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Volume 30 Issue 5
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New Fractal Evidence of Pacific Plate Subduction in the Late Mesozoic, Great Xing'an Range, Northeast China

  • Late Mesozoic granitoids are widespread in the Great Xing'an Range (GXR), which is part of a large igneous province in eastern China. The geodynamic setting of the Late Mesozoic granitoids is still debated, and there have been two dominant models proposed, subduction and thermal erosion. This study discusses the geodynamic mechanisms from a new perspective on ages of the granitoids and fractal dimensions of their shape. Our results show that granitoids become gradually older from South GXR to North GXR to Erguna Block (EB) in the Jurassic, and opposite in the Cretaceous. The fractal dimensions of the Perimeter-area model (DAP) exhibit the same features. The values of DAP are smaller from South GXR (0.673 1) to North GXR (0.628 0) to EB (0.607 9) in the Jurassic, and larger from South GXR (0.609 6) to North GXR (0.630 2) to EB (0.639 9) in the Cretaceous. This implies that the geometrical irregularities of the granitoids are shaped by subduction rather than thermal erosion. These spatial variations could be best explained by the subduction of the Pacific Plate and consequent granitoid magmatism in the Late Mesozoic, thus providing a new fractal evidence for Pacific Plate subduction mechanism and opening a new possibility method for studing plate movement.
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New Fractal Evidence of Pacific Plate Subduction in the Late Mesozoic, Great Xing'an Range, Northeast China

    Corresponding author: Qiuming Cheng, qiuming.cheng@iugs.org
  • 1. School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
  • 2. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing 100083, China
  • 3. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China

Abstract: Late Mesozoic granitoids are widespread in the Great Xing'an Range (GXR), which is part of a large igneous province in eastern China. The geodynamic setting of the Late Mesozoic granitoids is still debated, and there have been two dominant models proposed, subduction and thermal erosion. This study discusses the geodynamic mechanisms from a new perspective on ages of the granitoids and fractal dimensions of their shape. Our results show that granitoids become gradually older from South GXR to North GXR to Erguna Block (EB) in the Jurassic, and opposite in the Cretaceous. The fractal dimensions of the Perimeter-area model (DAP) exhibit the same features. The values of DAP are smaller from South GXR (0.673 1) to North GXR (0.628 0) to EB (0.607 9) in the Jurassic, and larger from South GXR (0.609 6) to North GXR (0.630 2) to EB (0.639 9) in the Cretaceous. This implies that the geometrical irregularities of the granitoids are shaped by subduction rather than thermal erosion. These spatial variations could be best explained by the subduction of the Pacific Plate and consequent granitoid magmatism in the Late Mesozoic, thus providing a new fractal evidence for Pacific Plate subduction mechanism and opening a new possibility method for studing plate movement.

0.   INTRODUCTION
1.   GEOLOGICAL SETTING
  • The study area located in the GXR (Fig. 2) consists of the Erguna Block (EB), the North Great Xing'an Block (North GXR), and the South Great Xing'an Block (South GXR). These subblocks are separated by the Tayuan-Xiguitu, Hegenshan, and Xar Moron faults (Shi et al., 2010; Ren et al., 1999). The EB, considered as the eastern extension of the central Mongolian microcontinent, is located in the northwestern part of the region. The area is heavily forested, and the block is composed of Proterozoic to Mesozoic strata and granitoids (Zhang et al., 2008). The North GXR and South GXR are characterized by extensive Mesozoic granitoids and volcanic rocks, although some of them were formed during the Paleozoic (Wu et al., 2002), particularly in the South GXR. The Hegenshan suture which indicates the closure of the Paleo-Asian Ocean between the Siberian and North GXR, was finally emplaced in the Permian (Ding et al., 2016; Shao et al., 2012; Li, 2006). Although the closure of the Paleo-Asian Ocean had a minor effect on voluminous granitoid intrusions in the Mesozoic, the subduction of the Pacific Ocean Plate played a key role in magmatic activities in the Jurassic and the Cretaceous (Zhu et al., 2017; Wang et al., 2015; Zhang J H et al., 2010).

