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Acta Geophysica

, Volume 66, Issue 4, pp 541–557 | Cite as

Three-dimensional distributed DC/IP method for altered tectonite-type gold ore deposits exploration: a case study of the Jiaojia gold metallogenic belt, Eastern China

  • Jiayong Yan
  • Yawei Zhang
  • Kun Zhang
  • Yongqian Zhang
  • Hao Hu
  • Zhihui Wang
Research Article - Applied Geophysics
  • 63 Downloads

Abstract

To develop an effective method to identify ore-controlling faults, we studied the Jiaojia gold metallogenic belt, a most typical altered tectonite-type gold metallogenic belt in the Jiaodong Peninsula, China, and conducted experiments using the 3D distributed direct current-induced polarization (DC/IP) method. Firstly, we tested the ability of using 3D distributed DC/IP method to identify altered tectonite-type gold ore deposits by 3D synthetic modelling. We then collected real data of the Sizhuang gold deposit using the 3D distributed DC/IP method. The resistivity model obtained of this region is generally consistent with the known geological setting. Moreover, to obtain the information about the southern extension of the Jiaojia gold metallogenic belt, we conducted a 3D distributed DC/IP experiment in the Shijia area in the southern segment of the Jiaojia fault. The southern extension of the Jiaojia fault and tectonic evolution of shallow magma in this region were inferred from the 3D resistivity and chargeability models. Based on all the information above, we concluded that the 3D distributed DC/IP method has the advantages of 3D observations, high spatial resolution and great detection depth and will be one of the most effective methods for detecting altered tectonite-type gold ore deposits.

Keywords

3D direct current-induced polarization (DC/IP) Altered tectonite-type gold ore deposit Jiaojia gold metallogenic belt Ore-controlling fault 

Introduction

Altered tectonite-type gold ore deposits are disseminated deposits of fine-grained gold ore in uniformly fractured clastic rocks and porphyritic rocks around the principal section of ore-controlling faults. The main ore rocks include disseminated beresite, beresitized granoclastic rocks with fine veins and beresitized granite with fine veins. The ore bodies occur as quasi-bedded forms and as lode gold deposits, with the main ore being parallel to the principal section of the ore-controlling fractures (Li et al. 2007). Faults control the formation of altered tectonite-type gold ore deposits, and the ore deposits are mainly located at the intersections of faults or in the curved sections of fractured and altered belts along their strike or dip directions. They are mainly altered by metasomatism and appear as large-scale groups and bands. Most of the altered tectonite-type gold ore deposits in China are located on the Jiaodong Peninsula. Although the Jiaodong Peninsula covers only about 0.17% the total area of China, current data suggest that it contains more than 3700 tons of gold reserves, which represent approximately one-fourth of the gold reserves in China. Thus, the Jiaodong Peninsula is the most important gold region in China and has become the third biggest gold region in the world (Song 2015). The Jiaojia gold metallogenic belt is one of the most important metallogenic belts in the Jiaodong Peninsula, and it contains three giant gold deposits (Xincheng, Jiaojia and Hexi) and several large- and medium-sized gold deposits, including Sizhuang, Hedong, Dongji, Shangzhuang, Wang’ershan and Matang. The Jiaojia gold belt is controlled by the Jiaojia Fault, and the metallogenic belt generally overlaps the fractured zone. The location and shape of the North Jiaojia Fault are generally well known, but the southern extension of the fault from the Sizhuang gold deposit is unclear because the Quaternary cover thickens gradually, limiting the ability to detect new gold deposits(Deng and Wang 2016),

