Home Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long-period magnetotellurics
Article Open Access

Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long-period magnetotellurics

  • Zhi-He Xu , Guan-Wen Gu EMAIL logo , Ji-Yi Jiang , Fei-Da Li and Xing-Guo Niu
Published/Copyright: February 16, 2023
Become an author with De Gruyter Brill

Abstract

The Hongqiling magmatic Cu–Ni sulfide deposit is one of the most important deposits in the easternmost segment of the Central Asian Metallogenic Belt, northeast China. However, the existence or non-existence of magmatic chambers is still not been determined, which is the key to decipher the formation of Hongqiling Cu–Ni deposit. Therefore, this study proposes to adopt long-period magnetotellurics method to image the deep-seated metallogenic system in Hongqiling Cu–Ni deposit. Two-dimensional (2D) nonlinear conjugate gradient inversion technology allows us to describe entire magma plumbing system, including the mantle-derived magma channels (banded low-resistivity anomalies), deep separated chamber (low-resistivity anomaly C2), and shallow magma conduits (low-resistivity anomaly C1). These results indicated that the mantle-derived primitive magma gave rise to the parental magma for the mafic–ultramafic intrusions in Hongqiling Cu–Ni deposit and triggered the segregation of Cu–Ni sulfides in the shallow chamber. By combining the experimental simulation, geochemistry, geochronology, and geotectonics data in the study area, we suggest that the partial melting processes which produced the large magma plumbing system probably have been triggered by lithospheric delamination.

1 Introduction

As an ultimate case of small intrusions that produced large to giant magmatic deposits, Hongqiling magmatic Cu–Ni sulfide deposit has been concerned and researched widely by geologists [1,2,3,4]. Extensive geochemical and geochronological data have been previously reported and have established some key hypotheses for the mineralization and tectono-magmatic evolution including:

  1. The differentiation of a single mantle-derived magma [5];

  2. Multi-stage sulfide segregation in different magma conduits [6];

  3. A crust–mantle interaction model [7]; and

  4. An asthenosphere underplating lithospheric mantle model [8].

In these models, it is generally accepted that the primitive for these ore-fertile magmas originated from partial melting of the upper mantle and then experienced complex evolution including multi-stage sulfide segregations in deep magma chambers. However, the existence or non-existence of magmatic chambers, and the size and morphology of them at depth, is still not been determined, which brings challenge to the applicability of existing metallogenic models to Hongqiling listed above. Long-period magnetotellurics (MT) has been used in depicting the deep magma plumbing system and structures in the lithospheric domain [9,10]. Based on the long-period MT results and previous studies, we propose a metallogenic model to illustrate the magmatic evolution and the formation of Hongqiling Cu–Ni deposit.

2 Geological setting

The Hongqiling mafic–ultramafic intrusions in the easternmost segment of the Central Asian Orogenic Belt (CAOB) are distributed north side of the northeast Dunhua-Mishan fault [7] (Figure 1a). The CAOB is bounded in the north by the Siberian Block and in the south by the Tarim and North China blocks and has traditionally been referred to as a series of orogenic or fold belts [11,12]. During the period from the Permian to the Triassic, the final closure of the Paleo-Asian Ocean occurred, and extensive ore-bearing mafic–ultramafic intrusions were subsequently extensively emplaced from the west to the east (e.g., Kalatongke, Poyi, Huangshandong, Huangshanxi, Huangshannan, Tianyu, Erbutu, Chajianling, Hongqiling, and Piaohechuan) [13,14,15,16,17,18] (Figure 1a). The formation ages of these intrusions indicate the diachronous closure of the Paleo-Asian Ocean from the west to the east [19,20].

Figure 1 
               (a) Simplified geological map of the CAOB showing the distribution of Cu–Ni–(PGE) sulfide deposits in China, and the sizes of Ni resources. (b) Regional geologic map showing the distribution of the Chajianliang–Hongqiling–Piaohechuan mafic–ultramafic intrusions in the central Jilin area.
Figure 1

(a) Simplified geological map of the CAOB showing the distribution of Cu–Ni–(PGE) sulfide deposits in China, and the sizes of Ni resources. (b) Regional geologic map showing the distribution of the Chajianliang–Hongqiling–Piaohechuan mafic–ultramafic intrusions in the central Jilin area.

3 Long-period MT method

3.1 Data acquisition and processing

Long-period MT data were collected along a north–south-trending section (251 km) from the North China Craton (NCC) to Song Liao Basin (SLB), which vertically cut across the east–west-directed accretion of the East Asian Pre-Pacific Province (Figure 2). This included 22 MT sites at intervals between 3 and 15 km.

Figure 2 
                  Topography and main tectonic boundaries in the central Jilin area. The red rectangle outlines the Hongqiling Cu–Ni deposit. The black circles indicate the long-period MT site locations of the section that originates in the NCC and passes through the middle of the Zhang Guang Cai Ling (ZGCL), before extending to the SLB.
Figure 2

Topography and main tectonic boundaries in the central Jilin area. The red rectangle outlines the Hongqiling Cu–Ni deposit. The black circles indicate the long-period MT site locations of the section that originates in the NCC and passes through the middle of the Zhang Guang Cai Ling (ZGCL), before extending to the SLB.

Long-period MT data were acquired using an Aether 200 instrument (Crystal Earth Co., Ltd., USA) with a collection time of no more less than 10 h and a period range of 0.001–1,000 s. Then, this time series are processed by a statistically robust algorithm [21]. The data from each MT site include five components: two horizontal electric field components (E x and E y ), two horizontal magnetic components (H x and H y ), and one vertical magnetic component (H z ). Information regarding lithospheric geoelectric structure can be embodied by the apparent resistivities (ρ a) and phases (φ) [22]. Figure 3 displays the apparent resistivity (R xy or R yx ) and impedance phase (φ xy and φ yx ) curves of partial sites and the error bars are corresponding to the measurement errors of the observed data. The MT skin depth is the depth at which the MT field attenuates to (1/e) of its amplitude at the surface of the Earth [23] and can be calculated by equation (1):

(1) δ = 2 ρ / ( μ ω ) = 503 ρ / f ,

where δ, ρ, μ, ω, and f represent the skin depth, bulk resistivity, magnetic permeability, angular frequency, and frequency, respectively. In geological terrains with a high resistivity, the MT signal has a larger penetration depth [24,25]. In this study, the bulk resistivity was estimated to be about 300 Ω m, and the lowest frequency was estimated to be 0.003 Hz. The skin depth and effective detection depth were nearly 160 and 100 km, respectively.

