**A 3D Predictive Method for Deep-Seated Gold Deposits in the Northwest Jiaodong Peninsula and Predicted Results of Main Metallogenic Belts**

**Mingchun Song 1,\*, Shiyong Li 2,3, Jifei Zheng 4,5, Bin Wang 1,2, Jiameng Fan <sup>1</sup> , Zhenliang Yang <sup>1</sup> , Guijun Wen <sup>1</sup> , Hongbo Liu <sup>3</sup> , Chunyan He <sup>3</sup> , Liangliang Zhang <sup>1</sup> and Xiangdong Liu <sup>1</sup>**


**Abstract:** With the rapid depletion of mineral resources, deep prospecting is becoming a frontier field in international geological exploration. The prediction of deep mineral resources is the premise and foundation of deep prospecting. However, conventional metallogenic predictive methods, which are mainly based on surface geophysical, geochemical, and remote sensing data and geological information, are no longer suitable for deep metallogenic prediction due to the large burial depth of deep-seated deposits. Consequently, 3D metallogenic prediction becomes a critical method for delineating deep prospecting target areas. As a world-class giant gold metallogenic province, the Jiaodong Peninsula is at the forefront in China in terms of deep prospecting achievements and exploration depth. Therefore, it has unique conditions for 3D metallogenic prediction and plays an important exemplary role in promoting the development of global deep prospecting. This study briefly introduced the method, bases, and results of the 3D metallogenic prediction in the northwest Jiaodong Peninsula and then established 3D geological models of gold concentration areas in the northwest Jiaodong Peninsula using drilling combined with geophysics. Since gold deposits in the northwest Jiaodong Peninsula are often controlled by faulting in the 3D space, this study proposed a method for predicting deep prospecting target areas based on a stepped metallogenic model and a method for predicting the deep resource potential of gold deposits based on the shallow resources of ore-controlling faults. Multiple characteristic variables were extracted from the 3D geological models of the gold concentration areas, including the buffer zone and dip angle of faults, the changing rate of fault dip angle, and the equidistant distribution of orebodies. Using these characteristic variables, five deep prospecting target areas in the Jiaojia and Sanshandao faults were predicted. Moreover, based on the proven gold resources at an elevation of −2000 m and above, the total gold resources of the Sanshandao, Jiaojia, and Zhaoping ore-controlling faults at an elevation of −5000–−2000 m were predicted to be approximately 3377–6490 t of Au. Therefore, it is believed that the total gold resources in the Jiaodong Peninsula are expected to exceed 10,000 t. These new predicted results suggest that the northwest Jiaodong Peninsula has huge potential for the resources of deep gold deposits, laying the foundation for further deep prospecting.

**Keywords:** deep prospecting; 3D metallogenic prediction; characteristic variables; stepped metallogenic model; resource potential; Jiaodong Peninsula

**Citation:** Song, M.; Li, S.; Zheng, J.; Wang, B.; Fan, J.; Yang, Z.; Wen, G.; Liu, H.; He, C.; Zhang, L.; et al. A 3D Predictive Method for Deep-Seated Gold Deposits in the Northwest Jiaodong Peninsula and Predicted Results of Main Metallogenic Belts. *Minerals* **2022**, *12*, 935. https:// doi.org/10.3390/min12080935

Academic Editor: Stanisław Mazur

Received: 24 May 2022 Accepted: 21 July 2022 Published: 25 July 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

