Spatial Analysis of Fe Deposits in Fujian Province, China: Implications for Mineral Exploration

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1 Journal of Earth Science, Vol. 26, No. 6, p , December 2015 ISSN X Printed in China DOI: /s Spatial Analysis of Fe Deposits in Fujian Province, China: Implications for Mineral Exploration Ziye Wang 1, Renguang Zuo* 1, Zhenjie Zhang 2 1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan , China 2. School of the Earth Sciences and Resources, China University of Geosciences, Beijing , China ABSTRACT: Spatial point pattern statistics, fractal analysis and Fry analysis in support of GIS were applied to explore the spatial distribution characteristics of mineral deposits and the spatial relationships between mineralization and geological features in Fujian Province (China). The results of Ripley s K(r) revealed a clustered distribution of Fe deposits in space with a fractal dimension of Fry analysis showed that Fe deposits distributed mainly along a NNE-NE trend. Buffer analysis showed that most of the known Fe deposits developed within 4 km buffer zones of the NNE-NE-trending faults, Yanshanian intrusions, and Late Paleozoic marine sedimentary rocks and the carbonate formations (C P Formation), indicating that they possibly control the spatial distribution of Fe mineralization. This is possibly because the NNE-NE-trending faults, Yanshanian intrusions, and C P Formation provided pathways of fluids, energy and a part of metal, and zones of deposition for the Fe mineralization, respectively. The fractal relation of the number of Fe deposits occurring within the buffer zones of geological features was observed. The fractal dimension suggested that the significance of Yanshanian intrusions and C P Formation are greater than that of NNE-NE-trending faults in controlling the formation of Fe mineralization. These findings are useful for better understanding the formation of the mineralization and provide significant information for further mineral exploration. KEY WORDS: Ripley s K(r) function, fractal dimension, Fry analysis, Fe deposits, Fujian. 0 INTRODUCTION Mineral deposits occurring in a group or camps are a general geological phenomenon and can be observed in many metallogenic provinces in the world (e.g., Lisitsin, 2015; Blenkinsop, 2014, 1994; Agterberg, 2013; Carranza, 2009; Mamuse et al., 2009; Zuo et al., 2009a, b; Raines, 2008; Blenkinsop and Sanderson, 1999; Cheng and Agterberg, 1995; Agterberg et al., 1993; Carlson, 1991). The clustered spatial relationship of deposits can be quantified by using spatial analysis in support by a geographic information system (GIS), such as the second-order property of a point pattern and fractal analysis, which can help unravel the geological processes that were crucial in the formation of the particular type of mineral deposits (e.g., Lisitsin, 2015; Agterberg, 2013; Gumiel et al., 2010; Carranza, 2009; Zuo et al., 2009a; Raines et al., 1996; Carlson, 1991). Exploring the spatial distribution of mineral deposits and the relationship between mineral deposits and the controlling geological factors by using spatial analysis with the aid of GIS could be great help in better understanding the formation of the mineralization and in further mineral prospecting. Systematic *Corresponding author: zrguang@cug.edu.cn China University of Geosciences and Springer-Verlag Berlin Heidelberg 2015 Manuscript received February 12, Manuscript accepted June 13, spatial data analysis can facilitate the identification of the properties of the spatial distribution of mineral deposits within a metallogenic province (e.g., Lisitsin, 2015; Carlson, 1991; Agterberg, 1984, 1975). The analysis of spatial point patterns has a long history in geology, and there are a number of tests available to characterize and explore such data (Perry, 2004). These tests can provide important insights into the spatial distribution of mineral deposits. In this study, spatial point pattern statistics, fractal analysis, and Fry analysis were applied to investigate the spatial distribution characteristics of mineral deposits. The main goals of this study are to (1) explore the spatial distribution characteristics of mineral deposits and (2) identify likely major metallogenic controls on the spatial distribution of mineralization using a case study of Fe deposits in Fujian Province, China. 