Characterizing the distribution of ore-controlling faults is crucial for discovery of altered tectonite-type gold ore deposits. When exploring for shallow deposits, surface geological observations or traditional geophysical methods can be used to trace faults and provide target areas for prospecting (Liang et al. 2000; Zhang et al. 2017). However, the widths of ore-controlling faults or fractured and altered zones decrease with increasing depth, and the corresponding surface geophysical information becomes weaker; therefore, common methods have difficulty, which severely limits their effectiveness in prospecting in distinguishing ore deposits in these cases. In recent years, due to increasing demands for precise electric prospecting, the 3D distributed electric prospecting method was developed. In this method, several receivers are arranged in a grid, and data are collected by transmitting an electric current into the subsurface at different locations. The receivers are distributed as nodes to form a detection network, and their built-in GPS chips can be used for spatial and temporal synchronization. As a result, the full waveform data collected during the measurement process are time stamped. After data collection, the time stamps can be used to match the voltage data collected by the distributed receivers with the current from the transmitter, based on which a 3D resistivity and chargeability model can be obtained by inversion processing (Eaton et al. 2010; Fischanger et al. 2013). This method has been applied in several fields of research. For example, Francese et al. (2009) used 3D resistivity imaging to study a fractured aquifer in the mountainous area of Tuscany, Italy, and demonstrated that high-resolution 3D geophysical imaging is an effective method to study small-scale fractured reservoirs. Papadopoulos et al. (2011) set up numerous parallel 2D detection lines on the surface and studied the internal structures of ancient tombs and their filling materials using the resistivity imaging method; their results showed that the method is a useful tool for non-destructive archaeological prospecting. Gharibi et al. (2012) used the ORION 3D distributed collection system from QUANTEC to collect resistivity, chargeability and magnetotelluric data of an ore deposit in Nevada, USA; their results showed that the resistivity and chargeability are closely related to the known ore body’s location. Bournas et al. (2013) conducted 3D direct current-induced polarization (DC/IP) measurements of polymetallic porphyry in Silver Queen, Canada, using TITAN-24, and the 3D inversion results identified zones of stockwork mineralization. Spicer (2016) conducted 3D DC/IP measurements at the Vitoria Cu/Ni deposit in the south-western Sudbury Basin; the inverted resistivity and chargeability clearly characterize the distribution of the Sudbury Igneous Complex (SIC), which provides a direct basis for prospecting of deep deposits.

3D distributed electric detection using large transmitting electrode spacings and densely distributed full waveform collection stations can provide abundant resistivity and chargeability information, and thus, it is a potential method to search for altered tectonite-type gold ore deposits. In order to test the effectiveness of this method in detecting altered tectonite-type gold ore deposits, to trace the southward extension of the Jiaojia gold metallogenic belt and predict the future targets of gold reserves in this region, we studied the Jiaojia gold metallogenic belt on the Jiaodong Peninsula, where the altered tectonite-type gold ore deposits are densely distributed, and conducted detection experiments using the 3D distributed DC/IP method.

Geological setting

The Jiaodong Peninsula is located in eastern China (Fig. 1). Its western region is located on the North China block and includes the Jiaobei Uplift and the Jiaolai Basin, and its eastern region is located in the Weihai Uplift in the Dabie-Sulu orogenic belt. The Jiaobei Uplift is mainly composed of a Neoarchaean granite–greenstone belt (Jiaodong Group, Qixia gneissic suite), Paleoproterozoic medium–high-grade metamorphic neritic and littoral facies sedimentary strata (Jingshan Group, Fenzishan Group, Zhifu Group), Neoproterozoic littoral facies sedimentary strata (Penglai Group) and Mesozoic granite (Li et al. 2012), and the Jiaolai Basin is mainly composed of Cretaceous continental volcanic-sedimentary strata (Laiyang Group, Qingshan Group, Wangshi Group). The Weihai Uplift is a high to ultrahigh pressure metamorphic belt that is mainly composed of Neoproterozoic eclogite-containing granitic gneiss and Mesozoic granite with a small amount of Paleoproterozoic metamorphic strata. Paleogene, Neogene and Quaternary strata are scattered in the Cenozoic mountain basins, modern rivers and coastal areas in the Jiaodong region. The primary Jiaodong Mesozoic intrusive rocks are the Triassic Liuli Zhuang diorite, Ningjinsuo syenite, Cuoshan syenogranite, Jurassic Duogushan granodiorite, Wendeng granite, Linglong monzogranite, Cretaceous Guojialing granodiorite, Weideshan granite and Laoshan granite (Fig. 1). The gold ore is mainly produced in the Jiaobei Uplift and the surrounding areas, and the ore-bearing wall rocks are primarily Cambrian metamorphic rocks (Archaean granite–greenstone belt and Paleoproterozoic and Neoproterozoic metamorphic strata) and Mesozoic blocky rocks (Early Cretaceous volcanic-sedimentary rock sequence and Jurassic-Early Cretaceous granitic intrusive rock). In the Jiaodong region, abundant NE–NNE faults provide a favourable environment for gold ore formation. The dominant ore-controlling faults are the Sanshandao, Jiaojia, Zhaoping, Xilin, Douya and Jinniushan faults (Song et al. 2015).
Fig. 1

