Abstract
The Luanchuan polymetallic mining district (LPMD) covers many major deposits of the East Qinling metallogenic belt in Henan Province, central China. It has attracted much attention because of its various types of minerals, abundant mineral reserves and huge metallogenic potential. Systematic research on the geology of LPMD has been carried out previously, and it is concluded that the formation of the ore deposits is dominantly controlled by fault structure. However, there are few reports on the quantitative characterization of fault structure and spatial distribution of deposits and the coupling relationship between them. This study applies fractal theory to the illustration of the characteristics of interrelationship between fault structure and spatial distribution of the ore deposits in LPMD, which then may serve as a basis for further ore exploration. Our results indicate that the capacity dimension (CPDs) of integrated fault structure, NWtrend fault and NEtrend fault for the area investigated are 1.7206, 1.6539 and 1.2145, respectively; while the information dimension (IND) of which is 1.7143, 1.6559 and 1.2222, respectively. The studied area has superior geological environment for potential ore deposits, with major contribution from the NWtrend fault. Spatial distribution dimension value of metallic ore is 0.8873. Quantity fractal dimension and Density fractal dimension values of the metallic deposits are 1.1154 and 1.115, respectively. Quantitatively, the main ore deposits discovered in this area mainly fall on either CPD ≥ 1.49, IND ≥ 1.38 or 1.49 > CPD ≥ 1.00, 1.38 > IND ≥ 1.00. Qualitatively, conditions that facilitate ore deposits require higher fractal dimension values. Among the areas studied, we found that Rank I area is the most ideal region that fulfilled the quantitative and qualitative conditions, which is consistent with the actual location of the ore deposits discovered.
1 Introduction
The Luanchuan polymetallic mining district (LPMD) is located in Luanchuan County, Henan Province, China, where northern Qinling orogenic belt adjuncts southern Huabei continental craton [1,2]. With superior geological background, the LPMD hosts large to superlarge metallic deposits including Sandaozhuang, Nannihu and Shangfanggou, which make it one of the many major components in East Qinling metallogenic belt [1,3]. With the production of many types of abundant mineral deposits and potential reserves, the LPMD has drawn much attention of numerous researchers in the past. Much progress was achieved on topics such as genetic and metallogenic age [4,5,6,7,8,9], fluid characteristics [3,10] and ore genesis [1,2,5,11,12]. Formation of the LPMD was dominantly controlled and affected by fracture systems [13,14]. They are also intimately associated with the location of intermediatefelsic intrusion in spatial distribution. Therefore, it is meaningful for the exploration of mineral resource to proceed research work on structurecontrolled oreforming processes. Being able to carry out precise description of complex structure as well as provide quantified unrevealed rules, fractal theory has been widely applied to the investigation of quantitative characterization of fracture [15,16,17], spatial distribution of ore deposits [18,19,20,21], principle of ore formation and ore exploration [21,22,23,24,25,26,27,28,29]. However, the fractal dimension value and the quantitative coupling relationship between the geological structure and spatial distribution of ore deposits are seldom reported. This study applies fractal theory to the investigation of the relationship between fault structure and spatial distribution of LPMD, which can then serve as a basis for potential ore exploration.
