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Volume 32 Issue 2
Apr.  2021
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Keyan Xiao, Jie Xiang, Mingjing Fan, Yang Xu. 3D Mineral Prospectivity Mapping Based on Deep Metallogenic Prediction Theory: A Case Study of the Lala Copper Mine, Sichuan, China. Journal of Earth Science, 2021, 32(2): 348-357. doi: 10.1007/s12583-021-1437-8
Citation: Keyan Xiao, Jie Xiang, Mingjing Fan, Yang Xu. 3D Mineral Prospectivity Mapping Based on Deep Metallogenic Prediction Theory: A Case Study of the Lala Copper Mine, Sichuan, China. Journal of Earth Science, 2021, 32(2): 348-357. doi: 10.1007/s12583-021-1437-8

3D Mineral Prospectivity Mapping Based on Deep Metallogenic Prediction Theory: A Case Study of the Lala Copper Mine, Sichuan, China

doi: 10.1007/s12583-021-1437-8
More Information
  • With the decrease in surface and shallow ore deposits, mineral exploration has focused on deeply buried ore bodies, and large-scale metallogenic prediction presents new opportunities and challenges. This paper adopts the predictive thinking method in this era of big data combined with specific research on the special exploration and exploitation of deep-earth resources. Four basic theoretical models of large-scale deep mineralization prediction and evaluation are explored: mineral prediction geological model theory, multidisciplinary information correlation theory, mineral regional trend analysis theory, and mineral prediction geological differentiation theory. The main workflow of large-scale deep resource prediction in the digital and information age is summarized, including construction of ore prospecting models of metallogenic systems, multiscale 3D geological modeling, and 3D quantitative prediction of deep resources. Taking the Lala copper mine in Sichuan Province as an example, this paper carries out deep 3D quantitative prediction of mineral resources and makes a positive contribution to the future prediction and evaluation of mineral resources.
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3D Mineral Prospectivity Mapping Based on Deep Metallogenic Prediction Theory: A Case Study of the Lala Copper Mine, Sichuan, China

doi: 10.1007/s12583-021-1437-8

Abstract: With the decrease in surface and shallow ore deposits, mineral exploration has focused on deeply buried ore bodies, and large-scale metallogenic prediction presents new opportunities and challenges. This paper adopts the predictive thinking method in this era of big data combined with specific research on the special exploration and exploitation of deep-earth resources. Four basic theoretical models of large-scale deep mineralization prediction and evaluation are explored: mineral prediction geological model theory, multidisciplinary information correlation theory, mineral regional trend analysis theory, and mineral prediction geological differentiation theory. The main workflow of large-scale deep resource prediction in the digital and information age is summarized, including construction of ore prospecting models of metallogenic systems, multiscale 3D geological modeling, and 3D quantitative prediction of deep resources. Taking the Lala copper mine in Sichuan Province as an example, this paper carries out deep 3D quantitative prediction of mineral resources and makes a positive contribution to the future prediction and evaluation of mineral resources.