The supply of gold in China does not meet the present demand. In 2021, the gold production in China was 330 t, while the gold consumption was 1121 t. As the shallow-surface mineral resources in East China are increasingly low, deep prospecting has inevitably become a method for resolving the resource crisis. The prediction of deep mineral resources is the premise and foundation of deep prospecting. However, metallogenic prediction is a complex engineering task [1,2]. As relevant methods, technologies progress and geological understandings deepen, the predicted and prospecting results will change significantly. As the most important base of gold in China, the Jiaodong Peninsula has witnessed the complexity and uncertainty of metallogenic prediction in its prospecting and metallogenic prediction history. At the beginning of the 21st century, the gold resources in the northwest Jiaodong Peninsula were predicted to be 2492 t of Au in total, based on previous regional metallogenic predictions [3,4]. During 2007–2012, the gold resources at a depth of 0–2000 m in the Jiaodong Peninsula were predicted to be 3963 t of Au using the "three-in-one" prospecting prediction theory that integrates metallogenic geological bodies, metallogenic structural plane, and metallogenic characteristics [5]. At present, the accumulative proven gold resources in the Jiaodong Peninsula total more than 5000 t of Au [6], far exceeding previously predicted results. Since the Jiaodong Peninsula is a world-class giant gold metallogenic province, the scientific and accurate assessment of gold resource potential in this area plays an important exemplary role in promoting the development of deep prospecting.

Deep-seated gold orebodies are covered by rock layers with a thickness of more than 1000 m or even thousands of meters. The mineralization information is strongly suppressed, thus, metallogenic predictive methods based on surface geophysical, geochemical, remotesensing data and geological information are ineffective. High-precision deep geophysical exploration (gravity, electromag, seismic) and 3D visualization analysis are effective for predicting and exploring deep resources [7–11]. Furthermore, 3D metallogenic prediction based on 3D geological modeling and fault ore-controlling law has achieved important research achievements in many areas of the world [12–19]. Based on the deep geophysical exploration and 3D modeling of gold concentration areas in the northwest Jiaodong Peninsula, this study proposed two new methods: a method for predicting deep prospecting target areas based on a stepped metallogenic model and a method for predicting the deep resource potential based on gold resources in shallow parts. Applying both methods, this study predicted the deep prospecting target areas and resource potential of major gold metallogenic belts in the northwest Jiaodong Peninsula, revealing an exciting deep prospecting prospect. This study lays the foundation for further deep prospecting in the Jiaodong Peninsula.

#### **2. Geological Background and Overview of Gold Deposits**

The Jiaodong Peninsula lies at the southeastern margin of the North China Craton and at the northeastern end of the Dabie-Sulu ultrahigh-pressure (UHP) metamorphic belt [20]. The Jiaobei terrane in the western Jiaodong Peninsula and the Weihai terrane in the eastern Jiaodong Peninsula fall in the North China Craton and the Sulu UHP metamorphic belt, respectively. Moreover, the Jiaolai Basin is superimposed on the Jiaobei terrane and the southern part of the Weihai terrane (Figure 1). The Jiaobei terrane mainly consists of Neoarchean granite-greenstone belts and Paleoproterozoic-Neoproterozoic metamorphic strata, the Weihai terrane is mainly composed of Neoproterozoic granitic gneiss bearing UHP eclogites, and the Jiaolai Basin mainly includes Cretaceous volcanic-sedimentary rock series [21–24]. Jurassic-Cretaceous granitic intrusive rocks are widely emplaced in the Jiaobei and Weihai terranes [25–32], whereas only a small number of Triassic granitoids are exposed in the Weihai terrane.

Faults are well-developed in the Jiaodong Peninsula. Among them, NE-NNE-trending faults with dip of SE or NW are the most developed. They are followed by nearly EW-NEE-trending faults. Additionally, EW-trending faults are sporadically exposed, showing

poor continuity. The gold deposits in the Jiaodong Peninsula are mainly controlled by NE-NNE faults including the Sanshandao, Jiaojia, Zhaoping, Xilin-Douya, and Jinniushan faults [32–35]. poor continuity. The gold deposits in the Jiaodong Peninsula are mainly controlled by NE-NNE faults including the Sanshandao, Jiaojia, Zhaoping, Xilin-Douya, and Jinniushan faults [32–35].

