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Element geochemical differences in lower Cambrian black shales with hydrothermal sedimentation in the Yangtze block, South China

  • Yanran Huang EMAIL logo , Man Luo , Fan Zhang , Taotao Cao , Ye Yu , Chenzhang Duan and Junjian Gao
Published/Copyright: March 18, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

Hydrothermal activities occurred in the Yangtze block, South China, and affected the process of black shale sedimentation in the early Cambrian. Their specific influences, such as the sources, sedimentary environment, and mineralization, have not yet been revealed. Fortunately, the influences are explained through the geochemistry comparison of different wells FY1, XJ1, HY1, and XA1 in northwestern Hunan. The outcomes of the tectonic setting, distinguishable by element indicators, are disorganized. This is caused by the variable element composition, sedimentary recirculation of material source, and hydrothermal materials. FY1, the closest well to Zhangjiajie where Ni–Mo ores were formed by hydrothermal sedimentation, has more different features on the elemental geochemistry, but many indexes still indicate that it is normal sediments. XA1, which is far from the other wells and deposited in the deep-water basin, has significantly more differences in geochemical properties and shows more about normal marine deposition. XJ1 and HY1 are intermediate between them. Based on these wells, the hydrothermal contribution to the black shale sedimentation is not significant. However, some contents of trace and rare earth elements change widely because hydrothermal materials can diffuse and deposit over long distances. The concentration of Ag, As, Ba, Mo, Ba, U, and V generally has dozens of times higher than that of Upper Continental Crust. The early Cambrian environment was primarily anoxic/euxinic with enough sulfur, which is beneficial for enriching metal elements and organic matter. The sequence FY1, XJ1, HY1, and XA1 in turn has the same performance on the distance with Zhangjiajie, different intervals of suboxic environment and element enrichment, and hydrothermal-fluid-addition Ni abundance. Therefore, hydrothermal processes indeed provide materials for element enrichment and support the reducing environment, but the impact of hydrothermal activity decreases on the plane.

Keywords: black shales; hydrothermal sedimentation; low Cambrian; northwestern Hunan; element geochemistry

1 Introduction

Since hydrothermal activity was discovered at the bottom of Lake Kivu and the Red Sea in the 1960s, hydrothermal activity in modern mid-oceanic ridges and geological hydrothermal sedimentation has been studied [1,2]. Unlike magma and hydrothermal fluids, hydrothermal activity takes place at the lakebed or seabed, which was near the deposition interface at that time. The hydrothermal process is linked to the deep source through fundamental faults but has not yet been clearly described [3,4]. Most hot water comes from the surface (including seawater, lakes and meteoric water). It continues to seep lower via the basement faults, heat up by the deep heat source (temperature can reach 400°C), reacts with surrounding rocks, and then extracts materials, eventually coming out from the hydrothermal vent [5,6]. As a result, hydrothermal activities are unique geological occurrences, and hydrothermal materials are deposited in the strata during this time. Compared with the normal deposition rate in seawater (approximately 10 cm/ka), the hydrothermal deposition rate is much higher (more than 40 cm/ka) [7]. Therefore, hydrothermal sedimentation is a new name for the sedimentation and another mechanism that occurs when the hydrothermal activities have happened [8]. The syngenetic sedimentation is formed by underground hydrothermal fluids and mixed with terrigenous clastics and the bottom water of a sea or lake [9]. Then they undergo the diagenetic processes like normal deposition. Their lithified sediments are even termed as “hydrothermal sedimentary rock” or “exhalative rock” because hydrothermal fluids bring the enormous heat and materials from the deep [9,10]. Actually, hydrothermal sedimentation has a long history of study and currently is still a hot research topic [2,7]. Many hydrothermal sedimentation reports have been published about oceanic island arcs, back-arc basins, and marine strata [11]. These studies have yielded detailed knowledge of hydrothermal systems and have established a set of indicators for hydrothermal sedimentation, including rock textures, sedimentary structures, and geochemical discrimination diagrams [8,12].

Some indications, such as hydrothermal sedimentation and organisms, multi-metal element enrichment, sedimentary minerals, and geochemical properties, suggest that Lower Cambrian black shales in the Yangtze block are of syngenetic hydrothermal sedimentary origin [13,14,15]. Many enriched elements, such as Ag, As, Ba, Cu, Mo, Ni, Se, U, V, Zn, and platinum group element (PGE), can be linked to hydrothermal activity in South China [12,16]. The supernormal enrichment and mineralization may have resulted from the combination of hydrothermal activities, anoxic/euxinic sedimentary environments, prosperous biological activities, maximum transgression at that time, and the chemical properties of the element itself [3,8,11]. However, there is still a lack of research and explanation on the impact of hydrothermal activity, especially on the surroundings of stratabound deposits. The influence of hydrothermal activity in the Yangtze block with many different deposits is not clear. In this paper, several exploratory wells in northwestern Hunan are used as research objects. The geological information including sedimentary environment, hydrothermal sedimentation, element enrichment, and others is revealed based on the geochemical differences in these wells.

That will help to know more about hydrothermal activity, which can be compared with the examples in South China and other places in the world.

2 Geological background

In South China, there is a transition between the Yangtze and Cathaysian blocks. Lower Cambrian black shales, which include mudstone, carbonaceous, or siliceous shale, are primarily developed in the Yangtze block’s interior and close to the Cathaysian block with a zonal distribution (Figure 1). There are many sedimentary mineral resources with different distributions in the Yangtze block [17]. Ni-Mo-PGE has many mineralized spots in South China, but only in Zhangjiajie and Zunyi are there industrial-scale deposits [3,18]. The Ni–Mo ores typically range from 10 to 30 cm in thickness and are not widely distributed [19]. Ba deposits as barites are mainly found in Xinghuang of Hunan and adjacent TianZhu of Guizhou. The black shale-hosted barite deposits have thick beds with thicknesses between 1 and 3 m and high BaSO4 contents ranging from 85 to 95% [20]. In southern Qingling, Ba deposits are also found as barite and barolite [21]. V deposits are mostly concentrated in the area between Hunan and Guizhou and in southern Qingling [22]. Compared to V resource, P deposits are more dispersed throughout almost the whole Yangtze block [23]. Phosphorite or phosphorus nodules can easily be found in the black shales of lower Cambrian [24]. In the southern margin of the Yangtze block, the ore-forming element association is Mo-Ni-V-P-Ba-PGE, while in the northern margin it is V-Mo-Ba-P-Au (Figure 1). U is also highly enriched in the Ni–Mo deposit. It can be taken as a long-term supplementary and alternative type of resource in black shales [25]. Elemental enrichment is so popular that there are many other associated elements in the black shales. These shales even can be called metalliferous shales [26]. The intervals of element enrichment are mostly at the bottom of lower Cambrian, which is close to the boundary between Ediacaran and Cambrian [22,27,28]. In addition, there is an impressive abundance of organic matter in the black shales. The content of total organic carbon (TOC) can exceed 2% on average and sometimes more than 30% [29]. Black shales with high calorific value even qualify as a coal resource [22].

