characterize weathering degree is insignificant and the effect of weathering grade is very significant. This approach can be applied to similar geomaterials.

1. Introduction Characterizing the extent of weathering is very important because weathering affects the engineering behavior of rocks. Weathering processes have been found to affect the mechanical behavior of rocks in both intact and reconstituted states (e.g., de Jayawardena and Izawa, 1994; Begonha and Sequeira Braga, 2002; Sequeira Braga et al., 2002; Basu et al., 2009; Okewale and Coop, 2017; Okewale, 2019a, 2019b). Weathering profiles and the extent of weathering are commonly characterized using chemical weathering indices that incorporates major elemental oxides (e.g., Irfan, 1999; Gupta and Rao, 2001; Ng et al., 2001; Duzgoren-Aydin et al., 2002; Price and Velbel, 2003). In addition, quantification of the states of rock in terms of weathering using chemical weathering indices is useful in ground investigation (e.g., DuzgorenAydin et al., 2002), analyzing and predicting the behavior of rocks (e.g., de Jayawardena and Izawa, 1994; Okewale and Coop, 2018b), estimating fertility and development of soil (e.g., Delvaux et al., 1989), interpreting the history of weathering in sediments and determination of the effect of climate on rock weathering (e.g., Neall, 1977).

1

The most common method is by applying the chemical indices along a vertical profile in order to characterize the degrees weathering in a rock as well as qualitatively correlating the indices with degrees of weathering (e.g., Ng et al., 2001). However, there are few studies characterizing degrees of weathering using chemical indices vertically as the weathering degree varies, and horizontally by comparing data from different locations. Also, studies linking chemical indices to weathering degrees in a quantitative way are very few. Weathered rocks are abundant in tropical and subtropical areas of the world and decomposed granitic and volcanic rocks are numerous in Hong Kong due to the nature of climate. These materials require greater attention due to their positive importance, such as, usefulness in engineering construction as fill materials and negative effects like geotechnical problems and slope failure caused by landslides and debris flow. Although, there are substantial studies on the mechanical behavior of weathered granitic and volcanic rocks (e.g., Begonha and Sequeira Braga, 2002; Okewale, 2018a; Okewale, 2019c), the understanding is still limited because samples of the same weathering degrees can behave in different ways (e.g., Okewale, 2017; Okewale, 2020). More

E-mail address: [email protected]. Present address: Federal University of Technology, P.M.B 704, Akure, Nigeria.

https://doi.org/10.1016/j.catena.2020.104475 Received 21 May 2019; Received in revised form 6 January 2020; Accepted 14 January 2020 0341-8162/ © 2020 Elsevier B.V. All rights reserved.

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Nomenclature ACN AKN ATI CF CIA CIW Cl D50 Et Fe Hc IP Kr

Lc M PI PIA Q Rc SOC SA v VRI WIP WPI µ σ CV

alumina to calcium-sodium ratio alumina to calcium-potassium ratio alumina-titania index clay fraction chemical index of alteration chemical index of weathering clay mean particle size tangent Young’s modulus feldspar hydration coefficient plasticity index silica-sesquioxide ratio

complications are found in decomposed volcanic rocks, where fresh rock is often found below residual soil due to closely spaced joints which aids uniform weathering and fine-grained nature which prevents percolation of water thereby reducing deeper penetration of weathering profile. This paper presents the applicability of chemical weathering indices to characterize degrees of weathering on soils resulting from decomposed volcanic rocks. This was achieved by estimating chemical indices using molecular proportions obtained from geochemical analysis on samples from different formations, depths and locations. This work is very important because it characterizes the extent of weathering vertically as it alters and horizontally, comparing weathering degrees from different sites. Also, another indicator has been used to characterize weathering in decomposed volcanic rocks which can be applied to other geomaterials. This paper is also unique in the way chemical indices are

leaching coefficient strength parameter weathering product index plagioclase index of alteration quartz residual coefficient sesquioxide content silica-to-alumina ratio specific volume Vogt’s residual index weathering index of Parker weathering potential index mean standard deviation coefficient of variation

related to weathering degrees in quantitative way. This has not been done for weathered igneous rocks and decomposed volcanic rocks the way it is systematically presented in this paper. While Okewale and Coop (2018b) uses physical, mineralogical and chemical indices to predict mechanical behavior of decomposed volcanic rocks, this study presents many new geochemical tests and new statistical analysis in order to characterize the degrees of weathering. Effects of weathering degrees and geological formations are also investigated in a qualitative and quantitative manner. 2. Materials and methodology The samples used were decomposed volcanic rocks. The samples were extrusive fine ash vitric tuff and coarse ash crystal tuffs. They belonged to Ap Lei Chau (ALC), Mount Davis (MD) and Tai Mo Shan

N

0

5

10km

Fig. 1. Geological map of sample locations (After Okewale and Coop, 2018b). 2

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Table 1 Details of sample characteristics, basic index and mechanical properties. Location/Weathering degree

A ewCDV B ewCDV C ewCDV D wmwHDV E vwwHDV I ewCDV K vwCDV L ewCDV M ewCDV N ewCDV O ewvwCDV P wCDV

Acronym

V-4 V-4 V-4 IV-1 IV-2 V-4 V-2 V-4 V-4 V-4 V-3 V-1

Formation

ALC ALC ALC ALC ALC ALC ALC TMS TMS MD ALC ALC

Depth (m)

0.7–1 1.7–2 2.4–3.5 0.7–1 1.5–2.6 3.6–4.6 4.5–5.5 4–5 10.7–11.7 5.86–6.31 8.1–8.35 13.3–13.95

D50 (mm)

0.008 0.06 0.025 0.08 0.0015 0.055 0.012 0.15 0.4 0.06 0.025 0.4

CF (%)