NEE-trending faults. Additionally, EW-trending faults are sporadically exposed, showing

*Minerals* **2022**, *12*, x FOR PEER REVIEW 3 of 22

There are more than 200 gold deposits with proven resources in the Jiaodong Peninsula (Figure 1), with gold resources greater than 5000 t [6]. The gold deposits in this peninsula are intensively distributed and are divided into three metallogenic sub-regions, namely Jiaoxibei (Laizhou-Zhaoyuan), Qipengfu (Qixia-Penglai-Fushan), and Muru (Muping-Rushan). These three regions consist of six metallogenic belts, namely Sanshandao, Jiaojia, Zhaoping, Qixia-Daliuhang, Taocun, and Muru. These metallogenic belts are composed of 13 gold orefields, namely Sanshandao, Jiaojia, Lingbei, Anshi, Dazhuangzi, Linglong, Dayingezhuang, Jiudian, Qixia, Daliuhang, Laishan, Pengjiakuang, and Denggezhuang. The gold mineralization in the Jiaodong Peninsula primarily include altered rock in fractured zones and quartz vein, followed by a small quantity of altered breccia, altered conglomerate, interlayer decollement-detachment zone, and pyrite-carbonate vein types. The characteristics, ore-controlling regularity and genesis of Jiaodong gold deposits have been studied extensively by predecessors [36–41]. The deep prospecting carried out in the peninsula since the beginning of this century has discovered more than 3000 t of proven gold resources at a depth of 600–2000 m, exceeding the previously proven gold resources at a depth of 500 m and less [6,42]. There are more than 200 gold deposits with proven resources in the Jiaodong Peninsula (Figure 1), with gold resources greater than 5000 t [6]. The gold deposits in this peninsula are intensively distributed and are divided into three metallogenic sub-regions, namely Jiaoxibei (Laizhou-Zhaoyuan), Qipengfu (Qixia-Penglai-Fushan), and Muru (Muping-Rushan). These three regions consist of six metallogenic belts, namely Sanshandao, Jiaojia, Zhaoping, Qixia-Daliuhang, Taocun, and Muru. These metallogenic belts are composed of 13 gold orefields, namely Sanshandao, Jiaojia, Lingbei, Anshi, Dazhuangzi, Linglong, Dayingezhuang, Jiudian, Qixia, Daliuhang, Laishan, Pengjiakuang, and Denggezhuang. The gold mineralization in the Jiaodong Peninsula primarily include altered rock in fractured zones and quartz vein, followed by a small quantity of altered breccia, altered conglomerate, interlayer decollement-detachment zone, and pyrite-carbonate vein types. The characteristics, ore-controlling regularity and genesis of Jiaodong gold deposits have been studied extensively by predecessors [36–41]. The deep prospecting carried out in the peninsula since the beginning of this century has discovered more than 3000 t of proven gold resources at a depth of 600–2000 m, exceeding the previously proven gold resources at a depth of 500 m and less [6,42].

**Figure 1.** Map showing the regional geology and gold deposit distribution in the Jiaodong Peninsula. 1—Quaternary; 2—Cretaceous; 3—Paleoproterozoic and Neoproterozoic; 4—Neoproterozoic bearing eclogite granitic gneiss; 5—Archean granite-greenstone belt; 6—Cretaceous Laoshan granite; 7—Cretaceous Weideshan granite; 8—Cretaceous Guojialing granite; 9—Jurassic Linglong granite; 10—Triassic granitoid; 11—Geological boundary of conformity/unconformity; 12—Fault; 13— Shallow gold deposits (very large and large/medium-scale and small); 14—Deep-seated gold deposits (very large and large/medium-scale and small); 15—Gold deposit of altered-rock-type/quartzvein-type/altered-breccia- type. **Figure 1.** Map showing the regional geology and gold deposit distribution in the Jiaodong Peninsula. 1—Quaternary; 2—Cretaceous; 3—Paleoproterozoic and Neoproterozoic; 4—Neoproterozoic bearing eclogite granitic gneiss; 5—Archean granite-greenstone belt; 6—Cretaceous Laoshan granite; 7—Cretaceous Weideshan granite; 8—Cretaceous Guojialing granite; 9—Jurassic Linglong granite; 10—Triassic granitoid; 11—Geological boundary of conformity/unconformity; 12—Fault; 13— Shallow gold deposits (very large and large/medium-scale and small); 14—Deep-seated gold deposits (very large and large/medium-scale and small); 15—Gold deposit of altered-rock-type/quartz-veintype/altered-breccia-type.