Figure 1 Map of the distribution of different enrichment element deposits in the Yangtze block, South China (modified after [22,33]).
Figure 1

Map of the distribution of different enrichment element deposits in the Yangtze block, South China (modified after [22,33]).

Early Cambrian transgression from southeast to northwest occurred in the Yangtze block, including northwestern Hunan [30]. The slope of the continental edge, which lies between Zunyi and Zhangjiajie, functions as the transition zone between the deep-water shelf and basin [31]. There are several growing synsedimentary faults in this region. Furthermore, hydrothermal activities had occurred frequently in Zunyi, Zhangjiajie, and Xinhuang-Tianzhu with many different evidences [14,16]. Because of the transformation from tectonic extension to subsidence in the early Cambrian, there was a reduction in faulting activity. That, in turn, led to the hydrothermal activities decreasing as a whole [12,32].

3 Materials and methods

The core samples used in this study were collected from four exploration wells, FY1, HY1, XJ1, and XA1 (Figure 1). All samples are in the bottom of the Niutitang Formation and close to the boundary of the Ediacaran-Cambrian. Each well has 8 samples, and there are 32 samples in total (Figure 2). The lower Cambrian Niutitang Formation in northwestern Hunan is in contact with the underlying strata of the Dengying (DY) or Liuchapo (LCP) Formations. The DY Formation in the Ediacaran is deposited in platform facies with most dolomite. The LCP Formation with siliceous rock was deposited in deep water and was diachronous from Ediacaran to early Cambrian [27]. The lithological distinction between Ediacaran and Cambrian is evident in these wells, as well as in some outcrops in northwestern Hunan [12,33]. According to the lithofacies and palaeogeography at that time, FY1, HY1, and XJ1 were in the slope, while XA1 was in the basin. Samples were cleaned, dried, and then ground into 200 mesh for TOC, major and trace elements, and rare earth elements (REEs). Therefore, their analysis results were easily compared with each other referencing the Ediacaran-Cambrian boundary (Figure 2).

Figure 2 The samples from the bottom of lower Cambrian black shales in different wells.
Figure 2

The samples from the bottom of lower Cambrian black shales in different wells.

To determine TOC levels, a porous crucible was filled with 200 mg of samples. Dilute hydrochloric acid was continuously supplied to the crucible to dissolve and remove the inorganic carbon, especially carbonate. Finally, the crucible was placed in an infrared carbon/sulfur analyzer, in Hunan Provincial Key Laboratory of Shale Gas Resource Utilization. The relative deviation and relative errors were controlled under 5 and 3.5%, respectively.

For major element concentration, the core samples were divided into two parts. One of them was placed in a muffle furnace and burned at about 1,000°C. The loss on ignition (LOI) was got by weighting the cooling residue and calculating the difference from the initial weight. The other part of the sample weighing 500 mg was combined with lithium nitrate and lithium borate as the flux. After blending and high-temperature melting thoroughly, the products were put in a platinum mold to make a sheet of glass. Then, the contents of major elements, which were shown in the form of oxides, were got with an X-ray fluorescence spectrometer. Each sample has 100% content including its component of major element and LOI. Both the relative deviation and error of the contents were controlled under 5%.

For the contents of trace elements and REE, samples (50 mg) were digested with a variety of acids including hydrofluoric, hydrochloric, nitric, and perchloric acids. Then, the samples were maintained in a constant volume by adding diluted hydrochloric acid. The contents of trace element were determined after correcting the interference between the spectrum of different elements based on inductively coupled plasma-atomic emission spectrometry and inductively coupled plasma-mass spectrometry. REEs were analyzed in a manner akin to that of trace elements. The difference is the samples must be melted with lithium borate as the flux and digested with multiple acids before making a consistent volume. Finally, the contents of REE were got through inductively coupled plasma-mass spectrometry. The relative deviation and error of trace element and REE contents were controlled under 10%. These contents were examined in the minerals laboratory of Australia Laboratory Services Chemex (Guangzhou) Company Limited.

4 Results

4.1 TOC and major elements

Some important results are presented in Table 1. The values of TOC are generally high, with an average value of 5.94%. The TOC distribution in the vertical direction in different wells can also be found in the table. Therefore, there is some trend that decreases upwards in XJ1 and is not very clear in FY1 and HY1, while is basically opposite in XA1. The reasons TOC distribution is not regular are different in FY1 and HY1. The FY1 trend is according to the data fluctuation, but the sample location is not close to the E-C boundary in HY1. The correlation coefficient between TOC and LOI is 0.71 in all samples and 0.97 and 0.99 in XJ1 and XA1, respectively. Meanwhile, LOI with 12.04% on average is the second most important component only less than SiO2. That indicates the burning losable components are mainly abundant organic matter in the black shales with generally high TOC.

Table 1

The results of TOC and the contents of primary major and trace elements in different samples and wells

