METHOD FOR EVALUATING METALLOGENIC POTENTIAL OF SKARN DEPOSIT BASED ON MAGNETITE COMPOSITION

Information

  • Patent Application
  • 20240290437
  • Publication Number
    20240290437
  • Date Filed
    October 23, 2023
    a year ago
  • Date Published
    August 29, 2024
    3 months ago
Abstract
The present invention discloses a method for evaluating a metallogenic potential of a skarn deposit based on the magnetite composition, including collecting geological, geophysical, geochemical, and remote sensing data in a studying area, systematically, and delineating a favorable area for mineralization; collecting magnetite-bearing samples in the favorable area for mineralization, and describing the lithology, alteration and mineralization characteristics of each sample; selecting the most representative magnetite samples for chemical analysis to obtain average contents of trace elements Ti, Ni, V, K and Al+Si+Mg, denoted as c(Ti), c(Ni), c(V), c(K), and c(Al+Si+Mg) in ppm; and calculating discriminant factors F1, F2, F3, and F4 by substituting data, and performing discrimination; and when the four discriminant factors all discriminate the metallogenic potential to be better, determining the skarn deposit in the favorable area for mineralization to have a good metallogenic potential; and discriminating as a poor metallogenic potential in the remaining cases.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No. 202310184807.4 with a filing date of Feb. 23, 2023. The content of the aforementioned application, including any intervening amendments thereto, is incorporated herein by reference.


TECHNICAL FIELD

The present invention belongs to the technical field of exploration, and in particular relates to a method for evaluating a metallogenic potential of a skarn deposit.


BACKGROUND

In the fragile ecological environment area of a plateau, the conventional exploration method is relatively costly and time-consuming, making it difficult to provide a clear exploration direction quickly. How to predict and evaluate a resource potential at a scale of an ore concentration area through limited exploration and evaluation techniques, guiding deposit exploration effectively, is the focus of domestic and foreign mineral exploration scientists.


Skarn deposits are mostly developed near contact zones between intermediate acidic magmatic rocks and carbonates. The occurrence and morphology of ore bodies are relatively complex, the continuity of the ore bodies is poor, the composition of minerals is complex, and the temperature range of formation is wide. During skarn formation, there is obvious zoning, with magnetite mostly formed in a late skarn stage and an oxide stage, and the formation temperature is relatively high. Skarn is developed in skarn deposits of different sizes, and how to quickly evaluate a metallogenic potential of this deposit (point) through the characteristics of skarn is a difficult point at present.


The conventional evaluation of a metallogenic potential of the skarn deposits requires to be based on large-scale geological mapping, geophysical and geochemical exploration work, and final drilling verification, and requires to complete mineral exploration stages such as general investigation and detailed investigation to evaluate the potential of the deposits, and has the following disadvantages: a long exploration and evaluation period, and a high cost, which cannot meet the urgent needs of rapid exploration and evaluation.


SUMMARY OF PRESENT INVENTION

An object of the present invention is to provide a new method for evaluating a metallogenic potential of a skarn deposit, which organically combines mineral geochemistry and deposit potential evaluation based on the differences in major and trace elements of magnetite in the skarn deposit, and solves the technical problem of rapid exploration and evaluation of skarn deposits in a plateau area.


To achieve the above object, the technical solutions adopted are as follows:

    • a method for evaluating a metallogenic potential of a skarn deposit based on the magnetite composition, including the steps of:


(1) Regional Data Collection and Comprehensive Analysis





    • collecting geological, geophysical, geochemical, and remote sensing data in a studying area, systematically, and delineating a favorable area for mineralization;





(2) Magnetite Sample Collection





    • collecting magnetite-bearing samples in the favorable area for mineralization by zoning, and describing the lithology, alteration and mineralization characteristics of each sample;





(3) Analysis of Major and Trace Elements in the Samples





    • selecting the most representative magnetite samples for chemical analysis to obtain average contents of trace elements Ti, Ni, V, K and Al+Si+Mg, denoted as c(Ti), c(Ni), c(V), c(K), and c(Al+Si+Mg) in ppm; and





(4) Discrimination of Evaluation of the Metallogenic Potential of the Deposit





    • calculating a discriminant factor F1 by substituting c(Ni) into F1=−3.1484*c(Ni)+13.301, and when c(V)>F1, discriminating as a poor metallogenic potential, and otherwise, discriminating as a better metallogenic potential;

    • comparing c(V) with a discriminant factor F2=2, and when c(V)>2, discriminating as a poor metallogenic potential, and otherwise, discriminating as a better metallogenic potential;

    • calculating a discriminant factor F3 by substituting c(K) into F3=0.0437*c(K)+0.4093, and when c(V)>F3, discriminating as a poor metallogenic potential, and otherwise, discriminating as a better metallogenic potential;

    • calculating a discriminant factor F4 by substituting c(Ti) into F4=−115.11*c(Ti)+34361, and when c(Al+Si+Mg)>F4, discriminating as a poor metallogenic potential, and otherwise, discriminating as a better metallogenic potential; and

    • when the four discriminant factors all discriminate the metallogenic potential to be better, determining the skarn deposit in the favorable area for mineralization to have a good metallogenic potential; and discriminating as a poor metallogenic potential in the remaining cases.