Regional geological setting of the Jiaodong Peninsula (JJF—Jiaojiao fault, SSDF—Sanshandao Fault, ZPF—Zhaoping fault, XLDYF—Xilin Douya fault)

The Jiaojia fracture belt is the southern section of the Longkou–Laizhou fracture belt on the Jiaodong Peninsula and is the main ore-controlling fault on the Jiaodong Peninsula, where the Jiaojia gold metallogenic belt is located. Numerous giant-, large- and medium-sized gold ore deposits have been identified in the Jiaojia gold metallogenic belt, which contain approximately 600 tons of gold ore. The Jiaojia Fault extends from Yaojia Village in Longkou to Zhuqiao Township, Laizhou City, to the south, and it is > 60 km long and 50–500 m wide. The fault generally strikes approximately 35°–40° and dips to the north-west at 30°–50° even can locally reach 78°. The fault is arc-shaped, and the fault’s extension bends to form an open S shape. The altered tectonite-type gold ore deposit is mainly located in the contact zone between metamorphic rocks and granite, and the gold ore mainly exists in the altered granite and cataclasite in the footwall of the main fault plane. A 5- to 40-m-thick continuous shale gouge is located along the main fracture plane of the Jiaojia Fault, which can be used in geological prospecting to identify the main plane of the Jiaojia Fault and the edge of the main ore body. The Jiaojia Fault was formed in the Early Cretaceous and was active later in the Paleogene. The Jiaojia Fault is generally believed to be a metallogenic fault, whose time of formation is consistent with both the main metallogenic period of the gold ore and the peak period of stretching and thinning of the lithosphere is in the Early Cretaceous (Song et al. 2010). The Jiaojia Fault extends south from Sizhuang to the Jijia section with a nearly N–S strike and continues towards Shijia Village, where the Quaternary deposits thicken gradually. As a result, the unclear strike and shape of the fault severely limits the ability to detect new gold deposits.

3D synthetic modelling test

To test the ability of the 3D distributed DC/IP method to identify altered tectonite-type gold ore deposits, we studied a typical gold deposit, the Sizhuang gold deposit in the Jiaojia gold metallogenic belt. We developed a resistivity model from the initial geological model and forward-derived its apparent resistivity response, based on which the resistivity model was inverted. By comparing the resulting model with the initial model, we analysed the ability of this method to identify ore-controlling fault.

The surface of the Sizhuang gold deposit is mainly covered by loose, low-resistivity Quaternary, and the fracture zone is mainly located along the contact between the gabbro and monzogranite. The hanging wall is primarily composed of relatively low-resistivity metagabbro and altered metagabbro. In contrast, the footwall is mainly composed of high-resistivity monzogranite and corresponding altered rocks, and the fracture zone is a transition zone between the hanging wall and the footwall. Because of the strong effects of its cataclastic structure and alteration, the fracture belt is characterized by medium- and low-resistivity values. This local region is enriched in gold, which forms mineral deposits (Fig. 2).
Fig. 2

Geological map of the Jiaojia gold metallogenic belt, location of 3D DC/IP test area and typical geological cross section of the Sizhuang gold deposit

Based on a statistical analysis of the rock resistivities of the Jiaojia gold belt (Wan 1994) and the geological information shown in Fig. 2, we constructed a 3D geological model of the Sizhuang gold deposit (Fig. 3). The model is 900 m long, 500 m wide and 1000 m deep. Considering the effects of fractures, mineralization alteration and groundwater, the Quaternary at the surface is considered to be a low-resistivity region with a resistivity of 50 Ω m. The fault’s hanging wall, which is composed of metagabbro, is also considered a low-resistivity region of a 300 Ω m resistivity. The footwall, which is composed of monzogranite, is considered a high-resistivity region with a resistivity larger than 1000 Ω m. The fractured and altered transition zone along the contact between the hanging wall and the footwall is considered to be a medium- to high-resistivity region with a resistivity about 1000 Ω m.
Fig. 3

