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Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China

  • Dan Wang , Yong Fu EMAIL logo , Lie-Meng Chen EMAIL logo , Qian Hu and Zhi Zhang
Published/Copyright: August 15, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

There has been a longstanding debate on whether plutonic and subvolcanic rocks have a petrogenetic connection, and the Jurassic Shuikoushan caldera complex in South China serves as an ideal case study for unraveling this geological puzzle. SIMS U–Pb dating of zircon indicates the formation age of plutonic (granodiorite) and subvolcanic rocks (dacite porphyry and rhyolite porphyry) from the Shuikoushan caldera complex at ca.159 Ma. In terms of geochemical composition, granodiorite and dacite porphyry exhibit higher levels of MgO (1.98–3.63%), MnO (0.07–0.11%), FeOt (5.12–6.15%), Sr (342–547 ppm), and Ba (754–1200 ppm) compared to the rhyolite porphyry. Conversely, they show lower concentrations of Rb (2.04–27.1 ppm), as well as lower ratios of Rb/Sr (0.004–0.055) and Rb/Ba (0.004–0.023). The distinct but complementary geochemical characteristics between these rock types are evident in the Shuikoushan caldera complex. Overall, zircon grains from all three rock types exhibit similar negative ε Hf(t) values (–8.1 to –12.4) with high δ 18O values (8.3–9.7‰), suggesting a common source region. Based on lithological and geochemical evidence, it is proposed that the rhyolite porphyry represents extracted melt from the mush reservoir. In contrast, the granodiorite and dacite porphyry are residual melts enriched in cumulates. This interpretation supports the idea that crystal–melt segregation processes were crucial in connecting the late Jurassic subvolcanic and plutonic rocks within the Shuikoushan complex.

Keywords: subvolcanics; pluton; crystal; melt segregation; Shuikoushan caldera complex; South China

1 Introduction

In recent decades, establishing a comprehensive framework connecting plutonic and volcanic rocks has been pivotal for comprehending deeper magmatic processes [1,2]. Nevertheless, substantial questions remain regarding the spatiotemporal and evolutionary relationships in which they undergo or co-evolve. Lately, studies using geochemistry and isotopes, analyzing zircon grains and whole-rock samples, have offered crucial insights into the intricate processes of storage, evolution, recharge, and crystal–melt segregation within magmatic systems [2–5]. The widely accepted notion posits that silicic plutons represent residual melt after volcanic eruptions. This framework needs a complementary cumulate residue in the shallow crust, suggesting a direct link between volcanic and plutonic phases in that geological layer [3,6,7]. However, identifying cumulates in silicic rocks proves challenging due to the cryptic nature of accumulation across geochemical criteria, mineral chemical, microstructural, and outcrop scales [8,9].

To investigate the connection between plutonic and volcanic phases, the coexisted intrusive and volcanic phases within the same igneous complex are essentially important. The Shuikoushan caldera complex in South China consists mainly of granodiorites, associated with well-exposed subvolcanic facies igneous rocks, including the dacite porphyry and rhyolite porphyry, and they thus offer an opportunity to explore the petrogenetic relationship between volcanic and plutonic rocks. In this article, we provide new insights into the above questions through U–Pb ages and Hf–O isotopes of zircon, and bulk rock major-trace element data.

2 Regional geology and sample description

Bounded by the Songpan-Ganzi Terrane to the west, the Dabie-Qinling orogenic belt to the north, and the Indochina Craton to the south, the South China Craton is geographically delineated (Figure 1a). This Craton is composed of the Yangtze and Cathaysia Blocks, which were welted together during the Neoproterozoic time [12]. Voluminous silicic magmatism occurred during the Early Jurassic – Late Cretaceous period in South China. This magmatic activity spans an extensive area, exceeding 90,790 km2 for volcanic rocks and approximately 135,000 km2 granitoids, extending from coastal southeast China through the Wuyi region into the Nanling Range [13]. The majority of the silicic volcanics are temporally and spatially associated with granitoids.

Figure 1 The South China Craton is composed of Yangtze Block and Cathaysia Block, and the Shuikoushan plutonic-subvolcanic complex is located near the suture zone between the Yangtze Block and Cathaysia Block (a). The Shuikoushan regional geological map showing Shuikoushan caldera complex consists of an unusual coexisting assemblage of plutonic (granodiorite) and subvolcanic rocks (dacite porphyry and rhyolite porphyry) (b). (a) was modified after Mao et al. [10] and (b) was modified after Yang et al. [11].
Figure 1

The South China Craton is composed of Yangtze Block and Cathaysia Block, and the Shuikoushan plutonic-subvolcanic complex is located near the suture zone between the Yangtze Block and Cathaysia Block (a). The Shuikoushan regional geological map showing Shuikoushan caldera complex consists of an unusual coexisting assemblage of plutonic (granodiorite) and subvolcanic rocks (dacite porphyry and rhyolite porphyry) (b). (a) was modified after Mao et al. [10] and (b) was modified after Yang et al. [11].

The Shuikoushan igneous complex is situated at the junction of the Yangtze Block and the Cathaysia Blocks (Figure 1a). The subvolcanic facies of dacite porphyry and rhyolite porphyry, as well as intermediate-acid intrusive body consisting predominantly of granodiorite, have simultaneously exposed in this complex due to continental uplift and erosion. The globally renowned Shuikoushan Zn–Pb–Cu–Au deposit and Kangjiawan Zn–Pb–Ag deposit are believed to be linked to these granodiorites [11,14,15].

The primary exposure of rhyolite porphyry is in the Xinmengshan region, situated to the northwest of the Shuikoushan district (Figure 1b). K-feldspar phenocrysts, varying in size from 5 to 15 mm, coexist with quartz and plagioclase phenocrysts (Figure 2). Accessory minerals in the assemblage contain oxides, zircon, apatite, and titanite. The mean groundmass content reaches up to 80%, and weak Pb–Zn–Ag mineralization has been locally observed in the rhyolites.

Figure 2 The hand specimens (a–c) and petrographic features (d–f) for both plutonic and subvolcanic rocks. Abbreviations: Mic, mica; Qtz, quartz; Amp, amphibole; Pl, plagioclase.
Figure 2

The hand specimens (a–c) and petrographic features (d–f) for both plutonic and subvolcanic rocks. Abbreviations: Mic, mica; Qtz, quartz; Amp, amphibole; Pl, plagioclase.

Granodiorite and its eruptive counterpart, dacite porphyry, are distributed in the Laoyachao and Laomengshan regions, respectively (Figure 1b). Both rock types share similar mineral assemblages, with ubiquitous phenocrysts including plagioclase, biotite, quartz, hornblende, and magnetite, accompanied by accessory minerals like zircon, oxides, apatite, and titanite (Figure 2). The groundmass exhibits textural variability, ranging from granophyric and microcrystalline in granodiorite to glassy in dacite porphyry.