40 30 30 24 52 – 22 34 17 10 26 10

Ip (%)

34.7 23 19.1 10.2 32.6 13.2 9.9 9.4 – 9.9 12.9 9.2

v

2.27 1.92 2.07 2.05 1.89 1.91 1.57 1.93 1.64 1.88 1.70 1.49

M

Et (MPa)

1.25 1.43 1.49 1.62 1.34 1.38 1.51 1.35 1.46 1.49 1.5 1.6

100 – – – – 300 520 – 700 – 50 200

Mineralogy Q

Fe

Cl

11 33 – 49 34 – 40 37 43 – – 34

– 33 – 8 15

81 18 – 11 31

18 20 30 – – 45

18 30 17 – – 14

ew extremely weak, ewvw extremely weak to very weak, vw very weak, w weak, vww very weak to weak, wmw weak to medium weak, V, CDV Completely Decomposed Volcanic rocks, IV, HDV Highly Decomposed Volcanic rocks, ALC Ap Lei Chau formation, TMS Tai Mo Shan formation, MS Mount Davis formation, D50 mean particle size, CF clay fraction, Ip plasticity index, v in-situ specific volume, M strength, Et tangent Young’s modulus, Q quartz, Fe feldspar, Cl clay.

(TMS) formations. The ALC formation is fine-grained and both MD and TMS formations are coarse-grained. The samples were collected as tubes and blocks at different depths and locations around Hong Kong under the supervision of Geotechnical Engineering Office (GEO) of Hong Kong. The samples were Highly Decomposed volcanic rocks (Grade IV, CDV) and Completely Decomposed volcanic rocks (Grade V, HDV) based on international six grade classifications (GSL, 1995; ISRM, 2007). The GEO (1988) classifications of Hong Kong geomaterials which primarily depends on international standards added some descriptions to weathered materials of Hong Kong and in this work, the CDV and HDV. The descriptions are ew extremely weak, ewvw extremely weak to very weak, vw very weak, vww very weak to weak, w weak and wmw weak to medium weak. The geological map of sample locations is presented in Fig. 1.

Table 1 presents the details of sample characteristics. It is worth mentioning that the descriptions, though subjective, were made by experienced geologists and engineers at the sampling site but they were verified by the author in the laboratory using slaking method. In order to make it clear, acronyms are used for the samples of different degrees of weathering. In the acronyms, letter(s) represent weathering degrees followed by number which stands for the various descriptions. The CDV is represented by V and for the V, 4 is for the ew, 3 for the ewvw, 2 for the vw and 1 for the w. Also, the HDV is represented by IV and for the IV, 2 is used the vww and 1 for the wmw (Table 1). Fig. 2 shows a typical example of lithological column of V-4 sample from location M and belonged to MD formation. Table 1 shows basic index, mineralogical and mechanical properties of the samples. The mean particle size (D50) and clay fraction (CF) were

Depth (m)

V-4 (M)

Fig. 2. Typical example showing the lithology of sample V-4 from location M of MD formation (Drill log from Geotechnical Engineering Office of Civil Engineering and Development Department of Hong Kong). 3

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V-4 (M)

V-3

V-2

V-1

IV-2

IV-1

Fig. 3. Micrographs of the samples.

obtained from grading curves determined using wet sieving and sedimentation techniques. Plasticity index (Ip) was determined from Atterberg’s limit. The in-situ specific volume (v) was obtained by measuring sample dimensions, weight and water contents at the initial stage and after the tests. The strength parameter (M) and tangent Young’s modulus (Et) were obtained from triaxial tests. The strength parameter M (slope of critical state line), which is one of the basic properties used for this material reduces with weathering. The tangent Young’s modulus Et (δq/δεa, where q is deviatoric stress and εa is axial strain) was obtained using least square regression function (LINEST) and it does not have trend with weathering. Apart from few scattered data resulting from heterogeneity, the index and mechanical properties generally vary according to degrees of weathering. The mineralogy was determined using Bruker X-ray diffractometer (XRD). The apparatus was equipped with compact desktop design, integrated PC/monitor and Diffrac Suite software. The samples of different degrees of weathering were ground into powder and the

powdered samples were placed in a flat sample holder for the tests. The minerals were identified in the range of 5° ≤ θ ≤ 50° and 10° ≤ 2θ ≤ 100° with Cu-kα radiation at 30 kV and 10 mA. The samples were scanned at an interval of 0.02°/0.8 s. Diffrac Plus v3 software was used to analyze the data from XRD. The details of mineralogy are given in Table 1. The parent rocks of samples consist of quartz, feldspars, biotite and other secondary minerals. As a result of weathering, samples comprise clay minerals, quartz (which is the most resistant to weathering) and feldspar in different proportions (Table 1). The most weathered samples have higher percentages of clay minerals. The clay minerals are kaolinites, halloysites, illites and montmorillonites. The chemical weathering indices were evaluated using major bulk elemental oxides. The bulk elemental oxides were obtained using a Philips XL30 FEG environmental scanning electron microscope (ESEM) equipped with energy dispersive spectrometer (EDS) and Shimadzu EDX-720 energy dispersive X-ray fluorescence spectrometer (XRF). The microstructures of the samples were studied using ESEM. For the ESEM 4