2 Geological background
LPMD distributes among Luanchuan fracture system and Machaoying fracture system, in which both Huabei cratonic and intracontinent orogenic structure characters are present [1,2]. LPMD shows typical binary structure [14]. The crystalline basement, Taihua Group, middle to highgrade metamorphic complex of Archaean age, is overlain by midProtozoic xiong’ershan Group, Guandaokou Group, upper Protozoic Luanchuan Group and Kuanping Rock Group [2,30]. LPMD can be mainly divided into two ore fields: one is Nannihu ore field and the other is Yuku ore field [3,5,8]. Polymetallic ores of Mo, W, Pb and Zn are mainly stratabounded in the rock formations of Guandaokou Group and Luanchuan Group. Research on these deposits shows that there are many different genetic types in LPMD, including porphyryskarntype molybdenumtungsten deposit, skarntype polymetallic deposit and hydrothermal filling metasomatic lead–zinc–silver deposit [1,2,3,5,6,7,8]. LPMD is well developed with fractures, mainly controlled by thrust nappe of southern margin of Huabei Craton and extensional stretching movement during the Yanshan stage. These are represented by NW (including NWW) trend and NE (including NNE) trend faults (Figure 1). Among them, the NW (including NWW) trend faults are mainly composed of a series of thrust faults napped from north to south, with great extension and scale, which is an important orecontrolling fault in this area [1,3]. The NE (including NNE) trend faults are superimposed on the NW trending fault zone in the later stage, with a much smaller scale and mainly in intermittent extension or echelon arrangement [1,3]. Both of them control not only the spatial intrusion of the magma but also the distribution of W, Mo, Pb and Zn ores [1,13,14,31]. Heavy magmatic activities were revealed, with multicycle, multistage signatures [32,33], among which granitic porphyry is related most intimately to metallic ores [3,34,35].
3 Methods
Fractal theory provides important messages including parameters of capacity dimension (CPD) and information dimension (IND) values of fracture systems as well as ore deposits, which are essential for the inference of whether there exists any coupling relationship between structure and ore bodies and thus assists the exploration of new ore deposits. Detailed procedure is described as follows.
3.1 Fractal calculation of fault structure
Among the many methods available for the calculation of linear structural CPD values, the countingbox dimension method is proven the best method, which has the benefits of being direct understandable, offering precise statistics and easy to operate. Therefore, we adopt in this study, the countingbox dimension method for the calculation of the various fractal dimension values based on the fault structure and oredeposit distribution map. CPD and IND values are calculated.
3.1.1 CPD, D _{0} calculation
The algorithm of fractal calculation by means of countingbox method is as follows: first, arbitrarily select a finite area with peripheral length of L (r = L/2n, where n = 1, 2, 3, …; for instance, L/1, L/2, L/4, L/8) as the studied area. The whole area is arbitrarily covered by square grids of various lengths, r/n, and then calculate the number of the grid that registers various fault types, N(r). The object under investigation can be regarded as fractal body which satisfies the following equation:
where C is a constant and D _{0} is the CPD value that attempted to acquire. Taking the logarithm of equation (1) yields equation (2) from which the fractal dimension value, D _{0}, is obtained by taking the absolute value of the slope of the straight line.
In practical, the CPD values are obtained by procedures as follows: (1) choose six square grids with different r values, namely, 15.6747, 7.8373, 3.9187, 1.9593, 0.9797 and 0.4898 km to cover the 2D map in this area (31.3494 km × 15.6747 km) and thus formed six sets of gridnets. One of the examples is shown in Figure 2, where a 2D orthogonal gridnet is established to cover the studied area by taking r = 3.9187 km and each grid is numbered; (2) once a rvalued gridnet is set, calculate the number of the grid that records if any fracture in each of the three categories, i.e., all faults, NEtrend fault and NWtrend fault found, separately to obtain N(r); (3) repeat the above procedure for different r values and plot on the X–Y plane six sets of data, using ln r as horizontal axis and ln N(r) as vertical axis to acquire linear regression on each of the three fault types and hence obtain their fractal dimension values, D _{0} following equation (2). The D _{0} thus obtained represent the integrated fracture CPD values for all faults, NEtrend fault and NWtrend fault, separately.
Another sets of D _{0} values called subdivided CPD values are required to more specifically quantify the spatial distribution of fracture system. This is achieved by dividing the studied area into 32 grids as shown in Figure 2. In each grid, subgridnets are established by taking r = 3.9187, 1.9593, 0.9797 and 0.4898 km as unit length to cover the grid, respectively, and thus form four sets of gridnet in each grid. In each subgridnet (division) with r as unit length, count total number of faults that show up in each subgrid area, N(r). Plot on the X–Y plane four rvalued sets of data, using ln r as horizontal axis and ln N(r) as vertical axis to acquire linear regression and hence obtain fractal dimension values, D _{0} following equation (2). The D _{0} thus obtained represent the subdivided fracture CPD values for all faults.