Sample Depth (m) TOC (%) CIA Major element (%) Trace element (ppm)
SiO2 CaO K2O Na2O Al2O3 TFe2O3 P2O5 TiO2 LOI Mo U Ni V Ba
FY1-1 2745.9 1.8 82.4 17.9 31.0 0.2 0.2 3.8 5.4 0.1 0.2 10.5 2.9 2.6 13.1 63.0 1320.0
FY1-2 2762.2 6.9 61.7 71.2 1.1 1.8 1.3 8.1 2.6 0.1 0.3 12.5 52.7 41.4 123.0 1220.0 7570.0
FY1-3 2778.1 4.4 77.8 48.7 8.8 1.1 0.5 8.0 4.6 0.4 0.4 11.7 115.0 54.4 142.5 1760.0 2000.0
FY1-4 2785.8 7.6 63.2 79.3 0.4 0.9 0.7 3.7 3.4 0.1 0.2 12.2 34.8 27.6 172.0 774.0 3120.0
FY1-5 2816.1 7.0 75.4 68.4 2.0 1.2 0.3 6.0 3.6 0.2 0.3 15.0 158.0 74.5 513.0 12158.0 8170.0
FY1-6 2822.0 7.9 67.6 71.9 0.4 1.5 0.6 5.8 4.4 0.0 0.3 10.0 22.8 12.4 173.0 2090.0 6950.0
FY1-7 2823.0 3.2 83.4 27.2 20.7 1.7 0.1 8.9 1.5 1.0 0.1 23.6 22.3 45.7 14.0 2530.0 9890.0
FY1-8 2823.7 8.5 72.6 67.5 6.0 0.8 0.3 3.7 2.4 0.1 0.2 21.7 93.1 43.0 237.0 7100.0 4260.0
HY1-1 2510.2 1.2 64.7 59.9 2.7 2.9 1.7 13.1 4.8 0.2 0.6 10.1 68.5 33.6 128.0 274.0 8950.0
HY1-2 2515.5 2.0 55.2 46.1 8.5 4.5 1.9 11.2 4.1 0.1 0.6 8.6 20.0 14.1 62.7 185.2 7384.5
HY1-3 2548.7 10.8 63.3 38.9 16.4 1.7 1.5 9.5 5.1 0.1 0.4 19.9 10.8 10.1 56.7 119.0 3030.0
HY1-4 2554.5 8.5 55.3 58.8 2.8 2.6 1.9 9.2 4.7 0.2 0.5 14.3 171.5 66.9 231.0 2878.6 19979.0
HY1-5 2560.0 7.6 65.9 60.6 1.5 2.3 1.2 10.2 3.8 0.2 0.5 14.1 120.5 91.8 257.0 3380.0 22660.0
HY1-6 2564.7 6.9 71.7 40.3 15.5 1.2 0.9 9.1 2.8 0.2 0.3 11.7 108.9 88.1 252.4 881.0 5269.0
HY1-7 2595.8 1.6 69.0 61.0 0.5 2.5 1.9 11.8 4.5 0.2 0.8 12.0 85.2 42.9 262.0 243.0 2071.0
HY1-8 2598.9 0.5 59.8 73.8 8.1 2.4 0.9 7.0 1.3 0.0 0.2 4.3 4.7 2.1 124.8 51.0 1945.0
XJ1-1 1934.6 1.6 74.0 67.8 0.5 2.9 1.1 13.6 5.2 0.1 0.5 5.6 25.8 8.6 74.8 214.0 2530.0
XJ1-2 1978.1 2.5 68.2 63.3 1.7 3.2 1.3 13.8 4.3 0.1 0.5 7.6 21.3 9.1 76.4 192.0 3360.0
XJ1-3 1998.3 3.1 64.7 52.8 6.3 2.7 1.9 13.7 4.3 0.1 0.5 8.0 10.1 8.0 60.9 162.0 29730.0
XJ1-4 2008.8 10.0 70.4 52.8 2.5 1.8 0.8 9.0 6.0 0.3 0.4 18.6 347.0 106.5 352.0 1240.0 15320.0
XJ1-5 2018.1 10.0 71.7 60.8 0.5 2.6 0.9 9.8 5.0 0.2 0.5 14.7 108.5 77.0 172.5 404.0 34390.0
XJ1-6 2025.0 7.9 62.8 53.6 5.5 2.3 1.2 9.1 6.2 0.8 0.4 13.4 917.0 97.7 319.0 1370.0 25880.0
XJ1-7 2035.3 5.0 80.0 69.7 1.2 0.7 0.1 3.4 1.6 0.1 0.1 8.2 80.0 36.3 242.0 6920.0 10210.0
XJ1-8 2040.1 11.3 81.4 73.5 0.9 0.2 0.1 1.5 1.3 0.1 0.1 19.8 46.9 23.4 237.0 3670.0 8950.0
XA1-1 798.6 6.0 77.8 79.9 1.3 0.7 0.1 2.7 2.4 0.8 0.1 8.3 66.5 30.5 111.5 1170.0 1245.0
XA1-2 813.9 9.3 68.0 62.4 0.8 2.8 0.7 9.5 5.2 0.2 0.5 14.3 161.0 122.5 298.0 1730.0 19080.0
XA1-3 821.6 14.4 81.5 64.3 1.0 1.1 0.1 5.1 3.5 0.3 0.3 18.5 109.0 68.0 47.3 291.0 9150.0
XA1-4 827.8 8.4 74.5 70.9 0.8 1.9 0.2 6.4 3.7 0.3 0.4 12.2 198.5 92.0 261.0 2780.0 16210.0
XA1-5 840.2 10.5 76.0 73.0 0.9 1.2 0.1 3.8 4.1 0.2 0.2 13.8 89.0 64.6 176.5 1240.0 16930.0
XA1-6 853.0 1.5 92.6 94.2 1.3 0.0 0.0 1.1 1.1 0.0 0.0 2.2 8.0 4.1 19.3 204.0 1060.0
XA1-7 866.2 1.1 74.5 85.5 1.1 1.0 0.2 3.9 2.8 0.0 0.2 3.1 1.3 1.2 18.3 38.0 1950.0
XA1-8 869.3 1.1 78.1 76.5 3.1 1.5 0.1 5.4 3.8 0.7 0.3 4.7 1.5 2.7 24.7 62.0 1450.0

CIA was calculated by the following equation: CIA = [Al2O3/(Al2O3 + CaO* + Na2O + K2O)] × 100, where CaO* must be corrected and the contents used in the formula should be the number of moles. The detailed procedures follow those of [50].

SiO2 is the major component accounting for 62.26% on average and ranging from 17.96 to 94.17% in these wells. That is the usual concentration when compared with that in the Upper Continental Crust (UCC) (65.90%) [34]. However, XA1 generally and significantly has more SiO2 than the others suggesting that the deep-water basin is a far greater source of Si than other wells on the slope. In comparison to the content of P2O5 (0.16%) in UCC, only a few samples have more P2O5 in all wells (Table 1). Most samples also have very low Al2O3 concentrations compared with UCC (15.19%). Total iron concentration is reported as oxide TFe2O3. However, there are abundant pyrites in the black shales [20,23]. There is a chemical index of alteration (CIA) based on major element concentration and used for judging the weathering degree in clastic rocks [35]. Most samples are distributed from 60 to 80 (Table 1), which is the same as the Average Shale and indicates the weathering of middle level with warm and humid climates [35]. That is also in accordance with the beginning of great transgression at that time [8].

4.2 Trace and rare earth elements

There will be misdirected when using Al normalization to describe the element enrichment according to the extreme low content of Al2O3 in the study area. Therefore, the element abundance in this study was determined directly through the absolute abundance (AA), which is calculated by the ratio of the samples and UCC. Different elements in the wells have different AA distributions (Table 2). There are some inherent regularities in these data. First, because of the hydrothermal activity, the elements are generally enriched but some elements, such as Sr, Li, Pb, Cs, Th, and Co have no enrichment. Second, the enriched components in different wells can be comparable, which indicates there are similar sedimentary conditions. They also follow a similar order for the elements at various enrichment levels. Finally, XA1 generally has lower enrichment degrees than the other wells, especially in the moderate enrichment elements (Table 2). That indicates all wells had similar sedimentary environment and there were some hydrothermal activities but not close to them at that time. However, they were still affected by hydrothermal activity but XA1 was much less than other wells.