Conceptual geophysical and resistivity model of the Sizhuang gold deposit

The forward calculation uses a 3D tetrahedral finite-element mesh (Zhou and Greenhalgh 2001) and mixed boundary conditions. The ERTLab64 software (http://www.ertlab.com), which was developed by the Geostudi Astier Company in Italy, is used for the forward and inversion procedures; it consists of the ERTLab-Sequencer forward module and the ERTLab-Solver inversion module. The main simulation steps of the forward module are as follows. First, the ERTLab-Sequencer forward module is used to divide the geological model into cells (Fig. 3) and then to fill the cells with corresponding resistivity values based on a 50 m × 50 m × 25 m uniform 3D mesh with a depth of 1000 m to obtain a theoretical resistivity model (Fig. 4a). Then, the observational system that is used to collect the actual data (Table 1), the collection parameters and the theoretical resistivity model are loaded into the ERTLab-Sequencer forward module for forward simulation calculations to obtain the resistivity response data. After the forward simulation, the forward data are used as inputs to the ERTLab-Solver module for the inversion calculations (the initial model for inversion was a uniform half-space of 1000 Ω m), which obtains the 3D resistivity model (Fig. 4b).
Fig. 4

a Synthetic 3D resistivity model of the Sizhuang gold deposit. b Inverted resistivity model from the forward sounding data based on the synthetic model (shown in Fig. 3)

Table 1

Acquisition scheme of transmitters in the Sizhuang study area

Transmit no.

Transmit

Injection direction

Current injection A

Current injection B

1

A1

B1

E–W

2

A2

B1

E–W

3

A3

B1

E–W

4

A4

B1

E–W

5

A4

B2

E–W

6

A5

B1

E–W

7

A6

B1

E–W

8

A7

B1

E–W

9

A8

B1

E–W

10

A8

B3

E–W

11

A9

B1

E–W

12

A10

B1

E–W

13

A11

B1

E–W

14

A12

B1

E–W

15

A12

B4

E–W

16

A13

B1

E–W

17

A14

B1

E–W

18

A15

B1

E–W

19

A16

B1

E–W

20

A17

B1

E–W

21

A18

B1

E–W

22

A19

B1

E–W

23

A20

B1

E–W

24

A21

B5

N–S

25

A21

B6

N–S

26

A21

B7

N–S

27

A22

B8

N–S

28

A23

B9

N–S

29

A24

B10

N–S

30

A25

B11

N–S

A comparison of Figs. 3 and 4a, b shows that the inverted resistivity model accurately reflects the low-resistivity characteristics of the metagabbro in the hanging wall and the high-resistivity characteristics of the monzogranite in the footwall. In addition, the fractured altered zone can clearly be seen. The forward and inversion modelling demonstrates that the 3D distributed DC/IP method can be used to identify ore-bearing fault and to characterize the wall rocks.

Data acquisition and methodology

To validate the method, test its reliability and apply the model to unknown regions for prospecting, we first conducted 3D distributed DC/IP tests in the well-known Sizhuang gold deposit, located in the middle segment of Jiaojia fault (Fig. 2). We then chose the Shijia region along the southern extension of the Jiaojia Fault to conduct 3D distributed DC/IP tests and obtained 3D resistivity and chargeability models of the region. Finally, based on a comprehensive analysis of the resistivity and chargeability data, we identified the southern extension of the Jiaojia Fault.

Data collection at the Sizhuang gold deposit

The 3D observational system at the Sizhuang gold deposit is shown in Fig. 5. Above the main ore body, 15 V-Full Waver receivers were arranged in 200 m × 200 m grids. Each V-Full receiver includes two receiving dipoles (L-shaped) with an anode–cathode distance of 100 m that formed an observational system with 30 × 15 tensors. We designed a combination of 30 electrodes to transmit currents, which includes 23 E–W combinations with a maximum transmitting distance of 5.6 km and 7 N–S combinations with a maximum transmitting distance of 4.8 km. The E–W and N–S direction arrangements ensured greater signal intensities in different directions. The electrode device used a combination of the middle gradient array and the pole–dipole array (Table 1), which resulted in vastly improved resolution and detection depth. The transmitter utilized a VIP10000 transmitter with an output power of 10 kW and a supply current of no less than 10 A. The transmitted waveform took bipolar square waves with duty cycles 50% and current period was set to 8 s. We conducted transmissions with different transmitting dipole distances 30 times in the field (23 times E–W and 7 times N–S direction as Table 1 shows). We continuously recorded the transmitted current intensities using an I-Full Waver, and each of the 30 current transmissions lasted for 10 min. To obtain high-quality data, we used unpolarized electrodes to connect the ground. Induced polarization (chargeability) measured every 10 ms after current shut-off (200 IP windows for a 2 s pulse), after setting up the equipment, the 15 V-Full Waver started to continuously record full waveform voltage data. Based on the 30 transmissions, we collected 900 data points at the Sizhuang gold deposit. Because the gold deposit was being mined during the data collection, the surface and underground mining equipment caused large amounts of noise in the collected data. As a result, some of the data could not satisfy the induced polarization effect. Therefore, we only inverted the resistivity data in this deposit.
Fig. 5

