Journal of Geochemical Exploration 128 (2013) 14–24

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Journal of Geochemical Exploration

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Element behaviors due to rock weathering and its implication to geochemicalanomaly recognition: A case study on Linglong biotite granite in Jiaodongpeninsula, China

Qingjie Gong ⁎, Jun Deng, Changming Wang, Zhongliang Wang, Liangzhuang ZhouState Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing, 100083, China

⁎ Corresponding author.E-mail address: qjiegong@cugb.edu.cn (Q. Gong).

0375-6742/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.gexplo.2013.01.004

a b s t r a c t

a r t i c l e i n f o

Article history:Received 18 July 2012Accepted 10 January 2013Available online 21 January 2013

Keywords:Linglong graniteWeathering index of granite (WIG)Background valueEnrichment factor

Trace element contents of regolith materials are often orders of magnitude higher than those of their parentrocks. This weathering enrichment is often confused with mineralization. In order to quantitatively describetrace element behaviors due to weathering, we selected a regolith profile developed over the Linglong biotitegranite in the western part of the Jiaodong peninsula, China. Eleven samples were collected from weatheredsoil to fresh biotite granite sequentially from top to bottom of the profile and 39 elements were analyzed foreach sample according to the analytical specification of regional geochemical surveys in China. Based on thebehaviors of major elements in samples from the studied regolith profile we present a newWeathering Indexof Granite (WIG), which is more sensitive than the Chemical Index of Alteration (CIA), Weathering Index ofParker (WIP), and Weathering Index of Colman (WIC). The weathering behaviors of 12 trace elements arequantitatively described using an exponential relationship between element contents and WIG values inthe studied regolith profile. Trace element contents calculated according to this exponential relation can beviewed as background values resulting only from differences of weathering degree of the same parentrock. For the case study in the Linglong–Jiaojia Au deposit region, an enrichment factor was defined as theratio of trace element content to its background value calculated from WIG. The results of the case study in-dicate that the enrichment factor is a useful index to determine anomalies in regional geochemical surveydata in a region where stream sediments and regolith materials are derived from the same parent material.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

China's National Geochemical Mapping Project (RegionalGeochemistry — National Reconnaissance, RGNR project), whichhas been implemented since 1979 and has covered more than 7 millionsquare kilometers of Chinese land surface (Xie et al., 1997; Yang et al.,2007c), has analyzed 39 elements in composite stream sedimentsamples each composed of four stream sediment samples in 4 squarekilometers. The geochemical survey data obtained by the project areuseful for geological, environmental, ecological, and agricultural studies.Many newmineral discoveries, especially Au, have been made based ongeochemical anomalies in those data (Xie et al., 2009). The theory ofmineral exploration based on the RGNR data is that ores or alteredrocks were weathered firstly, and then weathered materials weretransported and deposited as sediments into stream systems, and finallyfractions of stream sediments were sampled (Xie, 2002, 2008). Theinheritance of enriched pathfinder and target element contents frommineralized materials (ores and altered rocks) to stream sedimentsduring surficial geochemical processes is the integral step of drainage

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geochemical exploration. Pathfinder and target element contents ofores or altered rocks are several orders of magnitude higher than thoseof their host rocks, and their contents of stream sediments containingmaterials sourced wholly or partly from ores or altered rocks are clearlygreater than those of stream sediments sourced from the host rocks(Gong et al., 2011). However, enrichment of trace element contents instream sediments can be influenced by weathering especially whentrace element contents of ores or altered rocks are not significantlygreater than those of their host rocks. That is, the enrichment of specificelements in stream sediments due to weathering can be significantenough to mimic the level of enrichment of these elements in streamsediments due to incorporation of materials from ores or altered rocks(Deng et al., 2010a; Gong et al., 2010). Therefore, it is very importantto quantitatively describe trace element behaviors due to weatheringwhen assessing and discriminating anomalies in stream sediment geo-chemical survey data.

Element behaviors during weathering are commonly described onweathering indices and the percentage of gain or loss (Xgp) or the τvalue. Weathering indices are presented by the components of majoroxides. More than 30 different chemical weathering indices werereviewed and reassessed by Duzgoren-Aydin et al. (2002) with detailedcomments on their advantages and limitations. Some new indices were