We also collected granodioritic samples from the Laoyachao mining adits in the Shuikoushan district. These samples were taken at locations distanced from ore veins. Rhyolite porphyry and dacite porphyry samples were sourced from Xinmengshan and Laomengshan, respectively. All samples are relatively fresh, and their specific locations are depicted in Figure 1b.

3 Analytical procedures

3.1 Zircon SIMS O isotopes and U–Pb dating

Zircon crystals were obtained from crushed rock samples (1.5 kg) by magnetic and heavy liquid separation techniques at the Bureau of Geology and Mineral Resources (BGMR) in Hebei Province, China. Subsequently, zircon crystals were meticulously chosen under a binocular microscope and embedded within epoxy discs. The epoxy discs were polished to expose the smooth zircon grains. Each zircon grain was meticulously documented using both transmitted and reflected light images, alongside cathodoluminescence (CL) images, to reveal their structures. The specimen mount underwent a vacuum-coating process with a high-purity gold film before SIMS analyses.

Oxygen isotope analysis and U–Pb dating of zircon were conducted on an SIMS (Cameca IMS 1280) at the Institute of Geology and Geophysics (IGG), Chinese Academy of Sciences (Beijing). The methods for analyzing zircon oxygen isotopes remain consistent with the methodology outlined by Li et al. [16]. The Cs+ primary ion beam intensity was adjusted to approximately 2 nA and accelerated at 10 kV. The analytical point had a diameter of about 20 μm. Oxygen isotopes were measured utilizing a multi-collection mode. The measurements were conducted over 20 cycles, each lasting 3 s, achieving in-house precision surpassing 0.2‰ (1σ). Calibration of instrumental mass fractionation utilized the in-house standard (zircon of Penglai) [16]. To evaluate external uncertainties in oxygen isotope measurements, the zircon standard (Qinghu), treated as an unknown, was alternately examined and analyzed alongside the zircon sample. The Qinghu zircon standard yielded a weighted mean δ 18O values of 5.39 ± 0.13‰ for six analyses, in line with the recorded value of δ 18O = 5.4 ± 0.2‰ (2SD) [17].

Following the oxygen isotope analysis, the mounted zircon crystals underwent re-polishing for U–Pb dating. These measurements were conducted in close proximity to the O isotope analysis spot using the same instrument at IGG. The detailed analytical processes closely followed those described by Li et al. [18]. The size of analytical ellipsoidal pits was around 30 μm × 20 μm. Each measurement was comprised 7 cycles with an overall analytical duration of ca. 14 s. Calibration for Pb/U utilized zircon standard (91500) with a Th content of 29 ppm and a U content of 81 ppm [19]. Zircon U–Pb isotope ratios were calculated against the analyzed ratios of the zircon standard (Plésovice) [20]. The long-term uncertainty for 206Pb/238U measurements of the zircon standard applied to the unknown samples was 1.5% (1SD). The measured composition was adjusted for common Pb using non-radiogenic 204Pb. The Isoplot/Ex software was used for data reduction [21]. To evaluate external uncertainties in U–Pb analyses calibrated against the standard (Plésovice), the standard (Qinghu) was alternately measured alongside the unknown zircons. A total of five analyses on Qinghu generated a U–Pb Concordia age of 161.0 ± 1.2 Ma (2SD), in harmony with the recommended value of 159.2 ± 0.2 Ma within the uncertainties [17].

3.2 LA-MC-ICPMS Lu–Hf isotope analyses

Zircon Hf isotope analysis was accomplished at the State Key Laboratory of Geological Processes and Mineral Resources (SKLGRMR), China University of Geosciences (Wuhan), China. The analytical system comprises a laser ablation system (Geolas-2005 excimer Arf) and a Neptune multi-collector ICPMS (LA-MC-ICPMS). Lu–Hf isotope measurements were conducted on the same domains of zircon crystals previously analyzed for U–Pb–O isotopes. A 44-μm diameter ablated pit is used for this study. Each analysis consists of a 3-s background signal collection and a 50-s signal capture. The related procedures closely adhere to those reported by [22]. ICPMSDateCal was used for Offline selection and integration of the signal, as well as mass bias calibrations [23]. Assuming identical fractionation, the value 176Lu/175Lu = 0.02656 was used for interference corrections of 176Lu on 176Hf [24]. The interference of 176Yb on 176Hf was rectified by applying the mass bias acquired online, assuming 176Yb/173Yb = 0.79639 [25]. Zircon (91500) was employed as the external standard. Zircon TEM and GJ-1 were used as unknowns alongside analyzing samples to check external uncertainties of Lu–Hf analyses calibrated against 91500 standards. The external standard zircon 91500 was used for calibration. Zircon TEM and GJ-1 were analyzed alongside the samples as unknowns to monitor external uncertainties in Lu–Hf analysis calibrated against the standard (91500).

3.3 Major-trace elements of whole-rock

The fresh rocks were chosen for analyses of major and trace elements after petrographic examination. The analysis of major element oxides was performed on fused glass beads using a RANalytical Axios-advance (Axios PW4400) X-ray fluorescence spectrometer at the State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences (IGCAS). Analytical precision, assessed using the National standards GSR-1 and GSR-3, reveals analytical errors within the range of 1–5%. The determination of loss on ignition involved heating powder (1 g) at 1,100°C for 1 h.

The inductively coupled plasma mass spectrometer (Perkin-Elmer Sciex ELAN 6000) was used for trace elements at IGCAS. Related procedures closely follow those delineated by Qi et al. [26]. The powdered sample (ca. 50 mg) was dissolved in a mixture (HNO3 + HF) using a high-pressure Teflon bomb for 48 h at approximately 190°C. Signal drift during counting was monitored by an internal Rh standard solution. The standard of GBPG-1 was employed for measurement quality control. The experimental errors for all elements were within 5%.

4 Results

4.1 Zircon U–Pb dating

Zircon crystals from the dacite porphyry (samples LMS-01) and rhyolite porphyry (samples XMS-01) exhibit identical morphology and internal structures. The majority of zircon crystals exhibit colorless, transparent, and euhedral characteristics, ranging from 50 to 200 μm in length, with varied aspect ratios (2:1 to 4:1). Concentric zoning is prevalent in most grains under CL images. Occasionally, some grains contain mineral inclusions and inherited cores (Figure 4a).

Twenty-two analyses were conducted from dacite porphyry sample LMS-01 (Table 1). These grains exhibit variable U (153–1340 ppm) and Th (13–364 ppm), with high Th/U (0.08–0.33) and relatively low common Pb (f 206 ＜ 0.5%). A concordant age of 159.5 ± 1.4 Ma (2σ, MSWD = 0.97) was obtained, consistent with the weighted mean 206Pb/238U age of 159.5 ± 1.6 Ma (n = 22, MSWD = 0.97) (Figure 3a).