Figure 3 
                  Curves of partial long-period MT sites. (a–c) Observed resistivity curves for sites 1, 4, 17, and 20 from the transverse electric (TE) (R
                     
                        xy
                     ) and transverse magnetic ™ (R
                     
                        yx
                     ) model apparent resistivity curves. (a′–c′) The impedance phase curves of the TE (φ
                     
                        xy
                     ) and TM (φ
                     
                        yx
                     ) models.
Figure 3

Curves of partial long-period MT sites. (a–c) Observed resistivity curves for sites 1, 4, 17, and 20 from the transverse electric (TE) (R xy ) and transverse magnetic ™ (R yx ) model apparent resistivity curves. (a′–c′) The impedance phase curves of the TE (φ xy ) and TM (φ yx ) models.

3.2 Analysis and inversion

After data processing, the phase-impedance tensor decomposition is used to estimate the regional dimensions [22]. The pseudosection map of resistivity inversion in Figure 4 shows that the 2D deviations vary with frequency. The value of the 2D deviations is generally less than 0.3 over different frequencies [26]. Only a few sites in low frequencies exhibit large 2D deviation values. This result indicates that the long-period MT section is generally compliance with 2D inversion [27].

Figure 4 
                  Pseudosection map. The 2D deviations vary with frequency.
Figure 4

Pseudosection map. The 2D deviations vary with frequency.

The geoelectric strike can be identified by the rose diagram [22]. Data in the frequency bands 100–1 Hz showed geoelectric strikes in the range N50°E–N60°E and S30°E–S40°E. Data in the frequency bands 0.1–0.001 Hz showed geoelectric strikes in the range N60°E–N80°E and S10°E–S30°E (Figure 5). The NE-NNE regional geological strike directions correlate well with the geoelectric strike. Moreover, the distribution of Carboniferous to Permian subduction-related igneous rocks have been found to be generally related to the north–south-directed subduction that accompanied the east–west closure of the Paleo-Asian Ocean [2]. Thus, the inherent 90° ambiguity could be resolved well and the data were rotated into a S20°E direction [28].

Figure 5 
                  Results of the tensor decomposition and rose diagram of the main electrical axes. The red wedges correspond to the two solutions for the strike direction.
Figure 5

Results of the tensor decomposition and rose diagram of the main electrical axes. The red wedges correspond to the two solutions for the strike direction.

The inversion was consistent with the 2D nonlinear conjugate gradient (NLCG) inversion in the TE and TM modes [29]. The inversion models were discretized by a grid size of 46 × 90 in the X and Y directions with an initial uniform half-space resistivity of 100 Ω m. To ascertain the optimum value for the root mean square (RMS) misfit and the roughness for the 2D NLCG inversion, a number of inversions were performed [25]. We finally choose the parameter τ = 10 for the regularization factor [22]. After 200 iterations, the model responses are well fitted to the observed data, and the preferred model is obtained with an average normalized RMS of 2.12 for all sites.

3.3 Model test

To verify that the preferred model was properly inversed, the long-period MT result was test by some forward modeling (Figure 6). First, the C1 and C2 anomalies are displaced by 100 and 300 Ω m in forward model 1 and model 2. Then, forward calculation was adopted to assess the impact. The average normalized RMS misfits increase from 2.32 to 3.24 in forward model 1, and this trend is more sharply in forward model 2.

Figure 6 
                  Model assessments verify the sensitivity of MT data. In (a)–(c), the preferred inversion model result; the forward model 1 which displaced the C1 and C2 anomalies with the 100 Ω m; the forward model 2 which displaced the C1 and C2 anomalies with the 300 Ω m.
Figure 6

Model assessments verify the sensitivity of MT data. In (a)–(c), the preferred inversion model result; the forward model 1 which displaced the C1 and C2 anomalies with the 100 Ω m; the forward model 2 which displaced the C1 and C2 anomalies with the 300 Ω m.

Figure 7 shows the results of 12 and 13 sites data fitting. The preferred inversion fitting curve (the purple) fits well with the observed data, while the modified model response curve (the green and red) cannot fit well under the period of 10 s. The forward model test results indicate that reliability of C1 and C1 anomalies in preferred inversion.

Figure 7 
                  Site-by-site RMS misfits for the preferred model, the modified model 1 and the modified model 2. (a) The RMS of each site. In (b) and (c), the inversion results of the resistivity and phase curves. The red and blue triangles represent the observed Ryx and Rxy model data. The blue curve, the green curve, and the red curve represent the preferred inversion result, the model 1, and the model 2 results, respectively.
Figure 7

Site-by-site RMS misfits for the preferred model, the modified model 1 and the modified model 2. (a) The RMS of each site. In (b) and (c), the inversion results of the resistivity and phase curves. The red and blue triangles represent the observed Ryx and Rxy model data. The blue curve, the green curve, and the red curve represent the preferred inversion result, the model 1, and the model 2 results, respectively.

3.4 Interpretation of the MT profile result

The high-resolution lithospheric electrical properties were inversed using the 2D NLCG method and long-period MT data (Figure 8). The lithospheric geoelectric structure was found to be generally consistent with the major surface geological features. Based on the locations of regional faults and the similarities and differences in the inversion model, the long-period MT section was laterally divided into three segments: the NCC, ZGCL, and SLB [30] (Figure 8).