*3.1. 3D Modeling of Main Gold Concentration Areas in the Northwest Jiaodong Peninsula*

**3. 3D predictive Method of Deep-Seated Gold Deposits**

#### **3. 3D Predictive Method of Deep-Seated Gold Deposits** This study established the geological models of the Sanshandao supergiant gold de-

#### *3.1. 3D Modeling of Main Gold Concentration Areas in the Northwest Jiaodong Peninsula* posit in the Sanshandao fault zone, the Jiaojia supergiant gold deposit in the Jiaojia fault

This study established the geological models of the Sanshandao supergiant gold deposit in the Sanshandao fault zone, the Jiaojia supergiant gold deposit in the Jiaojia fault zone, and the Lingnan-Shuiwangzhuang and Dayingezhuang gold deposits in the Zhaoping fault zone. zone, and the Lingnan-Shuiwangzhuang and Dayingezhuang gold deposits in the Zhaoping fault zone. The basic data used for modeling included regional geological data on scales of 1:50,000 and 1:10,000, exploration line sections, borehole histograms, digital elevation

The basic data used for modeling included regional geological data on scales of 1:50,000 and 1:10,000, exploration line sections, borehole histograms, digital elevation data, and high-precision geophysical profiles. The modeling parameters included the scale of planar geological maps of 1:10,000, the scale of exploration line sections of 1:2000, and the grid density in the horizontal direction of 60 m × 60 m. Meanwhile, the minimum thickness was set to be 0.1 m. data, and high-precision geophysical profiles. The modeling parameters included the scale of planar geological maps of 1:10,000, the scale of exploration line sections of 1:2000, and the grid density in the horizontal direction of 60 m × 60 m. Meanwhile, the minimum thickness was set to be 0.1 m. The 3D geological models of the Sanshandao, Jiaojia, Lingnan-Lijiazhuang, and Day-

The 3D geological models of the Sanshandao, Jiaojia, Lingnan-Lijiazhuang, and Dayingezhuang gold deposits were established using data of 311, 500, 680, and 291 boreholes, respectively, as well as the basic data used for modeling. For example, Figure 2 shows the 3D geological model of the Sanshandao gold deposit (Figure 2a) and the superposition of gold orebodies on the plane of the Sanshandao fault (Figure 2b). Each of the 3D geological models was composed of two parts: the known model of the shallow part and the inferred model of the deep part. The former was controlled by systematic drilling engineering. As a known part, it was used to summarize metallogenic rules and extract favorable metallogenic information through spatial comprehensive analysis. The latter, which was constructed based on the former and relevant geophysical interpretation and inference, was used to provide intuitive attributes for deep metallogenic prediction. ingezhuang gold deposits were established using data of 311, 500, 680, and 291 boreholes, respectively, as well as the basic data used for modeling. For example, Figure 2 shows the 3D geological model of the Sanshandao gold deposit (Figure 2a) and the superposition of gold orebodies on the plane of the Sanshandao fault (Figure 2b). Each of the 3D geological models was composed of two parts: the known model of the shallow part and the inferred model of the deep part. The former was controlled by systematic drilling engineering. As a known part, it was used to summarize metallogenic rules and extract favorable metallogenic information through spatial comprehensive analysis. The latter, which was constructed based on the former and relevant geophysical interpretation and inference, was used to provide intuitive attributes for deep metallogenic prediction.