Survey layout of the Sizhuang gold deposit

Data collection in the Shijia study area

The Shijia study area is located in the Sizhuang study area approximately 8 km south along the main Jiaojia Fault (Fig. 2). The middle section and the western side are primarily composed of the Qixia gneissic–biotite–diorite, the north-west side is composed of small amounts of the Malianzhuang medium- and fine-grained metagabbro, and the east side and the remainder of the area are composed of the Linglong medium-grained monzogranite. The Quaternary covers thicken along the extension of the Jiaojia Fault, so it is difficult to use common geological methods to trace the fault’s strike and spatial distribution.

The geometry system for the Shijia study area is similar to that of the Sizhuang deposit. Fifteen V-Full Waver receivers were arranged in 200 m × 200 m grids, and each of the receivers included two rows of L-shaped receiving dipoles with an anode–cathode distance of 100 m, which formed an observational system with 30 × 15 tensors to simultaneously obtain E–W and N–S information (Fig. 6). The devices also used the combination of the intermediate gradient method and pole–dipole electrical sounding method with a maximum transmitting distance of 5.6 km. However, this is different from the Sizhuang gold deposit, because the electric–magnetic noise is relatively low in this region; we cancelled the N–S direction current transmission and only utilized the E–W current transmission method. In addition, we increased the number of current transmissions in the target areas. Because of the relatively little interference from mining in the Shijia study area, the quality of the data was high, resulting in smooth curves of primary field and secondary field. So, it was not required to manually pick up valid IP data from the time series.
Fig. 6

Geological map and survey layout of the Shijia study area

Inversion method

We utilized the 3D inversion method based on the smoothness-constrained least squares method (Sasaki 1994). In the inversion process, we introduced the preconditioned conjugate gradient (PCG) method to solve the inversion equation. The inversion process included a global inversion iteration and the PCG method, which are nested to solve the inversion equation by iteration. The details are given below.
  1. (1)

    First, construct an initial 3D electric model.

     
  2. (2)

    For the global inversion iteration, the initial model is used to calculate the theoretical distribution of the resistivities using finite-element forward simulations. Then, the error function based on the least squares method is used to calculate the error between the calculated theoretical resistivity and the corresponding observed resistivity. If the error satisfies the convergence criterion, the theoretical resistivity distribution approximates the actual distribution, and the predicted electric model approximates its actual subsurface electric mode. The degree of approximation clearly depends on the calculated error. If the error does not satisfy the convergence criterion, the process proceeds to the next step.

     
  3. (3)

    The PCG internal core iteration is used to solve the inversion equation. If the error does not satisfy the convergence criterion, the initial PCG value is redefined, and the internal core iteration is repeated to solve the set of inversion functions until the result satisfies the convergence criterion. When the convergence criterion is satisfied, a new model parameter is obtained by solving the set of inversion functions.

     
  4. (4)

    The newly obtained model parameter is substituted into the finite-element forward simulation calculation, and the next global inversion cyclic iteration starts. The iteration continues until the error satisfies the convergence criterion.

     

Results

Results of Sizhuang gold deposit

The cell affects the precision of the inversion. Although small cells can improve the precision of both the cell conversion and the solution calculation, the cell resolution affects the inversion efficiency. In addition, in inversions that use different methods and models, the cells influence the inversion precision differently. Considering both the inversion precision and calculation speed, we adopted a 50 m × 50 m × 25 m uniform 3D mesh with a depth of 1000 m.

Sizhuang gold deposit is being mined and many factories are located in the Sizhuang study area, which resulted in large amounts of EM noise with the secondary curve and consequently low-quality chargeability data. Therefore, we only inverted the resistivity data but not the chargeability data. The inversion utilized the least squares 3D inversion method based on the smoothness constraint in the ERTLab-Solver module and adopted a 1000 Ω m uniform half-space as the initial resistivity model for the inversion. The standard estimated error was set at 5%, and the inversion depth was set at 1000 m for the unconstrained resistivity inversion. The maximum number of PCG internal iterations was set to 30.