15Q. Gong et al. / Journal of Geochemical Exploration 128 (2013) 14–24

also presented by Riebe et al. (2003) and Ohta and Arai (2007). Theseindices can be classified into two types: parent normalized indicesand the non-normalized indices. Most studies on regolith profiles andwater sediments lack element concentration data of protoliths or parentrocks, which limits the application of parent normalized indices. Amongthe non-normalized indices, the Chemical Index of Alteration (CIA;Nesbitt and Young, 1982), Weathering Index of Parker (WIP; Parker,1970), andWeathering Index of Colman (WIC; Colman, 1982) are com-monly used by many authors (Aristizabál et al., 2005; Borges and Huh,2007; Braun et al., 2009; Duzgoren-Aydin and Aydin, 2003; Price andVelbel, 2003). The percentage of gain or loss (Xgp; Ng et al., 2001) orthe τ value (Brantley and White, 2009; Brantley et al., 2007; Ma et al.,2011) is based on immobile elements in protoliths to quantitativelydescribe element behaviors during weathering. Based on the idea thatan immobile element is just a relative notation, Gong et al. (2011)presented amethod to calculate the Xgp or τ value for a progressive geo-chemical process. However, the above-mentioned studies about ele-ment behaviors are seldom linked to mineral exploration. The linkageof regolith profiles to mineral exploration was well documented byAustralia researchers (Anand et al., 2001; Butt, 2001; Butt et al., 2000;Robertson et al., 2001; Smith, 1996), but they also did not give quanti-tative descriptions on element behaviors during weathering.

In this paper, we selected a regolith profile developed over theLinglong granite located in Jiaodong peninsula in China to quantita-tively describe element behaviors due to weathering. Firstly, we com-pared the commonly used weathering indices such as CIA, WIP, and

Fig. 1. Geological map of the northwest of Jiaodong peninsula and the location of the regolit1 — Quaternary silts and clay; 2 — Early Cretaceous medium-acid volcanic; 3 — Late Jurassgneiss, schist and marble; 6 — Archean granulite, amphibolite and gneiss; 7 — Aishan type gite; 11 — Wendeng type granite; 12 — Geological boundary; 13 — Fault; 14 — Au deposit; 1

WIC in the regolith profile data and presented a more sensitiveindex, Weathering Index of Granite (WIG), to quantitatively describethe weathering degree. Then, we adopted an exponential relationshipbetween contents of specific elements and values of WIG to quantita-tively describe trace element behaviors due to weathering. Finally, weused the quantitative relationship between Au concentrations andvalues of WIG to calculate background values due to different degreesof weathering. This empirical relationship is then applied to the dis-crimination of geochemical anomalies in the Linglong–Jiaojia Au de-posit region.

2. Materials and methods

2.1. Site setting

The Jiaodong Au province, located in the Jiaodong peninsula of easternChina (Fig. 1a), is the largest Au mining and production area in China.The peninsula accounts for 0.3% of China's area, but possesses morethan 30% of the total national Au reserves and currently provides overone-fourth of China's total Au production per annum (Deng et al.,2006; Yang et al., 2007a). The Linglong–Jiaojia Au deposit region inthe northwestern part of Jiaodong peninsula (Fig. 1b) is the mostfamous Au area in China. Gold deposits in the Jiaodong peninsula aremainly divided into two types: lode Au–quartz–sulfide veins (Linglongtype) and shear zone-hosted disseminated sulfides in granitoids (Jiaojiatype) (Deng et al., 2000; Yang et al., 2009). Supracrustal rocks in the

h profile of Z01 in the Linglong–Jiaojia Au deposit region (modified after Li et al., 2007).ic siltstone; 4 — Late Proterozoic quartzite, slate and limestone; 5 — Early Proterozoicranite; 8 — Laoshan type granite; 9 — Guojialing type granite; 10 — Linglong type gran-5 — Sampling site of the Z01 regolith profile; 16 — Linglong–Jiaojia Au deposit region.

16 Q. Gong et al. / Journal of Geochemical Exploration 128 (2013) 14–24

Linglong–Jiaojia Au deposit region includemetamorphosed Precambriansequences andMesozoic intrusions. The Precambrian sequence ismainlycomposed of basement rocks of the Late Archean Jiaodong Group (Ar)consisting of metamorphosed mafic to felsic volcanic and sedimentaryrocks with amphibolite to granulite grades (Deng et al., 2011). Plutonicrocks that intruded the Precambrian basement have been traditionallydivided into two suites, the Linglong suite and the Guojialing suite.The Linglong suite (γ5l) consists of medium-grained metaluminous toslightly peraluminous biotite granites whereas the Guojialing suite(γ5g) is composed of porphyritic hornblende–biotite granodiorites(Deng et al., 2011). The regional faults generally strike NE and have con-trolled thedistributions of themain Audeposits. The Jiaojia fault has con-trolled the locations of the Xincheng, Jiaojia, and Sizhuang large depositsfrom NE to SW (Deng et al., 2008), whereas the Zhaoyuan fault has con-trolled the locations of the Damoqujia (Yang et al., 2007b), Linglong(Deng et al., 2006), Dayingezhuang (Deng et al., 2009; Yang et al.,2009), and Xiadian (Deng et al., 2003) large deposits from NE to SW(Fig. 1b).