Figure 8 
                  (a) Elevation of the long-period section. (b) 2D resistivity model obtained following NLCG inversion of the TM data. The gray curve represents the topographic relief, the black triangle represents the MT site position, and the black cross represents the inversion depth. The low-resistivity geological bodies are represented by C1, C2, C3, C4, C5, C6, and C7, while the high-resistivity geological bodies are represented by R1 and R2.
Figure 8

(a) Elevation of the long-period section. (b) 2D resistivity model obtained following NLCG inversion of the TM data. The gray curve represents the topographic relief, the black triangle represents the MT site position, and the black cross represents the inversion depth. The low-resistivity geological bodies are represented by C1, C2, C3, C4, C5, C6, and C7, while the high-resistivity geological bodies are represented by R1 and R2.

In the ZGCL segment, at sites 7–10, a sudden change in the resistivity was observed, where the value decreased by one order magnitude from 700 to 60 Ω m. According to the previous research, this structure is understood to be loose, fractured, and filled with some low-resistivity filling materials [31] (Figure 8). Moreover, seismic imaging revealed a transition zone between high- and low-velocity anomalies, which is consistent with the site of the long-period MT survey. Thus, we infer that this structure, exposed near site 7, dipped steeply to the north is the Dunhua–Mishan fault [30]. At sites 7–11 and sites 15–17, two low-resistivity anomalies (C1 and C3) were observed with the resistivity values of 101.8 and 101.6 Ω m in the upper crust (Figure 8). The petrophysical parameter of minerals such as the ore-bearing amphibolite pyroxenite, altered gabbro, and Cu–Ni sulfides are characterized by low-resistivity anomalies (Table 1). In addition, according to the deep seismic-reflection sections in the CAOB, the short and strong reflections represent multiple volcanisms [32]. Thus, we infer that the low-resistivity anomalies C1 and C3 may represent the relics of ancient shallow magma chambers emplaced in the Triassic.

Table 1

Petrophysical data for Hongqiling Cu-Ni deposit in Jilin province

Name Number Geological age Specific resistivity (average)
log10  ρ (Ω m)
Altered Gabbro 25 Triassic 2.23
Amphibole pyroxenite 20 Triassic 3.23
Ore-bearing Pyroxenite 6 Triassic 2.06
Ore body 5 Triassic 1.72
Biotite gneiss 23 Ordovician 3.38
Mica schist 10 Ordovician 3.45
Skarn siliceous marble 20 Ordovician 3.44
Amphibole gneiss 10 Archaean 3.96
Granitic gneiss 22 Archaean 4.05
Fracture zone 4 Unknown 2.16

Vertically, the lithospheric geoelectric structure could be generally segmented into three layers. The first layer with an resistivity value ranging from 102.5 to 104.1 Ω m (except for conductor C1) represents crust at a depth from 28 to 40 km [32]. The crustal structure beneath northeastern China as imaged by the Northeast China Extended Seismic (NECESS) Array receiver indicates that the Moho depth varies from 26.7 to 42.3 km from the NCC to the SLB [33]. Thus, we argue that the first geoelectric layer represents the Moho depth (Figure 8). In Section 3.1, we have calculated the skin depth was nearly 160 km. Thus, the second layer with a value of 102.0 to 102.5 Ω m at depths from 52 to 100 km represents the lithospheric mantle, and the third layer with a value of 101.8 to 102.0 Ω m represents upwelling asthenosphere. Geomagnetic three-component depth sounding and terrestrial heat-flow data acquired for the region between Changchun and Tonghua had similar trends to that in our data [34]. Furthermore, previous deep exploration investigations showed that the lithosphere–asthenosphere boundary (LAB) depths in this region are marked on the MT section [35] (Xiong et al., 2011, p. 31). In addition, considering the fact that the study area is located near the Solonker Suture and far away from the Jiayin–Mudanjiang Suture, we infer that its geoelectric structure should be closely related to the evolution of the Paleo-Asian Ocean rather than the evolution of the Paleo-Pacific Ocean. Moreover, Jurassic magmatism (185–158 Ma) in the Hongqiling deposit has been found to be dominated by granitoids with few mafic–ultramafic rocks, which indicates that the degree of upwelling of the asthenosphere and the reworking of the LAB were quite limited during the Late to Middle Mesozoic evolution [3].

4 Discussion

4.1 Partial melting of the mantle

Ore-fertile magma formed by high degree of partial melting of the mantle generally has high Ni/Cu ratio and low Pd/Ir ratio [6]. In geophysics, minute quantities of partial melting can have a tremendous influence on the overall electrical performances [37]. Thus, resistivity can be used to estimate the volume of fluid/melt that is present in the mantle, although the geothermal model of the lithosphere requires simplification prior to calculating the degree of partial melting of the mantle. The geotemperature is 1,300 K at the depth of 50 km, and then, the geothermal gradient increment is 0.4 K/km [33] (Figure 9). The resistivity of olivine, which has a variable fluid content that ranges from 0 to 1,000 ppm, can be obtained using the Grades laboratory database [38]. The Grades laboratory database states that the resistivity of dry olivine with a olivine fluid content of 350 ppm ranges from 60 and 120 Ω m (conductivity of 0.0083–0.0167 S/m) (Figure 9). However, at the same depth, the long-period MT section had a resistivity of ∼50 Ω m (conductivity of 0.02 S/m) between sites 10 and 13, which had lower resistivity than that the olivine fluid content of 350 ppm (Figure 9). The preferred explanation is that Hongqiling ore-bearing mafic-ultramafic intrusions are generally produced by the partially melting of mantle.

Figure 9 
                  Lithospheric mantle resistivity; at a depth of 50–400 km, and a fluid content of 350–1,000 ppm, as computed from laboratory results of Karato and Dai [36].
Figure 9

Lithospheric mantle resistivity; at a depth of 50–400 km, and a fluid content of 350–1,000 ppm, as computed from laboratory results of Karato and Dai [36].