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equipped with EDS, the samples tested were taking by breaking surfaces, placed in sample holder using tape and then inserted into sample stage located relative to the beam for the tests. The focusing and controlling of electron beam were controlled by an electron optical system. A vacuum system removes air molecules that might impede the passage of high energy electron down the column and permit low energy secondary electrons to travel to the detector. The electrons were collected by a secondary electron detector thereby generating a signal that was displayed on the viewing and recording monitor. The EDS allows the quantitative chemical analysis to be conducted. The spectra collected were analyzed to obtain geochemical data in percentages using eDX software. Fig. 3 presents the SEM images of the samples. The micrographs are shown for 50 μm field of view. The fabrics are characterized by well-arranged flat particles that aggregated to form continuous clusters. The inter-cluster and inter-particle voids are small, and the fabrics are heterogeneous. For the XRF, powdered samples were used. The samples were put in the cell, covered with film and secured with ring. The powdered samples were excited from an X-ray radiation source. As the radiation hits the atom from the sample with sufficient energy, there will be an expulsion of electrons from one of the lowest energy levels of K- and Lshell and it leaves its former position. The vacancy created by electron expelled is immediately filled by an electron from shell above in order to restore original arrangement. The X-ray fluorescence or the secondary energy was released by this process. The energy condition detected for each element allows the quantitative analysis to be carried out on the principle that the radiation of the fluorescence is proportional to the quantity of element in the sample. The X-ray fluorescence emitted by the samples was detected by the semi-conductor and this was processed by Multi Channel Analyzer (MCA). Seventeen chemical indices were evaluated, and the details of the formulae and the relevant references are given in Table 2, as modified from Okewale and Coop (2018b). These formulae have been used for different types of geomaterial (e.g., Duzgoren-Aydin et al. (2002) for igneous rocks and Price and Velbel (2003) for metamorphic rocks). The weathering potential index (WPI) measures the removal of cations from rock during the processes of weathering. The weathering product index (PI) measures non-mobile elemental oxides. The

weathering index of Parker (WIP) considers mobility of some elements as well as quantifying the amount of cations present in the rock. The SA defines the ratio of silica to alumina. The chemical index of alteration (CIA) measures the conversion of feldspars to clays. The chemical index of weathering (CIW) is similar to CIA except that potassium oxide is removed from the formula. The plagioclase index of alteration (PIA) is used as an alternative to CIW. The silica-titania index (STI) relates silica and titania and the alumina-titania index (ATI) relates alumina to titania. The Vogt’s residual index (VRI) measures the maturity of residual sediments and sesquioxides content (SOC) measures the combination of oxides of iron and aluminum. The leaching coefficients (Lc) relates silica to mobile elemental oxides and residual content (Rc) relates SOC to mobile elemental oxides. Hydration coefficients (Hc) relates water content to mobile elemental oxides and silica-sesquioxides ratio (Kr) relates silica to sesquioxides. Alumina to calcium-sodium ratio (ACN) is similar to CIW and alumina to potassium-sodium (AKN). 3. Results and discussions In order to describe the relationships between chemical indices and degrees of weathering, regression statistics is used throughout this work. Statistics properties used are correlation coefficient (r) and p value. Correlation coefficient defines the quantitative strength between chemical indices and weathering degree and p value defines probability of a true relationship between chemical indices and weathering degree. A statistically significant relationship indicates high correlation coefficient and low p value. In this paper, the figures are shown for the six (6) most significant indices and the figures are presented in order of reducing regression statistics. Apart from r and p value, standard deviation (σ) and coefficient of variation (CV) are also used for selecting most significant indices. 3.1. Weathering progress using in-situ specific volume In-situ specific volume has been found to increase with weathering degree (Okewale and Coop, 2017). Fig. 4 presents the relationship between chemical indices and in-situ specific volume (v). This is

Table 2 Details of the chemical weathering indices evaluated (modified from Okewale and Coop, 2018). Index

Formula

Reference +

Weathering Potential Index (WPI) Weathering Product Index (PI) Weathering Index of Parker (WIP) Silica-to-Alumina Ratio (SA) Chemical Index of Alteration (CIA) Chemical Index of Weathering (CIW) Plagioclase Index of Alteration (PIA) Silica-Titania Index (STI)

100*[(K2O + Na2O + CaO + MgO-H2O )/(SiO2 + Al2O3 + Fe2O3 + TiO2 + K2O + Na2O + CaO + MgO)]

Reiche (1943)

100*[SiO2/(SiO2 + TiO2 + Fe2O3 + Al2O3)] [(2Na2O/0.35)+(MgO/0.9)+(2K2O/0.25)+(CaO/0.7)]

Reiche (1943) Parker (1970)

SiO2/Al2O3 100*[Al2O3/(Al2O3 + CaO + Na2O + K2O)]

Ruxton (1968) Nesbit and Young (1982)

100*[ Al2O3/(Al2O3 + CaO + Na2O)]

Harnois (1988)

100*[(Al2O3-K2O)/(Al2O3 + CaO + Na2O-K2O)]

Fedo et al. (1995)

100*[(SiO2/TiO2)/((SiO2/TiO2)+(SiO2/Al2O3)+(Al2O3/TiO2))]

Alumina-Titania Index (ATI)

100*[(Al2O3/TiO2)/((SiO2/TiO2)+(SiO2/Al2O3)+(Al2O3/TiO2))]

Vogt’s Residual Index (VRI) Sesquioxide Content (SOC) Leaching Coefficient (Lc) Residual Coefficient (Rc) Hydration Coefficient (Hc) Silica-Sesquioxide Ratio (Kr) Alumina to Calcium-Sodium Ratio (ACN) Alumina to Potassium-Sodium Ratio (AKN)

(Al2O3 + K2O)/(MgO + CaO + Na2O) Al2O3 + Fe2O3 SiO2/(K2O + Na2O + CaO + MgO) (Al2O3 + Fe2O3)/(K2O + Na2O + CaO + MgO) H2O+/(K2O + Na2O + CaO + MgO) SiO2/(Al2O3 + Fe2O3) Al2O3/(Al2O3 + CaO + Na2O)

de Jayawardena and Izawa (1994) de Jayawardena and Izawa (1994) Vogt (1927) Irfan (1996) Li et al. (1995) Li et al. (1995) Li et al. (1995) Moignien (1966) Harnois and Moore (1988)