The topography in Linglong–Jiaojia Au deposit region is character-ized by foothills and the elevation decreases from east to west. In theLinglong Au deposit district, the elevation varies mainly from ca. 100m to 500 mwith the highest elevation of 756 m at the apex of LuoshanMountain. In contrast, in the Jiaojia Au deposit district the elevationvaries mainly between 20 and 60 m with the highest elevation of 177m at the apex ofWang'ershanMountain. In both districts, soils are thin-ly developed, but regolith thickness commonly varies between 0.2 and4 m depending on the relief. The land is commonly covered by cropssuch as wheat, corn, and peanut. The hilltops are covered by shrubs.

The Jiaodong peninsula is situated in a warm temperature zoneand experiences a semi-humid monsoonal climate. The average tem-perature is −3 °C in winter and 25 °C in summer, and the mean an-nual temperature is ca. 12 °C. The annual rainfall is ca. 600 mm,about 60% of which falls in summer. The average yearly evaporationis 1200 mm and the relative humidity is 70% (Tian et al., 2012;Wang, 2000).

2.2. Sampling

Regolith samples were collected from the Z01 profile formed on theLinglong biotite granite, which is located east of the gold mine in theWang'ershan area in the Jiaojia Au deposit district (Fig. 1b). Samplesnumbered from Z01B1 to Z01B11 were collected sequentially fromthe topsoil downward to the saprolite (Fig. 2) and are described inTable 1.

2.3. Analytical methods

The collected samples (about 1 kg each) were ground andball-milled to −250 μm for chemical analysis. Seven major elementsas oxides (SiO2, Al2O3, Fe2O3, MgO, CaO, Na2O, and K2O) were analyzedby X-ray fluorescence spectrometry (XRF) frommelted-powder pelletsof each sample and LoI was determined by gravimetry. Silver, B, andSn were measured by emission spectrometry (ES), and As, Hg, and Sbby atomic fluorescence spectrometry (AFS). Gold was analyzed byatom absorption spectrometry using graphite furnace (AAS). Frompressed-powder pellets of each sample, Ba, Cr, Mn, P, Sr, Ti, V, and Zrwere analyzed by XRF. Beryllium, Bi, Cd, Co, Cu, La, Li, Mo, Nb, Ni, Pb,Th, U, W, Y, and Zn were measured by inductively coupled plasmamass spectrometry (ICP-MS). Fluorine was analyzed by ion selectivityelectrode (ISE). The analytical results for each sample are listed inTable 1.

All analyses were performed at the Hubei Geological ResearchLaboratory in China. The accuracy of analyses is better than 5%for major elements and better than 10% for most trace elements.The relative errors of repeated sample analyses were mostly lessthan 10% (Table 1).

3. Results

3.1. Major elements

The contents of the seven major elements as oxides in the samplesare plotted versus profile depths (Fig. 3). Values of LoI, Ti, and P arealso plotted in Fig. 3 because they are commonly analyzed or de-scribed as oxides. Fig. 3 indicates:

(1) In the Z01B1 top soil sample, values of SiO2, Al2O3, Fe2O3, Na2O,MgO, and LoI clearly deviate from their variation trends fromthe bottom to the top of the profile.

(2) Excluding sample Z01B1, values of SiO2, Na2O, and CaO are de-creasing from the bottom to the top in the regolith profile,while values of Al2O3, Fe2O3, MgO, LoI, and Ti are increasingfrom the bottom to the top of the profile.

(3) From the bottom to the top of the profile, values of P are de-creasing firstly and then increasing, while values of K2O are in-variant firstly in the saprolite samples, then decrease sharplyfrom the Z01B4 to the Z01B3 samples, and finally increasefrom the Z01B3 to the Z01B1 samples.

3.2. Indicator elements

Data for 15 of the analyzed 30 trace elements are plotted versusdepth in the Z01 profile (Fig. 4). These 15 trace elements are called in-dicator elements here because they are commonly enriched in the hy-drothermal mineralization process and were often selected in furtherlocal scale or detailed scale surveys after the RGNR. Fig. 4 indicatesthat:

(1) In the Z01B1 top soil sample, values of Sn, Bi, Cu, Zn, Co, Ni, As,and Sb clearly deviate from their variation trends from the bot-tom to the top of the profile depth.

(2) Excluding sample Z01B1, values of W, Sn, Co, Ni, As, and Sb areincreasing from the bottom to the top of the profile.

3.3. Other elements

Other elements analyzed in the samples from the Z01 profile areplotted in Fig. 5. Fig. 5 indicates that:

(1) In the Z01B1 top soil sample, values of Li, V, Cr, F, Th, and Uclearly deviated from their variation trends from the bottomto the top of the profile.