4.2 Mineralization model of Cu–Ni sulfide deposits

Long-period MT section successfully imaged the Dun-Mi structures which formed the magma conduit system for mantle-derived metalliferous magmas intrusion and fluids migration through the crust, as well as the shallow chamber (C1), the deep chamber chamber (C2), and the emplacement of the ore-bearing intrusions.

Positive εNd(t) value (3–5) of Hongqiling mafic-ultramafic intrusions indicated that [6]. The long-period MT section imaged the partial melting magma intruded the middle crust and formed a shallow chamber (C1) (Figure 10a). Wu et al. reported that the extensive distribution of Late Triassic A-type granites was the result of the direct partial melting of the juvenile crust [39]. We note that upwelling in the asthenosphere can realize such high temperatures and that mafic–ultramafic intrusions are generally coeval with the A-type granites in this region [3]. Thus, we suggest that the deep, separated chamber may have assimilated the SiO2-rich A2-type granites that were formed by underplated mantle-derived magma and that this triggered the PGE sulfide segregation prior to emplacement. The PGE-depleted magma subsequently intruded into the shallow chamber (C1) and then underwent variable degrees of crustal contamination before generating the second-stage sulfide segregation [6] (Figure 10b). In summary, the mineralization process involved voluminous, undersaturated, sulfide-mafic magma passing through the large magma plumbing system, where it reached sulfur saturation due to the addition of additional crust materials and triggered sulfide segregation [3]. The lithospheric-scale Dun–Mi structure and its secondary structures control the magma conduit system in the study area (Figure 10c).

Figure 10 
                  Simplified schematic representation of the tectono-magmatic evolution and mineralization in the Hongqiling ore-bearing intrusions. (a) Long-period MT section images of the low-resistivity bodies (C1 and C2). (b) Geological interpretation based on the long-period MT results. (c) The processing of multi-time immiscible segregations in the deep chamber.
Figure 10

Simplified schematic representation of the tectono-magmatic evolution and mineralization in the Hongqiling ore-bearing intrusions. (a) Long-period MT section images of the low-resistivity bodies (C1 and C2). (b) Geological interpretation based on the long-period MT results. (c) The processing of multi-time immiscible segregations in the deep chamber.

The top surface of the low-resistivity body in the upper mantle was consisent with the LAB. The trend of gradual thinning in the first layer indicates that a rapid uplift event occurred in this region. The depth variation in the third layer reflects the various degrees of asthenospheric upwelling, which suggests an extensional geodynamic regime that was probably induced by delamination. The volume of mafic–ultramafic magma in the shallow and deep chambers (C1 and C2) was found to be much larger than the volume of surface ore-related intrusions. This indicates that the strong extensional geodynamics regime in the study area is associated with intensive mantle-derived magmatism.

Three potential geodynamic processes could have formed such a large volume of mafic–ultramafic magma and the associated huge magmatic Cu–Ni–PGE deposits: (i) a mantle plume [39]; (ii) slab breakoff [40]; and (iii) lithospheric delamination [3]. First, it is impossible that a mantle plume existed during the Triassic when the CAOB was strongly associated with the evolution of the Paleo-Asian Ocean. Second, unlike delamination, slab breakoff cannot trigger a significant thermal perturbation in the overriding lithosphere; thus, slab breakoff cannot account for the intensive magmatism or additional diverse nature of the magmatic observations [41,42,43]. Therefore, the generation of intensive mafic–ultramafic magmatism was triggered by the intensive delamination of the lithosphere. Besides, the long-period MT section from the south Inner Mongolia to the Bainaimiao provided an image of the residual lithosphere in the asthenosphere [13]. Thus, we inferred that the genesis of the Hongqiling Cu–Ni deposits is related to lithospheric delamination.

5 Conclusions

  1. The long-period MT section allowed the inversion of entire magma plumbing system, including the mantle-derived magma channels, deep separated chamber, and shallow magma conduits;

  2. By combining the experimental simulation with findings of previous studies, we inclined that the primary magma of Hongqiling was formed by partial melting of the mantle, which are triggered by lithospheric delamination.

Acknowledgments

This research has been funded by the Engineering Research Center of Geothermal Resources Development Technology and Equipment, Ministry of Education, Jilin University and Science and Technology Plan Project, Langfang Heibei province.

  1. Funding information: This research benefited from the support of Science and Technology Research Project in Higher Learning Institutions of Hebei Province (ZC2022106); Science and Technology Plan Project, Langfang (2022013081); Engineering Research Center of Geothermal Resources Development Technology and Equipment, Ministry of Education (22006), Jilin University, Changchun, 130026, China; and Shandong Provincial Engineering Laboratory of Application and Development of Big Data for Deep Gold Exploration Funds (SDK202221).

  2. Author contributions: ZHX designed the surveys and carried them out. GWG performed the geophysical data processing. JYJ, FDL, and XGN draw relevant diagrams and prepared the article with contributions from all co-authors.