Keyan Xiao, Jie Xiang, Mingjing Fan, Yang Xu. 3D Mineral Prospectivity Mapping Based on Deep Metallogenic Prediction Theory: A Case Study of the Lala Copper Mine, Sichuan, China. Journal of Earth Science, 2021, 32(2): 348-357. doi: 10.1007/s12583-021-1437-8
Citation: Keyan Xiao, Jie Xiang, Mingjing Fan, Yang Xu. 3D Mineral Prospectivity Mapping Based on Deep Metallogenic Prediction Theory: A Case Study of the Lala Copper Mine, Sichuan, China. Journal of Earth Science, 2021, 32(2): 348-357. doi: 10.1007/s12583-021-1437-8
  • Mineral resources are the basis of both industry and agriculture. China's demand for mineral resources is increasing, and its dependence on foreign countries remains high, which seriously affects national energy resource security. Scientific prediction and evaluation of potential undiscovered mineral resources are related to the sustainable development of a country, and they are important weapons in the political game between major powers and the struggle for international power. In recent years, domestic mineral exploration and resource potential evaluation have shown that deep-earth elements have great prospecting possibilities, and the advancement of deep-earth prospecting is an important trend in the development of future mineral resource potential prediction and evaluation. Xiao and Zhao (1994) discussed the basic issues and research approaches of large-scale metallogenic prediction and concluded that the ore field structure, metallogenic and comprehensive prospecting models, metallogenic series deposition and quantitative prediction are all large-scale, important research issues that are inseparable from mine forecasting. The traditional quantitative prediction of mineral resources is based on the overlay analysis and prediction of two-dimensional (2D) layers. With the continuous development of computer graphics technology and three-dimensional (3D) spatial data processing, the 3D cube model is used for hidden ore bodies (that is, the second prospecting space). Resource prediction and evaluation has become a significant focus in mineral exploration. Zhao and Li (1992) used statistical methods to establish a 3D spatial positioning prediction model for the quantitative relationship between the deposit value and ore-controlling factors. Chen et al.(2014, 2007) established a "cubic block prediction model" prospecting method for the 3D prediction and evaluation of regional deep mineral resources. Wang et al.(2015, 2011) built a 3D geological model based on multisource data and applied evidence weights and fractal and other technical means to predict deep ore bodies and quantitatively estimate resources. Xiao et al.(2015, 2012) established a large-scale 3D prediction workflow based on 3D models of geological bodies and mineralization prediction theory and established a cube prediction model to carry out 3D ore body simulation and resource assessment. Mao et al.(2014, 2010, 2009) proposed a 3D prediction process for deep mineral resources called "geological information integration-quantitative extraction of mineralization information three-dimensional quantitative prediction" and studied the 3D quantitative analysis of geological bodies, quantitative extraction of ore-controlling geological factors, and 3D quantitative prediction methods for ore bodies. Yuan et al. (2014) and Li et al. (2015) used 3D comprehensive information and metallogenic prediction methods to carry out medium- and large-scale 3D metallogenic prediction case studies. Yousefi et al. (2019) classified ore-forming processes into pre-, syn-, and post-mineralization parts and proposed an exploration information system (EIS) for mineral exploration targeting. Zuo (2020) introduced the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on the analysis of spatial associations between geological prospecting big data (GPBD) and locations of known mineralization.

    The above research shows that prediction and evaluation methods for mineral resources are moving toward model integration, information intelligence analysis and deep nonlinear 3D models. However, the current 3D prediction of mineral resources has faced the basic contradiction of a relative lack of effective deep information when the prospecting prediction depth is increased. The problem is that the reliability of the prediction results gradually decreases with increasing prediction depth, which restricts the exploration for and development of deep resources. This paper summarizes the basic theory and process of deep mineralization prediction and provides a reference for the prediction of deep resource prospecting.

  • Faced with ever-increasing depth, geoscientists have explored the basic theoretical basis of the prediction and evaluation of mineral resources using the prediction thinking method of the big data era combined with specific research on deep mine resource exploration (Chen et al., 2016). The correlation prediction method of big data is considered to be consistent with the commonly used comprehensive information mineral forecasting method. The mineral forecasting model theory, multidisciplinary information correlation analysis, forecasting geological difference theory, and mineral regional trend analysis method are the four types of mineral resource evaluation.