Table 2

The distribution of absolute abundance in different elements and wells

Element FY1 HY1 XJ1 XA1
AAmin AAmax AAave AAmin AAmax AAave AAmin AAmax AAave AAmin AAmax AAave
Ag 35.2 263.0 121.1 3.6 114.0 46.3 5.2 136.0 56.7 2.4 185.4 77.5
Mo 1.9 105.3 41.8 3.1 114.3 49.2 6.7 278.0 88.1 0.9 132.3 52.9
As 18.1 56.0 33.8 8.0 48.1 26.2 10.0 294.7 63.2 3.8 49.7 29.7
Ba 2.4 18.0 9.8 3.5 41.2 16.2 4.6 62.5 29.6 1.9 34.7 15.2
U 0.9 26.6 13.5 0.7 32.8 15.6 2.9 38.0 16.4 0.4 43.8 17.2
V 0.6 113.6 32.4 0.5 31.6 9.4 1.5 64.7 16.6 0.4 26.0 8.8
Se 2.0 68.0 32.5 2.0 12.0 5.5 2.0 110.0 26.5 2.0 16.0 5.0
Zn 0.7 51.8 15.4 1.0 48.5 9.9 1.3 72.3 13.1 0.3 17.7 3.7
Cu 1.2 11.5 4.9 1.0 9.0 4.2 2.0 17.2 7.2 0.4 4.9 2.0
Ni 2.8 11.6 4.6 1.3 6.0 3.9 1.4 8.0 4.4 0.4 6.8 2.7
Cr 0.5 10.8 3.9 0.7 3.2 1.7 1.0 10.6 2.4 0.5 1.2 0.8
Sr 0.1 8.7 2.2 0.3 5.2 2.4 0.3 3.2 1.2 0.1 0.4 0.3
Li 0.2 4.7 1.7 1.2 2.8 1.8 0.6 2.9 1.4 0.1 2.1 0.8
Pb 0.5 3.2 1.5 0.4 1.7 1.1 0.6 1.7 1.2 0.3 2.7 1.4
Cs 0.2 2.1 1.1 0.5 1.7 1.1 0.3 2.5 1.2 0.1 0.8 0.5
Th 0.3 1.7 0.6 0.4 1.3 0.8 0.1 1.2 0.8 0.1 0.9 0.5
Sc 0.3 0.8 0.5 0.7 1.0 0.9 0.1 1.0 0.6 0.1 0.9 0.5
Co 0.1 0.7 0.4 0.2 1.0 0.7 0.2 1.1 0.7 0.1 0.8 0.5
Ti 0.1 0.6 0.4 0.2 0.9 0.7 0.1 0.8 0.6 0.1 0.7 0.4

Abbreviations: AAmin, the minimum value of absolute abundance; AAmax, the maximum value of absolute abundance; AAave, the average value of absolute abundance.

REEs have good chemical stability in the water and short time for sedimentation, so their change in sedimentary rocks can reveal much information such as the source and the features of the sedimentary environment [16,27]. Positive anomaly of Eu (Eu/Eu* > 1) is the most significant indicator for hydrothermal sedimentation [8]. Ce/Ce* is good index that implies the water depth in the sedimentary environment [14]. In addition, LaN/YbN and LREE/HREE can reveal the differentiation between light rare earth elements (LREEs) and heavy rare earth elements (HREEs) [27]. These indexes in our wells and in UCC and North American Shale Composite (NASC) are all shown in Table 3 [34,36]. It is easy to find that our samples have generally negative Eu/Eu* and are close to UCC and NASC, which are normal sediments. That means our wells might be normal marine sedimentation. However, Ce/Ce* basically increases upwards in XJ1, HY1, and even in FY1. These wells within the slope have a clear trend indicating that the seawater was becoming shallow at that time. Meanwhile, the change in Ce/Ce* in deep-water basin XA1 is not noticeable. All wells have generally low ∑REE than UCC and NASC, especially XA1. The other indexes in XA1 are changed a lot caused by low ∑REE. Except that, most samples mainly have similar characteristics based on the REE indicators.

Table 3

Some important indices of REE in different samples and wells

Sample Eu/Eu* Ce/Ce* LaN/YbN LaN/CeN ΣREE (ppm) LREE/HREE
FY1-1 0.80 0.97 7.85 1.35 52.34 6.82
FY1-2 0.67 0.88 4.75 1.44 79.61 4.91
FY1-3 0.79 0.87 4.23 1.41 101.22 4.53
FY1-4 0.61 0.81 5.08 1.64 73.56 4.14
FY1-5 0.59 0.51 3.26 2.54 189.28 2.85
FY1-6 0.42 0.48 4.61 3.60 40.41 3.98
FY1-7 0.26 0.81 5.08 1.49 378.24 4.53
FY1-8 0.61 0.47 2.83 2.80 104.22 2.36
Average value 0.59 0.73 4.71 2.03 127.36 4.27
HY1-1 0.51 0.99 12.32 1.35 173.55 12.07
HY1-2 0.38 0.90 5.98 1.42 129.03 6.17
HY1-3 0.58 0.96 7.07 1.30 107.99 7.00
HY1-4 0.61 0.75 5.92 1.79 120.96 5.62
HY1-5 0.49 0.82 6.34 1.58 178.06 5.94
HY1-6 0.86 0.73 4.40 1.96 84.15 4.23
HY1-7 0.88 0.77 4.43 1.57 72.69 4.68
HY1-8 0.88 0.74 4.89 1.56 65.87 5.29
Average value 0.65 0.83 6.42 1.57 116.54 6.38
XJ1-1 0.62 1.02 7.29 1.18 121.29 7.50
XJ1-2 0.62 1.01 8.22 1.22 159.06 8.15
XJ1-3 0.69 1.04 9.03 1.21 153.75 9.26
XJ1-4 0.53 0.82 6.85 1.73 177.55 6.29
XJ1-5 0.45 0.96 9.74 1.42 142.49 8.60
XJ1-6 0.56 0.74 7.42 1.72 155.40 5.65
XJ1-7 0.48 0.66 3.23 1.76 96.97 2.93
XJ1-8 0.50 0.51 3.54 2.41 45.51 3.56
Average value 0.56 0.85 6.91 1.58 131.50 6.49
XA1-1 0.57 0.83 2.82 1.28 110.81 3.19
XA1-2 0.50 0.87 6.48 1.55 157.58 6.03
XA1-3 0.58 0.78 5.14 1.71 116.34 4.61
XA1-4 0.41 0.71 5.74 1.80 120.56 6.15
XA1-5 0.53 0.67 2.58 1.64 61.02 2.48
XA1-6 0.84 0.99 0.53 1.05 14.15 13.44
XA1-7 0.66 0.88 4.31 1.58 41.10 13.22
XA1-8 0.63 0.89 2.49 0.98 69.23 2.84
Average value 0.59 0.83 3.76 1.45 88.38 6.49
UCC 0.69 1.08 10.74 1.14 148.10 9.36
NASC 0.70 1.15 7.01 1.22 173.21 7.50