  3. Conflict of interest: We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long period magnetotellurics”.

References

[1] Tang ZL, Ren DJ, Xue ZR, Mu YL. Nickel deposits of China. In: Editorial committee of mineral deposits, China, editors. Mineral deposits of China. Vol. 2. Beijing: Geological Publishing House; 1992. p. 59–99.Search in Google Scholar

[2] Xiao WJ, Windley BF, Hao J, Zhai MG. Accretion leading to collision and the Permian Solonker suture, Inner Mongolia, China: Termination of the Central Asian Orogenic Belt. Tectonics. 2003;22:1069.10.1029/2002TC001484Search in Google Scholar

[3] Wu FY, Wilde SA, Zhang GL, Sun DY. Geochronology and petrogenesis of the post-orogenic Cu–Ni sulfide-bearing mafic–ultramafic complexes in Jilin province, NE China. J Asian Earth Sci. 2004;23:781–97.10.1016/S1367-9120(03)00114-7Search in Google Scholar

[4] Hao LB, Zhao X, Boorder DH, Lu JL, Zhao YY, Wei QQ. Origin of PGE depletion of Triassic magmatic Cu–Ni sulfide deposits in the central-southern area of Jilin province, NE China. Ore Geol Rev. 2014;63:226–37.10.1016/j.oregeorev.2014.05.017Search in Google Scholar

[5] Konnikov EG, Yan HQ, Xi AH, Deyou S. Sulfide nickel deposits of the Hongqiling ore field (Jilin Province, China). Geol Ore Depos. 2004;46:299–306.Search in Google Scholar

[6] Wei B, Wang CY, Li C, Sun Y. Origin of PGE-depleted Ni–Cu sulfide mineralization in the Triassic Hongqiling No. 7 orthopyroxenite intrusion, central Asian orogenic belt, northeastern China. Econ Geol. 2013;108:1813–31.10.2113/econgeo.108.8.1813Search in Google Scholar

[7] Lü LS, Mao JW, Li HB, Franco P, Zhang ZH, Zhou ZH. Pyrrhotite Re–Os and SHRIMP zircon U-Pb dating of the Hongqiling Ni–Cu sulfide deposits in Northeast China. Ore Geol Rev. 2011;43:106–19.10.1016/j.oregeorev.2011.02.003Search in Google Scholar

[8] Xie W, Song XY, Deng YF, Wang YS, Ba DH, Zheng WQ, et al. Geochemistry and petrogenetic implications of a Late Devonian mafic–ultramafic intrusion at the southern margin of the Central Asian Orogenic Belt. Lithos. 2012;144:209–30.10.1016/j.lithos.2012.03.010Search in Google Scholar

[9] Dong ZY, Tang J, Unsworth M, Chen XB. Electrical resistivity structure of the upper mantle beneath Northeastern China: Implications for rheology and the mechanism of craton destruction. J Asian Earth Sci. 2015;100:115–31.10.1016/j.jseaes.2015.01.008Search in Google Scholar

[10] Yang B, Gary D, Egbert AK, Naser MM. Three-dimensional electrical resistivity of the north-central USA from EarthScope long period magnetotelluric data. Earth Planet Sci Lett. 2015;422:87–93.10.1016/j.epsl.2015.04.006Search in Google Scholar

[11] Kovalenko VI, Yarmolyuk VV, Kovach VP, Kotov AB. Isotope provinces, mechanisms of generation and sources of the continental crust in the Central Asian mobile belt: geological and isotopic evidence. J Asian Earth Sci. 2004;23:605–27.10.1016/S1367-9120(03)00130-5Search in Google Scholar

[12] Li JY, Zhang J, Yang TN, Li YP, Sun GH, Zhu ZX, et al. Crustal tectonic division and evolution of the southern part of the North Asian Orogenic Region and its adjacent areas. J Jilin Univ (Earth Sci Ed). 2009;39(4):584–605 (in Chinese with English abstract).Search in Google Scholar

[13] Han BF, Ji JQ, Song B, Chen LH, Li ZH. SHRIMP zircon U-Pb ages and implications of the Kalatongke and Huangshandong sulfide-bearing mafic–ultramafic intrusions. Chin Sci Bull. 2004;49:2324–28 (in Chinese with English abstract).10.1360/04wd0163Search in Google Scholar

[14] Gao YQ, Liu L, Hu WX. Petrology and isotopic geochemistry of dawsonite-bearing sandstones in Hailaer basin, northeastern China. Appl Geochem. 2009;24:1724–38.10.1016/j.apgeochem.2009.05.002Search in Google Scholar

[15] Zhou MF, Lesher CM, Yang ZX, Li JW, Sun M. Geochemistry and petrogenesis of 270 Ma Ni–Cu-(PGE) sulfide-bearing mafic intrusions in the Huangshan district, Eastern Xinjiang, Northwest China: implications for the tectonic evolution of the Central Asian orogenic belt. Chem Geol. 2004;209:233–57.10.1016/j.chemgeo.2004.05.005Search in Google Scholar

[16] Mao JW, Pirajno F, Zhang ZH, Chai FM, Wu H, Chen SP, et al. A review of the Cu–Ni sulphide deposits in the Chinese Tianshan and Altay orogens (Xinjiang Autonomous Region, NW China): Principal characteristics and ore-forming processes. J Asian Earth Sci. 2008;32:184–203.10.1016/j.jseaes.2007.10.006Search in Google Scholar

[17] Qin KZ, Su BX, Sakyi PA, Tang DM, Li XH, Sun H, et al. SIMS zircon U-Pb geochronology and Sr-Nd isotopes of Ni–Cu-bearing mafic–ultramafic intrusions in Eastern tianshan and Beishan in correlation with flood basalts in Tarim basin (NW China): Constraints on a Ca. 280 Ma mantle plume. Am J Sci. 2011;311:237–60.10.2475/03.2011.03Search in Google Scholar

[18] Lu YG, Lesher CM, Deng J. Geochemistry and genesis of magmatic Ni–Cu-(PGE) and PGE-(Cu)–(Ni) deposits in China. Ore Geol Rev. 2019;107:863–87.10.1016/j.oregeorev.2019.03.024Search in Google Scholar

[19] Li JY. Permian geodynamic setting of Northeast China and adjacent regions: closure of the Paleo-Asian Ocean and subduction of the Paleo-Pacific Plate. J Asian Earth Sci. 2006;26:207–24.10.1016/j.jseaes.2005.09.001Search in Google Scholar

[20] Wilhem C, Windley BF, Stampfli GM. The Altaids of Central Asia: a tectonic and evolutionary innovative review. Earth Sci Rev. 2012;113(3–4):303–41.10.1016/j.earscirev.2012.04.001Search in Google Scholar