According to the above solution, the sample collecting process in the step 2 includes recording a drill hole number and a drill hole depth, taking a field picture, and making a detailed field record at each sample collecting position; wherein the number of the samples is not less than five.


According to the above solution, selecting the most representative magnetite samples in the step 3 includes:

    • grinding the collected samples into laser in-situ targets, observing the characteristics of magnetite corresponding to the collected samples under a microscope, recording the mineral associations and their magnetite morphology in detail, and selecting the most representative magnetite samples according to the results under the microscope.


According to the above solution, the chemical analysis in the step 3 includes:

    • performing in-situ micro-area elemental analysis by using laser ablation inductively coupled plasma mass spectrometry to obtain recorded data for each analytical point.


According to the above solution, the step 3 further includes processing the recorded data obtained from the chemical analysis by using data processing software, including:

    • {circle around (1)} data importing, namely batch-importing elemental analysis recorded data obtained from in-situ micro-area analytical points of each magnetite sample into ICPMSDataCal software;
    • {circle around (2)} data interpretation, namely obtaining an integral curve of micro-area elements in the samples at each observation point, and adjusting the start time and the end time of the integral curve for each observation point one by one according to a principle of ensuring that a signal range of the integral curve of the selected elements is the flattest and the widest;
    • {circle around (3)} data screening, namely rejecting invalid data according to an abnormal peak of the integral curve of the elements; and
    • {circle around (4)} data exporting, namely summarizing and batch-exporting interpreted and screened micro-area data for each single point into a file in a csv format.


According to the above solution, the discriminant factors F1, F2, F3, and F4 in the step 4 are obtained by a method including the following steps of:

    • (1) separately collecting magnetite-containing samples in an area where the skarn deposit is known to have a good metallogenic potential and an area where the skarn deposit is known to have a poor metallogenic potential;
    • (2) selecting the most representative magnetite samples for chemical analysis to obtain average contents of trace elements Ti, Ni, V, K and Al+Si+Mg, denoted as c(Ti), c(Ni), c(V), c(K), and c(Al+Si+Mg) in ppm; and
    • (3) calculating the discriminant factors
    • performing diagram projection on data of sample collecting points with c(Ni) as an abscissa and c(V) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F1: F1=−3.1484*c(Ni)+13.301;
    • performing diagram projection on data of sample collecting points with c(Ti) as an abscissa and c(V) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F2: F2=2;
    • performing diagram projection on data of sample collecting points with c(K) as an abscissa and c(V) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F3: F3=0.0437*c(K)+0.4093; and
    • performing diagram projection on data of sample collecting points with c(Ti) as an abscissa and c(Al+Si+Mg) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F4: F4=−115.11*c(Ti)+34361.


Compared with the prior art, the beneficial effects of the present invention are as follows:

    • magnetite is a widely occurring mineral in magma and hydrothermal solution, and the formation of magnetite is not only influenced by crystallographic factors, but also controlled by changes in physical and chemical conditions. The temperature is one of the main factors controlling the composition of the magnetite. The present invention inventively proposes that the magnetite is used as a discriminating characteristic mineral to rapidly distinguish skarn with a larger metallogenic potential from skarn with a smaller metallogenic potential according to the change of major and trace elements of the magnetite, which is a new technical method for prospecting that is economical, efficient, and green, and can shorten the exploration and evaluation period, reduce exploration costs, and improve the exploration and evaluation efficiency, and meets the urgent needs of rapid exploration and evaluation of mining rights holders.


The present invention inventively proposes the use of trace elements of Ti, Ni, V and major elements of Al, K, Si and Mg in the magnetite, and inventively proposes the optimum discrimination ranges of the elements. The elements are sensitive to changes in temperature, water-rock interactions and redox conditions, and within the optimal discrimination ranges, accurate evaluation of the metallogenic potential of the skarn deposit can be made.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1: a diagram showing discrimination of a metallogenic potential of a skarn deposit in Detailed Description.



FIG. 2: a geological map showing sample collecting in a studying area in Detailed Description.