The anomalies of Eu and Ce can be obtained by the formulas Eu/Eu* = EuN/(SmN × GdN)1/2 and Ce/Ce* = CeN/(LaN × PrN)1/2, respectively. The subscripts N used in the formulas above and in the ratio indices such as LaN/YbN and LaN/CeN indicate the normalization to chondrites. In addition, ∑REE is the sum of all REE contents, and LREE/HREE is the ratio between LREEs and HREEs. UCC and NASC values are from [34] and [36] respectively.

5 Discussion

5.1 Tectonic setting

Except for FY1-1 and XA1-5, all other samples with Al2O3/(Al2O3 + TFe2O3) values are in the range of continental margins (0.5–0.9). MnO/TiO2 shows that most samples are less than 0.5, except FY1-1 and XA1-5. This implies that the samples may be formed in the continental margins [37]. The tectonic setting can be determined using several element assessment methods [38,39], but the outcomes are disorganized. Research shows that only XA1 is in the passive continental margin (Figure 3a–c). Many samples are in active continental margins and continental island arc (Figure 3d and g) or in continental island arc (Figure 3e and f). Some samples are even out of the judgment range. These processes were first employed successfully in magmatic rocks and some sedimentary basins. Passive continental margins are relatively stable, and protoliths may have the forming information of active continental margin or continental island arc when they have experienced magmatic activity. Instead, the protolith formed in the sedimentary basin of an active continental margin or continental island arc may generally have no information about passive continental margin because of the large amount of volcanic materials and rapid accumulation [33]. The rocks in the Wuqiangxi Formation of Qinbaikou, underlying lower Cambrian in Zhangjiajie, show mixed features including the active continental margin based on major elements and continental island arc based on the trace elements (Figure 3) [34]. This shows similar features to our samples. The terrestrial clastic deposits in the black shales are generally abundant siliceous. Therefore, the rocks in the Wuqiangxi Formation may serve as the source for the early Cambrian sedimentation of black shales. The sedimentary recirculation of material source is an adverse factor for the judgment. The results are further unreliable when there are hydrothermal materials from the deep unlike the normal sediments in the Niutitang Formation. Based on the palaeogeography of the early Cambrian and other evidence, northwestern Hunan is part of a passive continental margin [8]. The judgment procedures of the tectonic setting need to be considered when applied to sedimentary rocks with complicated sources.

Figure 3 The tectonic settings in the samples and wells based on elemental judgment methods (the methods are from [38,39]). (a) K2O/Na2O vs SiO2; (b) SiO2/Al2O3 vs K2O/Na2O; (c) K2O/(Na2O+CaO) vs SiO2/Al2O3; (d) Ti/Zr vs La/Sc; (e) the classification triangle of La, Th and Sc; (f) the classification triangle of Th, Sc and Zr/10; (g) the classification triangle of Th, Co and Zr/10. Abbreviations: PM is passive continental margin; ACM is active continental margin; OIA is oceanic island arc; CIA is continental island arc; A1 is felsic source, and A2 is basaltic and andesitic source.
Figure 3

The tectonic settings in the samples and wells based on elemental judgment methods (the methods are from [38,39]). (a) K2O/Na2O vs SiO2; (b) SiO2/Al2O3 vs K2O/Na2O; (c) K2O/(Na2O+CaO) vs SiO2/Al2O3; (d) Ti/Zr vs La/Sc; (e) the classification triangle of La, Th and Sc; (f) the classification triangle of Th, Sc and Zr/10; (g) the classification triangle of Th, Co and Zr/10. Abbreviations: PM is passive continental margin; ACM is active continental margin; OIA is oceanic island arc; CIA is continental island arc; A1 is felsic source, and A2 is basaltic and andesitic source.

5.2 Material sources

Aluminum, as the dissolved component, can respectively react with hydroxide and organometallic compounds and then deposit in rocks, but Sc cannot [40]. Sometimes Al2O3 and TiO2 with good correlation can reveal the contribution of terrestrial clastics [36]. Under moderate weathering conditions, many terrestrial elements (such as Al, Sc, Th, and Ti) have low contents in the samples (Table 2). The elemental concentration is still able to reveal the information about the source and provenance. All samples in XA1 have much lower contents of the above elements and higher CIA than others (Table 1). It is probably because XA1 is in the deep-water basin and far from the continent. REEs and provenance have a substantial inheritance in sedimentary rocks [34]. Generally, terrestrial clastics as the source can obviously increase ∑REE [29]. In XJ1 and XA1, the correlation mentioned above is positive and strong, which means that Al2O3 and TiO2 are mainly from terrestrial clastics. But the relationship in HY1 is relatively weaker, and there is basically no correlation in FY1 (Table 4). Therefore, terrestrial matter has a significant influence on the direction from continent to the deep-water basin (Figure 1). Perhaps in environments far from the continent, it is easy to identify the terrestrial materials from different provenances. Vertically, the amounts of Al2O3 and TiO2 in XJ1 are certainly increasing, while they are not as clear as in HY1 (Table 1). The trend is also increasing in Ce/Ce* (Table 3), which suggests that the seawater becomes shallower. However, why does FY1 display the same trend on Ce/Ce* and does not have the similar trend on Al2O3 and TiO2 like XJ1 and HY1? A reasonable explanation is that FY1 has a more complicated source than the other wells.

Table 4

The correlation among important contents in different wells

Well Al-Sc Al2O3-TiO2 Al2O3-∑REE TiO2-∑REE
FY1 0.49 0.32 0.56 −0.42
HY1 0.66 0.84 0.55 0.31
XJ1 0.99 0.94 0.74 0.85
XA1 0.85 0.99 0.79 0.79
All wells 0.85 0.85 0.48 0.22

The chondrite-normalized patterns of REEs can be found in Figure 4 [41]. Except for XA1-6, which has an extremely low ∑REE content (14.15 ppm), the other samples have similar features. LREEs are relatively enriched. The distribution curves have high slopes, are clear rightward in LREEs, and become flattened in HREEs. There is a V-shape with Eu anomalies but an obscure V-shape with Ce anomalies in the curves. Many indices are close to usual UCC or NASC (Table 3). In HY1, XJ1, and XA1, the curves in different samples are very concentrated. All these phenomena show that they were deposited in marine environments with normal sources. However, LaN/CeN is generally high, and LREE/HREE is low in FY1. The curves and some indicators are also varied (Figure 4 and Table 3), which means that FY1 truly has different sources than the other wells. This is the same performance as the source analysis in the previous paragraph.