  • The application of ore deposit models for scientific prediction is the prediction method most commonly used by geologists. For example, with the application of porphyry deposit models to the exploration and discovery of a series of porphyry copper deposits in China, Wang et al. (2010) summarized and promoted the "five-story building plus basement" tungsten ore model. The prediction and evaluation of deep concealed tungsten deposits has been applied in China; Tang et al. (2011) applied the "absence" theory of mineralization series and discovered a series of copper deposits, such as the Jiama copper deposit. Leading mineral resource evaluation plans are also based on models. For example, in the 1980s, the United States' native resource evaluation used the Macamen deposit model method to quantitatively predict resources through characteristic analysis. The global "tripartite" evaluation method advocated by Singer in the United States also started by establishing a descriptive model of global mineral deposits. Recent important national work on mineral resource prediction has developed a comprehensive geological information prediction method for mineral deposit models based on a series of mineral deposits, emphasizing the guiding role of mineral deposit models (Xiao et al., 2013). The out-of-phase positioning prediction theory and the "absent" prediction theory summarized by Yusheng Zhu can be unified and placed into this category (Zhu, 2006). The prediction theory of the ore deposit model conforms to causal theory. Through the study of mineral deposits, the formation environment and mechanism of an ore deposit are ascertained to make scientific predictions. Due to the complex and multigenetic origin of ore deposits, it is difficult to fully ascertain the formation of ore deposits at the current level of technology, as a result, researchers have proposed multiple origins for ore deposits, which causes prediction and evaluation to yield incorrect results. Mineral deposit model prediction theory requires that when using big data to mine prospecting information, the mineral deposit model must be used as a model, and similar analogy methods are used to identify areas and resource potentials that are most similar to the model area for multiple prospecting information.

  • The era of big data has also brought about a major change in the way scientists think about data analysis to understand and predict the objective world. Big data philosophy requires that when predicting and analyzing the patterns of natural and social phenomena, the phenomena must be known but not the reason behind them. The related analysis theory conforms to the objective law of connectivity. Mineral geological surveys and evaluation work are carried out based on the discovery and evaluation of mineral resources. Multidisciplinary and multifield surveys and evaluation work in the Earth sciences, physics, chemistry, remote sensing, and mineral resources are involved in the search for minerals. Prediction evaluation provides a large amount of data, which implicitly predict information about potential minerals and deposit types. The main purpose of correlation analysis is to predict the types of deposits; thus, big data correlation methods should be used to study the metallogenic correlation of this multidisciplinary information. Regarding the use of correlation analysis methods, Wang (2010) proposed a comprehensive information mineral forecast method system whose purpose is similar to those of big data correlation analysis methods. It summarizes the three major difficulties of mineral forecasting: the imbalance in geological work, the multiple possible origins of minerals, and the diversification of remote physical and chemical information. The origin of an ore deposit can be understood differently from exploration to completion, and considerable time and labor are needed to study the genesis of an ore deposit. Physical, chemical, and remote information is not unique to predicting and evaluating mineral geology. In areas with minor geologic work, geological data are relatively sparse, and conversion of the relevant information is required. Big data correlation analysis refers to linear or nonlinear analysis of 2 sets of variables or multiple variables. For example, a linear relationship model between the scale of mineral deposit systems and the amount of resources was summarized in the recent evaluation of potential important mineral resources in the country, and the S-type distribution model of the demand for mineral resources was summarized by Gao and Wang (2010). Resource prediction and evaluation using big data should be guided by basic mineralization theory. Based on the types of mineral predictions, data cleaning, integration and comprehensive correlation analysis should be carried out through multi-professional comprehensive information prediction and mapping, which is related to the use of big data mining technology to establish mineral prediction. This model allows multidisciplinary data to discover possible mineral deposits and their resource potential.

  • Trends are changes in the objective world. In mineral resource prediction and evaluation, studying the trends of temporal and spatial variations in mineral deposits is an effective method. Geological prospecting experts make prospecting predictions based on the trends and tendencies of ore bodies. For example, in evaluating and predicting the potential of the Panzhihua Iron Mine, when forecasters compiled and studied the exploration history of the mining area, they found that the deep part of the main ore body did not thin but tended to thicken. Deep ore prospecting prediction of the Jiaojia-style gold deposit in Jiaodong, Shandong Province, was also performed by studying the 3D variations in the mineralized body and its ore-controlling structure. The prospecting space tends to extend the ore bodies into the offshore area outside of the mining area. Through scientific drilling, the reserves of China's largest gold mining district have doubled. Geological trend analysis shows that the changes in geological objects (phenomena) in time and space are both random and have related trends. In the prediction and evaluation of mineral resources, it is necessary to study the variations in mineral deposit formation in a region over time and find a rule governing this formation. For example, China's iron ore has the greatest potential for mineralization in the Archean, and this period is the most important metallogenic age. To study the spatial variations in the deposit and its ore-controlling elements or geological bodies, eliminating random factors and finding trends are necessary. Quantitative research methods include moving averages, trend analysis, and geostatistical methods. The theory of predicting trends is an extension of and supplements big data theory in space and time.