(2) Excluding sample Z01B1, values of Li, B, Zr, V, Cr, F, Th, U, andNb are increasing from the bottom to the top of the profile,while values of Sr are basically decreasing.

4. Discussion

4.1. Weathering index of granite

To investigate the element behaviors due to weathering in the Z01profile, chemical indices were used to quantitatively assess weatheringdegree. Here, the commonly used non-normalized indices CIA,WIP, andWIC were selected to discuss element behaviors due to weathering inthis profile.

The CIAwas developed byNesbitt andYoung (1982) in reconstructingpaleoclimate from Early Proterozoic sediments of the Huronian Super-group, north of Lake Huron. The CIA is defined as

CIA ¼ 100Al2O3= Al2O3 þNa2Oþ K2Oþ CaO�ð Þ ð1Þ

inmolecular ratio, where CaO* is derived from silicateminerals. If the car-bonate mineral and/or apatite are present in the bulk sample, the CaO*value can be calculated as CaO*=CaO–CO2–(10/3)P2O5 in molecular

Fig. 2. The Z01 profile located east of a mine in the Wang'ershan area in the Jiaojia Au deposit district. The red dots indicate where samples were collected, and the arrow indicates adeeper location of the Z01B11 sample.

17Q. Gong et al. / Journal of Geochemical Exploration 128 (2013) 14–24

proportions. The CIA is a molecular ratio of immobile tomobile elements,it assumes that Al2O3, Na2O, K2O and CaO reside exclusively in feldspars,and it assumes that Al2O3 is conserved during weathering. The WIP wasintroduced by Parker (1970) for silicate rocks, and is defined as

WIP ¼ 100 2Na2O=0:35þ 2K2O=0:25þMgO=0:9þ CaO � =0:7ð Þ ð2Þ

in molecular proportion. The WIP is a weighted atomic sum of mobileelements, it takes into account the individual mobilities of Na, K, Mgand Ca, based on their bond strengths with oxygen, and it considers hy-drolysis the main agent of silicate weathering. The WIC was presentedby Colman (1982), is and defined as

WIC ¼ 100 Na2Oþ K2Oþ CaOþMgOð Þ= Al2O3 þ Fe2O3 þ TiO2ð Þ ð3Þ

in molecular ratio. These three indices are all calculated in molecularunits and are not normalized by parent sample. TheWIC is also amolec-ular ratio of immobile to mobile elements. The CIA, WIP, andWIC were

calculated for the Z01 regolith profile samples and are plotted withdepth (Fig. 6). The CIA and WIP involve four oxides while the WIC in-volves seven oxides. Hence, the CIA and WIP show narrow ranges rela-tive to WIC (Fig. 6), meaning that WIC is a more sensitive weatheringindex than CIA and WIP.

Excluding the top soil sample Z01B1, values of CIA increase grad-ually from the bottom to the top of the Z01 profile. Excluding thetop soil sample Z01B1, values of WIP and WIC generally decreasefrom the bottom to the top of the Z01. These variant trends reflectthe decrease of Na2O and CaO and the increase of Al2O3 duringweathering from the bottom to the top of the Z01 profile. Therefore,the CIA, WIP, andWIC are all good indices of the degree of weatheringof the Linglong biotite granite.

However, excluding the top soil sample Z01B1, values of MgO in-crease from the bottom to the top in the Z01 profile whereas varia-tions in values of K2O show a complicated behavior. Behaviors ofMgO and K2O are inconsistent with the behaviors and the definitionsof the weathering indices in the Z01 profile. This inconsistency may

Table 1Major and trace element data from samples in the Z01 regolith profile.

Sample no. Z01B1 Z01B2 Z01B3 Z01B4 Z01B5 Z01B6 Z01B7 Z01B8 Z01B9 Z01B10 Z01B11 Analysis Detection Relative

Depth (cm) 10–50 50–100 100–150 150–200 200–250 250–300 300–350 400–450 450–550 550–650 650–750 Method Limits ErrorsSample type Soil Clastic granite Weathered granite Fresh granite %Sample color Grey-brown Yellow-brown Light brown Grey-brown Grey-brown Grey-brown Grey-brown Grey-brown Grey-brown Grey-brown Grey-brownGranite texture Disappear Retain Retain Retain Retain Clear Clear Clear Clear