Al2O3/(K2O + Na2O)

Harnois and Moore (1988)

5

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(b)

(a)

25

20

r = 0.79 p = 5.82E-08

20 0

Hc

WPI

15 -20

10

-40

5

r = 0.87 p = 4.61E-11

0

-60

1

1.5

2

v (-)

(c)

1

2.5

1.5

2

2.5

120

25

r = 0.73 p = 3.39E-09

V-4 V-3

100

20

V-2 V-1

80

AKN

WIP

15 10

(e)

IV-2

IV-1

60

40

5

0

3

v (-)

(d)

20 0 1

1.5

v (-)

2

2.5

(f)

100

r = 0.72 p = 7.15E-09 1

1.5

2

2.5

v (-) 120 100

80

80

CIA

PIA

60 40

40

20

0

60

20

r = 0.72 p = 4.05E-09 1

0 1.5

2

2.5

v (-)

r = 0.65 p = 4.59E-07 1

1.5

2

2.5

v (-)

Fig. 4. Weathering progress plots for the samples; (a) weathering potential index, (b) hydration coefficient, (c) alumina to calcium-potassium ratio (d) weathering index of Parker, (e) chemical index of alteration and (f) plagioclase index of alteration.

presented as an independent measurement of progress of weathering in the samples. In-situ specific volume is used because it gives direct insitu behavior resulting from decomposition of rock. This is introduced because the samples were taken from different locations and belonged to different formations which is different from traditional approach (along vertical profile). However, this is done in order to make comparisons and provide alternative, and not to say that plotting indices with depth is not important. Fig. 4 shows the weathering progress plots for the indices with best correlation, but the details of statistics are given Table 3. Regression lines are used to show the relationship between the indices and specific volume and both correlation coefficient and p value are included in each plot. The weathering potential index (WPI) reduces with specific volume (v) with strong relationship as depicted by regression statistics (Fig. 4(a)). This invariably means WIP reduces with weathering as

expected, because as the weathering progresses, the cation reduces. Fig. 4(b) shows that the hydration coefficient (Hc) increases with v and weathering. This is due to increase in water content as the intensity of weathering increases. The relationship is statistically significant. Similarly, the aluminum to potassium-sodium (AKN) increases with v and weathering with strong regression statistics (Fig. 4(c)). This can be linked to reduction in cations and an increase in oxides of aluminum as the intensity of weathering increases. The weathering index of Parker (WIP) reduces with v and weathering as a result of reduction in cations (Fig. 4(d)). The relationship is also statistically significant. The chemical index of alteration (CIA) and plagioclase index of alteration (PIA) increase with v and weathering with significant statistics (Fig. 4(e) and (f)). This is because, as the weathering progresses, majority of feldspars will be converted to clays, thereby increasing in-situ v. Apart from VRI, the chemical indices give 6

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PIA, Lc and Rc yield strong correlations as given in Table 3. The SOC, STI, ATI, CIW and ACN have moderate correlations and Kr has weak correlation. The correlation of VRI is very weak (r = 0.07 and p value = 0.63). The weak and very weak correlation statistics of Kr and VRI show they are unsuitable for characterizing decomposed volcanic rocks. This means that the WPI, Hc, AKN, WIP, CIA, PIA, Lc and Rc are the most suitable for characterisation using specific volume approach. Some chemical indices show better regression statistics considering weathering progress using specific volume. This is due to the presence of many cations or mobile elements and water content in their formulations. In order to determine the overall performance of the most significant indices, a criterion (r ≥ 0.60) is set in this work based on the classification used by Okewale and Coop (2018b). The r values between 0.60 and 0.79 are classified as strong correlation and those between 0.80 and 1.00 are classified as very strong correlation. The average values of r and p for the variations of indices with specific volume for the most suitable indices are 0.71 and 9.96E−07 respectively.

Table 3 Summary of statistics for weathering progress using in-situ specific volume. Chemical index

Regression statistics

WPI PI SA SOC Lc Rc Kr ACN AKN Hc WIP VRI CIA CIW PIA STI ATI

r

p value

0.87 0.38 0.38 0.52 0.63 0.60 0.30 0.46 0.73 0.79 0.72 0.07 0.72 0.46 0.65 0.52 0.55

4.61E−11 0.45 0.007 0.0001 1.16E−06 6.28E−06 0.037 0.0008 3.38E−09 5.82E−08 7.15E−09 0.63 4.05E−09 0.0007 4.59E−07 0.0001 4.95E−05

3.2. Variation of chemical indices with depth Fig. 5 presents the variation of chemical indices with depth. Relating chemical indices with depth is very important in characterizing the degree of weathering to highlight the trend along vertical profile. Again, the relationships are shown using regression lines and the

appropriate trend with in-situ v and degree of weathering, but the regression statistics varies from one index to the other (Table 3). The WPI yields a very strong correlation and the Hc, AKN, WIP, CIA,

(a)

-20

0

(b)

20

0

0

Hc

10

20

30

40

(c) 0 2

4

4

4

6

6

6

10 12

20 V-3

4

V-1

16

60

80 100

(e) 0 0 2

V-2

IV-1

Filled markers and broken line for Price and Velbel (2003)

r = 0.62 p = 1.76E-06

Depth (m)

IV-2

12 14

r = 0.69 p = 1.07E-05

V-4

2

10

14

CIA

40

10

Lc

20

30

40

50

8 10 12

16

(d) 0

8

10

0

20

PIA

40

60

r = 0.63 p = 1.56E-06

14 16

80 100

(f) 0 0

r = 0.60 p = 5.33E-06

4

4

6

6

8

10

30

WIP 60

90

2

Depth (m)

r = 0.86 p = 3.11E-10

16

6

8

12

14

0

Depth (m)

2

8

Depth (m)

-10

2

Depth (m)

Depth (m)

0

WPI -40

8

10

12

12

14

14

16

16

Fig. 5. Vertical profiles of the weathering indices. 7

r = 0.60 p = 6.5E-06

120

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Table 4 Summary of statistics for variations of weathering indices with depth. Index

WPI PI SA SOC Lc Rc Kr ACN AKN Hc WIP VRI CIA CIW PIA STI ATI

Table 6 Classification of weathering degrees (After GEO, 1988).