1 METHODS 1.1 Ripley s K(r) Function and the Fractal Dimension Mineral deposits can be regarded as point events in a regional mineral exploration (e.g., Zuo et al., 2009b) and can be modeled by point pattern statistics. The first-order property of a point process is its intensity (λ), which is estimated as n/a. Here, n represents the total number of mineral deposits and A is the total area of the specific study area. The second-order property λ 2 (r) of a point process can be modeled by the Ripley s K(r) function (Ripley, 1988, 1981, 1977), which is proportional to the expected number of points within distance r of an arbitrary point. Let r ij is the distance between points i and j in A, Wang, Z. Y., Zuo, R. G., Zhang, Z. J., Spatial Analysis of Fe Deposits in Fujian Province, China: Implications for Mineral Exploration. Journal of Earth Science, 26(6): doi: /s

2 814 Ziye Wang, Renguang Zuo and Zhenjie Zhang the K(r) can be written as K r 1 w I r (1) 1 () ( ) 2 ij r i j A i j Such that K(r) is estimated by the empirical cumulative distribution function of the pairwise distances between all distinct pairs of points x i and x j (i j) in the point pattern (Ripley, 1977). Here, I r (r ij ) is an indicator function assuming the value 1 if r ij <r, 0 otherwise. The weight w ij denotes the proportion of the circumference of the circle around point i with radius r that lies within A. The K(r) function can be used to determine whether or not a point pattern is clustered, random, or regularity. For a randomly distributed point pattern, according to the Poisson process, the expected number of points within distance r of an arbitrary point is equal to πλr 2. If there are more points in the vicinity than that predicted by the Poisson model, the point pattern is clustered; otherwise, it belongs to a regularity pattern (Diggle, 2003). The relation between λ 2 (r) and K(r) is λ 2 (r) K'(r)/r (2) where represents proportionality and K'(r) denotes the first derivative of K(r) with respect to r. According to the fractal cluster theory, the following relations can be obtained (Agterberg, 2014; Cheng and Agterberg, 1995) λ 2 (r) r Dc-2 (3) and K(r) r Dc (4) where denotes proportionality and Dc is the cluster determination. The fractal dimension of the point pattern can be estimated from relation (3) (Agterberg, 2014; Cheng and Agterberg, 1995). basement formed in the Archean and the metamorphic (folded) basement formation due to Caledonian movement in the Early Paleozoic; (2) the Late Paleozoic Middle Triassic sedimentary cover stage; and (3) the continental margin activities stage that occurred since the Late Triassic (Zhang et al., 2008). In the Middle Late Jurassic, South China rotated from the EW-trending Tethyan tectonic domain to the NE-trending Pacific tectonic domain (Mao et al., 2008, 2007, 2004). The massive granitic intrusions and the NE-NNE-trending folds are regarded as a response to the low-angle subduction of the paleo-pacific Plate under South China (Zhang et al., 2009; Shu and Zhou, 2002). Four terrains, i.e., the (A) NW-, (B) SW-, (C) E-, and (D) SE-Fujian terrains, have been identified in the Fujian Province based on the different lithologies, formations, deformation characteristics, and tectonic evolutions (Zhang et al., 2008). The most conspicuous structures of the Fujian Province are the Zhenghe-Dapu, Nanping-Ninghua, and Pingtan-Dongshan faults (Fig. 1). The Zhenghe-Dapu fault defines the boundary of Terrain C and terrains A and B, the Nanping-Ninghua fault defines the boundary of terrains A and B, and the Pingtan-Dongshan fault defines the boundary of terrains C and D. The dominant strata outcropping in Terrain A are the Late Archean metamorphic basement and the Proterozoic metamor phic rocks. The main characteristic differentiating Terrain A from the other terrains is that Terrain A does not include the Late Paleozoic formations because of long-term uplift that occurred during that time. On the contrary, Terrain B was mostly covered by continuous marine sedimentary formations in the Late Paleozoic. The primary ore-hosting strata are Middle Lower Carboniferous carbonate and clastic rock formations discontinuously distributed along the regionally NE-controlling Zhenghe-Dapu fault (Han and Ge, 1983). The 1.2 Fry Analysis Fry analysis (Hanna and Fry, 