Major oxides (%)SiO2 73.22 67.89 69.34 70.09 69.27 70.77 72.58 70.62 72.80 74.14 74.19 XRF 0.1 1Al2O3 13.46 15.70 15.24 15.94 16.10 14.93 14.62 15.06 13.86 13.68 13.56 XRF 0.1 1Fe2O3 1.56 2.66 2.22 1.47 1.52 1.50 1.10 1.30 1.11 0.97 0.68 XRF 0.1 2K2O 3.53 3.39 3.13 3.79 3.59 3.64 4.09 3.68 3.80 3.65 3.69 XRF 0.05 2Na2O 2.95 2.44 3.02 3.44 3.79 4.04 3.76 4.17 3.93 3.96 4.13 XRF 0.05 3CaO 0.73 0.71 0.87 0.93 1.22 1.31 1.04 1.31 1.27 1.31 1.40 XRF 0.05 1MgO 0.33 0.57 0.52 0.28 0.26 0.23 0.15 0.16 0.15 0.10 0.08 XRF 0.05 1LoI 1.66 2.72 2.26 1.57 1.48 0.98 0.90 0.92 0.64 0.56 0.38 Gravimetry 0.1 2

Trace elements (μg/g)Ag 71 35 36 29 31 30 30 32 36 35 30 ES 20 3As 2.5 5.6 4.3 1.5 1.6 0.8 0.8 0.8 0.5 0.6 0.6 AFS 1 5Au 7.1 3.3 8.8 3.2 2.5 1.0 1.1 0.8 0.4 0.4 0.3 AAS 0.2 25B 15.1 16.8 14.9 2.7 2.3 1.5 1.5 1.3 1.8 1.1 1.6 ES 1 15Ba 1748 1778 1599 2052 1989 1991 1934 1754 2144 2048 2237 XRF 5 4Be 1.28 1.43 1.53 1.45 1.45 1.50 1.48 1.44 1.40 1.21 1.25 ICP-MS 0.5 11Bi 0.07 0.12 0.07 0.03 0.02 0.01 0.01 0.02 0.02 0.03 0.03 ICP-MS 0.05 15Cd 45 29 32 12 21 36 16 31 36 21 17 ICP-MS 30 16Co 5.2 6.0 5.8 4.9 3.5 3.6 3.0 2.7 3.0 2.9 3.0 ICP-MS 1 8Cr 16.7 29.1 20.2 7.1 4.6 3.4 3.1 3.3 1.4 0.8 1.9 XRF 5 13Cu 7.5 12.5 9.1 4.2 4.8 11.6 4.3 4.8 3.0 2.8 3.7 ICP-MS 1 3F 204 277 284 224 197 199 152 152 148 123 107 ISE 50 18Hg 21 6 2 3 2 2 4 2 1 2 3 AFS 2 12La 20.9 18.3 20.0 12.1 12.6 13.7 12.5 17.3 11.4 9.0 8.8 ICP-MS 1 5Li 12.2 20.1 18.0 13.8 14.5 13.3 13.0 12.6 11.8 8.2 6.2 ICP-MS 1 4Mn 337 339 350 333 378 358 270 268 283 278 228 XRF 20 3Mo 0.34 0.37 0.38 0.29 0.23 0.28 0.18 0.18 0.18 0.14 0.23 ICP-MS 0.3 12Nb 8.1 7.8 7.7 5.9 5.3 5.5 5.5 4.9 4.6 3.8 3.6 ICP-MS 2 8Ni 7.9 14.5 12.1 4.9 4.3 2.3 2.0 1.5 1.4 1.3 1.3 ICP-MS 2 9P 133 106 79 53 62 73 58 80 70 112 99 XRF 20 5Pb 24.8 24.8 24.0 22.5 24.4 26.5 25.1 23.2 21.9 20.5 21.6 ICP-MS 2 5Sb 0.27 0.52 0.50 0.24 0.28 0.19 0.18 0.21 0.16 0.17 0.16 AFS 0.05 7Sn 1.4 2.1 1.5 0.9 0.8 0.9 0.8 0.7 0.9 0.7 0.8 ES 1 4Sr 398 387 426 569 583 651 545 621 665 631 770 XRF 5 4Th 5.0 7.2 7.6 4.7 4.7 5.1 4.6 4.5 3.6 3.2 3.3 ICP-MS 2 7Ti 1983 2150 2153 1345 1352 1355 1052 1238 1138 982 846 XRF 100 3U 0.82 0.99 0.87 0.34 0.31 0.33 0.29 0.26 0.25 0.26 0.31 ICP-MS 0.2 7V 24.7 37.2 33.7 13.9 13.8 10.9 8.3 9.7 6.9 5.5 4.5 XRF 5 12W 1.65 1.34 1.20 0.62 0.74 0.62 0.74 0.51 0.39 0.33 0.33 ICP-MS 0.3 8Y 8.4 10.2 10.0 5.0 4.0 4.0 3.5 6.0 4.3 3.8 2.6 ICP-MS 1 3Zn 30.5 36.3 33.0 26.6 31.4 35.6 20.3 21.3 22.2 23.3 17.6 ICP-MS 2 7Zr 202 173 178 117 111 114 109 126 97 101 89 XRF 10 3

Note: Unit of Au, Ag, Cd, and Hg is ng/g.