3.1.2 IND, D _{1} calculation
Fracture IND, D _{1} contains not only messages of whether there are faults transecting the 2D grids but also considers the number of faults (or the probability) that are distributed in the grids. For a 2D orthogonal gridnet with the number of the grid that contains the fault N(r), the probability that fault structure shows up in the ith grid, P _{ i }(r) can be expressed as equation (3), the total information message I(r) is defined as equation (4):
By changing the grid length r in the 2D orthogonal net, providing that a linear relationship exists between I(r) and ln r, the IND D _{1} can be obtained by the following equation:
In this case, the grid lengths adopted are 15.6747, 7.8373, 3.9187, 1.9593, 0.9797 and 0.4898 km. The IND value, D _{1}, of various fault types can be obtained through equations (3)–(5). The IND value, D _{1}, of division integral structure is calculated by taking r = 1.9593, 0.9797 and 0.4898 km as grid lengths and yields separately their parameters, N(r), P _{ i }(r), I(r), for D _{1} follow equation (5).
3.2 Calculation of fractal dimension values of ore deposits
Consider ore deposits as point set, the fraction dimension values of ore deposits can be calculated by the countingbox method similar to that applied to the treatment of fractal dimension values for fracture systems described in the previous sections. To quantitatively determine the distribution character of ore deposits within a circular area of radius r, we normally adopt probability density function, which is defined as:
where d(r) is the probability density function, denoting the number of ore deposits per unit area within radius r, take a known ore deposit as the center of the circle. K is a constant and D is the fractal dimension value. In a nonscale section, the higher the D value is, the more number of ore deposits can be located [19]. The fractal distribution function is proposed to represent quantitatively the number of possible ore deposits N(r) that is likely to be explored within a definite radius from the center:
In practical calculation, we take three wellknown ore deposits, i.e., Laowanggou Fe deposit, Nannihu W–Mo deposit and Niandaogou Pb–Zn deposit as the center of the circle. The number and density of the ore deposits covered by areas of various radii, rs, are calculated and take the averaged values of the three deposit centers.
4 Results
4.1 Fractal characteristics of fault structures
4.1.1 CPD values
The CPD values of fault structure in this area are listed in Table 1 and accordingly the ln r versus ln N(r) plots for integrated faults, NW faults and NE faults with their linear regression parameters are shown in Figure 3. Within r scale between 0.4898 and 15.6747 km, there exists a high statistical selfsimilarity. The absolute value of the slopes of the three straight lines, i.e., D _{0} of integrated faults, NW faults and NE faults, are 1.7206, 1.6539 and 1.2145, respectively, with excellent R ^{2} of ∼0.99, signifying very good linear fits among the data. D _{0} of integrated faults is close to that of NWtrend faults, which is greater than that of NEtrend faults, indicating that the fault structure in the studied area is dominated by the NWtrend faults. This is in consistent with the structure characteristics of geological observation.