1979; Fry, 1979) is a geometrical method of spatial autocorrelation analysis of point objects that can be implemented by the following steps (Carranza, 2009; Zuo et al., 2009a; Vearncombe and Vearncombe, 1999). First, a point pattern is marked by means of series of parallel reference lines, and a second origin empty map is characterized by the intersection of an NS-trending line and an EW-trending line. Second, the origin of the overlay map is placed successively on top of each point such that the reference lines for the same direction on both maps are kept parallel and the positions of all points are recorded on the second map. The procedure is repeated until all points have been used as the origin on the overlay map. The large number of interrelated points generated by Fry analysis can indicate which geological features are plausible geological controls. Distances and orientations between pairs of translated points in a Fry plot can be used to construct a rose diagram (Carranza, 2009; Zuo et al., 2009a). 2 GEOLOGICAL SETTING AND DATA The study area is located in the southeastern margin of the Eurasian Plate. It is a major component of the South China Block and has experienced three major stages of evolution: (1) the basement formation stage including the primitive (stabilized) Figure 1. Simplified geological map of Fujian Province, China and spatial distribution of Fe deposits (c.f., China Geological Survey).

3 Spatial Analysis of Fe Deposits in Fujian Province, China: Implications for Mineral Exploration 815 voluminous Indosinian and Yanshanian (i.e., Jurassic Cretaceous) granitoids were also emplaced in this terrain. Terrain C is characterized by voluminous Late Mesozoic continental intermediate-acidic volcanic rocks and Yanshanian granitoids. Because Terrain D has very small size compared to the other terrains and because it shares relatively similar evolutionary characteristics with Terrain C, the Terrain D in this paper was included in the Terrain C. Terrain B is one of the most important iron metallogenic belts in China. Almost 98% of the proved iron ore reserves in Fujian Province, such as the Makeng, Yangshan, Pantian, Luoyang, Zhongjia, Dapai, and Zhangkeng Fe deposits, have been discovered in this terrain (Zhang and Zuo, 2014). A total of 271 Fe ore deposits was compiled from the Geological Survey Institute of Fujian (China). The Fe mineralization age and the intrusion age of the granites are coincident in some Fe deposits (Lai et al., 2014; Zhang Z J et al., 2014; Yuan et al., 2013; Zhang D et al., 2012). Most of the Fe deposits are skarn-type deposits, which formed in the contact zones between the Yanshanian granitoids and the Middle Lower Carboniferous carbonate and clastic rock formations. Other Fe deposits in terrains A and C are generally small proved reserves. 3 RESULTS AND DISCUSSION 3.1 Application of Ripley s K Function and Fractal Analysis The K(r) values were calculated using SpPack (Baddeley and Turner, 2005; Perry, 2004), which is an Excel plug-in for point pattern statistics. Plots of r versus K(r) (Fig. 2) suggest that the Fe deposits within both the entire study area and within subareas A, B, and C are clustered, because their K(r) values are greater than those of Poisson patterns. Log-log plots of r versus K(r) demonstrate a fractal relation of the spatial distribution of Fe deposits. The estimated fractal dimensions of Fe deposits in the whole study area and in subareas A, B, and C are 1.38, 1.19, 1.37, and 1.21, with R 2 greater than 0.98, respectively (Fig. 3). These results indicate that Fe deposits occurred in groups both in the whole study area and the subareas follow a fractal statistic. The fractal dimension is a function of the point pattern intensity and the probability of the distance function (Zuo et al., 2009a), which are linked to the geological setting. 