[21] Xue S, Bai DH, Chen Y, Ma XB, Chen L, Li X, et al. Contrasting crustal deformation mechanisms in the longmenshan and West Qinling orogenic belts, NE Tibet, revealed by magnetotelluric data. J Asian Earth Sci. 2019;176:120–8.10.1016/j.jseaes.2019.01.039Search in Google Scholar

[22] McNeice GW, Jones AG. Multisite multi-frequency tensor decomposition of magnetotelluric data. Geophysics. 2001;66:158–73.10.1190/1.1444891Search in Google Scholar

[23] Roshan KS, Ved PM, Shalivahan, Sahendra S. Imaging Regional Geology and Au – Sulphide mineralization over Dhanjori greenstone belt: Implications from 3-D inversion of audio magnetotelluric data and petrophysical characterization. Ore Geol Rev. 2019;106:369–86.10.1016/j.oregeorev.2019.01.027Search in Google Scholar

[24] Simpson F, Bahr K. Practical magnetotellurics: Numerical forward modelling. Cambridge London: Cambridge University Press; 2005.10.1017/CBO9780511614095Search in Google Scholar

[25] Singh RK, Maurya VP, Shalivahan, Singh S. Imaging regional geology and Au - Sulphide mineralization over Dhanjori greenstone belt: Implications from 3-D inversion of audio magnetotelluric data and petrophysical characterization. Ore Geol Rev. 2019;106:369–86.10.1016/j.oregeorev.2019.01.027Search in Google Scholar

[26] Swift C. A magnetotelluric investigation of an electrical conductivity anomaly in the southwestern United States, PhD thesis. 1967.Search in Google Scholar

[27] Booker JR. The magnetotelluric phase tensor: A critical review. Surv Geophys. 2014;35:7–40.10.1007/s10712-013-9234-2Search in Google Scholar

[28] Yin JN, Lindsay M, Teng SR. Mineral prospectivity analysis for BIF iron deposits: A case study in the Anshan-Benxi area, Liaoning province, North-East China. Ore Geol Rev. 2018;120:102746.10.1016/j.oregeorev.2018.11.019Search in Google Scholar

[29] Rodi W, Mackie RL. Nonlinear conjugate gradients algorithm for 2-D magnetotelluric inversion. Geophysics. 2000;66:174–87.10.1190/1.1444893Search in Google Scholar

[30] Liang HD, Gao R, Hou HS, Liu GS, Han JT, Han S. Lithospheric electrical structure of the Great Xing’an Range. J Asian Earth Sci. 2015;113:501–7.10.1016/j.jseaes.2015.01.026Search in Google Scholar

[31] Tian J, Ye G, Ding Z, Wu Q, Wei W, Jin S, et al. A study of the deep electrical structure of the northern segment of the tan-lu fault zone, ne china. J Asian Earth Sci. 2019;170:118–27.10.1016/j.jseaes.2018.10.029Search in Google Scholar

[32] Hou H, Wang H, Gao R, Li QS, Xiong XS, Li WH, et al. Fine crustal structure and deformation beneath the Great Xing'an Ranges, CAOB: Revealed by deep seismic reflection profile. Asian J Earth Sci. 2015;113:491–500.10.1016/j.jseaes.2015.01.030Search in Google Scholar

[33] Tao K, Niu FL, Ning JY, Chen J, Grand S. Crustal structure beneath NE China imaged by NECESSArray receiver function data. Earth Planet Sci Lett. 2014;398:48–57.10.1016/j.epsl.2014.04.043Search in Google Scholar

[34] Zhang GB, Shen NH. Geomagnetic three-components depth sounding and the electrical conductivity of the crust and upper mantle in Changchun and Tonghua. Chin J Geophys. 1994;37:296–303.Search in Google Scholar

[35] Xiong X, Gao R, Zhang X. Revealing Moho surface depth in North-Northeast China by deep seismological detection. Acta Geosci Sin. 2011;32:46–56.Search in Google Scholar

[36] Karato SI, Dai LD. Comments on “Electrical conductivity of wadsleyite as a function of temperature and water content” by Manthilake et al. Phys Earth Planet Inter. 2009;174:19–21.10.1016/j.pepi.2009.01.011Search in Google Scholar

[37] Bai DH, Unsworth MJ, Meju MA, Ma XB, Teng JW, Kong XR, et al. Crustal deformation of the eastern Tibetan plateau revealed by magnetotelluric imaging. Nat Geosci. 2010;3:358–62.10.1038/ngeo830Search in Google Scholar

[38] An MJ, Shi YL. Three-dimensional thermal structure of the Chinese continental crust and upper mantle. Chin Sci Bull. 2007;50(10):1441–51.10.1007/s11430-007-0071-3Search in Google Scholar

[39] Wu FY, Sun DY, Li H, Jahn BM, Wilde S. A-type granites in northeastern China: Age and geochemical constraints on their petrogenesis. Chem Geol. 2002;187:143–73.10.1016/S0009-2541(02)00018-9Search in Google Scholar

[40] Liu YG, Lü XB, Wu CM, Hu XG, Duan ZP. The migration of Tarim plume magma toward the northeast in Early Permian and its significance for the exploration of PGE-Cu–Ni magmatic sulfide deposits in Xinjiang, NW China: As suggested by Sr–Nd–Hf isotopes, sedimentology and geophysical data. Ore Geol Rev. 2016;72:538–45.10.1016/j.oregeorev.2015.07.020Search in Google Scholar

[41] Peng RM, Zhai YS, Li C, Ripley EM. The Erbutu Ni–Cu deposit in the Central Asian orogenic belt: a Permian magmatic sulfide deposit related to boninitic magmatism in an arc setting. Econ Geol. 2013;108:1879–88.10.2113/econgeo.108.8.1879Search in Google Scholar

[42] Freeburn R, Bouilhol P, Maunder B, Magni V, Van Hunen J. Numerical models of the magmatic processes induced by slab breakoff. Earth Planet Sci Lett. 2017;478:203–13.10.1016/j.epsl.2017.09.008Search in Google Scholar