Fault types  CPD values  

r (km)  N(r)  ln r  ln N(r)  
Integrated faults  15.6747  2  2.7520  0.6931 
7.8374  8  2.0589  2.0794  
3.9187  32  1.3658  3.4657  
1.9593  108  0.6726  4.6821  
0.9797  310  −0.0205  5.7366  
0.4898  738  −0.7138  6.6039  
NWtrend faults  15.6747  2  2.7520  0.6931 
7.8374  8  2.0589  2.0794  
3.9187  30  1.3658  3.4012  
1.9593  95  0.6726  4.5539  
0.9797  260  −0.0205  5.5607  
0.4898  601  −0.7138  6.3986  
NEtrend faults  15.6747  2  2.7520  0.6931 
7.8374  7  2.0589  1.9459  
3.9187  15  1.3658  2.7081  
1.9593  34  0.6726  3.5264  
0.9797  77  −0.0205  4.3438  
0.4898  146  −0.7138  4.9836 
The statistics of the parameters of divided CPD values in the studied area are listed in Table 2. From Table 2, the 32divisionbased CPD values are obtained from linear regression of observed data with nearly perfect R ^{2} of 0.9608–1.0000, depicting that good statistic selfsimilarity is reached for fault structure when subgrid unit length r ranges between 0.4898 and 3.9189 km, with D _{0} falls between 0.8340 and 1.8475, mean value 1.4504, median level 1.4832 and standard deviation 0.2585.
Scale, r (km)  CPD D _{0}  Coefficient of determination (R ^{2})  

Location of subgrid  3.9187  1.9593  0.9797  0.4898  
N(r)  1  1  4  12  27  1.5849  0.9865 
2  1  3  8  20  1.4380  0.9983  
3  1  2  5  13  1.2423  0.9949  
4  1  2  5  10  1.1287  0.9968  
5  1  4  14  40  1.7773  0.9962  
6  1  4  16  45  1.8475  0.9955  
7  1  3  9  29  1.6158  0.9998  
8  1  2  5  9  1.0831  0.9937  
9  1  4  10  21  1.4498  0.9790  
10  1  4  15  39  1.7763  0.9933  
11  1  4  13  38  1.7444  0.9966  
12  1  3  10  23  1.5307  0.9949  
13  1  2  4  10  1.0966  0.9949  
14  1  2  3  6  0.8340  0.9902  
15  1  4  9  16  1.3170  0.9608  
16  1  4  9  20  1.4135  0.9791  
17  1  4  11  20  1.4425  0.9698  
18  1  4  13  29  1.6274  0.9866  
19  1  4  12  28  1.6007  0.9883  
20  1  4  15  36  1.7416  0.9901  
21  1  4  15  39  1.7763  0.9933  
22  1  4  13  32  1.6700  0.9912  
23  1  3  10  22  1.5115  0.9932  
24  1  2  4  8  1.0000  1.0000  
25  1  3  8  16  1.3415  0.9902  
26  1  4  9  16  1.3170  0.9608  
27  1  3  9  22  1.4963  0.9977  
28  1  4  10  22  1.4700  0.9820  
29  1  4  10  28  1.5744  0.9919  
30  1  4  13  30  1.6421  0.9883  
31  1  3  6  13  1.2101  0.9892  
32  1  3  5  11  1.1115  0.9797 
4.1.2 IND value, D _{1}
The calculated parameters of IND of fault structure in this area are listed in Table 3, and accordingly the ln r versus I(r) plots for integrated faults, NW faults and NE faults with their linear regression parameters are shown in Figure 4. Within r scale between 0.4898 and 15.6747 km, there exists a high statistical selfsimilarity. The absolute value of the slopes of the three straight lines, i.e., D _{1} of integrated faults, NW faults and NE faults, are 1.7143, 1.6559 and 1.2222, respectively, with excellent R ^{2} >0.99, signifying very good linear fits among the data. D _{1} of integrated faults is close to that of NW faults, which is greater than that of NE faults, similar to that of CPD, indicating that the fault structure in the studied area is dominated by the NWtrend faults. This is also in consistent with the structure characteristics of geological observation.