18Q.G

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eochemicalExploration

128(2013)

14–24

66 69 72 75

SiO2

800

600

400

200

0

800

600

400

200

0

Dep

th (

cm)

Dep

th (

cm)

13 14 15 16 17

Al2O3

0 1 2 3

Fe2O3

0 1 2 3

LoI600 1200 1800 2400

Ti

3 3.5 4 4.5

K2O

2 3 4 5

Na2O

0.6 0.9 1.2 1.5

CaO0 0.2 0.4 0.6

MgO40 80 120 160

P

Fig. 3. Major elements in the Z01 profile (% concentrations except Ti and P in μg/g).

19Q. Gong et al. / Journal of Geochemical Exploration 128 (2013) 14–24

be attributed to weathering products, such as montmorillonite, illite,and muscovite, which retain the MgO and K2O of the parent rock.In order to better describe the weathering intensity of the granitein the Z01 regolith profile, we modified the WIC to WIG, which isdefined as

WIG ¼ 100 Na2Oþ K2 þ CaO� 10=3P2O5ð Þ= Al2O3 þ Fe2O3 þ TiO2ð Þð4Þ

0 0.5 1 1.5 2W

800

600

400

200

0

Dep

th (

cm)

800

600

400

200

0

Dep

th (

cm)

800

600

400

200

0

Dep

th (

cm)

0.5 1 1.5 2 2.5Sn

0.1 0.2

18 21 24 27

Pb10 20 30 40

Zn0 15

0 3 6 9

Au20 40 60 80

Ag0 2

Fig. 4. Indicator trace elements in the Z01 profile (μg/g

in molecular ratio. The WIG excludes MgO but uses P2O5. Here, we donot distinguish between CaO in feldspar or in carbonate mineralsbecause CO2 is not among the chemical variables in the RGNR surveydata. Variations in values of WIG are similar to those of WIC, havemore sensitivity than CIA andWIP (Fig. 6), and avoid the inconsistentbehaviors of MgO and K2O. Therefore, the WIG is effective and amore appropriate index of weathering degree in the Z01 profile.

0.3 0.4Mo

0 0.05 0.1 0.15Bi

0 4 8 12 16Cu

30 45 60

Cd2 4 6 8

Co0 5 10 15

Ni

4 6

As0 0.2 0.4 0.6

Sb0 8 16 24

Hg

concentrations except Cd, Au, Ag, and Hg in ng/g).

5 10 15 20 25

Li

800

600

400

200

0

Dep

th (

cm)

800

600

400

200

0

Dep

th (

cm)

800

600

400

200

0

Dep

th (

cm)

1.2 1.3 1.4 1.5 1.6

Be200 400 600 800

Sr1500 1800 2100 2400

Ba0 5 10 15 20

B

80 120 160 200 240

Zr0 10 20 30 40

V0 10 20 30

Cr200 250 300 350 400

Mn80 160 240 320

F

5 10 15 20 25

La0 3 6 9 12

Y2 4 6 8

Th0.2 0.4 0.6 0.8 1

U2 4 6 8 10

Nb

Fig. 5. Other elements in the Z01 profile (concentrations in μg/g).

20 Q. Gong et al. / Journal of Geochemical Exploration 128 (2013) 14–24

Furthermore, the WIG is also a good index of weathering degree in aregolith profile developed over pyroclastic rocks under subtropicalconditions of Hong Kong in a case study (Fig. 6) by Duzgoren-Aydinet al. (2002).

40 60 80 100

CIA

800

600

400

200

0

Dep

th (

cm)

in Z

01

40 60 80 100

WIP

40 60 80 100

CIA

1

2

3

4

5

Wea

ther

ing

grad

e in

Hon

gkon

g

0 20 40 60 80

WIP

Fig. 6. Weathering indices in the Z01 profile plotted with depth and in th

4.2. Quantitative behaviors during weathering

In order to quantitatively describe trace element behaviors due toweathering, we plotted trace element contents versus values of WIG

40 60 80 100

WIC40 60 80 100

WIG

0 20 40 60 80 100

WIC0 20 40 60 80 100

WIG

e subtropical profile from Hong Kong plotted with weathering grade.