  • Zhao and Hu (1992) proposed the theory of geological anomaly prediction. In the model, various geological anomalies, especially ore-causing geological anomalies, are delineated to carry out mineralization prediction. Similar to the analogy prediction method in model theory, difference theory is abbreviated from the equivalent difference in mineral forecasting. Difference theory sorts through different positioning mechanisms from known deposits on the basis that the analogy and the same deposit cannot achieve prediction. This theory identifies mineralization information differences between unknown areas and known deposits and scientifically infers new types and new mineral species. The "absence" prediction method for mineralization series is essentially a prediction of differences based on mineralization series. For example, the metallogenic series of iron, copper, molybdenum, gold, and silver deposits in the middle and lower reaches of the Yangtze River is related to Yanshanian crust-mantle intermediate and acidic superficial intrusive activity and includes porphyry type, contact metasomatic type, medium-temperature hydrothermal type, and medium-low temperature type. There are five main hydrothermal and weathered crust types, and the "full position" of this series contains these five types. If only porphyry and contact metasomatic deposits are found in an area with a similar geological background of mineralization, then there are three types: medium-temperature hydrothermal fluid, medium-low temperature hydrothermal fluid, and weathered crust. These three types of deposits may be discovered and are called "absence" deposits. The "full position" and "absence" of a mineralization series of a mineral deposit indicate the prospect for mineral resources in a mineralization zone (belt) or subregion (subzone) based on the mineralization series theory of the deposit. Understanding the difference theory requires that in actual prospecting prediction, the search and prediction of known minerals and ore deposit types, as well as the prediction and evaluation of new minerals, new types, and new deposits at depth, should always be considered. In big data processing and analysis, samples similar to those in the model area as well as anomalous samples that are inconsistent with those in the model area should be considered. For example, the discovery and identification of new types of Jiaojia-style gold deposits in Shandong in the 1980s doubled the gold deposits in the Jiaodong area. The recent discoveries of the large-scale Daqiao and Zhaishang gold deposits in Gansu are also breakthroughs achieved through long-term exploration of altered rock gold deposits and siliceous breccia-type gold deposits. The discovery of the Xiarihanmu nickel deposit in the Qimantage area of East Kunlun in Qinghai has turned the East Kunlun metallogenic belt into a newly developed nickel metallogenic belt. Discovery by prospecting is also an innovation in the methods of difference theory. A 1 : 50 000 mining survey was originally deployed in the area to find copper-lead-zinc polymetallic deposits. In the actual mining survey work, basic-ultrabasic rock bodies were found along with geochemical anomalies such as copper and nickel. By adjusting the direction of prospecting, a breakthrough in prospecting was obtained.

  • Under the guidance of the abovementioned basic prediction theory, the development of deep mineral resource prediction is focused on solving major scientific issues, such as the mechanism of 3D structural reconstruction of deep mineralization space, the positioning mechanism of deep mineralization, and the method of deep mineralization prediction. Research needs to be carried out from three aspects: geological prospecting models of metallogenic systems, multiscale 3D geological modeling, and 3D quantitative prediction (Fig. 1).

    Figure 1.  Flow chart of 3D mineralization prediction of deep mineral resources.

  • The mineralization system is a concept developed on the basis of systems science (Zhai, 1999; Li, 1996). The mineralization system is a product of the geological system process and is a specific part delineated based on economic needs and production technology. Wyborn et al. (1994) proposed that the original intent of the mineralization system was to carry out mineral resource prediction for elements of prediction. According to Wyborn's definition of the metallogenic system, this system includes all the elements that control the generation and preservation of the deposit and the genesis of the source-transport-storage-preservation process that formed the deposit. The metallogenic system is unique in geological history. A deposit and traces of preserved objects are the final products of the metallogenic system. Transforming the process of mineralization and its key factors (source, transport, storage, etc.) into predictable specific spatial elements of mineralization is the key to prediction and target delineation (Hagemann et al., 2016).