Regression statistics

Variability

r

p value

µ

σ

CV

0.86 0.27 0.23 0.31 0.63 0.45 0.09 0.56 0.55 0.69 0.60 0.18 0.62 0.57 0.60 0.23 0.41

3.11E−10 0.05 0.10 0.02 1.56E−06 0.001 0.53 2.42E−05 3.81E−05 1.07E−05 6.5E−06 0.20 1.76E−06 2.24E−05 5.33E−06 0.10 0.003

−9.12 68.86 3.53 28.30 10.90 5.67 2.57 0.88 5.05 3.63 43.10 9.71 74.85 88.77 82.97 73.59 21.62

12.22 10.83 1.12 9.91 5.24 5.80 1.13 0.07 3.61 4.01 27.22 5.08 14.44 7.64 14.23 6.18 5.81

1.34 0.15 0.31 0.35 0.48 1.02 0.44 0.08 0.71 1.10 0.63 0.51 0.19 0.08 0.17 0.08 0.26

Weathering degree

Acronym

Tentative numerical value

ewCDV ewvwCDV vwCDV wCDV mwCDV ewHDV ewvwHDV vwHDV vwwHDV wHDV wmwHDV mwHDV

V-4 V-3 V-2 V-1 – – – – IV-2 – IV-1 –

10 12 14 16 18 20 22 24 25 26 27 28

This indicates that the WPI, Hc, Lc, PIA, CIA, WIP are the most suitable, adopting a vertical profile for characterisation. Again, the indices with many cations or mobile elements and water content in them perform better than the others. The average values of r and p for the variations of indices with depth for the most suitable indices are 0.66 and 4.30E−06 respectively.

µ mean, σ standard deviation, CV coefficient of variation.

regression statistics is stated in each plot. However, the plots are shown for most significant indices, but the details of regression statistics are given in Table 4. Fig. 5(a) presents variation of weathering potential index (WPI) with depth. The WPI increases with depth and the relationship has significant statistics. The WPI reduces with weathering degree due to reduction of cations in the samples. This is similar to what has been established for other geomaterials (e.g., Irfan, 1999; Ng et al., 2001; Duzgoren-Aydin et al., 2002). The Hc reduces with depth and increases with weathering with strong correlation. Again, this can be attributed to increase in water content as a result of intense weathering (Fig. 5(b)). Similarly, the Lc reduces with depth and increases with weathering with strong correlation due to high ratio of silica to mobile elements resulting from increased weathering (Fig. 5(c)). Both CIA and PIA reduce with depth and increase with weathering as a result of reduction in mobile elements and increase in oxides of aluminum (Fig. 5(d) and (e)). The WIP, which is also based on the amounts of cations in sample has similar trend to WPI with significant statistics (Fig. 5(f)). For comparison, the data of the study of Price and Velbel (2003) in the saprolite zone are presented in Fig. 5(d)–(f). The data are shown in filled markers with broken lines along the profile. Their study revealed that both CIA and PIA reduce with depth and WIP increases with depth. This is similar to what has been found in this study. However, the variation of the indices with depth is small compared to this work. This may be as a result of samples used and samples obtained from a single vertical profile. Apart from SA, Kr and VRI, the chemical indices give appropriate trends along the profile with different regression statistics. Table 4 shows the summary of regression statistics for chemical indices against the depth. The WPI has a very strong correlation and Hc, Lc, CIA, PIA and WIP yield strong correlations (Table 4). The CIW, ACN, AKN, Rc and ATI have moderate correlations and the SOC, STI, SA and PI yield weak correlations. The Kr and VRI have very weak correlations.

3.3. Variability of chemical indices Attempt was made to use another statistical parameter for chemical indices used in weathering characterisation. This is essential because majority of chemical indices give appropriate trends with weathering although with different correlation statistics. Statistical comparison is made for the chemical indices in order to evaluate the variability of each index. The coefficient of variation (CV = σ/µ, ratio of standard deviation (σ) to mean (µ) of the index), which measures variability is used for the comparison and the result is presented in Table 4. The WPI has the highest value (1.34), indicating largest variation with weathering. The Hc, Rc, AKN and WIP have high variation with weathering. Low variation can be seen for Lc, Kr, VRI, SOC, SA, ATI, PI, ACN, CIA, CIW, PIA and STI. It is believed that a suitable index should give appropriate trends, provides meaningful regression statistics and varies greatly with degrees of weathering. The index with high variability can be used to characterize degrees of weathering in decomposed rocks similar to what has been seen for other studies (e.g., Price and Velbel, 2003). The WIP, Hc, Rc, WIP and AKN vary widely and could be used for characterizing weathering in decomposed volcanic rocks. 3.4. Comparison of weathering progress using in-situ specific volume and depth The comparison of the weathering progress plots using in-situ specific volume (an inherent property) and depth (vertical profile) is made. This was achieved using Figures (4) and (5) as well as Tables 3 and 4. In plotting chemical indices against depth, it is assumed that the weathering progresses steadily with depth. This may be true for homogenous rock along a vertical profile, but the contrast may be found for heterogeneous rock from different locations and formations. Strong