3.2 Application of Fry Analysis Fry analysis can provide more detailed information on the directional anisotropy of the spatial distribution of mineral deposits and the presence of multiple preferred orientations at different scales. In this study, DotProc, which is freeware available at was applied to implement Fry analysis. Within the whole study area, the Fry plot of Fe deposits clearly showed a dominant regional NNE-NE orientation. At different scales (i.e., 5, 15 and 100 km), the dominant orientation of the spatial distribution of Fe deposits varies from an NNE (i.e., 5 and 15 km) to an NE orientation (i.e., 100 km), indicating that the spatial distribution of mineral deposits may be controlled by different geological factors at different scales (Fig. 4). At a smaller scale (i.e., 5 and 15 km), the NNE-trending faults may control the Fe mineralization. At a regional scale, the NE-trending faults could control the spatial distribution of the Fe deposits. Figures 5, 6, and 7 show the resulting Fry analyses of Fe deposits within subareas A, B, and C, respectively. At a regional scale, these are similar to Figure 4 and the mineralization is controlled by regional NE-trending faults. When the scale is less than 5 km, the dominant orientation of the Fe deposits within subarea A is different from that of subareas B and C, which exhibit NW-trending in addition to NE-trending orientations. This may be due to the massive secondary conjugate faults occurring in subareas B and C, which also may be the reason why more Fe ore deposits occur in subareas B and C. 3.3 Probable Structural Controls on the Distribution of Fe Deposits The standard deviation ellipses (de Smith et al., 2007; Ebdon, 1988) of the Fe deposits, Yanshanian intrusions, and faults are 34.02, 39.8, and 33.84, which exhibit a preferred NNE orientation (Fig. 8). The standard deviational ellipse can Figure 2. Plot of r versus Ripley s K(r) for Fe deposits within (a) the whole study area, (b) subarea A, (c) subarea B, and (d) subarea C.

4 816 Ziye Wang, Renguang Zuo and Zhenjie Zhang Figure 3. Log-log plots of r versus Ripley s K(r) for Fe deposits in (a) the whole study area, (b) subarea A, (c) subarea B, and (d) subarea C. Figure 4. Fry analysis of Fe deposits in the whole study area. Figure 6. Fry analysis of Fe deposits within subarea B. Figure 5. Fry analysis of Fe deposits within subarea A. Figure 7. Fry analysis of Fe deposits within subarea C.

5 Spatial Analysis of Fe Deposits in Fujian Province, China: Implications for Mineral Exploration 817 create a new feature class containing an elliptical polygon centered on the mean center for all features. The attribute values of ellipse polygons include two standard distances (long and short axes), the orientation of the ellipse, and the case field. Here, the orientation denotes the rotation of the long axis measured clockwise from noon. When the features have a spatially normal distribution (meaning they are densest in the center and become increasingly less dense toward the periphery), one, two and three standard deviation will encompass approximately 68%, 95% and 99% percent of all input feature centroids, respectively (Mitchell, 2005). This suggests that the spatial distributions of the Fe deposits are spatially coincident with regional geological factors, especially that they may be controlled by the main faults and the Yanshanian intrusions in the study area. To investigate further the relationship between the spatial distribution of Fe deposits, faults, and Yanshanian intrusions, spatial buffer analysis was employed in support of ArcGIS The rose diagram of faults in the study area (Fig. 9) exhibits a dominant NE direction. The correlation between the number of Fe deposits occurring within the buffer zones of the NNE-NE-trending faults and their buffer distance is negative, and most of the Fe deposits are developed near NNE-NE-trending faults (Figs. 10a and 11). In the study area, the deep faults may have controlled the Fe mineralization related to magmatic and sedimentary activities, and the secondary NNE-NE-trending faults may have determined the Fe mineralization related to plutons and stocks, which determined the specific locations of Fe mineralization (Zuo et al., 2015). A clear negative correlation was observed between the number of Fe deposits occurring within the buffer zones of Yanshanian intrusions and their buffer distance (Figs. 10b and 11). Most of the Fe deposits developed within 4 km buffer Figure 9. Rose diagram of faults for (a) the whole study area, (b) subarea A, (c) subarea B, and (d) subarea C. Figure 8. Map showing the standard deviation ellipses for the distribution of Fe deposits, Yanshanian intrusions, and the mean direction of faults in Fujian Province, China. Figure 10. Maps showing buffer zones of faults (a) and Yanshanian intrusions (b).