    Studies of the geological prospecting model of the metallogenic system focus on summarizing the 3D structural characteristics of six metallogenic systems (on a geological basis); at the scale of metallogenic belts, guided by the theory of the metallogenic system, studies summarize the "source, transport, aggregation, and transformation of different types of mineralization". Studies on the preservation of 3D structural characteristics guided by the "trinity" prospecting prediction theory at the prospecting area scale summarize the 3D structures of "metallogenic geological bodies, metallogenic structures and structural planes, and metallogenic features" of different types of mineralization.

  • Research on multiscale 3D geological modeling focuses on breakthroughs in such work at two scales: the exploration area and the metallogenic belt (see Table 1). Multiscale layered and superimposed 3D geological modeling methods are key research areas. In an exploration area with detailed geological data and effective modeling depth, 3D modeling software is used in combination with an exploration profile and borehole data in a multivariate geological database, and a 3D geological model is established. In large-scale and small-scale areas (metallogenic belts) where geological data are scarce, the 3D spatial reconstruction of deep and marginal metallogenic geological anomalies can be conducted through geological knowledge reasoning and 3D gravity and magnetic inversion, and this work can be constrained by the shallow 3D solid model.

    Modeling scale Prospecting area Metallogenic belt
    Elevation range According to drilling depth 3 000 m underground
    Data Large amounts of (1) drilling and laboratory data; (2) a prospecting line profile; small amounts of (1) geophysical data, (2) geochemical data, etc. Large amounts of (1) surface geological survey measurement data and (2) geophysical data; a small number of (1) measured profiles; (2) borehole and laboratory data.
    Modeling method (1) Contour splicing based on adjacent sections (Micromine software, Surpac software, etc.); (2) radial basis function (RBF) spatial interpolation based on sampling points of adjacent boreholes (Leapfrog software, etc.). (1) Modeling method based on human-machine interactive editing (GeoCAD and other software); (2) modeling method based on geological knowledge and spatial interpolation; (3) geophysical 3D inversion method.
    Modeling content Ore body, specific lithology, ore-forming indicators, fracture zones, ore-forming structural surface, underground 3D primary halo, 3D model of grade distribution, etc. Surface model, stratum model, fold model, fracture model, rock model, alteration zone model, 3D gravity and magnetic model, etc.

    Table 1.  Two scales of 3D geological modeling work

  • With the development of mineral resource evaluation theory, various mineralization prediction methods are also constantly improving. Mineralization prediction methods can be classified into two categories: knowledge-driven and data-driven (Carranza, 2009). Knowledge-driven methods assign various parameters based on the knowledge and experience of experts to integrate multiple pieces of information. The data-driven method is based on quantitative analysis of the correlation between predicted factors and known mineral points, and mineralization prediction is carried out according to the established mathematical model. The mathematical models commonly used for mineralization prediction include weight of evidence, logistic regression, artificial neural networks, support vector machines and random forests (Ouyang et al., 2019).

    The delineation of the mineralization system based on the theory of mineralization systems and the deep learning method guarantees a high degree of accuracy both in theory and in data mining methods. For the final delineated range of the metallogenic system, combining specific field investigations and existing research on strata, rock masses, minerals and deposits, the prediction model of the metallogenic system can be perfected, and the reliability of the metallogenic system can be classified. A typical metallogenic system in the study area should be selected as the model area to explore the reflection of resource estimation parameters, such as the range, depth, and ore-bearing rate of the mineralized geological body in the area, to realize the "positioning" of the metallogenic system along with its "quantity" and "probability" (Xiao et al., 2010).