Fault type  IND  

r (km)  ln r  I(r)  
Integrated faults  15.6747  2.7520  0.6730 
7.8374  2.0589  1.9813  
3.9187  1.3658  3.3147  
1.9593  0.6726  4.5332  
0.9797  −0.0205  5.6370  
0.4898  −0.7138  6.5538  
NWtrend faults  15.6747  2.7520  0.6682 
7.8374  2.0589  1.9676  
3.9187  1.3658  3.2361  
1.9593  0.6726  4.3983  
0.9797  −0.0205  5.4724  
0.4898  −0.7138  6.3675  
NEtrend faults  15.6747  2.7520  0.6897 
7.8374  2.0589  1.8459  
3.9187  1.3658  2.5796  
1.9593  0.6726  3.4556  
0.9797  −0.0205  4.3122  
0.4898  −0.7138  4.9649 
As listed in Table 4, the 32divisionbased CPD values are obtained from linear fit with nearly perfect R ^{2} of 0.9777–1.0000, depicting that good statistic selfsimilarity is reached for fault structure when subgrid unit length r ranges among 0.4898–1.9593 km, with divided D_{1} falls between 0.7925 and 1.7510, mean value 1.3366, median level 1.3601 and standard deviation 0.2501.
Scale, r (km)  IND  Coefficient of determination (R ^{2})  

Location of subgrid  1.9593  0.9797  0.4898  
I(r)  1  1.3108  2.4255  3.2958  1.4319  0.9950 
2  1.0986  2.0228  2.9470  1.3333  1.0000  
3  0.6730  1.5596  2.5232  1.3346  0.9994  
4  0.6931  1.6094  2.3026  1.1609  0.9936  
5  1.3813  2.5123  3.5861  1.5904  0.9998  
6  1.3335  2.6845  3.7610  1.7510  0.9957  
7  0.9433  2.1286  3.3093  1.7066  1.0000  
8  0.6931  1.6094  2.1972  1.0849  0.9843  
9  1.1685  2.1661  2.9669  1.2972  0.9960  
10  1.3761  2.6515  3.7909  1.7419  0.9989  
11  1.2853  2.5116  3.5951  1.6661  0.9987  
12  1.0609  2.1597  3.0431  1.4299  0.9961  
13  0.6730  1.2770  2.2539  1.1403  0.9818  
14  0.6931  1.0986  1.7918  0.7925  0.9777  
15  1.3297  2.1640  2.7726  1.0408  0.9919  
16  1.3297  2.1458  2.9650  1.1796  1.0000  
17  1.3297  2.3694  2.9785  1.1893  0.9778  
18  1.3108  2.5070  3.3285  1.4554  0.9886  
19  1.3421  2.3933  3.2631  1.3857  0.9970  
20  1.3337  2.5896  3.5027  1.5645  0.9917  
21  1.3337  2.5824  3.6102  1.6421  0.9969  
22  1.3730  2.4762  3.4365  1.4885  0.9984  
23  1.0790  2.2539  3.0625  1.4308  0.9888  
24  0.6931  1.3863  2.0794  1.0000  1.0000  
25  1.0549  2.0253  2.7363  1.2128  0.9921  
26  1.3297  2.1972  2.7726  1.0408  0.9865  
27  0.9003  2.0692  3.0247  1.5324  0.9966  
28  1.3297  2.2719  3.0910  1.2705  0.9984  
29  1.2799  2.2327  3.2817  1.4440  0.9992  
30  1.3689  2.5053  3.3633  1.4386  0.9935  
31  1.0986  1.7918  2.5649  1.0577  0.9990  
32  1.0986  1.6094  2.3979  0.9372  0.9850 
4.2 Fractal analysis of ore deposits
4.2.1 Spatial distribution dimension values of ore deposits
The spatial distribution dimension values of ore deposits are listed in Table 5 and accordingly the ln r versus ln N(r) plots for metallic deposits and Pb–Zn deposits are shown in Figure 4. The absolute value of the slopes of the linear fit, i.e., spatial distribution dimension values for metallic ore and Pb–Zn ore are 0.8873 and 0.6752, respectively, with R ^{2} of the linear regression as 0.9150 and 0.8786 (Figure 5).