DETAILED DESCRIPTION OF THE EMBODIMENTS

The following embodiments further illustrate the technical solutions of the present invention, but are not intended to limit the scope of protection of the present invention.


A specific embodiment provides a process for obtaining discriminant factors F1, F2, F3, and F4 by using skarn deposits with a known metallogenic potential:

    • (1) magnetite-bearing samples are collected in an area where the skarn deposit is known to have a good metallogenic potential and an area where the skarn deposit is known to have a poor metallogenic potential, respectively;
    • (2) the most representative magnetite samples are selected for chemical analysis to obtain average contents of trace elements Ti, Ni, V, K and Al+Si+Mg, denoted as c(Ti), c(Ni), c(V), c(K), and c(Al+Si+Mg) in ppm; and
    • (3) the discriminant factors F1, F2, F3, and F4 are calculated; wherein a, b, c, and d are fitting processes for the discriminant factors F1, F2, F3, and F4, respectively referring to FIG. 1;
    • diagram projection is performed on data of sample collecting points with c(Ni) as an abscissa and c(V) as an ordinate, and a boundary of the good metallogenic potential and the poor metallogenic potential is fitted to calculate the discriminant factor F1: F1=−3.1484*c(Ni)+13.301;
    • diagram projection is performed on data of sample collecting points with c(Ti) as an abscissa and c(V) as an ordinate, and a boundary of the good metallogenic potential and the poor metallogenic potential is fitted to calculate the discriminant factor F2: F2=2;
    • diagram projection is performed on data of sample collecting points with c(K) as an abscissa and c(V) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential is fitted to calculate the discriminant factor F3: F3-0.0437*c(K)+0.4093; and
    • diagram projection is performed on data of sample collecting points with c(Ti) as an abscissa and c(Al+Si+Mg) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential is fitted to calculate the discriminant factor F4: F4=−115.11*c(Ti)+34361.


A specific embodiment also provides a process for discriminating skarn deposits with an unknown metallogenic potential:

    • a. geological, geophysical, geochemical, and remote sensing data in an ore concentration area is collected, systematically, and magnetite-bearing samples are collected in drill holes in two areas, respectively, and as shown in FIG. 2, the two areas are an area A and an area B, respectively.
    • b. Field sample collection


Magnetite samples are collected in 5 drill holes. During the sample collecting process, the following information is recorded truthfully in detail, as shown in Table 1.













TABLE 1








Hand



Sample
Magnetite

specimen


No.
morphology
Lithology
alteration
Mineralization







Zk3503-
Idiomorphic
Actinolite
Actinolitization
Galena


174.6

skarn

mineralization,






and sphalerite






mineralization


Zk1003-
Veined
Chlorite
Chloritization
Galena


524.7

skarn

mineralization,






and sphalerite






mineralization


Zk0301-
Other
Epidote
Epidotization
No mineralization


85.9
shaped
skarn


. . .
. . .
. . .
. . .
. . .











    • c. Sample analysis





The collected samples are ground into laser in-situ targets, the characteristics of magnetite corresponding to the collected samples are observed under a microscope, the mineral associations and their magnetite morphology (including an idiomorphic morphology or a veined morphology, etc.) are recorded in detail, the most representative magnetite samples are selected according to the results under the microscope, and marked with a marking pen, and in-situ micro-area elemental analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) is performed, wherein delineated areas are areas where magnetite develops, and the magnetite samples are subjected to in-situ analysis by LA-ICPMS, and the number of each analytical point is marked, and the in-situ analysis data is shown in Table 2 in ppm (10−6).

