[43] Lyros E, Kostelecky J, Plicka V. Detection of Tectonic and Crustal Deformation using GNSS Data Processing: The Case of PPGnet. Civ Eng J. 2021;7(1):14–23.10.28991/cej-2021-03091633Search in Google Scholar

Received: 2022-04-03
Revised: 2022-08-11
Accepted: 2022-10-12
Published Online: 2023-02-16

© 2023 the author(s), published by De Gruyter

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

Articles in the same Issue

  1. Regular Articles
  2. Diagenesis and evolution of deep tight reservoirs: A case study of the fourth member of Shahejie Formation (cg: 50.4-42 Ma) in Bozhong Sag
  3. Petrography and mineralogy of the Oligocene flysch in Ionian Zone, Albania: Implications for the evolution of sediment provenance and paleoenvironment
  4. Biostratigraphy of the Late Campanian–Maastrichtian of the Duwi Basin, Red Sea, Egypt
  5. Structural deformation and its implication for hydrocarbon accumulation in the Wuxia fault belt, northwestern Junggar basin, China
  6. Carbonate texture identification using multi-layer perceptron neural network
  7. Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long-period magnetotellurics
  8. Assessments of recent Global Geopotential Models based on GPS/levelling and gravity data along coastal zones of Egypt
  9. Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)
  10. Uncertainty assessment of 3D geological models based on spatial diffusion and merging model
  11. Evaluation of dynamic behavior of varved clays from the Warsaw ice-dammed lake, Poland
  12. Impact of AMSU-A and MHS radiances assimilation on Typhoon Megi (2016) forecasting
  13. Contribution to the building of a weather information service for solar panel cleaning operations at Diass plant (Senegal, Western Sahel)
  14. Measuring spatiotemporal accessibility to healthcare with multimodal transport modes in the dynamic traffic environment
  15. Mathematical model for conversion of groundwater flow from confined to unconfined aquifers with power law processes
  16. NSP variation on SWAT with high-resolution data: A case study
  17. Reconstruction of paleoglacial equilibrium-line altitudes during the Last Glacial Maximum in the Diancang Massif, Northwest Yunnan Province, China
  18. A prediction model for Xiangyang Neolithic sites based on a random forest algorithm
  19. Determining the long-term impact area of coastal thermal discharge based on a harmonic model of sea surface temperature
  20. Origin of block accumulations based on the near-surface geophysics
  21. Investigating the limestone quarries as geoheritage sites: Case of Mardin ancient quarry
  22. Population genetics and pedigree geography of Trionychia japonica in the four mountains of Henan Province and the Taihang Mountains
  23. Performance audit evaluation of marine development projects based on SPA and BP neural network model
  24. Study on the Early Cretaceous fluvial-desert sedimentary paleogeography in the Northwest of Ordos Basin
  25. Detecting window line using an improved stacked hourglass network based on new real-world building façade dataset
  26. Automated identification and mapping of geological folds in cross sections
  27. Silicate and carbonate mixed shelf formation and its controlling factors, a case study from the Cambrian Canglangpu formation in Sichuan basin, China
  28. Ground penetrating radar and magnetic gradient distribution approach for subsurface investigation of solution pipes in post-glacial settings
  29. Research on pore structures of fine-grained carbonate reservoirs and their influence on waterflood development
  30. Risk assessment of rain-induced debris flow in the lower reaches of Yajiang River based on GIS and CF coupling models
  31. Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt
  32. Surface deformation and damage of 2022 (M 6.8) Luding earthquake in China and its tectonic implications
  33. Differential analysis of landscape patterns of land cover products in tropical marine climate zones – A case study in Malaysia
  34. DEM-based analysis of tectonic geomorphologic characteristics and tectonic activity intensity of the Dabanghe River Basin in South China Karst
  35. Distribution, pollution levels, and health risk assessment of heavy metals in groundwater in the main pepper production area of China
  36. Study on soil quality effect of reconstructing by Pisha sandstone and sand soil
  37. Understanding the characteristics of loess strata and quaternary climate changes in Luochuan, Shaanxi Province, China, through core analysis
  38. Dynamic variation of groundwater level and its influencing factors in typical oasis irrigated areas in Northwest China
  39. Creating digital maps for geotechnical characteristics of soil based on GIS technology and remote sensing
  40. Changes in the course of constant loading consolidation in soil with modeled granulometric composition contaminated with petroleum substances
  41. Correlation between the deformation of mineral crystal structures and fault activity: A case study of the Yingxiu-Beichuan fault and the Milin fault
  42. Cognitive characteristics of the Qiang religious culture and its influencing factors in Southwest China
  43. Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data
  44. Interpretation of aeromagnetic and remote sensing data of Auchi and Idah sheets of the Benin-arm Anambra basin: Implication of mineral resources
  45. Building element recognition with MTL-AINet considering view perspectives
  46. Characteristics of the present crustal deformation in the Tibetan Plateau and its relationship with strong earthquakes
  47. Influence of fractures in tight sandstone oil reservoir on hydrocarbon accumulation: A case study of Yanchang Formation in southeastern Ordos Basin
  48. Nutrient assessment and land reclamation in the Loess hills and Gulch region in the context of gully control
  49. Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data
  50. Spatial variation of soil nutrients and evaluation of cultivated land quality based on field scale
  51. Lignin analysis of sediments from around 2,000 to 1,000 years ago (Jiulong River estuary, southeast China)
  52. Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi
  53. Transforming text into knowledge graph: Extracting and structuring information from spatial development plans
  54. A symmetrical exponential model of soil temperature in temperate steppe regions of China
  55. A landslide susceptibility assessment method based on auto-encoder improved deep belief network
  56. Numerical simulation analysis of ecological monitoring of small reservoir dam based on maximum entropy algorithm
  57. Morphometry of the cold-climate Bory Stobrawskie Dune Field (SW Poland): Evidence for multi-phase Lateglacial aeolian activity within the European Sand Belt
  58. Adopting a new approach for finding missing people using GIS techniques: A case study in Saudi Arabia’s desert area
  59. Geological earthquake simulations generated by kinematic heterogeneous energy-based method: Self-arrested ruptures and asperity criterion