We conducted six global inversion iterations until the error satisfied the convergence criterion and obtained the 3D resistivity model. Based on three clearly defined resistivity boundary surfaces, we divided the model into four layers with different electric properties (Fig. 7). The first layer is an extremely low-resistivity layer with resistivity values from 0 to 200 Ω; it is widely distributed on the surface and possibly corresponds to the Quaternary. The second layer has a large range of resistivity values from 200 to 600 Ω, it has a maximum depth of approximately 400 m, and the high-resistivity body in its middle section is uplifted and partially exposed at the surface. It is inclined towards the west, and it is generally shallower in the east and deeper in the west. Thus, we infer that the second layer represents the hanging wall of the Jiaojia Fault, which is mainly composed of metagabbro. The resistivities of the third layer are within a narrower range of approximately 600–700 Ω m. It has a relatively constant depth of several tens of metres, and it has clear layered characteristics. It dips towards the west at approximately 30°. We infer that the third layer is the ore-controlling fracture zone. The fourth layer is a high-resistivity layer (greater than 700 Ω m) in which the resistivity increases with depth. Thus, the fourth layer likely represents the monzogranite in the footwall. The resistivity characteristics of the inverted resistivity model generally agree with the geological features of the known fracture planes (Fig. 2) and the dip angles of the geological bodies. The test at the Sizhuang gold deposit shows that although the ore area was severely noisy during the test, the 3D distributed DC/IP method is still effective in detecting the ore-controlling fault of the altered tectonite-type gold ore deposit. Integrated with geology and drilling, it is maybe an effective method for searching altered tectonite-type gold ore deposit.
Fig. 7

Layers of the 3D inversion resistivity model of the Sizhuang gold deposit. a The low-resistivity layer, b the low- and moderate-resistivity layer, c the altered fracture zone, d the high-resistivity layer

Results of Shijia study area

We adopted a similar inversion procedure used for the Sizhuang gold deposit. To obtain the resistivity model for the Shijia area (Fig. 8), we divided its resistivity model into four layers based on three clearly defined resistivity boundary surfaces. The first layer is a low-resistivity layer with resistivity values between 0 and 200 Ω m and is widely distributed at the surface, and it likely corresponds to the Quaternary. The second layer has relatively large variations in its shape and resistivity (ranging from 200 to 650 Ω m). Its middle and south-east sections are relatively shallow with depths of approximately 200 m, and its thickness increases to a maximum thickness of approximately 600 m, from east to west and from south to north. The second layer is inferred to be metagabbro. The resistivity of the third layer varies in a range of approximately 650–750 Ω m. It is relatively shallow in the south-east and is nearly horizontal with large variations in its shape. Isosurfaces with nearly N–S and E–W strikes, which are inferred to be a nearly N–S fault and a nearly E–W fault, can be clearly observed. The two faults merge in this region, resulting in complicated structural shapes in the middle and south-west regions. The fourth layer has high resistivities (≥ 750 Ω m). The shape of the upper section is similar to that of the third layer, and at a depth of approximately 300 m, a high-resistivity body (> 1000 Ω m) with a variable shape intrudes from south-east to north-west; we infer this body to be biotite–diorite. The section below the high-resistivity body has relatively low resistivities and is inferred to be monzogranite.
Fig. 8

Layers of the 3D inversion resistivity model of the Shijia study area. a The low-resistivity layer, b the low- and moderate-resistivity layer, c the altered fracture zone, d the high-resistivity layer

Based on the inverted resistivity model, we further inverted the chargeability model. The initial chargeability model was set at 5 mV/V to obtain the 3D chargeability model for the Shijia study area. The 3D chargeability model with different threshold values is shown in Fig. 9. The high-chargeability anomalous body has an irregular columnar shape, and the long axis extends E–W. It generally dips to the west; it is nearly horizontal in the east, and the dips increase gradually in the west. The gross morphology is relatively simple, but it has a more complicated local morphology. The body is shallower (approximately 200 m) in the east and gradually deepens towards the west. The burial depth and location of the western section of this high-chargeability anomalous body generally correspond to the two fracture belts in the resistivity model. Based on our analysis of the contents of minerals with different chargeabilities, we infer that most of the rocks in the study area are low-chargeability metamorphic rocks and common granite, followed by granitic rocks altered by mineralization. The high-chargeability anomalous body likely resulted from the strong effects of mineralization, such as pyritization. Because pyritization, siliconization and sericitization processes are closely related to the formation of the gold deposit, the high-chargeability areas are of highest priority in the exploration for altered tectonite-type gold ore deposits.
Fig. 9