2.   MODELS
  • The perimeter-area (P-A) model is a mathematical model expressing the relationship between the perimeter and area of similarly shaped fractals. Taking granitoids in the GXR as an example, the basic formula (Zuo et al., 2009; Wang et al., 2007; Cheng, 1995) is

    where P is the perimeter and A is the area of granite plutons, "∝" means "proportional to", and DAP is the exponent of the power-law relationship. DAP can be further defined as DAP=DP/DA, where DP and DA are the fractal dimensions of the frequency-perimeter model and frequency-area model, respectively. When a box-counting method is applied, DP, DA and DV (volume of the granite plutons) can be estimated by (Cheng, 1995)

    where DP, DA and DV represent one-dimensional, two-dimensional, three-dimensional geometries of the granite plutons, repectively. If log P and log A data show a linear relationship, DAP could be estimated by fitting a straight line using the least squares method. The slope of the line can be taken as an estimate of DAP or DPA (Cheng, 1995).

    In the above equations, C1 and C2 are constants. DAP varies from 0.5 to 1, and DPA varies from 1 to 2. The larger the value of DAP, the flatter the shapes. When DAP=0.5, then PA0.5, regularly shaped sets are implied (e.g., circles or squares). If DAP=1, then PA1, extremely stratified sets are implied. In this limiting situation, perimeter fluctuates synchronously with area.

    This perimeter-area (P-A) model is a special case of the fractal model. The fractal model has the characteristics of self-similarity and multi-scale invariance (Cheng and Cheng, 2018; Cheng, 2017, 1995; Mandelbrot, 1967), which allows for the filtering of the datasets according to the lower-limits of measurement and the errors caused by the multi-scale invariance. Thus, the perimeter-area (P-A) fractal model can be used to reliably calculate the DAP value even when some datasets are excluded.

    The error of the DAP values is subject to the student's t-distribution (Zuo et al., 2009), and can be estimated by

    The error of the DAP values,

    where n is the number of granite plutons, i is an integer from 1 to n.

  • The frequency-area model can be used to characterize the relationship between the frequency and the area of granite plutons in different blocks as

    where A is the area of granite plutons, N is the cumulative frequency of granite plutons with area greater than A, and "∝" means "proportional to". To calculate the exponent D, the slope coefficient of the linear regression line for N versus A can be expressed as

    The value of the exponent D reflects the rate of change rate of the cumulative frequency and area of the granite plutons.

  • The activity index (AI) can be used to quantify the shape of granite plutons in relation to area by

    where A is the area and P is the perimeter. AI ranges from 0 to 10. An AI value of 10 expresses minimum compaction, indicating isometric shapes. If shapes become elongated, the AI value decreases. For a circle, square or triangle, the AI value decreases from 10 to 7.8 to 6.0 (Fig. 3), after which the irregularity of the shapes increases.

    Figure 3.  Schematic granite pluton's activity index according to the perimeter-area fractal model. The larger the area, the stronger the intensity of the magma activity. AI ranges from 0 to 10. The value of 10 expresses minimum compaction and maximum magma activity, meaning isotropic shapes. If the perimeter is a constant, the area could indicate the magma activity.

    The AI value indicates the magmatic activity. Considering the exposed surface of the pluton, the more violent the magmatic activity, the larger the AI value. Compared to the perimeter-area fractal model, the AI value and magmatic activity are positively correlated, which suggests that the variations in magmatic activities correspond to geological events and could be easily detected. The AI is a qualitative parameter rather than a quantitative one which describes the relationship between magmatic activities and fractal dimensions.

3.   MECHANISMS CORRESPONDING TO FRACTAL MODELS
  • The mechanisms of fractal models had been studied for many years and applied to explicate geological phenomenon (Ranguelov and Ivanov, 2017; Mallard et al., 2016; Sornette and Pisarenko, 2003; Turcotte, 2002; Triolo et al., 2000; Korvin, 1992; Lucido et al., 1988).

    This discussion will focus on the main mechanisms that correspond to fractal models and are likely relevant to magmatic activities and shapes of granite plutons. From mathematics and physics points of view, the mechanisms that have been proved to correspond to the generation of fractal models include but are not limited to phase transition (P-T), self-organized criticality (SOC) and multiplicative cascade processes (MCP) (Cheng, 2017; Lovejoy et al., 2009; Newman, 2005).

  • Common phases include liquid phase, solid phase and vapor phase of chemical components which exist under certain pressure and temperature (P-T) conditions. However, multiple phases coexist in the same system such as liquid and vapor in magma and hydrothermal systems under proper P-T conditions in phase transition conditions (Cheng, 2017). For example, the critical point for water is at temperature (374 ℃) and pressure (22 MPa). It has been found that the critical point is so peculiar that close to the critical point, small changes in pressure or temperature result in large changes in density and other density related properties such as heat capacity and the solubility (Cheng, 2017).