Table 5 Classification of rock material weathering (After ISRM, 2007; GSL, 1995). Class/Grade

Term

Description

Tentative numerical value

I II III IV V VI

Fresh (F) Slightly weathered (SW) Moderately weathered (MW) Highly weathered (HW) Completely weathered (CW) Residual soil (RS)

No change in the original material Material is slightly discolored Discoloration is deeper and material is weakened Not readily slake and can be broken by hand Readily slake and considerably weakened Material converted to soil

50 40 30 20 10 0

8

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(a)

(b)

15

20

0

Hc

WPI

10

5

-20

-40

Irfan (1999) Ng et al. (2001) Duzgoren-Aydin et al. (2002)

0

IV-1

(c)

IV-2

V-1

V-2

V-3

-60

V-4

(d)

Weathering degree

V-1

V-2

V-3

V-4

Price and Velbel (2003) Duzgoren-Aydin et al. (2002)

20 100

CIW

16

Lc

IV-2

IV-1

Weathering degree

120

24

Gupta and Rao (2001)

12

80

8 4 0

IV-2

V-1

V-2

60

V-3

V-4

(f)

Weathering degree

1.2

160

1

120

WIP

ACN

(e)

IV-1

0.8

IV-2 V-1 V-2 V-3 Weathering degree

V-4

V-4 V-3 V-2 V-1 IV-2 IV-1 Price and Velbel (2003)

40

0.6 Irfan (1999)

0.4

80

IV-1

IV-1

V-3 V-1 V-2 Weathering degree IV-2

0

V-4

IV-1

IV-2

V-1

V-2

V-3

V-4

Weathering degree

Fig. 6. Variations of chemical weathering indices with degree of weathering.

correlations of similar value are seen for WPI of both weathering progress plots. The Hc, AKN, WIP, CIA, PIA, Lc and Rc are the best considering specific volume. The Hc, Lc, CIA, PIA and WIP are the best using depth. Using the average values of r and p obtained for the best indices considering specific volume (r = 0.71, p value = 9.96E−07) and depth (r = 0.66, p value = 4.30E−06), the correlation is higher for specific volume than depth. In characterizing weathering degrees using chemical indices in decomposed volcanic rocks, plotting chemical indices against in-situ specific volume give a better weathering progress indicator than plotting chemical indices against depth.

index with another chemical index (e.g., Ng et al., 2001; DuzgorenAydin et al., 2002). However, this method is qualitative or semiquantitative in the sense that the measured chemical indices are related with international weathering classification (Grade I to VI). In this work, attempt is made to relate chemical indices with weathering degrees in both quantitative and qualitative way. In order to achieve this, a tentative numerical value is given to weathering classification (GSL, 1995; ISRM, 2007) as shown in Table 5. In Table 5, weathering class or grade, weathering term, the description of weathering and tentative numerical values is presented. The tentative values are carefully chosen to reflect weathering degrees. It is assumed that grade I (fresh) rock will have highest value according to its strength and the values reduce as grade increases (weathering increases). The interval between each classification is made equal. Considering the wide range of weathering within grades IV and V investigated in this study, the tentative numerical values are further

3.5. Variation of chemical indices with weathering degree Another common way of characterizing weathering degree is by relating chemical indices with weathering degrees or relating chemical 9

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slightly higher values but generally, the values are within the range found in this study. The ACN is fairly similar for different degrees of weathering (Fig. 6(e)) with moderate correlation. The results obtained by Irfan (1999) for study on weathered fine and coarse volcanic rocks are superimposed on Fig. 6(e). The data are very close to those found in this work. Fig. 6(f) shows that the trend of WIP with weathering is not clear due to HDV data and the correlation is moderate. Price and Velbel (2003) found similar values for their study on weathered metamorphic rock (Fig. 6(f)). The PI reduces with weathering and the statistics is insignificant (r = 0.38, p value = 0.45). Due to scattered data, the SA and SOC do not give a trend with weathering degree and they both have insignificant statistics. The Rc increases, decreases and then increases with weathering and the statistics is insignificant. The Kr has weak correlation with varied trend. The ACN is similar for different degrees of weathering and has moderate correlation. The AKN reduces and increases with weathering with very weak correlation (Table 7). The VRI does not really change with weathering and the correlation is very weak. The CIA and PIA do not show clear trend with weathering and the correlations are weak. The STI and ATI have opposite trend with weathering, but the correlations are weak. The Hc and WPI give very strong correlations with weathering, the Lc yields strong correlation with weathering and ACN, CIW and WIP have moderate correlations with weathering. The other chemical indices have weak and very weak correlations. This indicates that using this approach, the WPI, Hc, Lc, WIP, CIW and ACN can be used for characterizing decomposed volcanic rocks. Considering weathering degrees, indices that perform better also have many cations or mobile elements and water content in their formulae. However, using the criterion set in this study, the Hc, WPI and Lc are the best and the average values of r and p obtained for the best indices are 0.86 and 0.06 respectively.