Table 1

SIMS zircon U–Pb dating of the dacite porphyry and rhyolite porphyry

Sample/ [U] [Th] Th/U 207Pb ±σ 206Pb ±σ 207Pb ±σ t 207/206 ±σ t 207/235 ±σ t 206/238 ±σ
spot # ppm ppm ratio 235U % 238U % 206Pb % Ma Ma Ma
Dacite porphyry
LMS-1@1 767 302 0.394 0.17645 2.39 0.0262 1.79 0.04892 1.58 144.0 36.7 165.0 3.7 166.5 2.9
LMS-1@2 372 13 0.034 0.16679 2.67 0.0254 2.06 0.04765 1.69 81.8 39.7 156.6 3.9 161.6 3.3
LMS-1@3 991 120 0.121 0.16754 2.06 0.0252 1.51 0.04816 1.41 107.0 33.0 157.3 3.0 160.6 2.4
LMS-1@4 153 108 0.704 0.17551 3.10 0.0260 1.71 0.04896 2.58 145.7 59.5 164.2 4.7 165.5 2.8
LMS-1@5 1225 248 0.202 0.16518 1.78 0.0248 1.51 0.04834 0.94 116.0 22.1 155.2 2.6 157.8 2.4
LMS-1@6 693 223 0.322 0.15908 2.03 0.0239 1.52 0.04822 1.36 110.0 31.7 149.9 2.8 152.4 2.3
LMS-1@7 884 141 0.159 0.17364 1.86 0.0256 1.51 0.04912 1.09 153.6 25.3 162.6 2.8 163.2 2.4
LMS-1@8 986 335 0.340 0.16535 2.31 0.0246 1.50 0.04876 1.75 136.2 40.7 155.4 3.3 156.6 2.3
LMS-1@10 560 89 0.159 0.18016 2.53 0.0260 1.55 0.05033 2.00 210.2 45.8 168.2 3.9 165.2 2.5
LMS-1@12 371 33 0.090 0.16362 3.36 0.0249 1.64 0.04775 2.93 86.7 68.1 153.9 4.8 158.3 2.6
LMS-1@13 534 72 0.134 0.16818 2.35 0.0250 1.52 0.04882 1.79 139.4 41.4 157.8 3.4 159.1 2.4
LMS-1@14 829 128 0.154 0.16975 1.99 0.0254 1.55 0.04851 1.25 124.1 29.1 159.2 2.9 161.6 2.5
LMS-1@15 451 303 0.672 0.16464 2.21 0.0247 1.50 0.04844 1.62 120.6 37.8 154.8 3.2 157.0 2.3
LMS-1@16 957 215 0.225 0.15945 2.34 0.0237 1.55 0.04874 1.75 135.5 40.7 150.2 3.3 151.2 2.3
LMS-1@19 921 102 0.111 0.17310 1.98 0.0252 1.53 0.04979 1.26 185.4 29.2 162.1 3.0 160.5 2.4
LMS-1@21 493 47 0.096 0.16741 2.34 0.0251 1.50 0.04831 1.79 114.4 41.8 157.2 3.4 160.0 2.4
LMS-1@22 778 364 0.468 0.17028 2.08 0.0249 1.53 0.04962 1.41 177.4 32.6 159.7 3.1 158.5 2.4
LMS-1@23 1340 350 0.261 0.17331 1.86 0.0257 1.50 0.04886 1.10 141.3 25.6 162.3 2.8 163.7 2.4
LMS-1@24 549 289 0.527 0.17309 2.30 0.0255 1.52 0.04923 1.72 159.0 39.7 162.1 3.4 162.3 2.4
LMS-1@25 1007 240 0.238 0.16912 2.00 0.0251 1.52 0.04883 1.29 139.5 30.1 158.7 2.9 159.9 2.4
LMS-1@26 479 111 0.232 0.17209 2.39 0.0254 1.51 0.04917 1.85 155.9 42.7 161.2 3.6 161.6 2.4
LMS-1@27 243 166 0.681 0.17549 2.99 0.0248 1.55 0.05137 2.56 257.4 57.8 164.2 4.5 157.8 2.4
Rhyolite porphyry
XMS-1@1 274 416 1.517 0.16556 2.93 0.0249 1.60 0.04832 2.45 114.8 56.9 155.6 4.2 158.2 2.5
XMS-1@2 349 134 0.385 0.16771 2.67 0.0249 1.55 0.04878 2.18 137.1 50.4 157.4 3.9 158.8 2.4
XMS-1@5 1584 419 0.264 0.16738 2.94 0.0250 2.68 0.04847 1.21 122.3 28.3 157.1 4.3 159.5 4.2
XMS-1@6 816 159 0.195 0.16510 3.92 0.0251 1.57 0.04764 3.59 81.5 83.1 155.2 5.7 160.0 2.5
XMS-1@7 392 459 1.170 0.17178 2.96 0.0252 1.53 0.04949 2.54 171.2 58.2 161.0 4.4 160.3 2.4
XMS-1@8 1545 221 0.143 0.16888 2.08 0.0252 1.52 0.04869 1.43 132.8 33.3 158.5 3.1 160.2 2.4
XMS-1@9 342 332 0.971 0.16378 3.08 0.0245 1.51 0.04850 2.69 123.9 62.1 154.0 4.4 156.0 2.3
XMS-1@10 822 421 0.513 0.16696 2.28 0.0249 1.56 0.04861 1.66 129.1 38.7 156.8 3.3 158.6 2.4
XMS-1@12 641 176 0.274 0.17507 3.28 0.0247 1.57 0.05141 2.89 259.3 65.0 163.8 5.0 157.3 2.4
XMS-1@13 1327 345 0.260 0.16838 2.10 0.0248 1.51 0.04918 1.47 156.6 34.0 158.0 3.1 158.1 2.4
XMS-1@14 792 191 0.241 0.16813 2.45 0.0246 1.55 0.04959 1.90 175.9 43.7 157.8 3.6 156.6 2.4
XMS-1@15 261 215 0.825 0.16365 2.87 0.0249 1.58 0.04761 2.39 80.1 55.8 153.9 4.1 158.7 2.5
XMS-1@16 988 146 0.147 0.17581 2.04 0.0259 1.55 0.04917 1.32 156.0 30.6 164.4 3.1 165.0 2.5
XMS-1@21 362 420 1.158 0.16941 2.90 0.0256 1.51 0.04804 2.48 101.4 57.6 158.9 4.3 162.8 2.4
XMS-1@22 149 124 0.833 0.18818 4.07 0.0253 1.50 0.05399 3.78 370.7 83.0 175.1 6.6 160.9 2.4
XMS-1@23 484 199 0.412 0.17514 2.34 0.0255 1.51 0.04986 1.79 188.7 41.2 163.9 3.6 162.2 2.4
XMS-1@24 194 287 1.479 0.16879 3.63 0.0249 1.55 0.04918 3.29 156.5 75.2 158.4 5.3 158.5 2.4
XMS-1@25 963 396 0.412 0.17505 2.71 0.0254 1.55 0.04996 2.22 193.1 50.9 163.8 4.1 161.8 2.5
XMS-1@26 1093 229 0.209 0.17073 2.19 0.0249 1.51 0.04967 1.58 179.8 36.3 160.0 3.2 158.7 2.4
Figure 3 Zircon U–Pb Concordia diagrams for dacite porphyry (a) and rhyolite porphyry (b).
Figure 3

Zircon U–Pb Concordia diagrams for dacite porphyry (a) and rhyolite porphyry (b).