  • The phenomena associated with continuous phase transitions are called critical phenomena which are often related to so called self-organized criticality (SOC). SOC is commonly illustrated conceptually with avalanches as piles of sand which generate a fractal model of avalanches (Bak et al., 1987). At the criticality in a SOC phenomenon a small continuous input to the system can cause sudden and discontinuous outputs or avalanches.

  • Multiplicative cascade processes (MCP) involve iterative multiplicative processes across multiple scales, which involve positive or negative feedback processes to generate extreme values that follow fractal models with self-similarities and singularities (Cheng, 2017, 2014; Meakin, 1987).

    The results obtained from analyzing the granitoids' perimeter and area data suggest that the DAP values characterized by the fractal model might be mainly ascribed by superlative events such as slab rollback, slab breakoff, phase transition of magma, etc..

4.   DATASETS
  • The datasets used for the study are drawn from data published in previous studies, include 171 ages in the Jurassic and the Cretaceous (Table S1). The geological map used to analyze the spatial distribution of the granitoids is from the China Geological Survey (Ye et al., 2017; Xu et al., 2008). The newest edition of the 1 : 2.5 million scale geological map can be found at http://dcc.ngac.org.cn. The GXR is divided into subblocks according to Ren (2013). After loading the geological map and ages in ArcGIS 10.2, the DAP value of the P-A model can be estimated by a log-log method from output sheets in statistical software, such as Microsoft Excel 2016.

5.   RESULTS AND DISCUSSION
  • Magmatism during the Late Mesozoic occurred not only in the GXR, but was also widespread in Northeast China and adjacent areas, such as southern and eastern Mongolia, the Korean Peninsula and southwestern Japan (Wang et al., 2006; Jahn, 2004). Wang et al. (2006) argued that the ages of the magmatism were concentrated mainly in four periods: 163–160, 147–140, 125–120, and 116–113 Ma, which suggested that a magmatic succession pulsed from 160 Ma in the Late Jurassic to the Early Cretaceous. Zhang (2010) reported that Jurassic rocks were younger to the West, whereas the Cretaceous rocks became younger to the East, and the 'magma gap' increased eastward. Wu (2011) discussed the geochronology of the Phanerozoic granitoids in northeast China from 50 to 550 Ma based on 425 ages and posited that the ages of the granitoids varied from 50 to 200 Ma and were likely related to pacific subduction with an obvious transition in subduction angle at ca. 150 Ma. Wang et al. (2015) revealed that Jurassic granitoids (200–145 Ma) occured predominately in the GXR and adjacent areas with a northwestward-youngling migration, whereas Cretaceous granitoids (145–100 Ma) occured mainly in the GXR with a southeastward youngling migration.

    The published ages (n=171, Table S1) of the granitoids used in the study show the same spatial characteristics as in Wang et al. (2015). The age peaks of the granitoids are gradually older from South GXR to North GXR to EB in the Jurassic, and exhibit an opposite trend during the Cretaceous (Fig. 4). There is an obvious transition at ca. 145 Ma (Wang T et al., 2015; Wang F et al., 2006; Wu et al., 2005).

    Figure 4.  Plot of the graintes' ages vs. frequency, Great Xing'an Range, Northeast China. The interval age is 5 Ma, the published ages are originated from plutons in Late Mesozoic ranging from 66 to 201 Ma, and the curves shown in the figure have a transition at ca. 145 Ma, respectively.

  • The results of the P-A model for Mesozoic granitoids in the GXR are shown in Figs. 5, 6 and 7. The perimeter and area of granitoids were plotted on a log-log graph. The exponent DAP was calculated using the least squares method. The results show a linear relationship between logarithms of perimeter and area (correlation coefficients R2 > 0.9), suggesting that there is a power law relationship between them. The error of the DAP values was estimated at a confidence interval of 95% (Eq. 7). The slopes (DAP values) for southeast increased from 0.609 6±0.010 to 0.628 0±0.017, 0.671 3±0.010 in the Jurassic, whereas they decreased from 0.639 9±0.033 to 0.630 2±0.027, 0.596 1±0.033 in the Cretaceous, and the corresponding DAP values increased simultaneously (Fig. 5). This method is qualitative and the results are impacted by many factors, such as the scale and the accuracy of the geological map.