Table 7 Summary of statistics for variations of chemical indices with weathering degrees. Index

WPI PI SA SOC Lc Rc Kr ACN AKN Hc WIP VRI CIA CIW PIA STI ATI

Regression statistics r

p value

0.91 0.38 0.34 0.28 0.70 0.16 0.39 0.52 0.10 0.98 0.45 0.20 0.37 0.52 0.37 0.34 0.23

0.08 0.45 0.50 0.58 0.10 0.74 0.43 0.28 0.84 0.01 0.36 0.70 0.46 0.28 0.46 0.50 0.64

broken down as presented in Table 6. The weathering classification is according to GEO (1988). Again, for simplicity and to use familiar notation, acronym is introduced for different weathering degrees (Table 6). In the acronym, the letters indicate weathering grade followed by number which indicates the descriptions that has been discussed earlier. The CDV is generally represented by V and the HDV is represented by IV. For clarity, since there are different weathering degrees within a grade, the initial tentative numerical value is divided into interval of 2. For example, in each weathering grade (V or IV), the ew takes the least value, followed by ewvw, vw, w and mw in that order. If there is another weathering degree between them, it is given another value and that is the reason for the odd number values for the vww and wmw of the HDV in Table 6. Fig. 6 presents the variations of chemical indices with weathering degrees. The plots show the qualitative and semi-quantitative relationship between chemical indices and degrees of weathering. In Fig. 6, arithmetic scale is used for weathering degree. The quantitative relationship is obtained using statistical analysis and the summary of the result is presented in Table 7. Tentative numerical value is used for the statistical analysis shown in Table 7. Again, the indices with higher correlations are presented and they are arranged in order of reducing regression statistics. Also, all the data of the V-4 samples are presented in each plot. The Hc increases with weathering with very strong correlation (r = 0.98 and p value = 0.01) (Fig. 6(a)). The WPI reduces with weathering degree due to reduction of cations resulting from intensity of weathering (Fig. 6(b)). The correlation is very significant (r = 0.91 and p value = 0.08). The data of the studies of Irfan (1999), Gupta and Rao (2001), Ng et al. (2001) and Duzgoren-Aydin et al. (2002) are presented in Fig. 6(b) for comparisons. Different filled symbols are used in the plot for different studies. The samples belonged to grade V and they are presented at different weathering grades here for clarity. Although, the values vary for different studies, the values are however within the range found for similar sample in this study. The trend of Lc with weathering degree seems not clear (Fig. 6(c)) due to the HDV data at shallow depth but the correlation is strong (Table 7). The CIW does not show clear trend with weathering (Fig. 6(d)) and the correlation is moderate. The data of the studies of Duzgoren-Aydin et al. (2002) and Price and Velbel (2003) are included in Fig. 6(d) for comparisons. Duzgoren-Aydin et al. (2002) obtained

3.6. Influence of geological formation Influence of formation on the characterisation of weathering degree using chemical indices is investigated by considering only one formation (Ap Lei Chau, ALC) in which majority of the samples belonged. Formation is the term used for volcanic rocks due to its complexity. Fig. 7 presents the variations of chemical indices with degrees of weathering using ALC formation. Six plots are also shown for the purpose of consistency as some indices are not really showing good correlations (Fig. 7(a)–(f)) and again, all the data points of V-4 samples are presented in each plot. Considering ALC formation, the variations of Hc, WPI and Lc with weathering degrees (Fig. 7(a)–(c)) show similarities to variations of the same indices with weathering degrees considering all the formations (Fig. 6(a)–(c)). The regression statistics is also similar. In general, the indices give direct similarities to Fig. 6 and the correlations are similar but with different values. This shows that the influence of formation is insignificant. Within the scatter data, the PI seems reducing with weathering with moderate correlation (r = 0.44 and p value = 0.69) (Fig. 7(d)). The weathered volcanic rocks investigated by Irfan (1999) and highly to completely weathered rocks studied by Gupta and Rao (2001) are included in Fig. 7(d). Apart from scattered data resulting from heterogeneity and slightly higher values for few data, the values can be said to be close. The Kr seems reducing with weathering with moderate correlation (Fig. 7(e)). Duzgoren-Aydin et al. (2002) showed slightly higher values for weathered pyroclastic rocks investigated and this can be attributed to the nature of the samples used (Fig. 7(e)). The STI seems reducing with weathering with moderate correlation. The values obtained by de Jayawardena and Izawa (1994) for weathered

10

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(a)

(b)

15

20

0

Hc

WPI

10

5

-20

-40

0

IV-1

(c)

IV-2

V-1

V-2

-60

V-3

V-4

IV-1

IV-2

(d)

Weathering degree

V-1

V-3

V-2

V-4

Weathering degree

100

24 20

80

12

PI

Lc

16 V-4 V-3

8 4 0

60

V-2 V-1

Irfan (1999)

IV-2 IV-1

IV-1

IV-2

V-1

V-2

V-3

40

V-4

Weathering degree

(e)

Gupta and Rao (2001)

IV-1

(f)

IV-2

V-1

V-2

V-3

V-4

Weathering degree

100

10 Duzgoren-Aydin et al. (2002)

8

80

Kr

STI

6 4

60

2 0

de Jayawardena and Izawa (1994)

IV-1

IV-2

V-1

V-2

V-3

40

V-4

Weathering degree

IV-1

IV-2

V-1

V-2

V-3

V-4

Weathering degree

Fig. 7. Variations of chemical indices with weathering degrees considering one formation (ALC).

metamorphic rocks are close to those found in this study (Fig. 7(f)). Based on criterion used in this work, the Hc, WPI and Lc are the best indices and the average values of r and p obtained for them are 0.87 and 0.08 respectively. Using the average values of r and p obtained for the best indices considering weathering degree (r = 0.86, p value = 0.06) and geological formation (r = 0.87, p value = 0.08), the values are very close and this confirms that there is no significant effect of geological formation.

degrees within one grade (CDV, Grade V). Plots of the variations of chemical indices with degrees of weathering in the CDV are not shown because they can be easily observed in Figs. 6 and 7. Generally, the chemical indices give appropriate trends with weathering degrees. The details of regression statistics are presented in Table 8. The correlation coefficients are very strong and strong for the chemical indices except for the SA. This may be as a result of the simplicity of the formula which is just relating silicon oxide to aluminum oxide and many authors have indicated that such formula should be avoided (e.g., Duzgoren-Aydin et al., 2002). Many more indices show significant statistics considering weathering degrees within the CDV. This might be attributed to fully develop

3.7. Influence of weathering grade The effect of weathering grade is studied by using the weathering

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general, the chemical indices that combine many mobile elemental oxides, those that contains cations in their formulae as well as those with water content in their formulations can be used to characterize weathering degrees in decomposed volcanic rocks.