Nineteen analyses from rhyolite sample XMS-01 were acquired (Table 1). They exhibit high U (149–1584 ppm) and Th (124–419 ppm) contents with varying Th/U ratios (0.14–1.52). f 206 values are also less than 0.5%. All the examinations are concordant with analytical uncertainties. A Concordia age of 159.5 ± 1.1 Ma (2σ, MSWD of concordance = 0.77) is yielded, consistent within uncertainties with the weighted mean 206Pb/238U age of 159.5 ± 1.8 Ma (n = 19, MSWD = 0.32) (Figure 3b).

The crystallized age of the granodiorite has been dated to 158.3 ± 1.2 Ma and 158.8 ± 1.1 Ma by SIMS U–Pb dating of zircon [11,14], similar to those ages of dacite porphyry and rhyolite in this study (Figure 4a–c).

Figure 4 SIMS zircon 206Pb/238U age (a–c), plots of zircon ε Hf(t) values (d–f), and plots of zircon δ 18O values (g–i) for both plutonic and subvolcanic rocks in the Shuikoushan complex. Data source for granodiorite from Yang et al. [11]. The plutonic and subvolcanic rocks were almost synchronously emplaced in the late Jurassic and have similar zircon ε Hf(t) and δ 18O values.
Figure 4

SIMS zircon 206Pb/238U age (a–c), plots of zircon ε Hf(t) values (d–f), and plots of zircon δ 18O values (g–i) for both plutonic and subvolcanic rocks in the Shuikoushan complex. Data source for granodiorite from Yang et al. [11]. The plutonic and subvolcanic rocks were almost synchronously emplaced in the late Jurassic and have similar zircon ε Hf(t) and δ 18O values.

4.2 Major-trace elements of whole rock

The granodioritic have large variable SiO2 (58–65 wt%) as reported by Yang et al. [11]. The dacite porphyry samples in this research have similar compositions but exhibit relatively narrow ranges of SiO2 (60.9–62.3 wt%), FeOt (5.1–5.5 wt%), and MgO (2.0–2.4 wt%) (Table 2). There is a compositional gap between the rhyolite porphyry and granodiorite-dacite porphyry (∼5 wt% interval for SiO2); some samples within the gap exhibit textural and compositional evidence of mixing. From the granodiorite-dacite porphyry to rhyolite, SiO2 increase, Na2O, MnO, TiO2, and P2O5 gradually decrease, whereas MgO, CaO, and K2O decrease drastically, but Al2O3 and FeOt remain nearly constant (Figure 5).

Table 2

Major and trace elements of the dacite porphyry and rhyolite porphyry from the Shuikoushan complex Yu and Liu [27]