Figure 4 Chondrite-normalized patterns of REEs in different samples and wells (the chondrite values are from [41]).
Figure 4

Chondrite-normalized patterns of REEs in different samples and wells (the chondrite values are from [41]).

5.3 The influence of hydrothermal activity

There are generally two ways for hydrothermal material to migrate and deposit. One is the sedimentary system close to the hydrothermal vent, which is made up of siliceous, metal sulfide and sulfate, and some carbonate. This sedimentary system can also collapse and be deposited not too far from the hydrothermal vents. The other way is that the materials are mixed with hydrothermal fluid and seawater and enter the clasts directly or through chemical reactions [5,12]. The clasts can progressively be deposited by the plume, dispersion flow, ocean currents, etc. The materials can also migrate through biological absorption [4]. Generally, significant geochemical differences exist and can be identified between hydrothermal and usual marine sediments [6]. The hydrothermal sediments are enriched in Fe and Mn but lack Al and Ti. The Al/(Fe + Mn + Al) value of silicalite in normal sediments is 0.60 when they are biogenetic and can decrease to 0.01 with increasing hydrothermal materials [42]. The amount of SiO2 is generally high, and many samples are silicalite, especially in XA1. Except for sample XA1-6, Al/(Fe + Mn + Al) ranges from 0.35 to 0.82 with an average of 0.58, and most samples are close to 0.60 (Table 5). Most samples are normal sediments also according to Al–Fe–Mn diagram of sedimentary origin judgment [43]. The location of Zhangjiajie, where it has been confirmed that there are many hydrothermal activities and Ni–Mo ores deposited, is far from all wells (Figure 1). FY1, the nearest well, is located approximately 70 km from Zhangjiajie. None of our wells have sedimentary systems close to hydrothermal vents based on core observation. The important indicator Eu/Eu* is also negative (Table 3). Therefore, it is easy to conclude that there is normal marine sedimentation in these wells.

Table 5

Some important geochemical indices in different samples and wells

Sample Al/(Al + Fe + Mn) Fe/Ti Co/Zn Ba/Sr Y/Ho U/Th Ni/Co V/(V + Ni) V/Cr δU
FY1-1 0.35 36.50 0.09 0.44 29.73 0.76 27.29 0.32 1.58 1.39
FY1-2 0.69 9.74 0.01 56.28 34.27 5.69 13.37 0.91 8.71 1.89
FY1-3 0.57 12.26 0.05 2.93 37.81 7.03 12.18 0.93 11.73 1.91
FY1-4 0.44 23.90 0.04 46.29 40.23 6.95 20.24 0.82 12.90 1.91
FY1-5 0.56 12.89 0.01 65.89 44.44 12.56 72.25 0.96 13.51 1.95
FY1-6 0.49 18.67 0.01 178.21 35.80 2.53 26.21 0.92 1.46 1.77
FY1-7 0.82 21.00 0.001 5.53 36.68 2.53 156.67 0.95 42.17 1.77
FY1-8 0.54 16.37 0.001 15.78 43.48 13.35 57.80 0.97 12.03 1.95
HY1-1 0.67 9.51 0.11 23.01 28.49 2.39 7.27 0.68 3.30 1.76
HY1-2 0.67 7.50 0.10 4.32 30.99 1.50 3.85 0.75 2.01 1.64
HY1-3 0.58 14.83 0.16 1.67 29.74 1.13 4.85 0.68 2.20 1.54
HY1-4 0.60 10.52 0.01 43.39 42.49 10.78 15.61 0.93 12.94 1.94
HY1-5 0.66 8.41 0.003 53.57 40.34 9.47 22.16 0.93 12.66 1.93
HY1-6 0.71 11.35 0.06 3.33 47.76 20.98 26.02 0.78 10.97 1.97
HY1-7 0.66 8.67 0.14 20.30 48.26 4.09 18.19 0.48 1.03 1.85
HY1-8 0.78 9.78 0.02 5.80 29.14 0.39 36.71 0.29 0.51 1.08
XJ1-1 0.65 13.11 0.07 28.05 30.66 0.67 5.02 0.74 2.38 1.34
XJ1-2 0.70 9.77 0.14 20.00 31.10 0.79 5.03 0.72 1.92 1.41
XJ1-3 0.70 9.38 0.21 26.66 28.50 0.75 3.21 0.73 1.62 1.39
XJ1-4 0.53 16.12 0.02 32.46 42.26 9.77 21.08 0.78 7.75 1.93
XJ1-5 0.59 11.67 0.10 251.02 37.56 8.47 13.80 0.70 5.11 1.92
XJ1-6 0.52 16.69 0.08 24.88 38.22 11.39 25.12 0.81 15.57 1.94
XJ1-7 0.61 14.13 0.001 37.40 46.71 11.03 75.63 0.97 7.86 1.94
XJ1-8 0.46 22.75 0.003 49.31 41.23 17.33 84.64 0.94 33.36 1.96
XA1-1 0.46 23.86 0.02 34.87 40.98 8.79 23.72 0.91 16.71 1.93
XA1-2 0.58 12.20 0.15 224.73 43.24 14.05 21.13 0.85 20.84 1.95
XA1-3 0.52 13.50 0.17 100.99 49.06 13.33 5.56 0.86 6.47 1.95
XA1-4 0.57 11.68 0.22 179.12 34.90 15.13 20.55 0.91 27.80 1.96
XA1-5 0.42 24.17 0.45 118.39 40.00 17.60 16.19 0.88 25.31 1.96
XA1-6 0.08 70.00 0.001 7.19 36.00 25.88 48.25 0.91 3.29 1.97
XA1-7 0.51 19.30 0.02 38.61 25.29 0.36 8.71 0.67 0.86 1.04
XA1-8 0.51 15.71 0.01 13.43 26.70 0.29 2.78 0.72 1.29 0.93