    Figure 5.  Log-log plot of perimeter-area relation of granite plutons in the Great Xing'an Range, (a) in the Jurassic, (b) in the Cretaceous (logs base 10). Perimeter and area are values at the geological map at the scale of 2 500 000.

    Figure 6.  (a) Log-log plot of area-frequency of granite plutons in the GXR; (b) Log-log plot of perimeter-frequency of granite plutons in the GXR (logs base 10).

    Figure 7.  Log-log plot of perimeter-area relation of granite plutons in the GXR with activity-index (AI), (a) in the Jurassic, (b) in the Cretaceous (logs base 10). High activity-index, 6–10; middle activity-index, 4–6; low activity-index, 0–4. The perimeter-area model shows a distinguishable relationship among activity-indexes. High activity-indexes approach the axis of area, whereas low activity-indexes stay away from the axis of area.

    In the study, a geological map with a scale of 1 : 2.5 million was used to estimate the fractal dimension of the shape of the granitoids. This meant that magmatic plutons of smaller than 2.5 km2 in size were neglected. According to the fractal model's characteristics of self-similarity and invariance in multi–scale (Cheng, 2017, 1995; Mandelbrot, 1967), the fractal dimension of shapes of the magmatic plutons smaller than 2.5 km2 would be the same as those of magmatic plutons larger than 2.5 km2. The shapes of the plutonic bodies considered were also simplified, which suggested the errors of DAP values should be discussed.

    Cheng (1995) proposed that the P-A model could provide unbiased estimates of the fractal dimensions in two-dimensional space. Zuo et al. (2009) demonstrated that fractal modeling was an effective tool to distinguish between mineral phases using simplified shapes. Mallard et al. (2016) reckoned that the P-A model could distinguish between distributions of large and small tectonic plates. These studies suggested that the fractal model was also effective under complicated circumstances. In order to test the invariance of the fractal model in different cases, we considered the Mesozoic the granite plutons of the GXR (Fig. 8). We selected 90% of the granite plutons at random to calculate DAP; the other part of 10% could be regarded as area and perimeter of the granite plutons were incorrect (Fig. 8b). The lower limits of area value set as 0.000 2 and 0.000 5 could be regarded as the geological map be amplified in scale and the area value less than 0.000 2 and 0.000 5 could not be shown in the map (Figs. 8c, 8d). The DAP value remained stable throughout with an error of ±0.004. Thus, the final results were not affected by the exclusion of some datasets. However, the DAP values are still uncertain and should be further examined in future studies.

    Figure 8.  Different estimated values of DAP in the different cases. (a) The granite plutons in the Mesozoic of the GXR, the granite plutons number is N1=530, which is also shown in Fig. 2; (b) random 90% of N1, in this case, some plutons ignored at random when DAP value was estimated; (c) and (d), the lower limit of area value set as 0.000 2 and 0.000 5 in the 1 : 2.5 million scale geological map. The datasets smaller than 0.000 2 and 0.000 5 were filtered when DAP values were calculated. These figures show the accuracy of DAP roughly.

    The exponent D in the cumulative frequency-area model was estimated, using the least squares method, from a log-log diagram for granitoids in the Jurassic and the Cretaceous (Fig. 6a). The results show that a linear relationship exists between the logarithms of cumulative frequency and area, and both R2 coefficients are larger than 0.93. The cumulative frequency-area exponent D decreases from 2.750 7 in the Jurassic to 1.733 0 in the Cretaceous, implying less magmatic activity and only small-scale plate movement. The exponent D in the cumulative frequency-perimeter model was also estimated from a log-log diagram for granitoids in the Jurassic and the Cretaceous using the least square method (Fig. 6b). The results show an opposite trend with the cumulative frequency-perimeter exponent D increasing from 2.479 4 in the Jurassic to 2.811 8 in the Cretaceous. This finding is explained by the assumption that the larger exponent D, the more violent of the magmatic activity and plate motions.

    Figure 7 shows the relationship between the P-A model and AI. There is a distinguishable relationship among the AI values. The larger the AI, the closer it is to the axis of the area. Figure 7a shows that two parallel lines with different intercepts can be constructed for the P-A model. The upper line with a larger intercept represents granite plutons with lower AIs, whereas the lower line represents granite plutons with higher AIs. If the granite plutons have the same DAP values, the intercept can provide a measure of AI.