Sample LMS-1 LMS-2 LMS-3 LMS-4 LMS-5 LMS-6 LMS-7 XMS-1 XMS-2 XMS-3 XMS-4 XMS-5 XMS-6 XMS-7
Yu and Liu [27] Yu and Liu [27]
Major element (wt%)
SiO2 61.4 61.4 62.0 60.9 62.1 62.2 62.3 62.7 61.0 60.9 70.4 67.0 66.7 69.0 69.8 66.9 69.7 72.2 70.9 68.8
Al2O3 15.1 15.0 15.2 15.0 14.9 15.1 15.4 16.8 15.0 15.1 15.6 16.4 17.3 16.1 15.0 16.2 16.2 16.8 17.2 17.0
Fe2O3 tot 5.28 5.21 5.50 5.30 5.12 5.14 5.43 6.65 6.41 6.00 5.93 7.23 6.60 7.06 6.31 7.34 5.96 4.14 5.23 6.47
MgO 2.29 2.09 2.35 2.14 1.98 2.07 2.38 1.70 3.63 2.23 0.236 0.355 0.456 0.106 0.136 0.140 0.120 0.060 0.060 0.050
CaO 2.75 2.11 2.19 2.36 2.44 3.16 2.85 2.20 2.34 3.23 0.216 0.081 0.080 0.073 0.351 0.073 0.069 0.050 0.030 0.060
Na2O 4.68 4.92 4.39 4.80 4.31 4.17 4.03 2.67 3.37 3.95 0.848 0.571 0.786 0.891 0.801 0.547 1.014 0.020 0.030 0.040
K2O 4.63 4.54 4.52 4.65 4.65 4.64 4.49 3.74 4.69 4.29 0.205 0.269 0.359 0.041 0.075 0.111 0.094 0.010 0.040 0.010
MnO 0.109 0.09 0.085 0.089 0.073 0.1 0.093 0.07 0.1 0.09 0.010 0.019 0.037 0.008 0.032 0.019 0.017 0.080 0.010 0.010
TiO2 0.625 0.634 0.638 0.629 0.627 0.623 0.644 0.590 0.680 0.550 0.683 0.716 0.749 0.691 0.658 0.710 0.714 0.460 0.420 0.400
P2O5 0.301 0.299 0.310 0.297 0.298 0.300 0.309 0.210 0.250 0.174 0.106 0.094 0.300 0.319 0.088 0.082 0.040 0.040 0.150
L.O.I 3.02 2.79 2.92 2.70 3.24 3.26 2.28 2.04 3.22 4.09 6.51 6.95 7.27 6.70 6.23 7.16 6.98 7.20 6.33 6.30
Total 100.2 99.1 100.0 98.9 99.8 100.8 100.2 99.4 100.7 100.4 100.8 99.7 100.4 101.1 99.7 99.4 100.9 101.1 100.3 99.3
Trace element (in ppm)
Li 23.6 25.1 19.6 19.8 22.4 14 24.1 54.8 37.7 37.8 39.4 34.6 33.3 24.6
Be 1.39 1.73 2.29 1.39 1.9 1.99 1.35 2.7 1.44 0.394 0.286 0.806 0.439 0.328
Sc 10.9 11.3 11.1 11.5 10.7 5.37 10.5 1.6 7.4 6.71 6.44 3.82 5.48 5.35
V 92 93.6 91.3 92.3 92 49.1 91.3 108 131 80.6 111 89.0 104 91.1
Cr 12.3 16.4 15.1 15.2 14.1 9.31 17.7 67.9 48.9 23.9 42.1 25.4 35.9 21.1
Co 50.8 46.5 47.3 51.7 41.9 28.1 60.8 37.3 25.6 24 31.5 38.6 29.5 30.2
Ni 5.52 6.32 5.77 5.97 6.36 3.44 6.95 6.07 3.83 7.33 7.68 6.38 5.28 5.81
Cu 18.8 20.3 23.1 20.6 18.1 9.0 19.9 15.5 7.9 33.2 23.6 15.0 15.8 16.7
Zn 86.5 83.1 90.0 83.9 85.6 50.9 87.4 32.5 32.6 43.1 31.3 31.9 34.8 29.5
Ga 17.9 17 17.5 17.9 17.1 10.7 18 17.2 17.6 17.6 17.2 13.7 15.8 15.8
Ge 2.00 1.81 1.61 1.76 2.14 0.92 1.7 1.88 1.89 1.34 1.44 1.89 1.85 1.42
As 1.38 1.3 1.28 1.21 0.859 0.779 0.934 22.3 34 17 4.31 19 38.1 30.7
Rb 13.1 17.1 27.1 2.04 4.71 7.82 4.8 143 136 127 141 145 92.7 136
Sr 543 518 496 530 511 342 547 159 469 256 117 240 336 331
Y 22.2 20.7 22.1 22.8 21.9 13.9 21.3 24.7 14.6 16.9 23.9 16.5 13.4 13.2
Zr 167 155 160 154 153 94.6 140 154 155 160 151 139 173 160
Nb 15.3 15.0 15.0 14.9 15.5 9.1 14.7 15.9 15.7 16.6 15.3 14.6 16.6 16.0
Mo 0.435 0.334 0.293 0.374 0.301 0.097 0.455 0.633 0.638 0.411 0.277 0.584 0.64 0.413
Cs 4.44 3.31 3.31 3.8 4.49 3.03 4.49 3.40 6.92 15.5 0.386 1.95 1.92 1.71
Ba 1190 1170 1200 1200 1190 754 1100 76.3 169 282 196 81.9 140 142
La 46.1 37.2 39.1 48.7 40.1 27.4 43.6 29.6 48.9 48.4 46.0 41.0 62.2 44.7
Ce 87.1 73.1 74.2 93.8 80.7 51.5 82.9 65.2 101 89 87.6 84.2 111 91.7
Pr 9.91 8.36 8.68 10.8 9.29 5.37 9.53 6.22 11.4 10.7 10.5 9.05 13.8 9.64
Nd 35.8 30.4 31.2 38.3 34.1 19.1 34.3 22.3 41.9 38.6 37.3 32.9 49 33.8
Sm 6.43 5.79 5.95 6.64 6.17 3.64 6.16 5.16 7.11 6.71 6.72 5.70 8.46 5.83
Eu 1.70 1.51 1.57 1.76 1.55 0.95 1.62 1.79 2.00 1.69 1.72 1.33 2.13 1.58
Gd 5.28 4.63 4.73 5.82 5.01 3.19 5.05 7.13 6.01 5.12 5.60 4.68 6.80 4.97
Tb 0.855 0.727 0.782 0.854 0.81 0.491 0.788 1.4 0.867 0.737 0.878 0.683 0.885 0.748
Dy 4.2 3.74 3.85 4.22 4.17 2.51 4.13 7.99 3.65 3.46 4.6 3.22 3.81 2.99
Ho 0.885 0.801 0.804 0.849 0.827 0.498 0.825 1.41 0.639 0.657 0.967 0.604 0.633 0.579
Er 2.37 2.14 2.29 2.4 2.34 1.47 2.16 3.43 1.63 1.86 2.61 1.68 1.73 1.63
Tm 0.347 0.318 0.345 0.339 0.37 0.22 0.319 0.423 0.222 0.266 0.383 0.25 0.212 0.214
Yb 2.23 1.95 2.11 2.23 2.12 1.23 2.04 2.33 1.44 1.7 2.55 1.4 1.55 1.49
Lu 0.332 0.265 0.295 0.323 0.312 0.213 0.303 0.272 0.216 0.262 0.337 0.213 0.248 0.198
Hf 5.08 4.52 4.35 4.53 4.06 2.65 3.8 4.19 4.07 4.28 4.08 3.88 4.89 4.8
Ta 1.95 1.73 1.76 1.7 1.75 1 1.74 2.64 1.6 1.72 1.65 1.57 1.81 1.7
W 271 228 244 260 195 138 329 155 137 121 185 222 172 184
Pb 30.2 25.5 26.2 27.5 25 15.9 25.8 27.6 47.8 29.3 18 33.5 57.4 33.8
Th 14.2 12.1 13.2 15.8 13.1 8.39 13.3 11.3 12.8 13.5 13.1 12.8 14.5 13.3
U 2.62 2.38 2.22 2.39 2.57 1.02 2.53 1.08 1.1 1.42 1.48 1.03 1.17 1.24
Zr/Hf 32.9 34.3 36.8 34.0 37.7 35.7 36.8 36.8 38.1 37.4 37.0 35.8 35.4 33.3
Nb/Ta 7.87 8.68 8.53 8.79 8.86 9.09 8.44 6.03 9.79 9.68 9.30 9.31 9.19 9.41
Eu/Eu* 0.89 0.89 0.90 0.87 0.85 0.85 0.89 0.90 0.94 0.88 0.86 0.79 0.86 0.89
Rb/Ba 0.011 0.015 0.023 0.002 0.004 0.010 0.004 1.874 0.805 0.450 0.719 1.770 0.662 0.958
Rb/Sr 0.024 0.033 0.055 0.004 0.009 0.023 0.009 0.899 0.290 0.496 1.205 0.604 0.276 0.411
La/Yb 20.7 19.1 18.5 21.8 18.9 22.3 21.4 12.7 34.0 28.5 18.0 29.3 40.1 30.0
Figure 5 Hark diagrams for both plutonic and subvolcanic rocks (a–i). Data source for granodiorite from Yang et al. [11]. The data related to granodiorite in the following figures are all sourced from Yang et al. [11].
Figure 5

Hark diagrams for both plutonic and subvolcanic rocks (a–i). Data source for granodiorite from Yang et al. [11]. The data related to granodiorite in the following figures are all sourced from Yang et al. [11].

The dacite porphyry, granodiorite, and rhyolite porphyry exhibit coherent linear correlations in the elemental diagrams (Figure 6). Granodiorite samples are located between dacite porphyry and rhyolite porphyry in terms of some trace elements, such as Rb, Sr, and Ba. The three types of intrusive and extrusive felsic igneous rocks share similar chondrite-normalized REE patterns, distinguished by enrichment of LREEs and negative Eu anomalies. They have La/Yb of 13–40 and Eu/Eu* ratios of 0.79–0.94 (Figure 7a). Positive Pb and negative Sr, P, Ti, and Nb anomalies are observed in the spider diagrams normalized by primitive mantle (Figure 7b).