10

100

V

40 60 80 100WIG

40 60 80 100WIG

40 60 80 100WIG

40 60 80 100WIG

40 60 80 100WIG

40 60 80 100WIG

40 60 80 100WIG

40 60 80 100WIG

40 60 80 100WIG

40 60 80 100WIG

40 60 80 100WIG

40 60 80 100WIG

lny = -0.0491 x +6.14r2

= 0.959 n=10

1

10

100

Cr

lny = -0.0760 x +7.16r2

= 0.902 n=10

1

10

Co

lny = -0.0198x + 2.80r2

= 0.852 n=10

1

10

100

Ni

lny = -0.0640x + 5.93r2

= 0.961 n=10

1

10

As

lny=-0.0584x + 4.58r2=0.941 n=10

0.1

1

Sb

lny = -0.0301x + 0.835r

2= 0.903 n=10

100

1000

F

lny = -0.0223x + 6.86r

2= 0.904 n=10

0.1

1

10

100

Au

lny = -0.0731x + 5.75r

2= 0.852 n=10

1

10

Nb

lny = -0.0172x + 2.97r

2= 0.889 n=10

1

10

Th

lny = -0.0191x + 2.98r

2= 0.841 n=10

100

1000

Zr

lny = -0.0148x + 5.90r

2= 0.818 n=10

0.1

1

10

W

lny = -0.0325x + 1.96r

2= 0.867 n=10

Fig. 7. Scatter plots show good exponential relationships of element contents to their WIG values for the Z01 profile samples (circle). The top soil sample Z01B1 was excluded forexponential fitting but also plotted here (plus symbol).

21Q. Gong et al. / Journal of Geochemical Exploration 128 (2013) 14–24

(Fig. 7), which show good exponential relationships. Here, we sup-pose that element contents in parent rock and in weathered materialsare represented by y0 and y, respectively, whereas values of WIG arerepresented by x. The enrichment coefficient due to weathering canbe defined as y/y0. If the enrichment coefficient shows an exponentialrelation with the WIG, this can be defined as

ln y=y0ð Þ ¼ axþ b ð5Þ

where a and b are fitting parameters. Eq. (5) can be rearranged as

lny ¼ axþ bþ lny0 ¼ axþ c ð6Þ

where b+lny0 is combined as c.The behaviors of 12 trace elements due toweathering of the Linglong

biotite granite follow the exponential relationship depicted in Eq. (6)quite strongly as indicated by high values of r2 (Fig. 7). These resultsindicate that an exponential relationship between the enrichment coef-ficient (y/y0) and the WIG is credible. The quantitative relationshipdepicted by Eq. (6) illustrates that element contents in weathered orregolith materials may vary exponentially with weathering gradesquantified as WIG values even if weathered or regolith materials aredeveloped directly over the same parent rock.

Although weathering grades represented as WIG values of streamsediments in the Linglong–Jiaojia Au deposit region are clearly differentfrom weathered or regolith materials in the Z01 profile, element con-tents in stream sediments in this district were likely derived from thesame rocks in the region. Therefore,we can use the exponential relationsof element contents with their corresponding WIG values to eliminatebackground fluctuations due to weathering when determining anoma-lies in regional stream sediment geochemical survey data, like the appli-cation of geochemical mineralization probability index (Yousefi et al.,2012), fractal (Deng et al., 2010b; Han et al., 2004), multifractal (Deng

et al., 2011; Wang et al., 2008; Yuan et al., 2012), spatially weightedprincipal component analysis (Cheng et al., 2011), singularity analysis(Cheng andAgterberg, 2009), and othermethods to delineate geochem-ical anomalies (Carranza, 2008, 2011; Carranza et al., 2009; Sun et al.,2009, 2010).

4.3. Application

The geological map of the Linglong–Jiaojia Au deposit region (Fig. 1)is shown in Fig. 8a for reference to the geochemical maps. The Linglongsuper large Au deposit (ca. 150 t Au) is located in the eastern part of theregion, which includes the Jiuqu and Dongfengmining districts, where-as the Jiaojia super large Au deposit (ca. 200 t Au) is located in thewest-ern part of the region, which includes the Wang'ershan and Sizhuangmining districts. The Xincheng super large Au deposit (ca. 120 t Au) isalso located in the western part near the Jiaojia Au deposit district.Most Au deposits in the Linglong–Jiaojia Au deposit region are hostedin the Linglong biotite granite (γ5l) (Fig. 8a). Therefore, we use the rela-tion of Au content with WIG, lnAu=−0.0731*WIG+5.75 in Fig. 7, tocalculate per sample the background values (Aubackground) in this regionignoring differences of the sediments' parent rocks as

Aubackground ¼ e −0:0731�WIGþ5:75ð Þ ¼ 314:2e −0:0731�WIGð Þ ð7Þ

The calculated background values per sample are not affected bymineralization or alternation effects, only by changes as a result ofweathering. The ratio of Au content to the normal background value(Au/Aubackground) per sample is called the enrichment factor in thisstudy, which reflects the gain or loss of Au as a result of geochemicalprocesses other than weathering.