Table 8 Summary of statistics for variations of chemical indices with weathering degrees for CDV. Index

WPI PI SA SOC Lc Rc Kr ACN AKN Hc WIP VRI CIA CIW PIA STI ATI

Regression statistics r

p value

0.99 0.69 0.34 0.76 0.89 0.85 0.60 0.73 0.86 0.99 0.86 0.96 0.77 0.73 0.69 0.59 0.82

0.001 0.30 0.65 0.23 0.10 0.14 0.39 0.26 0.13 0.01 0.13 0.03 0.22 0.26 0.30 0.40 0.17

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The author thanks Research Grants Council (RGC) of Hong Kong Special Administrative Region (HKSAR) for the award of fellowship for his PhD programme. The work presented in this paper is partially supported by grant from the Research Grant Council (RGC) of the HKSAR, China (T22-603/15N). Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.catena.2020.104475.

chemical components at the later stage of weathering.

References

4. Conclusions

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An extensive geochemical and statistical analyses have been carried out to investigate the applicability of chemical indices to characterize weathering degrees in decomposed volcanic rocks. This was achieved by combining elemental oxides obtained from environmental scanning electron microscope (ESEM) equipped with energy dispersive spectrometer (EDS) and energy dispersive X-ray fluorescence spectrometer (XRF) and statistical analysis for samples from different locations, depths and formations. The weathering progress in decomposed volcanic rocks can be characterized by chemical indices using both in-situ specific volume (v) and depth approach. The weathering potential index (WPI), hydration coefficient (Hc), weathering index of Parker (WIP), alumina to potassium-sodium ratio (AKN), chemical index of alteration (CIA), plagioclase index of alteration (PIA), leaching coefficient (Lc) and residual coefficient (Rc) are the best chemical indices that can be used for characterisation considering specific volume. The chemical indices that includes many cations, mobile elements and water contents in their formulations are the most suitable. Considering the depth, the WPI, Hc, Lc, PIA, CIA and WIP are the most suitable and again this can be attributed to their formulae which comprise cations, mobile elements and water contents. In using chemical weathering indices for characterizing weathering degrees, the indices worked much better using specific volume rather than depth, which is the common approach. Also, the variability of chemical indices is estimated, and it shows that the WPI, Hc, Rc, AKN and WIP vary widely and therefore they are suitable for use in characterisation of weathering degrees in decomposed volcanic rocks. Relating chemical indices with weathering degrees in a quantitative way using tentative numerical values and employing statistical analysis, the WPI, Hc, Lc, WIP, CIW and ACN are the best and can be used for characterizing weathering degrees. The few data from other formations appear to be consistent with those obtained for samples from ALC formation. The influence of formation on the characterisation of degrees of weathering using chemical indices is not significant. Weathering grade has significant effect on characterisation of weathering degrees using chemical indices in decomposed volcanic rocks. In

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I.A. Okewale volcanic rocks from Hong Kong. PhD Dissertation, City University of Hong Kong. Okewale, I.A., 2019a. Influence of fines on the compression behavior of decomposed volcanic rocks. Int. J. Geo-Eng. 10 (4), 1–17. https://doi.org/10.1186/s40703-0190101-y. Okewale, I.A., 2019b. Effects of weathering on the small strain behavior of decomposed volcanic rocks. J. GeoEng. 14 (2), 97–107. https://doi.org/10.6310/jog.201906_ 14(2).5. Okewale, I.A., 2019c. On the intrinsic behavior of decomposed volcanic rocks. Bull. Eng. Geol. Env. 1–12. https://doi.org/10.1007/s10064-019-01643-7. Okewale, I.A., 2020.. A study of dynamic shear modulus and breakage of decomposed volcanic soils. J. GeoEng. 15 (1) Accepted (in press). Okewale, I.A., Coop, M.R., 2018a. On the effects of weathering on the compression behavior of decomposed volcanic rocks. TuniRock 2018, Hammamet, Tunisia, 85-90. Okewale, I.A., Coop, M.R., 2018b. Suitability of different approaches to analyze and predict the behavior of decomposed volcanic rocks. J. Geotech. Geoenviron. Eng. 144 (9), 1–14. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001944.

Okewale, I.A., Coop, M.R., 2017. A study of the effects of weathering on soils derived from decomposed volcanic rocks. Eng. Geol. 222, 53–71. https://doi.org/10.1016/j. enggeo.2017.03.014. Parker, A., 1970. An index of weathering for silicate rocks. Geol. Mag. 107, 501–504. Price, J.R., Velbel, M.A., 2003. Chemical weathering indices applied to weathering profiles developed on heterogeneous felsic metamorphic parent rocks. Chem. Geol., 202, 397–416. doi: 10.10 16/j.che1ngeo.2002.11.001. Reiche, P., 1943. Graphic representation of chemical weathering. J. Sediment. Petrol. 13 (2), 58–68. Ruxton, B.P., 1968. Measures of the degree of chemical weathering of rocks. J. Geol. 76, 518–527. Sequeira Braga, M.A., Paquet, H., Begonha, A., 2002. Weathering of granites in a temperate climate (NW Portugal): granitic saprolites and arenization. Catena 49, 41–56. Vogt, T., 1927. Sulitjelmafelt's geology and petrography. The Geological Survey of Norway, 121, 1–560 (in Norwegian, with English abstract).

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