Figure 6 The binary diagrams of element concentrations and elemental ratios for the Shuikoushan subvolcanic-plutonic complex (a–f). The Rayleigh fractionation modeling (e and f) was evaluated from the granodiorite, dacite porphyry, and rhyolite porphyry by mass balance equation C o = f m × C l + (1 − f m) × C s, where C o, C l, and C s are trace element concentrations in the initial melt, extracted melt, and residual solids, respectively, and f m is the fraction of melt extracted from the mush reservoir. Tick marks are melt fraction increments of 0.1, ranging from f m = 0.9–0.1. The extracted melt was controlled by plagioclase-apatite-biotite-ilmennite-k-feldspar-quartz-magnetite-zircon mineral assemblage. The solid–melt partition coefficients used for modeling are from Schaen et al. [28] and Rollinson [29].
Figure 6

The binary diagrams of element concentrations and elemental ratios for the Shuikoushan subvolcanic-plutonic complex (a–f). The Rayleigh fractionation modeling (e and f) was evaluated from the granodiorite, dacite porphyry, and rhyolite porphyry by mass balance equation C o = f m × C l + (1 − f m) × C s, where C o, C l, and C s are trace element concentrations in the initial melt, extracted melt, and residual solids, respectively, and f m is the fraction of melt extracted from the mush reservoir. Tick marks are melt fraction increments of 0.1, ranging from f m = 0.9–0.1. The extracted melt was controlled by plagioclase-apatite-biotite-ilmennite-k-feldspar-quartz-magnetite-zircon mineral assemblage. The solid–melt partition coefficients used for modeling are from Schaen et al. [28] and Rollinson [29].

Figure 7 Chondrite-normalized rare earth element patterns (a) and primitive mantle-normalized trace element diagrams (b) for the subvolcanic and plutonic rocks. Chondrite and primitive mantle values from Rudnick and Gao [30].
Figure 7

Chondrite-normalized rare earth element patterns (a) and primitive mantle-normalized trace element diagrams (b) for the subvolcanic and plutonic rocks. Chondrite and primitive mantle values from Rudnick and Gao [30].

4.3 In situ zircon Hf–O isotope compositions

Hf and O isotope compositions of zircon grains from samples LMS-1 for dacite porphyry and XMS-1 for rhyolite porphyry were obtained by SIMS (Table 3). Zircon crystals from sample LMS-1 have homogenous Hf and O isotope compositions. Their present 176Hf/177Hf ratios range from 0.282327 to 0.282436, matching with ε Hf(t) values of −12.4 to −8.5 with two-stage Hf model ages (T 2DM) of 1.7–1.9 Ga. These zircon grains have high δ 18O values from 8.2 to 9.5‰.

Table 3

Zircon Hf–O isotope of the dacite porphyry and rhyolite porphyry from the Shuikoushan complex

Spot Age (Ma) 176Hf/177Hf ±σ 176Lu/177Hf ±σ ε Hf(t) ±σ T 2DM f Lu/Hf δ 18O (‰) ±2σ
Dacite porphyry
LMS-1@1 166.5 0.282405 0.000011 0.002060 0.000096 −9.6 0.7 1,610 −0.94 8.84 0.31
LMS-1@2 161.6 0.282436 0.000010 0.001708 0.000143 −8.5 0.6 1,548 −0.95 8.91 0.35
LMS-1@3 160.6 0.282409 0.000009 0.001815 0.000022 −9.5 0.6 1,602 −0.95 8.34 0.21
LMS-1@4 165.5 0.282367 0.000011 0.001081 0.000024 −10.8 0.7 1,679 −0.97 9.43 0.29
LMS-1@5 157.8 0.282395 0.000009 0.001702 0.000057 −10.1 0.6 1,631 −0.95 8.87 0.34
LMS-1@6 152.4 0.282387 0.000012 0.001386 0.000066 −10.4 0.7 1,647 −0.96 9.13 0.32
LMS-1@7 163.2 0.282398 0.000009 0.001364 0.000032 −9.8 0.6 1,620 −0.96 8.20 0.26
LMS-1@8 156.6 0.282383 0.000010 0.001283 0.000008 −10.4 0.6 1,651 −0.96 9.03 0.31
LMS-1@12 158.3 0.282415 0.000012 0.001261 0.000103 −9.3 0.7 1,589 −0.96 9.31 0.31
LMS-1@13 159.1 0.282395 0.000008 0.000979 0.000033 −9.9 0.6 1,626 −0.97 8.58 0.38
LMS-1@14 161.6 0.282417 0.000009 0.001385 0.000048 −9.2 0.6 1,584 −0.96 9.07 0.35
LMS-1@15 157.0 0.282397 0.000009 0.000787 0.000009 −9.9 0.6 1,622 −0.98 9.34 0.27
LMS-1@16 151.2 0.282354 0.000011 0.001138 0.000021 −11.6 0.7 1,710 −0.97 8.42 0.38
LMS-1@19 160.5 0.282414 0.000010 0.001259 0.000059 −9.3 0.6 1,590 −0.96 8.85 0.26
LMS-1@21 160.0 0.282372 0.000010 0.000468 0.000011 −10.7 0.6 1,668 −0.99 8.87 0.26
LMS-1@22 158.5 0.282369 0.000009 0.001408 0.000010 −10.9 0.6 1,679 −0.96 9.18 0.29
LMS-1@23 163.7 0.282385 0.000010 0.001438 0.000017 −10.2 0.6 1,645 −0.96 9.06 0.37
LMS-1@24 162.3 0.282368 0.000011 0.001266 0.000057 −10.9 0.7 1,679 −0.96 9.18 0.47
LMS-1@25 159.9 0.282390 0.000009 0.001876 0.000024 −10.2 0.6 1,641 −0.94 9.05 0.25
LMS-1@26 161.6 0.282365 0.000009 0.001139 0.000021 −11.0 0.6 1,684 −0.97 9.06 0.33
LMS-1@27 157.8 0.282372 0.000008 0.000778 0.000012 −10.8 0.6 1,670 −0.98 9.51 0.30
LMS-1@28 159.5 0.282327 0.000010 0.001227 0.000009 −12.4 0.6 1,760 −0.96
Rhyolite porphyry
XMS-1@1 158.2 0.282388 0.000010 0.001536 0.000013 −10.3 0.6 1,643 −0.95 8.40 0.36
XMS-1@2 158.8 0.282413 0.000009 0.001663 0.000118 −9.4 0.6 1,594 −0.95 8.97 0.22
XMS-1@5 159.5 0.282427 0.000009 0.001480 0.000049 −8.9 0.6 1,567 −0.96 9.62 0.22
XMS-1@6 160.0 0.282387 0.000009 0.000982 0.000027 −10.2 0.6 1,641 −0.97 9.24 0.26
XMS-1@7 160.3 0.282390 0.000011 0.001122 0.000013 −10.1 0.7 1,636 −0.97 9.36 0.33
XMS-1@8 160.2 0.282406 0.000009 0.001271 0.000019 −9.6 0.6 1,605 −0.96 8.98 0.33
XMS-1@9 156.0 0.282375 0.000010 0.000632 0.000030 −10.7 0.6 1,664 −0.98 9.43 0.46
XMS-1@10 158.6 0.282400 0.000010 0.001516 0.000008 −9.8 0.6 1,620 −0.95 9.06 0.33
XMS-1@12 157.3 0.282392 0.000011 0.001250 0.000037 −10.1 0.6 1,634 −0.96 9.16 0.63
XMS-1@13 158.1 0.282419 0.000010 0.001427 0.000015 −9.2 0.6 1,582 −0.96 9.12 0.26
XMS-1@14 156.6 0.282391 0.000010 0.001413 0.000024 −10.2 0.6 1,638 −0.96 9.14 0.29
XMS-1@15 158.7 0.282414 0.000010 0.001736 0.000030 −9.3 0.6 1,592 −0.95 8.99 0.35
XMS-1@16 165.0 0.282414 0.000009 0.001461 0.000007 −9.2 0.6 1,589 −0.96 9.40 0.41
XMS-1@21 162.8 0.282367 0.000010 0.000475 0.000033 −10.8 0.6 1,676 −0.99 9.09 0.34
XMS-1@22 160.9 0.282427 0.000012 0.000356 0.000058 −8.7 0.7 1,559 −0.99 9.38 0.36
XMS-1@23 162.2 0.282390 0.000009 0.000829 0.000019 −10.0 0.6 1,634 −0.98 9.34 0.43
XMS-1@24 158.5 0.282383 0.000009 0.000278 0.000014 −10.3 0.6 1,645 −0.99 8.69 0.33
XMS-1@25 161.8 0.282405 0.000008 0.000877 0.000061 −9.5 0.6 1,605 −0.97 8.98 0.33
XMS-1@26 158.7 0.282255 0.000012 0.001222 0.000036 −14.9 0.7 1,900 −0.96 8.75 0.29