However, there is unusual enrichment of many different elements related to hydrothermal activity (Table 2). Some geochemical indicators can indicate the hydrothermal materials in the samples as follows. According to the correlation curve of Al/(Al + Fe + Mn) and Fe/Ti [44], a few samples have the characteristics of both hydrothermal and normal sediments. Co/Zn is significantly lower in hydrothermal sediments than in normal sediments. It can have a higher value of 2.5 on average in seafloor iron-manganese concretions [45]. U/Th usually shows the redox conditions. There may be hydrothermal origin when its value is much higher than 1 [9]. In marine sedimentary rocks, Ba/Sr is generally lower than 1, but it can rise to more than 1 if there are abundant hydrothermal deposits [46]. Our samples in different wells generally have lower Co/Zn values, and some samples have extremely high U/Th and Ba/Sr values (Table 5), which indicate the presence of hydrothermal materials in the samples. Generally, hydrothermal sediments have different geochemical characteristics of REEs, such as high LaN/CeN, low Y/Ho, ∑REE, LREE/HREE, and especially positive Eu/Eu* [14,27,47]. In our samples, Y/Ho is mainly distributed between 25 and 44 (Table 5), which is generally lower than the usual sediments ranging from 44 to 74 [24]. Although there are negative Eu/Eu* in the samples, some higher LaN/CeN ratios and lower ∑REE and LREE/HREE ratios can be found in Table 3. The corresponding horizon with typical hydrothermal sediments in Zhangjiajie has 2.78 Eu/Eu*, 2.05 LaN/CeN, and only 29 Y/Ho. Their ∑REE are extremely low [14]. The black-shale-hosted barite deposits have similar geochemical features in Xinghuang-Tianzhu [20].

Therefore, there are two contradictory occurrences based on different geochemical markers. Our research suggests that the influence of hydrothermal activity on our wells is small because they are far from hydrothermal vents. Meanwhile, the materials from hydrothermal activities can diffuse and deposit over a long distance, which leads to wide changes in some contents of trace and rare earth elements. This is why different indicators show the geochemical properties with both the features of normal and hydrothermal deposits. In the process of hydrothermal materials downwards diffusion in the slope and basin, FY1 is the most affected because it is nearest to Zhangjiajie. The distributions of U/Th, Co/Zn, LaN/CeN, and LREE/HREE, which are related to hydrothermal sedimentation, can be found in Figure 5 [12,14]. There is a clear and similar trend in FY1 and XJ1, as well as in HY1, which is consistent with the reduced hydrothermal activity. In contrast, there is no trend in XA1. Therefore, these indexes can partly reflect the hydrothermal sedimentation. XA1, located in deep-water basin, needs more distance and time to migrate and deposit with hydrothermal materials. According to the distance from Zhangjiajie, the decreasing influences from hydrothermal activity are FY1, XJ1, HY1, and XA1.

Figure 5 The longitudinal distribution of some indicators tracing hydrothermal activity in different wells. Note: U/Th and LaN/CeN are higher with more muscular hydrothermal activities, but Co/Zn and LREE/HREE are opposite.
Figure 5

The longitudinal distribution of some indicators tracing hydrothermal activity in different wells. Note: U/Th and LaN/CeN are higher with more muscular hydrothermal activities, but Co/Zn and LREE/HREE are opposite.

5.4 Element enrichment

Molybdenum, U, Ni, and V are sensitive elements for redox conditions, and Ba is an important ore-forming element. The redox conditions can be identified with standardized Mo and U enrichment [48,49]. While some samples were formed with anoxic conditions, most samples were in a euxinic environment, and a few samples in each well were in a suboxic environment (Figure 6). In general, U/Th ≥ 1, Ni/Co ≥ 5, V/(V + Ni) ≥ 0.54, V/Cr ≥ 2, and δU ≥ 1 indicate anoxic and reduced sedimentary environments. δU is obtained by the formula δU = 2U/(U + Th/3) [16,31]. Although most samples were in strongly reducing environment, a few samples were in a suboxic environment in each well. The samples show a suboxic environment with two or three indices in Table 5 are the same within Figure 6 based on the indices of U and Mo. Comparing these redox indices, it is believed that δU or V/(V + Ni) may not be useful enough for judging the redox conditions but can combine with others. Based on different geochemical indicators, most samples are in a euxinic/anoxic environment. The suboxic environment occurred in a single piece (FY1-1) in the upper part of FY1, some samples (XJ1-1 to XJ1-3) in the upper part of XJ1, some samples (XA1-7 and XA1-8) in the lower part of XA1, and some (HY1-2, HY1-3, and HY1-8) in the upper part of HY1. This is a very regular sequence in which the suboxic environment from the bottom upwards agrees with the distance from Zhangjiajie. That order is also the same trend as the influence of hydrothermal activity among these wells.

Figure 6 Mo enrichment factor (MoEF) vs U enrichment factor (UEF). The model is modified after [47].
Figure 6

Mo enrichment factor (MoEF) vs U enrichment factor (UEF). The model is modified after [47].

In the Ni–Mo deposits of Zunyi and Zhangjiajie, the barite deposits of Xinhuang-Tianzhu, and the same strata in other places of South China, many researchers have found some significant minerals, such as millerite, hyalophane, sphalerite, chalcopyrite, gersdorffite, barite, pyrite, and Mo–sulfur–carbon–phase [17,20,23,28,32]. Most minerals are sulfide, except barites and hyalophane are sulfate. There is no doubt that there was an abundant source of sulfur at that time. Meanwhile, hydrothermal activity is also an important sulfur source [49]. Interestingly, these minerals mainly include the highly enriched metal elements (Table 2). With enough sulfur, some metal elements can easily transform as sulfide minerals and then enrich in an euxinic/anoxic environment [13,16,20]. Although Ba is not a redox-sensitive element, it has a similar enrichment trend with the other metal elements in different wells (Figure 7). Many different sulfide minerals are found in the strata, but barium sulfate is present as large-scale barite deposits in Xinhuang-Tianzhu at the same time. Because barium sulfide can be hydrolyzed in water, it cannot be stable. The transformation from sulfide ions to sulfate ions is an oxidation. Therefore, there had some oxidizing processes in the early Cambrian. Some studies have shown that there is increasing oxidation in surface water but anoxic and sulfur-rich environments in deep water [50,51]. This is in accordance with different samples showing their geochemical features on redox conditions, which are mostly in euxinic/anoxic and some in suboxic environments. This sedimentary environment is also beneficial for the enrichment of organic matter. Biological activity and adsorption are also helpful for element enrichment [28]. The horizons that are highly enriched in the elements and TOC are basically the same (Table 1).

Figure 7 The vertical distribution of absolute abundance in different elements and wells.
Figure 7

The vertical distribution of absolute abundance in different elements and wells.