6 818 Ziye Wang, Renguang Zuo and Zhenjie Zhang zones of Yanshanian intrusions. This is coincident with the genesis of these Fe deposits in Fujian Province because most of the Fe deposits occur within the contact zones between the Yanshanian intrusions and the Middle Lower Carboniferous carbonate and clastic rock formations. The Yanshanian intrusions provided heat and metal-bearing magmatic-hydrothermal fluids for the formation of the Fe mineralization, and the contact zones provided the migration pathways and space for the metal-bearing magmatic-hydrothermal fluids and Fe mineralization (Zhang and Zuo, 2014). Additionally, a power-law relation was observed between the buffer zones and the density (=the cumulative number of Fe deposits/the length of buffer zone) (Fig. 12). Such observation suggested a fractal statistic. The fractal dimensions are 0.53 and 0.62, respectively, indicating that they have a different significance in controlling the formation of Fe mineralization in the study area. Furthermore, previous studies (Zuo et al., 2015; Zhang et al., 2014) have revealed that most of the known Fe deposits in subarea B developed in the Late Paleozoic marine sedimentary rocks and the carbonate formations (C P Formation). One Hundred and twenty-one (121) Fe deposits occurred within 4 km buffer zones of C P Formation (Fig. 13) and a clear correlation with C P Formation with the known Fe deposits was observed (Fig. 14a). These observations suggest that the C P Formation is another indicator for the prospectivity of Fe mineralization because the C P Formation provided zones of deposition for the Fe mineralization (Zuo et al., 2015; Zhang et al., 2014). Figure 14b gives a fractal dimension of 0.65, which is approximately equal to the fractal dimension of Yanshanian intrusions but greater than that of NNE-NE-trending faults, indicating that the significance of C P formations and Yanshanian intrusions are greater than that of NNE-NE-trending faults in controlling the formation of Fe mineralization. Figure 15 shows the overlap areas of the 4 km buffer zones of the NNE-NE-trending faults, Yanshanian intrusions, and C P Formation, which contain most of the known Fe deposits within subarea B. This observation indicates that NNE-NE-trending faults, the Yanshanian intrusions, and the C P Formation are key factors for the existence of Fe mineralization in the study area. Figure 11. Plots of buffer zones of NNE-NE-trending faults and Yanshanian intrusions versus the number of Fe deposits. Figure 13. Maps showing buffer zones of Late Paleozoic marine sedimentary rocks and the carbonate formations. Figure 12. Plots of the buffer zone of (a) NNE-NE-trending faults and (b) Yanshanian intrusions versus of the density of Fe deposits. Figure 14. Plot (a) and log-log plot (b) of the buffer zone of Late Paleozoic marine sedimentary rocks and the carbonate formations versus the density of Fe deposits.