Ore type  r (km)  N(r)  ln r  ln N(r) 

Metallic ore  15.6747  2  2.7520  0.6931 
7.8374  6  2.0589  1.7918  
3.9187  14  1.3658  2.6391  
1.9593  18  0.6726  2.8904  
0.9797  25  −0.0205  3.2189  
Pb–Zn ore  15.6747  2  2.7520  0.6931 
7.8374  5  2.0589  1.6094  
3.9187  10  1.3658  2.3026  
1.9593  11  0.6726  2.3979  
0.9797  14  −0.0205  2.6391 
4.2.2 Quantity and density fractal dimension values of ore deposits
The quantity and density distribution of ore deposits within circular areas of various radius is listed in Table 6. The quantity and density fractal dimension values of ore deposits are obtained by quantityversusr plot and densityversusr plot, respectively, as shown in Figure 6. Within the radius of 2–10 km, there exists polynomial relationship between quantityr and densityr plots, with R ^{2} of 0.9977 and 0.9964, respectively. The fractal dimension values, D, of the quantity and density of ore deposits are 1.1154 and 1.115 (since D2 = −0.885), respectively (Figure 6).
Radius (km)  Averaged number of ore deposits  Density of ore deposits (No./km^{2}) 

2  2.3333  0.5833 
4  4.6667  0.2917 
6  7.6667  0.2130 
8  11.0000  0.1719 
10  13.6667  0.1367 
5 Discussion
5.1 Potential ore deposits of the studied area
According to selforganized criticality theory, fracture system was formed during dissipation process through which small fractures connected, evolved and organized by themselves so that the strain was focused on the main fracture belt that possessed fractal geometry [36,37]. Fracture fractal dimension values are highly related to the connectivity of the fracture system. When fracture fractal value is lower than the critical value, the deformation and penetration rate tend to be low, resulting in the isolation and poor connection among the fractures. On the other hand, when fracture fractal dimension value reaches or exceeds the critical value, then the intense deformation as well as high permeability results in good connectivity of the fracture, which in turn favors the transportation and accumulation of oreforming fluids for the formation of ore deposits [15,38,39,40]. It is reported that the fractal characteristics (fractal dimension values) of fault structure are highly related to the distribution of ore deposits and are of important indication for oreforming potential [15,16,17,21,22,29,41,42,43,44]. Twoaxis compression tests on rocks and numerical simulation indicate that the critical value for fracture fractal dimension value is 1.22–1.38 [45]. Fracture fractal dimension value is related to the scale of the divided gridnet, which in this study is comparable to the scale of fault structure. Therefore, critical fracture fractal dimension values still have important indication regarding to the investigation of integrated oreforming potential in this area. The CPD values of integrated fault and NWtrend fault are 1.7206 and 1.6539, respectively, which are much greater than the critical value of 1.22–1.38, demonstrating that the studied area owns great integrated oreforming potential, with NWtrend faults contribute most of the potential host for ore deposits, which is in consistent with practical investigation results reported so far [1,3,31,35,46,47,48].
5.2 Coupling relationship between fault structure fractal dimension values and spatial distribution of ore deposits
Fractal dimension values of fracture signify the connecting ability of a geological body, and higher dimension values mean better connection among fractures. From the geological significance of fracture fractal dimension, there are two fractal dimension requirements that benefit the host of ore bodies, high fractal values in the individual region favor the transportation and accumulation while low dimension values in the adjacent region facilitate the blocking and closure of oreforming fluids [17,21,29]. In contrast to our previous work [17,21,29], the studied area contains six polymetallic ores, including gold, iron, copper, lead and zinc, which cover much wider orehosting fractal dimension values. More specifically, there are two explored ore deposits in this area (Figure 7), (1) area that hosts Nannihu, Sandaozhuang, Majuan and Yuku ore deposits with CPD value ≥ 1.49 and IND value ≥ 1.38; (2) area that contains enriched ores of Lengshuigou and Sandaogou with 1.49 > CPD value ≥ 1.00 and 1.38 > IND value ≥ 1.00.