**Figure 2.** 3D geological model of the Sanshandao supergiant gold deposit (**a**) and superposition map of fault plane on gold orebodies (**b**). 1—Sea area; 2—Quaternary; 3—Early Precambrian metamorphic rock series; 4—Linglong-type granite; 5—Alteration zone of Sanshandao fault. **Figure 2.** 3D geological model of the Sanshandao supergiant gold deposit (**a**) and superposition map of fault plane on gold orebodies (**b**). 1—Sea area; 2—Quaternary; 3—Early Precambrian metamorphic rock series; 4—Linglong-type granite; 5—Alteration zone of Sanshandao fault.

#### *3.2. Method for Predicting Deep Metallogenic Target Areas Based on a Stepped Metallogenic Model 3.2. Method for Predicting Deep Metallogenic Target Areas Based on a Stepped Metallogenic Model* 3.2.1. Overview of Predictive Method

3.2.1. Overview of Predictive Method Breakthroughs have been made in deep prospecting in the Jiaodong Peninsula since the beginning of the 21st century, leading to the discovery of large-scale gold resources mainly at a depth of 700–2000 m. Based on the study of the characteristics and ore-hosting regularity of deep faults, Song MC et al. [41] proposed a stepped metallogenic model of deep-seated gold deposits. Specifically, ore-controlling faults show a stepped pattern due to alternate steep-gentle dip angles, and gold orebodies are mainly rich in stepped fault parts with gentle dip angles and the fault parts with a transition between steep-gentle dip angles. Given that it is difficult to capture the mineralization-related predictive information on the ground surface due to the large burial depth of deep-seated gold deposits, Breakthroughs have been made in deep prospecting in the Jiaodong Peninsula since the beginning of the 21st century, leading to the discovery of large-scale gold resources mainly at a depth of 700–2000 m. Based on the study of the characteristics and ore-hosting regularity of deep faults, Song MC et al. [41] proposed a stepped metallogenic model of deep-seated gold deposits. Specifically, ore-controlling faults show a stepped pattern due to alternate steep-gentle dip angles, and gold orebodies are mainly rich in stepped fault parts with gentle dip angles and the fault parts with a transition between steep-gentle dip angles. Given that it is difficult to capture the mineralization-related predictive information on the ground surface due to the large burial depth of deep-seated gold deposits, this study proposed a method based on the stepped metallogenic model according to the following three characteristics [43]. First, ore-controlling faults of gold deposits have large scales. Second, the hanging walls and footwalls of the faults are composed of Early

this study proposed a method based on the stepped metallogenic model according to the following three characteristics [43]. First, ore-controlling faults of gold deposits have large

fer in physical properties. Third, the deep locations and morphological characteristics of

Precambrian metamorphic rock series and Jurassic-Cretaceous granitoids, which greatly differ in physical properties. Third, the deep locations and morphological characteristics of faults can be detected using high-precision geophysical methods (gravity, electromag, seismic). This predictive method is designed to extract the predictive factors for identifying ore-controlling faults and the characteristics of metallogenic plane based on deep exploration and 3D geological modeling and to delineate the favorable ore-hosting locations of faults as prospecting target areas.

The 3D geological models of known gold concentration areas and their deep parts were divided into large numbers of 3D cubic blocks (120 m × 120 m × 10 m or 120 m × 120 m × 15 m). Each block was regarded as a homogeneous body with consistent properties, and thus the regularity of change in the properties of all blocks approximately reflected the regularity of internal change in geological bodies. Such cube blocks are called cell blocks. The storage address in a computer of each cell block corresponds to its location in the natural deposit. The prospecting target areas were determined as follows. Firstly, various quantitative information for deep prospecting assessment was comprehensively analyzed and processed according to the ore-controlling geological conditions of known deposits and the regularity of spatial (especially deep) change in prospecting indicators, and accordingly, 3D prospecting predictive models were established. Subsequently, the quantitative information described in Section 3.2.2. was assigned to each cell block, i.e., each favorable indicator is coded. Once the coding of the many prospective indicators is finished, a global favorable indicator is computed by combining the favorable coded indicator on each cell. Finally, the areas with higher prospective global scores were identified as prospecting target areas.