The acquired Hf–O isotopes conform to a normal Gaussian distribution and produced negative mean ε Hf(t) values of −10.2 ± 0.4 (2σ) (Figure 8a) with relatively high δ 18O values (average = 8.9 ± 0.2‰) (2σ) (Figure 8b). Similarly, the zircon grains in sample XMS-1 demonstrate a uniform composition of Hf and O isotopes. Their 176Hf/177Hf ratios differ from 0.282388 to 0.282427, ε Hf(t) values from −10.8 to −8.7, and T 2DM from 1.7 to 1.9 Ga. A normal distribution was observed, with a negative mean ε Hf(t) value of −9.8 ± 0.3 (2σ) (Figure 8c). Additionally, they consistently display high δ 18O values of 8.4–9.6‰, resulting in a mean δ 18O values of 9.1 ± 0.1‰ (2σ) (Figure 8d). The dacite porphyry and rhyolite porphyry have similar zircon H–O isotopic compositions to those of the granodiorites (Figure 4d–i), and the latter show strong negative ε Hf(t) values (−10.2 to −7.4) and high δ 18O values (8.4–9.7‰) [11].

Figure 8 Histogram of zircon ε Hf(t) and δ 18O values for the dacite porphyry (a, b) and rhyolite porphyry (c, d).
Figure 8

Histogram of zircon ε Hf(t) and δ 18O values for the dacite porphyry (a, b) and rhyolite porphyry (c, d).

5 Discussion

The early studies have dated the plutonic granodiorite nearby the Shuikoushan district using single-grained zircons U–Pb dating technique at 172–181 Ma [31–33]. Zircons may contain mineral inclusions and single-grained mineral dating probably produces unreliable U–Pb ages [34]. The granodiorite was confirmed to have been emplaced at 158–159 Ma through new in situ SIMS zircon U–Pb dating [11,14]. However, ages of the subvolcanic rocks, such as the dacite porphyry and rhyolite porphyry, were not dated and the geochronological framework of subvolcanic and plutonic rocks has not been constructed. The spatiotemporal connection between intrusive and extrusive phases in the region remains unclear.

5.1 Spatiotemporal connection between intrusive and extrusive phases

Our SIMS U–Pb dating of zircon shows that the dacite porphyry and rhyolite porphyry have crystallization ages of 159.5 ± 1.6 and 159.5 ± 1.8 Ma, respectively (Figure 3). These two types of porphyry rocks have the same ages and were synchronously emplaced in the Shuikoushan region. The granodiorite in the region also has a zircon SIMS U–Pb age of 158.3 ± 1.2 Ma [11], nearly similar to those of the dacite porphyry and rhyolite porphyry (Figure 4a), and is more likely to have been formed by produced at the final magmatic stage of the Shuikoushan caldera complex. Therefore, both the plutonic (granodiorite) and subvolcanic (dacite porphyry and rhyolite porphyry) phases of the Shuikoushan caldera complex were emplaced and generated by the same tecno-thermal event (Figure 4a–c).

5.2 Magmatic source of the subvolcanic–plutonic complex

The isotopic composition of magmatic zircon records the nature of the source region and the magma evolution from which they crystallized [35,36]. Magmatic zircons from the granodiorite, dacite porphyry, and rhyolite porphyry have similar ranges of Hf–O isotope compositions (Figures 4 and 9), suggesting that they were derived from similar source regions. All zircon grains show enriched Hf [ε Hf(t) = −8.1 to −12.4)] and relatively high O isotope compositions (δ 18O = 8.3–9.7‰), indicating that the subvolcanic-plutonic rocks were likely derived from fractional melting of crustal materials. Moreover, both the intrusive and extrusive rocks from the Shuikoushan caldera complex are characterized by relatively low MgO (0.05–2.29 wt%), FeOt (4.14–7.34 wt%), Ni (3.44–7.68 ppm), and Cr contents (9.31–67.9 ppm) and relatively high SiO2 (60.9–72.2 wt%) and K2O contents (0.01–4.69%) (Figure 5). These geochemical compositions are identical to those of melts generated by mafic rocks with medium-high K contents in the lower crust [42]. Compared to the Mesozoic supracrustal-derived S-type granites in the Cathaysia Block, the subvolcanic-plutonic rocks in the Shuikoushan district have relatively high zircon ε Hf(t) values (−8.1 to −12.4) and low zircon δ 18O values (8.3–9.7‰) (Figure 9a). Additionally, their zircon ε Hf(t) values are within the evolution range of the lower continental crust (Figure 9b). They also have Mesoproterozoic two-stage Hf model ages (1.75–1.90 Ga), consistent with the peak formation age of the Cathaysia crust at 1.85 Ga [43]. Thus, the subvolcanic–plutonic rocks of the Shuikoushan caldera complex are proposed to have originated from the same pre-existing lower crust. Subsequently, their parental magmas underwent crystal–melt segregation in the upper crust level during the magma emplacement.