There are apparent discrepancies based on the correlation between the content of five elements and the tracing indices of hydrothermal activity, as well as between the elements and some indicative major elements in Table 6. There is a consistent and high correlation between the enriched elements and the tracing indices in FY1, HY1, and XJ1. Some correlation coefficients are even high. However, the only association in XA1 is LeN/CeN. Al2O3 and TiO2 indicate the terrestrial clast input. Therefore, XA1 suggests that the hydrothermal materials may have experienced long-distance migration before being deposited, and the source is more dependent on the supply of distal debris. Except for Ag and U, the enrichment elements in XA1 are much weaker than those in FY1 and XJ1 (Table 2). The differences from V to Cr in each well, which are moderately enriched, are much more obvious between XA1 and the other wells. Different elements are basically enriched in the bottom of FY1, middle and lower part of XJ1, middle part of HY1, and upper part of XA1 (Figure 7). According to this tendency, the enriched horizon moves upwards in FY1, XJ1, HY1, and XA1. This is also the same order mentioned above. Furthermore, the abundance between Ni and Mo is very different in all wells, although Ni–Mo ores deposited are generally developed (Table 2). Mo usually comes from seawater and biological activity within an anoxic sedimentary environment, while Ni easily comes from hydrothermal fluid [17,52]. It has been demonstrated that Ni has a hydrothermal-fluid addition in the same strata in Zunyi [49]. In most sediments within euxinic environments, the Mo/TOC ratios do not exceed 100 and are usually below 50 [53]. Our samples are lower than 50 except for HY1-1 (57.6), HY1-7 (55.0), and XJ1-6 (116.2), indicating that Mo has a normal source. Ni, as the micronutrient element, has much higher Ni/TOC ratios than Mo/TOC based on Table 1. In turn, FY1, XJ1, HY1, and XA1 follow the same order as the AA distribution of Ni, which means that the hydrothermal activity provided the additional Ni abundance. But Mo has no sequence like that. All these phenomena adequately explain how hydrothermal materials were deposited in these wells and improved the element enrichment. All wells mainly were normal marine sedimentation except FY1 was a little different. The influences of hydrothermal activity rapidly decrease in the plane. XA1, located in a deep-water basin at that time, was far from the hydrothermal vents and had completely different geochemical characteristics compared with the other wells.

Table 6

The relationship between enrichment elements and the other indicators, including the tracing indices and the content of Al2O3 and TiO2 in different wells

Well Element Correlate with the tracing indexes of hydrothermal activity Correlate with Al2O3 Correlate with TiO2
U/Th Co/Zn LaN/CeN LREE/HREE
FY1 Ba\Mo\Ni –\–\ −0.82\–\ 0.59\0.57\ −0.57\–\ –\–\ –\0.56\
–\–\– –\−0.50\−0.46 0.65\0.70\0.88 –\−0.56\−0.42 –\–\– –\–\–
HY1 –\0.71\ −0.66\−0.49\ –\– –\–\ –\– –\–\
−0.44\−0.49
U\V
0.71\0.87\0.49 −0.71 –\–\– −0.43\–\– –\–\– –\–\–
XJ1 –\–\ –\–\ –\–\ –\–\ –\–\ –\–\
0.79\0.48\0.63 −0.70\–\−0.70 −0.62\–\−0.85 −0.62\–\−0.90 −0.62\–\−0.85 −0.41\–\−0.89
XA1 –\–\ –\–\ 0.69\0.67\ –\–\ 0.69\0.67\ 0.72\0.72\
–\–\– –\–\– 0.70\0.77\0.50 –\–\– 0.70\0.77\0.50 0.69\0.79\0.52

Note: U/Th and LaN/CeN are higher with stronger hydrothermal activity, while Co/Zn and LREE/HREE are opposite. “–” means that there is no correlation when the correlation coefficient is less than 0.40 or more than −0.40.

6 Conclusions

There is mainly normal marine sedimentation in different wells, but hydrothermal materials have a vast diffusion and deposition range and greatly affect change in some trace and rare earth elements. However, FY1, nearest to Zhangjiajie where it has been confirmed that there are many hydrothermal activities and Ni–Mo ores, has a complex material source and is more influenced by hydrothermal activity than the others.

All wells have an euxinic/anoxic sedimentary environment with enough sulfur. However, there were still some suboxic environments during the early Cambrian. The order, FY1, XJ1, HY1, and XA1, in turn, is shown in many elemental geochemistry. The same sequence can be found in the different parts of the suboxic environment and element enrichment strata and in the hydrothermal-fluid-addition Ni abundance. The influence of hydrothermal activity is regular with the distance.

Hydrothermal activities indeed provide the materials for element enrichment and strengthen the reduction in depositional environments. These wells, which are continually deposited with hydrothermal materials, have some geochemical differences, especially in the distant XA1. However, hydrothermal activities occur intensively in a region where sedimentary ore-bearing rocks have developed, and their effect is diminishing on the plane.

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Acknowledgements

The authors thank Prof. Jianhua Guo (South Central University) and Prof. Chuanbo Shen (Chinese Geology University, Wuhan) for the help on the samples and analysis. Sincere thanks and gratitude to the Natural Science Foundation of Hunan province (No. 2023JJ30231 and 2023JJ30239) for funding this research article.

  1. Author contributions: This work was carried out by notable contributions from all authors. All authors contributed critically to the drafts and gave final approval for publication. All authors have read and agreed to the published version of the manuscript.

  2. Conflict of interest: The authors declare no conflict of interest.

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Received: 2023-11-14
Revised: 2024-01-29
Accepted: 2024-02-03
Published Online: 2024-03-18

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/geo-2022-0614
Keywords for this article
black shales; hydrothermal sedimentation; low Cambrian; northwestern Hunan; element geochemistry
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Theoretical magnetotelluric response of stratiform earth consisting of alternative homogeneous and transitional layers
  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
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  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
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  19. Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes
  20. Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities
  21. Platform margin belt structure and sedimentation characteristics of Changxing Formation reefs on both sides of the Kaijiang-Liangping trough, eastern Sichuan Basin, China
  22. Enhancing attapulgite and cement-modified loess for effective landfill lining: A study on seepage prevention and Cu/Pb ion adsorption
  23. Flood risk assessment, a case study in an arid environment of Southeast Morocco
  24. Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression
  25. Evaluation of Viaducts’ contribution to road network accessibility in the Yunnan–Guizhou area based on the node deletion method
  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
  27. Element geochemical differences in lower Cambrian black shales with hydrothermal sedimentation in the Yangtze block, South China
  28. Three-dimensional finite-memory quasi-Newton inversion of the magnetotelluric based on unstructured grids
  29. Obliquity-paced summer monsoon from the Shilou red clay section on the eastern Chinese Loess Plateau
  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
  31. Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster
  32. New formula to determine flyrock distance on sedimentary rocks with low strength
  33. Assessing the ecological security of tourism in Northeast China
  34. Effective reservoir identification and sweet spot prediction in Chang 8 Member tight oil reservoirs in Huanjiang area, Ordos Basin
  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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