3D inversion results of the chargeability of the Shijia study area. a Chargeability > 8, b chargeability > 10, c chargeability > 12, d chargeability > 14 (units: mV/V)

Discussion

Resolution comparison

We use the Shijia study area as an example to compare the resolutions of the 2D, 3D and vector observational methods in detecting underground structures. Using a constant transmitting dipole location and current, we extracted observational data from the receiving dipoles along the same observational line (P − P′) using four different methods (Fig. 10): (a) 2D vector observations: remove all receiving dipoles that are not located on line P − P′ and acquire the data using the 2D vector method; (b) 3D vector observations: preserve all receiving dipoles in the experimental area and acquire the data using the 3D vector method; (c) 2D scalar observations: remove all receiving dipoles that are not located on line P − P′ and the line perpendicular to line P − P′ and acquire the data using the 2D scalar method; (d) 3D scalar observations: remove all N–S direction receiving dipoles, preserve only the E–W direction receiving dipoles and acquire the data using the 3D scalar method.
Fig. 10

Comparison between different survey layouts. a 2D vector acquisition, b 3D vector acquisition, c 2D scalar acquisition, d 3D scalar acquisition

After this process, the four types of observational data were imported into the ERTLab-Solver software. For convenience in the comparisons, we did not filter out any data points and inverted the resistivity and chargeability data using the same inversion parameters. Based on the inverted resistivity and chargeability models, we compared the cross sections along line P − P′ (Figs. 11 and 12).
Fig. 11

Resistivity slices of different survey layouts along profile P − P′ in Fig. 10. a 2D vector acquisition, b 3D vector acquisition, c 2D scalar acquisition, d 3D scalar acquisition

Fig. 12

Chargeability slices of different survey layouts along profile P − P′ in Fig. 10. a 2D vector acquisition, b 3D vector acquisition, c 2D scalar acquisition, d 3D scalar acquisition

The resistivity cross sections obtained from the different observational methods (Fig. 11) show that the 3D vector data (Fig. 11b) provide the greatest detail, followed by the 2D vector observations (Fig. 11a), whereas the 2D scalar (Fig. 11c) and 3D scalar (Fig. 11d) observations have worse resolutions and larger deviations from the observational results.

The chargeability cross sections obtained from the different observational methods (Fig. 12) show that the 3D vector observational data (Fig. 12b) provide the greatest detail, followed by 3D scalar observations (Fig. 12d), whereas the 2D vector (Fig. 12a) and 2D scalar (Fig. 12c) observations have worse resolutions. The inverted 3D observational results are deeper than the inverted 2D results.

The inverted results for both the resistivity and chargeability using the 3D vector observations have the highest resolution because the receiving dipoles have an L shape that are observed in both the N–S and W–E directions, which can obtain more detailed geoelectrical properties and thus results in more precise observations. Consequently, the vector observations have greater depths and higher horizontal and vertical resolutions than the scalar observations. Based on this discussion, we conclude that the 3D distributed DC/IP method is method that is capable of collecting sufficient geoelectrical properties in different directions and thus comprehensively records more detailed and higher-resolution 3D information about the electric field to greater prospecting depths. The larger number of receiving dipoles simultaneously collected the data and further improved the prospecting efficiency. In addition, the receivers can be discretely arranged and are thus unaffected by the surface morphology and obstacles. In summary, based on the detection resolution and depth and the working method, the 3D distributed DC/IP method can be reliably applied in prospecting for metal ore deposits.

Southern extension of the Jiaojia gold belt, shallow magma and tectonic evolution

Faults F1 and F2 can be recognized (Fig. 13) in the 3D inverted resistivity (Fig. 8) and chargeability (Fig. 9) models from the Shijia experiments.
Fig. 13

Geological model of the Shijia study area based on the resistivity and chargeability

Fault F1 strikes approximately N–NW and dips to the south-east at varying angles that decrease with depth. The hanging wall is composed of the Malianzhuang metagabbro, whereas the footwall is composed of the Linglong monzogranite. A fracture zone locates along the contact between the hanging wall and the footwall. The fault has a relatively constant structure at depth, but it is quite complicated in the shallow section of the north-west region of the model. The electrical properties of fault F1 (low resistivity and high polarization) are generally consistent with those of altered tectonite-type gold ore deposits. By comparing the physical properties of fault F1 with those of other altered tectonite-type gold ore deposits along the Jiaojia Fault, we infer that fault F1 is the southern extension of the Jiaojia Fault. However, the fault strikes more towards the east, which is different from the Sizhuang section.