Figure 9 (a) Diagram of Hf vs O isotopes of zircons from Shuikoushan subvolcanic and plutonic rocks. Hf–O isotopes of zircon from supracrustal-derived S-type granite are from Yang et al. [37]. (b) ε Hf(t)-age diagram for zircon from Shuikoushan subvolcanic-plutonic rocks, implying that these rocks formed by reworking of middle-lower continental crust. The contemporary mantle-derived (Qinghu granite) and mantle–crust mixing magmas (Lisong granite) in adjacent regions are from Li et al. [38]. The evolution of depleted mantle with present-day 176Lu/177Hf = 0.0384 and 176Hf/177Hf = 0.28325 [39]. Chondrite with present-day 176Lu/177Hf = 0.0332 and 176Hf/177Hf = 0.282772, continental upper crust and lower crust with 176Lu/177Hf = 0.0093 and 0.022, respectively [40]. δ 18O values of mantle are from Valley et al. [41].
Figure 9

(a) Diagram of Hf vs O isotopes of zircons from Shuikoushan subvolcanic and plutonic rocks. Hf–O isotopes of zircon from supracrustal-derived S-type granite are from Yang et al. [37]. (b) ε Hf(t)-age diagram for zircon from Shuikoushan subvolcanic-plutonic rocks, implying that these rocks formed by reworking of middle-lower continental crust. The contemporary mantle-derived (Qinghu granite) and mantle–crust mixing magmas (Lisong granite) in adjacent regions are from Li et al. [38]. The evolution of depleted mantle with present-day 176Lu/177Hf = 0.0384 and 176Hf/177Hf = 0.28325 [39]. Chondrite with present-day 176Lu/177Hf = 0.0332 and 176Hf/177Hf = 0.282772, continental upper crust and lower crust with 176Lu/177Hf = 0.0093 and 0.022, respectively [40]. δ 18O values of mantle are from Valley et al. [41].

5.3 Crystal–melt segregation processes

The deciphering of genetic relationships between volcanic and plutonic rocks often involves the widespread application of the crystal mush model [3–5,7]. Melt extraction process has been verified by trace elements of zircon and incompatible elements of highly silicic volcanic rocks in southeast China [4,7]. In the Shuikoushan caldera, the granodiorite, dacite porphyry, and rhyolite porphyry display a liner relationship in the Harker diagrams (Figure 5) and trace elemental binary diagrams (Figure 6), indicating that these rocks were probably connected by crystal fractionation. However, there is an obvious compositional gap in these diagrams (Figures 5 and 6). The granodiorite and dacite porphyry have higher MgO, MnO, FeO, and P2O5 than those of the rhyolite porphyry, which may have resulted from the accumulation of aplite and Fe–Mg minerals. Additionally, the former has lower Rb content and Rb/Sr and Rb/Ba ratios but higher Sr and Ba contents than the rhyolite porphyry (Figure 6). Generally, cumulate rocks are rich in compatible element (e.g., Ba, Sr, Eu, and Zr), but depleted in incompatible elements (e.g. Rb) than the extracted melt due to the crystal–melt segregation process [3,7,44]. The granodiorite and dacite porphyry show distinct but complementary geochemical features in comparison to the rhyolite porphyry. Therefore, the granodiorite and dacite porphyry are inferred to be residual cumulate-rich melts, while the rhyolite porphyry represents the extracted melts. It is worth mentioning that the granodiorite exhibits variable compositions, which may result from further differentiation of the interstitial melts in magma reservoirs after extraction of the rhyolite magma.

The Rayleigh model was employed to further validate the processes of crystal–melt segregation using incompatible trace elements (Figure 6e and f). The starting composition (C o) is assumed to be the average values of the granodiorite samples, including Rb = 106 ppm, Ba = 630 ppm, and Sr = 394 ppm. These values are very similar to those of the bulk continent crust of South China [45]. The bulk partition coefficients (D) are Rb = 0.2–0.5, Ba = 2.8–8.0, and Sr = 1.29–3.9. Published partition coefficients are utilized to establish the range for cumulates and extracted melts through bracketing.

The calculated results are shown in the Rb–Sr vs Rb–Ba binary diagrams (Figure 6). The dacite porphyry falls within the cumulate field, while the rhyolite porphyry is situated within the extracted melt field. Based on the above-mentioned geochemical features of the igneous rocks in the Shuikoushan caldera, the granodiorite and dacite porphyry are residual cumulates, while the rhyolite porphyry is highly silicic melts derived from the mush reservoirs. Crytal–melt segregation is therefore an effective mechanism for connecting plutonic and subvolcanic rocks in mush reservoirs.

6 Conclusions

  1. Zircon SIMS U–Pb dating reveals that the granodiorite, dacite porphyry, and rhyolite porphyry are comagmatic and originated from the same source region at ca. 158 Ma. They were produced by partial remelting of the pre-existing lower crust.

  2. The distinct but complementary geochemical features between the subvolcanic and the plutonic rocks, as well as geochemical modeling results, indicate that the magma system underwent crystal–melt segregation in the Shuikoushan region. The granodiorite and dacite porphyry are residual cumulate-rich melts after melt extraction, whereas the rhyolite porphyry is high silicic melt derived from the mush reservoir. Crytal–melt segregation is an effective mechanism connecting the plutonic and subvolcanic rocks in the mush reservoir.

Acknowledgments

We express our gratitude to the geologists at Shuikoushan Nonferrous-metal Corporation for their valuable assistance throughout our field investigations. We extend our heartfelt gratitude to Prof. Jan Barabach and three anonymous reviewers for their invaluable suggestions that helped to significantly improve the manuscript.

  1. Funding information: This work is financially supported by the National Key R&D Program of China (2023YFC2906401) and the National Natural Science Foundation of China (42073046, 42372113).

  2. Author contributions: Conceptualization: Dan Wang, Yong Fu, and Lie-Meng Chen; data curation: Dan Wang and Yong Fu; formal analysis: Dan Wang, Yong Fu, and Lie-Meng Chen; investigation: Dan Wang, Yong Fu, Zhi Zhang, and Qian Hu; resource: Dan Wang, Yong Fu, Lie-Meng Chen, Zhi Zhang, and Qian Hu.

  3. Conflict of interest: The authors declare that there is no conflict of interest.

  4. Data availability statement: Data available on request from the author.

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Received: 2024-01-22
Revised: 2024-03-27
Accepted: 2024-04-02
Published Online: 2024-08-15

© 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-2022-0634
Keywords for this article
subvolcanics; pluton; crystal; melt segregation; Shuikoushan caldera complex; South China
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
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  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
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  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
  15. Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district
  16. 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
  17. Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil
  18. Spatial and temporal changes in ecosystem services value and analysis of driving factors in the Yangtze River Delta Region
  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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