5.3 Prediction of ore deposits location in the studied area
Since the fractal dimension values are intimately associated with the ore deposits, the contour map of CPD and IND values is plotted and shown in Figure 8a and b, respectively, to further explore the area that may potentially host the ore deposits. As discussed above, the fractal dimension values for the host of the ore deposits in this area are (1) CPD value ≥ 1.49 and IND value ≥ 1.38 (Figure 8c, orange yellow area); (2) 1.49 > CPD value ≥ 1.00 and 1.38 > IND value ≥ 1.00 (Figure 8c, light purple area). The above conditions are required quantitatively for the host of ore deposits. Qualitatively, potential ore deposits are required to possess high individual fractal dimension values and low fractal dimension values in adjacent region.
Summing up the quantitative and qualitative requirements, the studied area can be classified as three ranks, i.e., Ranks I–III (Figure 8), based on their potential of hosting ore deposits. Rank I area facilitates the formation of ore deposits, and it contains three subregions, I1, I2 and I3. Most of all deposits discovered are located in Rank I area, including large deposits such as Nannihu, Sandaozhuang, Majuan, Yuku, Lengshuigou and Sandaogou ore bodies. Among them, subregions I1 and I3 have highest fractal dimension values, which favor the transportation and accumulation of oreforming fluids, and hence are of great potential for ore deposits. Situating between I1 and I3 regions, I2 region is on the pathway of fluid transportation. Having fractal dimension value next to the highest ones, I2 region can effectively block and enclose oreforming fluids, which makes it a target location for ore deposits. Except for the ore deposits mentioned above, middle to largescale lead, zinc and silver deposits such as Hetaocha, Yangshuwa and Hongdonggou are recently discovered in Rank I region. Rank II region locates along the direction of fluid migration, however, being lack of blocking and enclosure effect for fluid, the oreforming potential is limited. Rank III area is classified as of the lowest potential for hosting ore deposits in this studied area.
The present prediction on the ore location is based solely on the fractal analysis of fault systems that host the excavated ore deposits. Apparently, further geophysical and geochemical data are of vital importance for the justification of the present prediction on ore exploration.
6 Conclusions
Within the domains various r of 0.4898–15.6747 km, the fracture fractal dimension values of the integrated faults, NWtrend faults and NEtrend faults for CPD are 1.7206, 1.6539 and 1.2145, respectively, and for IND are 1.7143, 1.6559 and 1.2222, respectively. The studied area has superior integrated oreforming geological conditions with NWtrend fracture contributing most for the host of ore deposits.
Within the gridnets of 0.9797–15.6747 km, the spatial distribution fractal dimension values are 0.8873 and 0.6752, respectively. In the domains of radius ranging from 2 to 10 km, the metallic ore deposits show a quantity fractal dimension value of 1.1154 and a density fractal dimension value of 1.115.
Ore deposits in this area locate in areas containing the following fractal dimension values: CPD value ≥ 1.49 and IND value ≥1.38; 1.49 > CPD value ≥ 1.00 and 1.38 > IND value ≥ 1.00.
The studied area is classified as three ranks according to their potential for hosting ore deposits. Rank I is the most ideal location for the host of ore deposits.
Acknowledgments
This work was supported financially by the Science and Technology Project of Department of Education, Jiangxi Province (No. GJJ213014). The authors wish to thank Jianchao Song, Zhiqiang Liu, Yiyong Zhan and other colleagues of Jiangxi Institute of Applied Science and Technology, for their assistance during the process of this article.

Author contributions: Methodology, data curation, investigation, writing – original draft: Z.C.; methodology, supervision, writing – review and editing: E.H.; investigation, formal analysis, writing – review and editing: G.L.; formal analysis, data curation: H.C.; data curation, investigation, validation: X.G. All authors have read and agreed to the published version of the article.

Conflict of interest: Authors state no conflict of interest.
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