The statistical parameters of the regional geochemical data of Aucontents, calculated normal background values, and the enrichment

Fig. 8. Geological map of the Linglong–Jiaojia Au region (a) and the geochemical maps of Au contents (b), normal background values (c), and enrichment factors (d) in watersediments with the scale of 1:200000 in the RGNR. The + symbols are locations of composite stream sediment samples.

22 Q. Gong et al. / Journal of Geochemical Exploration 128 (2013) 14–24

factors in the Linglong–Jiaojia Au deposit region are listed in Table 2.The geochemical maps of Au contents, normal background values,and enrichment factors were produced by a cumulative frequencymethod with 19 classes (Fig. 8b–d) using the GeoExpl® software de-veloped by the Chinese Geological Survey.

In the geochemical map of Au contents (Fig. 8b), high values coin-cide well with areas where Au deposits have been discovered. In themap of Au enrichment factors (Fig. 8d), areas with discovered Au de-posits are characterized by high values and some values correspondingtoAudeposits are clearly enhanced compared to themap of Au contents(Fig. 8b).

Values of WIG, Au contents and Au enrichment factors (Au/Aubackground) data along AA′ and BB′ profiles in Fig. 8 were selected forfurther examination of the results (Fig. 9). In the AA′ profile, whichpasses through the Jiaojia, Jinling, Jinchiling, and Taishang depositsfrom west to east (Fig. 8a), high values of not only Au contents but alsoenrichment factors coincide with these deposits well (Fig. 9). This

Table 2Statistical parameters of regional geochemical data of Au contents, background values,and enrichment factors in the Linglong–Jiaojia Au deposit region.

Parametersof Au

Au(ng/g)

log Au Aubackground

(ng/g)Au/Aubackground log Au/Aubackground

Number ofsamples

371 371 371 371 371

Minimum 0.5 −0.30 0.15 0.09 −1.0Maximum 690 2.84 21.2 124.6 2.10Average 32.3 0.89 7.3 5.4 0.077Median 5.2 0.72 7.1 0.82 0.086Standarddeviation

86.4 0.63 3.1 15.3 0.668

Skewness 4.79 1.02 0.52 5.46 0.90Kurtosis 26.1 0.38 0.76 34.3 0.124

indicates that the proposed enrichment factor can retain the informationof the strong anomalies of the original Au contents. In the BB′ profile,which passes through the Jiaojia and Linglong deposits, a strong anomalyoccurs at the Linglong deposit and no clear anomaly coincides with theJiaojia deposit on the Au contents (Fig. 9), but a clear weak anomalyand a strong anomaly coincide well with the Jiaojia and Linglong de-posits, respectively, on the enrichment factors (Au/Aubackground). Theseresults indicate that the enrichment factors not only retain the stronganomalies of the original Au contents but also can enhance weak anom-alies of the original Au contents which were due to mineralization. Theeffect of the enrichment factor, which retains the strong anomalies andenhances weak anomalies due tomineralization, is also used for the rec-ognition of geochemical anomalies in the Pb–Zn, W, and Au deposits(Han et al., 2004), although the background values were determinedby other methods.

The enrichment factor used in this study was a useful index todetermine anomalies in regional geochemical survey data in a regionwhere stream sediment and regolith are likely derived from the sameparent material. However, the equations (e.g., Eq. (7)) derived in thispaper were based on only one regolith profile. More regolith profilesdeveloped on the granite in this region and in other regions should betested further for the WIG and the derived equations to be applied ona broader scale.

5. Conclusion

Based on the behaviors of major elements in a regolith profile overthe Linglong biotite granite located in the western part of the Jiaodongpeninsula, China, we presented a new weathering index of granite(WIG), which is more sensitive than the CIA, WIP, and WIC.

The weathering behaviors of 12 trace elements were quantitativelydescribed using an exponential relationship between element contents

Fig. 9. Geochemical curves of the contents, normal background values, and enrichment factors of Au and the WIG in profiles of AA′ and BB′ in Fig. 8.

23Q. Gong et al. / Journal of Geochemical Exploration 128 (2013) 14–24

and WIG in the regolith profile. Element contents calculated using thisexponential relationship can be viewed as background values showingelement trends due to varying degrees ofweathering of the same parentrock.

A case study in the Linglong–Jiaojia Au deposit region indicate thatthe calculated enrichment factor is a useful index to determine anom-alies in regional geochemical survey data from a region where streamsediments and regolith materials were derived from the same parentmaterial.

Acknowledgments

This work was financially supported by the Natural Science Founda-tion of China (GrantNo. 41230311 and 40872068), National Science andTechnology Support Program (Grant No. 2011BAB04B09), and ChinaGeology Survey (Grant No. 1212011120950). The authors would liketo thank staff members of the China University of Geosciences (Beijing)for constructive discussions and comments. We thank John Carranzaand Ryan Noble for their valuable time, suggestions and comments toimprove the article.

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