7 Spatial Analysis of Fe Deposits in Fujian Province, China: Implications for Mineral Exploration 819 Foundation of China (Nos and ). Figure 15. Overlap areas of 4 km buffer zones of NNE-NE-trending faults, Yanshanian intrusions, and Late Paleozoic marine sedimentary rocks and carbonate formations. 4 CONCLUSIONS In this study, various spatial analyses were applied to identify the spatial distribution characteristics of mineral deposits and the spatial relationship between mineralization and geological features. The following conclusions were obtained. (1) The spatial distribution of Fe deposits in Fujian Province (China) is clustered and satisfies fractal statistics. Subarea B has a higher intensity of mineral deposits and a greater value of fractal dimension, which is related to the geological setting. (2) The spatial distribution of the Fe deposits exhibits different characteristics at different spatial scales, indicating a complexity of the factors controlling the formation of the mineralization that could result in difficulties in the search for Fe mineralization. (3) Fry analysis and buffer analysis revealed that the NNE-NE-trending faults, the Yanshanian intrusions, and the C P Formation are three key controlling factors for Fe mineralization in the study area. The regional NNE-NE-trending faults may control the spatial distribution of the Fe deposits because they provided pathways for hydrothermal fluids for the Fe mineralization. Yanshanian intrusions provided energy, fluids, and a portion of the metal for the formation of the Fe mineralization. Late Paleozoic marine sedimentary rocks and the carbonate formations offered spatial zones of deposition for the Fe deposits. The fractal dimension is proposed to measure the significance of geological factors controlling the formation of mineralization. The results show that the importance of Late Paleozoic marine sedimentary rocks and the carbonate formations and Yanshanian intrusions are greater than that of NNE-NE-trending faults in controlling the spatial distribution of Fe deposits. ACKNOWLEDGMENTS This study was supported by the National Natural Science REFERENCES CITED Agterberg, F. P., Spatial Clustering and Lognormal Size Distribution of Volcanogenic Massive Sulphide Deposits in the Bathurst Area. Geological Survey of Canada, 15-1C: doi: / Agterberg, F. P., Use of Spatial Analysis in Mineral Resource Evaluation. Journal of the International Association for Mathematical Geology, 16(6): doi: /bf Agterberg, F. P., Fractals and Spatial Statistics of Point Patterns. Journal of Earth Science, 24(1): doi: /s Agterberg, F. P., Correlation, Method of Least Squares, Linear Regression and the General Linear Model. Springer International Publishing, 18: Agterberg, F. P., Cheng, Q. M., Wright, D. F., Fractal Modeling of Mineral Deposits. In: Elbrond, J., Tang, X. eds., Application of Computers and Operations Research in the Mineral Industry. Proceedings of 24th APCOM Symposium, Montreal. 1: Baddeley, A., Turner, R., Spatstat: An R Package for Analyzing Spatial Point Patterns. Journal of Statistical Software, 12: 1 42 Blenkinsop, T. G., Sanderson, D. J., Are Gold Deposits in the Crust Fractals? A Study of Gold Mines in the Zimbabwe Craton. Geological Society, London, Special Publications, 155(1): doi: /gsl.sp Blenkinsop, T., The Fractal Distribution of Gold Deposits: Two Examples from the Zimbabwe Archaean Craton. In: Kruhl, J. H., ed., Fractals and Dynamic Systems in Geoscience. Springer, Berlin Blenkinsop, T., Scaling Laws for the Distribution of Gold, Geothermal, and Gas Resources. Pure and Applied Geophysics, 172(7): doi: /s Carlson, C. A., Spatial Distribution of Ore Deposits. Geology, 19(2): Carranza, E. J. M., Controls on Mineral Deposit Occurrence Inferred from Analysis of Their Spatial Pattern and Spatial Association with Geological Features. Ore Geology Reviews, 35(3 4): doi: /j.oregeorev Cheng, Q. M., Agterberg, F. P., Multifractal Modeling and Spatial Point Processes. Mathematical Geology, 27(7): doi: /bf de Smith, M. J., Goodchild, M. F., Longley, P. A., Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. Matador, Leicester Diggle, P. J., Statistical Analysis of Spatial Point Patterns (2nd ed.). Arnold, London Ebdon, D., Statistics in Geography. Blackwell Publishing, Oxford Fry, N., Random Point Distributions and Strain Measurement in Rocks. Tectonophysics, 60(1 2): doi: / (79)

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