  • There are two primary models that have been proposed to address the geodynamic mechanism in the GXR. One is subduction coupled with lithospheric thinning or delamination (Liu et al., 2017; Müller et al., 2016; Wang et al., 2015; Zhang et al., 2010; Gao et al., 2004; Gao et al., 1998), and the other is the thermos-tectonic reactivation model (Wang and Chen, 2017; Tian and Zhao, 2011; Xu, 2007; Lu et al., 2005; Deng et al., 2004; Xu et al., 2004; Xu, 2001; Zheng, 1999). The existence of a thickened crust and the nature of the melt derived from a delaminated crust or asthenosphere in the Late Mesozoic are the key criteria for these two models (Xu et al., 2013; Wu et al., 2003).

    Geochemical and geophysical studies show that a thickened crust (> 60 km) exist due to high pressure mineral inclusions, wave velocities and the presence of adakites (Wang et al., 2017; Zhang J et al., 2017; Zhang Y et al., 2017; Zhang S H et al., 2014; Xu et al., 2013; Tian and Zhao, 2011; Zhang C et al., 2011). Images of P and S wave velocities in East Asia show a dramatically thinning lithosphere (Tian and Zhao, 2011). Zhang et al. (2011) proposed that subduction and a thickened crust could explain the geochronology and geochemistry of Late Mesozoic volcanic rocks. Xu (2013) studied mineral chemistry and oxygen isotopes and reported that the existence of recycled continental crust was evidence for a thickened crust and delamination. Zhang et al. (2014) suggested that a youngling or oldling trend for Mesozoic magmatic rocks was also evidence for lithospheric thinning. Thus, increasing evidence shows that there is historically a thickened crust in East China, including the GXR.

    There was also an obvious youngling or oldling trend in terms of the spatiotemporal of granitoids (Zhang Y et al., 2017; Wang et al., 2015; Zhang et al., 2011; Zhang et al., 2010; Wang et al., 2006; Wu et al., 2005; Jahn, 2004). The same spatiotemporal characteristics were exhibited by the granitoids in this study (Figs. 2, 4). The age peaks of the Jurassic granitoids increased from South GXR, North GXR and EB, and an opposite trend was shown in the Cretaceous (Fig. 4). Additionally, the DAP of the P-A model exhibited a similar trend (Figs. 6, 9). Wang (2015) speculated that the Jurassic granitoids were likely derived from a thickened-melted lower continental crust, while the Cretaceous granitoids were produced from a thinning-melted crust in an extensional setting. All these characteristic can be explained by a model of subduction coupled with lithospheric thinning or delamination; as the subduction zone continued to migrate, the active continental margin and back arc regimes played their roles successively in different parts of the GXR during the Late Mesozoic (Fig. 10). According to the spatial distribution of the Pacific seamount chain and K-Ar ages of basalt, the direction of the Pacific Plate turned from SW to NWW at ca. 130 Ma (Wessel and Kroenke, 2008). The reconstruction of the Pacific Plate indicated that there was a transition at ca. 150 Ma when the plate motion rates were set as 150–200 km/Ma (7.5–10 cm yr-1). Relatively high mean absolute plate motion rates of approximately 9–10 cm yr-1 between 140 and 120 Ma may have been related to transient accelerations in plate motion driven by the successive emplacement of a sequence of magmatic activities (Müller et al., 2016). The age peaks of the granitoids and the DAPs of the P-A model corresponded to Pacific Plate subduction. Subduction was often related to self-organized criticality (SOC), and had been discussed in many papers (Cheng, 2017; Mallard et al., 2016). In the processes of subduction, the phenomena of slab break-off, slab rollback, and variations of plate velocity could be the mechanism of the fractal model. In addition, the geochemistry results of grantoids also showed the temperature of magma was higher in the Jurassic than in the Cretaceous (Wu et al., 2011; Zhang et al., 2010), which could affect the phase transition. For mechanisms corresponding to P-A models, variation in subduction rates and phase transition of magma controlled the DAPs of the shapes of the granitoids, which explained why this model could create the observed variations.

    Figure 9.  Geodynamic evolutional directions of Pacific Plate with the dimension of perimeter-area (DAP) in Late Mesozoic, GXR, Northeast China. The value of DAP is from Fig. 5 calculated by the perimeter-area model. The varity of the evolution DAP from a mighty beginning into a small was consistent with the fluctuating stress of the Pacific Plate subduction from strong to weak. The transition time of ca. 145 Ma is from the plot of the graintes' ages vs. frequency (Fig. 4), combined with International Chronostratigraphic Chart v. 2016/04. Cohen (2013).