Fault F2 is relatively shallow, generally strikes north-west and dips to the north-east, and the dip angle increases with increasing depth. Similar to fault F1, the hanging wall of fault F2 is composed of metagabbro, whereas the footwall is composed of monzogranite. The north-western section of fault F2 in the model also has a complicated structural morphology, but its eastern section has a relatively constant morphology. Compared with fault F2, the resistivity of fault F1 does not correspond well to its chargeability. Therefore, we infer that fault F2 might be a branch of the southern extension of the Jiaojia Fault.

The intrusion of the Guojialing granodiorite resulted in the formation of faults F1 and F2, which are shown as clearly defined resistivity boundaries in the 3D model (Fig. 8). After the formation of the faults, continuous intrusions led to large variations in the structural morphologies of the faults and even reversed dip angles; the alteration process caused by mineralization resulted in the high-chargeability characteristics of the faults (Fig. 9).

Combined with the regional information, the magmatic and structural evolution of the southern section of the Jiaojia metallogenic belt is interpreted as follows (Fig. 13):

Neoarchaean volcanic rocks were deposited in the ancient Jiaodong strata, and the intrusion of mantle-derived ultrabasic magma resulted in the formation of the Malianzhuang metagabbro. From the Late Triassic to the Early Jurassic, the collision between the South China block and the North China block caused large-scale magmatic activity, and the crystalline basement melted to form the Linglong granite. During the Early Cretaceous, the lithosphere thinned, and mantle materials were brought upward to form the Guojialing granite, which intruded from the bottom to the top towards the south-east and resulted in the uplifted rock bodies of the Linglong monzogranite and Malianzhuang metagabbro. As a result, the fault changed from ductile to brittle, which finally resulted in two north-west-striking brittle faults (F1 and F2). Subsequently, the intrusion of hydrothermal solutions led to alteration processes such as siliconization and potassiumization, which likely resulted in the accumulation of gold deposits in locations within the F1 and F2 fracture belts.

Conclusions

  1. (1)

    The modelling and observations of the Sizhuang gold deposit showed that the 3D distributed DC/IP method can effectively detect ore-controlling faults of altered tectonite-type gold ore. Integrated with geology and drilling, it is maybe an effective method for searching altered tectonite-type gold ore deposits.

     
  2. (2)

    and it is an effective method for exploring for this type ore deposits.

     
  3. (3)

    The detection results using the 3D distributed DC/IP method in the Shijia study area preliminarily revealed the southern extension and spatial characteristics of the Jiaojia fault, thus providing a basis for prospecting in the southern section of the Jiaojia gold belt.

     
  4. (4)

    The collection of full waveform data can provide high-quality data in old mining areas with severe noise and thus provides resistivity and chargeability information for prospecting in deep areas and regions surrounding old mining sites.

     

Notes

Acknowledgments

This work was co-supported by National Natural Science Foundation of China Fund Project (No. 41574133), the fund from the Ministry of Science and Technology of People’s Republic of China (No. 2016YFC0600109) and geological survey Project (No. 12120114053301 & No. KY201401). Hengda century (Beijing) Geophysical Technology Co., Ltd, and IRIS instruments (France) provided equipment for filed survey; general manager of Guoqing Liu, Dr. Shuai Ruan, Shuyuan Wu, Jiong Zhang, Zhiming Zhou and other engineers provided strong support in the field data acquisition; Stefano Del Ghianda (Geostudi Astier company) carried out the acquisition and inversion software guide; Shandong Geologica Survey Institute provides geological data of Sizhuang gold deposit, thanks all of them!

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Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2018

Authors and Affiliations

  1. 1.China Deep Exploration Center (SinoProbe Center)Chinese Academy of Geological SciencesBeijingChina
  2. 2.MLR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral ResourcesCAGSBeijingChina
  3. 3.School of Resources and Earth SciencesChina University of Mining and TechnologyXuzhouChina
  4. 4.China University of GeosciencesWuhanChina

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