  • The study area is located in the middle section of the Kangdian axis of the Yangtze quasi-platform. It has undergone a long history of evolution: in the Early Proterozoic, it had a back-arc basin environment; in the Early Mesoproterozoic, it had an aulacogen environment; in the Mesoproterozoic to Late Proterozoic, it experienced a transition from an inter-rift valley to a postorogenic rift; and in the Paleozoic to the Mesozoic, it experienced uplift and subsidence, ground fissure movements, foreland basins, and mountain fault depressions (Liang et al., 2019). The main regional strata that crop out include the pre-Sinian Kunyang Group, the Hekou Group, and Triassic and Quaternary strata, among which the lower Proterozoic estuary groups are the main strata. The study area experienced very strong magmatic activity that displays multiphase and multicycle characteristics (Liu et al., 2019). Among the strata, the early and middle estuary groups reveal the most intense magmatic activity; there was a strong volcanic eruption during the deposition of the early estuary group, and a basic rock intrusion appears in the middle estuary group (Sun and Li, 1990). Although there are intrusions of basic rocks and acidic granites, the intensity and scale of research activity in Hercynian and Indosinian strata have been relatively small (Fig. 2).

    Figure 2.  Simplified geological map of Lala copper ore field.

  • Figure 2 shows the metallogenic model of the Lala copper deposit in Sichuan. Compared with foreign volcanic massive sulfide (VMS)-type deposits (Antoni et al., 2017), the metallogenic process of this deposit is relatively complex and can be roughly divided into volcanic eruptions and hydrothermal processes. The formation of this deposit involved two metallogenic stages: the volcanic eruption and deposition stage and the hydrothermal superimposition and transformation stage (Liu et al., 2018).

    (1) Source: The S isotope composition of most metal sulfides in the Lala copper deposit is similar to that of mantle-derived sulfur, implying that S in the ore-forming fluid came mainly from the mantle. The basic rocks and subvolcanic rocks of the same period in the region can be used as important factors for prediction (Xiang et al., 2019). In addition, studies have shown that the Lala copper deposit was based on the ore-bearing source layer of the early volcanic eruption and deposition stage and was enriched by later hydrothermal superimposition and transformation. The ore-bearing strata are an important source of ore-forming materials and play an indicative role in ore prediction.

    (2) Transport: The space created by structural deformation caused by the aggregation of the Neoproterozoic Rodinia supercontinent provided passages and places for the later superposition of ore-bearing hydrothermal fluids. The supercontinent underwent a cracking event after which polymerization occurred, a mantle plume ascended, large quantities of diabase intruded, and magmatic fluid or mantle fluid migrated along the fault to further enrich the minerals. There may have been two phases of metallogenic events in the Lala copper deposit. The early metallogenic events are of VMS origin, and the ore bodies were all produced at the interface of basic volcanic rock and sedimentary rock. Buffer analysis of basic volcanic rocks can be used to determine the optimal buffer distance, and intersection analysis with sedimentary rocks can then be used to express the structural surface of ore formation. Late mineralization occurred during the hydrothermal superimposition period, and the fault structures were the most important channels for mineral migration. Through the quantitative analysis of the structure, the migration characteristics of mineralization can be better characterized.

    (3) Storage: The location and trapping of ore-forming materials are crucial to the entire ore-forming process (Xiang et al., 2020). An analysis of the Lala metallogenic model reveals that the fold structure is a favorable metallogenic space. The east-west structure of the Lala area is affected by a north-south-trending early Proterozoic rift. The dominant stress is oriented in the east-west direction, and the main compressive stress is aligned in the north-south direction, forming a series of axial east-west folds, such as an estuary anticline and a red mud slope. These folds are oblique, which controls the overall distribution direction of the basement strata and ore bodies. The north-south-trending structure is affected by the collage of the Late Proterozoic Yangtze Plate and Cathaysia Plate. The dominant stress is oriented in the north-south direction, and the main compressive stress follows the east-west direction, which forms the north-south fold structure of the Lala mining area superimposed on the early east-west fold structure. This structure has obvious enrichment and transformation effects on the ore body. In this study, morphological analysis of folds is performed to determine favorable metallogenic positions, and favorable trap positions are then quantitatively characterized.