  60. Semi-automated classification of layered rock slopes using digital elevation model and geological map
  61. Geochemical characteristics of arc fractionated I-type granitoids of eastern Tak Batholith, Thailand
  62. Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data
  63. Analysis of artificial intelligence approaches to predict the wall deflection induced by deep excavation
  64. Evaluation of the current in situ stress in the middle Permian Maokou Formation in the Longnüsi area of the central Sichuan Basin, China
  65. Utilizing microresistivity image logs to recognize conglomeratic channel architectural elements of Baikouquan Formation in slope of Mahu Sag
  66. Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea
  67. Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm
  68. Spatial objects classification using machine learning and spatial walk algorithm
  69. Study on the stabilization mechanism of aeolian sandy soil formation by adding a natural soft rock
  70. Bump feature detection of the road surface based on the Bi-LSTM
  71. The origin and evolution of the ore-forming fluids at the Manondo-Choma gold prospect, Kirk range, southern Malawi
  72. A retrieval model of surface geochemistry composition based on remotely sensed data
  73. Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions
  74. Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin
  75. Study of fracturing fluid re-discharge based on percolation experiments and sampling tests – An example of Fuling shale gas Jiangdong block, China
  76. Impacts of marine cloud brightening scheme on climatic extremes in the Tibetan Plateau
  77. Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing
  78. The time-dependent deformation and damage constitutive model of rock based on dynamic disturbance tests
  79. Evaluation of spatial form of rural ecological landscape and vulnerability of water ecological environment based on analytic hierarchy process
  80. Fingerprint of magma mixture in the leucogranites: Spectroscopic and petrochemical approach, Kalebalta-Central Anatolia, Türkiye
  81. Principles of self-calibration and visual effects for digital camera distortion
  82. UAV-based doline mapping in Brazilian karst: A cave heritage protection reconnaissance
  83. Evaluation and low carbon ecological urban–rural planning and construction based on energy planning mechanism
  84. Modified non-local means: A novel denoising approach to process gravity field data
  85. A novel travel route planning method based on an ant colony optimization algorithm
  86. Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam
  87. Regional tectonic uplift indicated by geomorphological parameters in the Bahe River Basin, central China
  88. Computer information technology-based green excavation of tunnels in complex strata and technical decision of deformation control
  89. Spatial evolution of coastal environmental enterprises: An exploration of driving factors in Jiangsu Province
  90. A comparative assessment and geospatial simulation of three hydrological models in urban basins
  91. Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration
  92. Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features
  93. Numerical investigation of gravity-grouted soil-nail pullout capacity in sand
  94. Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants
  95. Road cut slope stability analysis for static and dynamic (pseudo-static analysis) loading conditions
  96. Forest biomass assessment combining field inventorying and remote sensing data
  97. Late Jurassic Haobugao granites from the southern Great Xing’an Range, NE China: Implications for postcollision extension of the Mongol–Okhotsk Ocean
  98. Petrogenesis of the Sukadana Basalt based on petrology and whole rock geochemistry, Lampung, Indonesia: Geodynamic significances
  99. Numerical study on the group wall effect of nodular diaphragm wall foundation in high-rise buildings
  100. Water resources utilization and tourism environment assessment based on water footprint
  101. Geochemical evaluation of the carbonaceous shale associated with the Permian Mikambeni Formation of the Tuli Basin for potential gas generation, South Africa
  102. Detection and characterization of lineaments using gravity data in the south-west Cameroon zone: Hydrogeological implications
  103. Study on spatial pattern of tourism landscape resources in county cities of Yangtze River Economic Belt
  104. The effect of weathering on drillability of dolomites
  105. Noise masking of near-surface scattering (heterogeneities) on subsurface seismic reflectivity
  106. Query optimization-oriented lateral expansion method of distributed geological borehole database
  107. Petrogenesis of the Morobe Granodiorite and their shoshonitic mafic microgranular enclaves in Maramuni arc, Papua New Guinea
  108. Environmental health risk assessment of urban water sources based on fuzzy set theory
  109. Spatial distribution of urban basic education resources in Shanghai: Accessibility and supply-demand matching evaluation
  110. Spatiotemporal changes in land use and residential satisfaction in the Huai River-Gaoyou Lake Rim area
  111. Walkaway vertical seismic profiling first-arrival traveltime tomography with velocity structure constraints
  112. Study on the evaluation system and risk factor traceability of receiving water body
  113. Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources
  114. Temporal dynamics of green urban areas in Romania. A comparison between spatial and statistical data
  115. Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
  116. Varying particle size selectivity of soil erosion along a cultivated catena
  117. Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins
  118. Influence of nappe structure on the Carboniferous volcanic reservoir in the middle of the Hongche Fault Zone, Junggar Basin, China
  119. Dynamic analysis of MSE wall subjected to surface vibration loading
  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
  125. Review Articles
  126. The actual state of the geodetic and cartographic resources and legislation in Poland
  127. Evaluation studies of the new mining projects
  128. Comparison and significance of grain size parameters of the Menyuan loess calculated using different methods
  129. Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
  130. Rainfall-induced transportation embankment failure: A review
  131. Rapid Communication
  132. Branch fault discovered in Tangshan fault zone on the Kaiping-Guye boundary, North China
  133. Technical Note
  134. Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data
  135. Erratum
  136. Erratum to “Forest cover assessment using remote-sensing techniques in Crete Island, Greece”
  137. Addendum
  138. The relationship between heat flow and seismicity in global tectonically active zones
  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
Downloaded on 11.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/geo-2022-0430/html
Scroll to top button