    Figure 10.  Sketh map of geodynamic mechanism of the Pacific Plate subdction. The subduction of ocean crust and the rollback of the slab is the key factor for geological events, e.g., granite plutons and volcanics spatial and temporal distribution, in Late Mesozoic, GXR, Northeast China. (a) in the Jurassic, from ca. 201 to ca. 145 Ma, (b) in the Cretaceous, from ca. 145 to ca. 66 Ma. (1) Erguna Block. EB; (2) North GXR; (3) South GXR; SlB. Songliao Basin; NCC. North China Craton.

    The thermos-tectonic reactivation model emphasized thermal processes, rather than subduction and delamination (Wang and Chen, 2017; Xu, 2001). This model was propitious for explaining the thinning effect of a thicken and low density lithosphere (Deng et al., 2004). The reasons that why this model can not explain the spatial and temporal characteristic of granitoids could be stated as follows: (1) Transition. The DAP values for the southeast increased in the Jurassic, whereas they decreased in the Cretaceous. Combined with ages, there was an obvious transition in the DAP values at ca. 145 Ma (Fig. 10), which was difficulty for this model to explain. The possible reasons for this phenomenon include the mantle convection or mantle plume (Schubert et al., 2001). However, it may also be difficult for this model to explain the transition because many studies have suggested that there was no mantle plume in the GXR (Zhu et al., 2017; Zhang et al., 2008); (2) Thermal source region. Previous studies indicated that a thermal source region was beneath the block, and this induced the generation of magma chambers (Wang and Chen, 2017; Zheng et al., 2007; Xu, 2001). However, it is difficult to illustrate why the peaks of the granitoid ages increased in the Jurassic and decreased in the Cretaceous based on this mechanism. Thus, it was very difficult to explain the spatiotemporal characteristics of granitoids and why a low density lithospheric mantle was delaminated (Wu et al., 2003).

    The GXR was reworked or superposed by the tectonic regimes of the Paleo-Asian and Pacific Oceans. The Hegenshan suture, which indicated the closure of the Paleo-Asian Ocean between the Siberian and North GXR, was finally emplaced in the Permian. Although the closure of the Paleo-Asian Ocean has a minor effect on voluminous granitoid intrusions in the Mesozoic, the subduction of the Pacific Ocean Plate played a key role in magmatic activities in the Jurassic and Cretaceous (Ding et al., 2016; Shao et al., 2012; Li, 2006).

    Therefore, Pacific subduction coupled with lithospheric thinning or delamination rather than thermal erosion is most likely the geodynamic mechanism for granitoids in the Late Mesozoic in the GXR.

6.   CONCLUSIONS
  • In the study, the age distribution and the fractal dimension of the P-A model for granite plutons were used to address the issue of Pacific Plate subduction in the GXR. Results of the study are summarized as follows.

    (1) The age peaks of granite plutons are gradually older from the South GXR to North GXR to EB in the Jurassic and the opposite trend is found for the Cretaceous. There is an obvious transition in the direction of plate motion at ca. 145 Ma.

    (2) The DAP values of the granitoids are gradually smaller from South GXR (0.673 1) to North GXR (0.628 0) to EB (0.607 9) during the Jurassic, and gradually larger from South GXR (0.609 6) to North GXR (0.630 2) to EB (0.639 9) in the Cretaceous.

    (3) Combined with ages, the DAP values of the granitoids may also serve as a possible tool for studying plate movement in other studies, although some uncertainties exist in the measured areas and perimeters.

    (4) The spatial and temporal characteristics of the studied granitoids may be best explained by the subduction of the Pacific Plate rather than by thermal erosion during the Late Mesozoic in the GXR, Northeast China.

ACKNOWLEDGMENTS
  • This paper is jointly supported by the National Key R & D Program of China (No. 2016YFC0600501), the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing 295 (No. KLIGIP-2017A03), the National and Nature Science Foundation of China (Nos. 41430320, 41572315). Eric C. Grunsky, from the University of Waterloo, is thanked for suggestions which greatly improve the quality of the manuscript. We thank Ziye Wang and Jian Wang for providing analytical assistance. We are grateful to Prof. Yongqing Chen and another anonymous reviewer for their thoughtful and constructive reviews on the manuscript. The final publication is available at Springer via https://doi.org/10.1007/s12583-019-1216-y.

    Electronic Supplementary Material: Supplementary material (Table S1) is available in the online version of this article at https://doi.org/10.1007/s12583-019-1216-y.

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