    (4) Preservation: Preservation conditions are not available for mineral resource evaluation based on the mineralization system. This analysis is mainly reflected in two factors: the protection afforded by the overlying strata and the destruction of the later structure. In mineralization prediction, especially 3D quantitative prediction, the protective effect of the overlying strata is often not considered, and quantitative analysis of the fault structure is mainly used to determine the later damage from the fault to the ore body and to quantitatively characterize the specific part of the fault with mineralization (Xiang et al., 2019).

  • 3D geological modeling is a current frontier in the geosciences. 3D models of geological units (objects) in the region based on data from geological surveys, exploration engineering, and geophysics can be built, and the spatial distribution of geological units and the evolutionary relationship between geological units can be visually depicted. Constructing a 3D geological model through 3D visualization technology is the basis of 3D quantitative prediction, and its essence is a fusion process of multiple pieces of comprehensive information (Li et al., 2018). This 3D geological modeling uses the 3D modeling software Surpac 6.3 from GEMCOM International Mining Software Company and carries out 3D modeling work based on planar geological maps, borehole data, exploration line profiles, comprehensive geophysical interpretation maps, etc. Using the section modeling method, a 3D solid model of the strata, rock mass, fracture, and ore body in the study area is constructed (Fig. 3).

    Figure 3.  3D geological model of Lala copper ore field.

  • Based on the 3D geological model, the "cube model" prediction method is adopted to realize the integration of multiple pieces of information, and various geostatistical analyses are carried out through the cube model to extract favorable conditions for 3D mineralization and quantitative prediction and evaluation of 3D minerals. In this study, the entire physical model area is divided into 50 m×50 m×50 m blocks. The total number of unit blocks in the entire study area is 3 825 347, of which the number of known ore blocks is 5 638 (Fig. 4i). Based on the above analysis of the metallogenic system and on the 3D cubic block model, the basic idea behind extracting favorable metallogenesis information is to construct a geographic information system (GIS) layer that can characterize the mineralization process and its key factors. The analysis of the known ore body can extract beneficial mineralization information.

    Figure 4.  3D favorable prediction information for mineralization of the Lala copper ore field. (a) Diabase and buffer zone; (b) Tianshengba Formation; (c) Luodang Formation; (d) fault and buffer zone; (e) bending of the fold; (f) ore-bearing strata greater than 500 m; (g) isodensity; (h) fracture frequency; (i) orebody.

    (1) There is favorable "source" information for mineralization. The Lala copper deposit is an "other type" of VMS copper deposit. Either as the source of mineralization or the supply of mineralization energy, the rock mass plays a very important role. The main rock mass related to mineralization in this area is diabase, which can be used as an important predictive element (Fig. 4a). The volcanic deposits or the ore-bearing source layers that formed in the early volcanic eruption and deposition stage are also important indicators for prospecting prediction in this area. Statistical analysis shows that 27.4% and 56.5% of known ore bodies are located in the Tianshengba and Luodang Formations, respectively, which confirms that the ore-bearing strata in this area are the Tianshengba and Luodang Formations; their spatial distributions are shown in Figs. 4b, 4c.

    (2) There is favorable "transport" information for ore-forming minerals. Two geological elements that can be used as ore-forming migration channels are included. On the one hand, the interface between basic rock and sedimentary rock is used to quickly construct different distances through the "expansion buffer method" (Li et al., 2016). In the diabase buffer zone, the optimal diabase buffer distance is determined based on an ore-bearing analysis to characterize the interface between the basic rock and the sedimentary rock. On the other hand, the ore-conducting structure is an important migration channel, and it is mainly characterized by fractures and its optimal fracture buffer. Through statistical analysis of the buffer zone mineralization between the gabbro and the fracture at different distances, the optimal buffer zone of the diabase is 50 m, and the optimal buffer zone of the fracture is 100 m. The spatial distribution is shown in Fig. 4d.

    (3) There is favorable "storage" information for mineralization. Fold structures and the resulting slip spaces are often important traps in a mineralization system. There are many east-west fold structures in the study area; the overall trend of the ore body is nearly east-west, the occurrence is consistent with that of the surrounding rock, and east-west folds occur along with the surrounding rock (Zhang et al., 2016). An analysis of the 3D morphology of the strata indicates that the surrounding folds in the area often bend or undulate and that the fold surfaces are often similarly curved, resulting in swelling and thickening collapsed spaces at the turning ends of the folds; these spaces are beneficial for mineralization enrichment. Their locations are shown in Fig. 4e. In addition, copper ore bodies are mostly distributed in pyroclastic or hornblende-bearing porphyritic volcaniclastic rocks. The main lithologies are biotite schist and quartz albite-bearing rock. The thicker the ore-bearing strata, the thicker the ore body generally is. 3D quantitative analysis confirms that the thickness of the ore-bearing strata is greater than 500 m, and this value can be used as a 3D prediction element. Its spatial distribution is shown in Fig. 4f.

    (4) There is favorable "guarantee" information for mineralization. The fault structure before and during the mineralization period is closely related to mineralization and is often an important factor in controlling mineralization. Destruction of the ore body did not occur, and due to the relative movements of the two fractured discs, the rising surface of the ore body was either eroded or buried deep underground to become a blind ore body. This study uses 3D structural analysis techniques to calculate multiple quantitative characteristics of fractures, such as isodensity, frequency, number of intersections, fracture superiority, anomalous azimuth, and azimuth anomaly. Finally, through correlation analysis, isodensity and frequency are selected. The characteristic index of fractures on the preservation of the ore body and the quantitative characteristic values of the isodensity (0.44, 0.76) and frequency (6.24, 9.51) are determined, and their spatial distributions are shown in Figs. 4g, 4h.

  • This study uses the prospecting information method to calculate the amount that each prospecting indicator contributes. The sum of the beneficial mineralization information (ore-controlling elements) contained in each cube unit is the information amount of the entire cube. The ore-bearing rate of the information volume for the entire research block is calculated. According to its statistical law, a selected information volume value of 1.6 is the lower limit for a favorable mineralization area, and an information volume value greater than 3.2 implies a high-value area (Fig. 5a). A cubic block with information greater than 1.6 is favorable for the Lala copper deposit. Combining the regional geological characteristics and the existing prospecting data, four prospecting target areas are delineated. Target area A4-1 has been verified by deep drilling to detect copper ore bodies and has great resource potential. Target areas A4-2, A4-3, and A4-4 intersect with multiple faults to provide minerals (Fig. 5b). They are located at the edges of gabbro and conglomerate rock bodies. Large-scale prospecting has not yet been carried out, but these areas have certain mineralization potential.

    Figure 5.  (a) Statistics of the amount of ore cubes in different mineralization information; (b) favorable area for mineralization and division of targets in the Lala mineral the Lala copper ore field.

  • In this study, 3D mineral prospectivity mapping based on deep metallogenic prediction theory was carried out in the Lala ore cluster. This paper analyzed the geological prospecting model and constructed a 3D geological model through 3D visualization technology. Based on the metallogenic system analysis, the favorable mineralization process was transformed to 3D predictor layers representing exploration criteria. The prospecting information method was used to calculate the metallogenic possibility, and four target areas were delineated. From this study case, we can draw the following basic conclusions.

    (1) The development of deep mineral resource mineralization prediction is a general trend in geoscientific research. The era of big data presents new requirements and working methods for mineral prediction and evaluation. The mineral prediction model theory, mineral prediction correlation theory, trend analysis method, and difference theory are the basic prediction theories.

    (2) The prediction and evaluation method of mineral resources is moving toward model integration, information analysis intelligence and deep nonlinear 3D models. The construction of mineralization system prospecting models, multiscale 3D geological modeling, and 3D quantitative prediction are the main workflows of deep resource prediction in the digital and information age.

    (3) Resolving the basic contradiction between the 3D prediction of mineral resources with increasing prospecting prediction depth and the relative lack of effective deep information is a key scientific problem for deep prospecting prediction that will not be solved in the near future.

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