TABLE 2







Ti
Al
Si
K
Mg
V
Ni























Area A









3503-179.1-1-2
60.467
1436.500
9733.066
68.260
1323
1.3
2.78


3503-179.1-1-1
68.137
511.454
7983.358
38.123
105
1.5
2.84


3503-179.1-2-1
54.535
459.804
7147.759
41.175
2016
1.2
1.38


3503-179.1-2-2
90.544
2236.533
28840.308
73.247
4639
1.10
1.50


3503-174.6-5
15.486
95.394
5088.362
0.000
32.9
1.06
1.41


3503-174.6-3
7.626
116.855
5947.653
0.000
143
1.49
0.47


3503-174.6-1
3.486
171.787
10192.266
22.590
237
1.57
0.01


ZK3503-175.7-1
9.948
577.014
18984.805
53.050
1032.53
0.65
0.93


ZK3503-175.7-2
21.114
216.157
13620.021
38.401
1555.23
1.59
0.87


ZK3503-175.7-3
16.034
104.017
6346.182
22.148
116.41
1.47
1.18


ZK3503-175.7-4
5.317
91.988
4948.058
39.393
303.42
1.29
0.95


ZK3503-175.7-5
6.091
57.576
4791.258
8.942
29.53
0.81
1.18


ZK3501-362.6-1
62.607
1089.103
9066.710
281.326
1577.00
1.7
10.2


ZK3501-362.6-2
75.984
1638.878
7285.634
445.974
1259.92
1.2
7.94


ZK3501-362.6-3
142.653
2023.462
3537.741
471.679
469.65
1.8
3.73


ZK3501-362.6-4
166.662
2092.711
2998.024
372.120
940.48
1.6
1.24


ZK3501-362.6-5
300.188
3263.152
4583.112
216.357
1660.41
1.7
1.50


ZK3501-249.2-1-1
0.549
287.673
7708.693
50.518
37.47
0.19
0.25


ZK3501-249.2-1-2
0.294
448.507
17884.927
34.613
94.01
0.25
1.20


ZK3501-249.2-2-1
1.851
375.254
12422.064
47.943
78.55
0.38
0.01


ZK3501-249.2-2-2
1.400
542.796
18991.180
32.422
129.30
0.50
1.22


ZK3501-249.2-2-3
0.576
420.187
19435.638
51.690
431.25
0.35
0.58


ZK3501-249.2-2-4
0.294
310.132
10022.957
36.752
60.41
0.16
0.21


Area B


1003-502.7-3
29.489
182.070
18736.323
21.482
1038
12.2
1.22


1003-502.7-5
30.971
147.452
14936.568
11.392
520
12.7
1.04


1003-526.4-1
76.427
1009.868
17001.766
67.746
695
27.7
4.03


1003-526.4-2
75.177
940.291
20003.946
36.865
1725
27.3
4.19


1003-526.4-3
79.179
376.170
15861.162
31.810
681
25.5
3.37


1003-526.4-5
79.464
332.812
12373.200
19.825
1683
27.3
5.79


ZK1003-489.8-1
13.508
203.418
38887.015
20.346
1555
2.63
4.53


ZK1003-489.8-2
50.036
360.121
6920.028
27.795
8086
11.8
8.03


ZK1003-489.8-4
26.775
168.499
29611.870
24.930
1744
4.22
5.04


ZK1003-489.8-6
61.167
135.880
9873.583
23.930
5449
9.87
4.36


1003-524.7-1-1
81.035
64.763
18005.991
20.424
66.4
19.1
1.80


1003-524.7-1.2
114.067
93.214
15598.418
19.492
89.7
11.9
3.35


1003-524.7-2-1
141.352
462.625
20571.914
6.239
862
18.7
3.94


1003-524.7-1-3
99.669
22.032
16767.866
15.320
103
19.6
2.33


1003-524.7-1-4
81.794
88.529
16807.792
20.009
138
20.6
2.81


1003-524.7-2-2
191.395
84.389
17700.137
50.856
186
20.5
1.55


1003-524.7-2-3
96.638
233.538
29932.377
49.872
149
11.0
3.57


1003-524.7-2-4
155.736
81.297
17347.322
29.360
151
22.4
3.20


1003-524.7-2-5
110.782
64.928
15155.376
8.267
109
14.6
3.87


ZK1003-530.35-7
118.779
58.634
11841.616
9.001
93.7
33.9
4.74


ZK1003-530.35-6
208.148
258.267
16016.726
102.451
426
54.7
33.1


ZK1003-530.35-5
291.339
241.188
14825.374
95.652
457
151
13.2


ZK1003-530.35-4
434.054
230.421
18728.838
119.517
512
170
7.77


ZK1003-530.35-3
137.531
202.924
13208.296
55.043
613
39.8
3.18


ZK1003-530.35-2
129.542
333.929
10864.143
40.153
215
26.5
3.16


ZK1003-530.35-1
161.552
479.486
19623.906
70.511
493
53.2
4.50


ZK0201-52.6-1
142.742
3385.151
12956.290
201.030
1534.68
23.9
4.38


ZK0201-52.6-2
113.913
2445.157
11929.951
213.052
1128.39
23.9
6.12


ZK0201-52.6-3
132.438
3659.402
12460.790
270.633
1110.53
23.1
4.96


ZK0201-52.6-4
145.578
2539.106
9744.320
225.854
892.38
18.8
3.41


ZK0201-52.6-5
87.019
2443.932
7472.444
133.973
783.09
12.3
3.80


ZK0201-80.5-1-1
196.613
251.772
26353.736
27.106
686.98
21.9
1.28


ZK0201-80.5-1-2
134.168
74.069
13250.259
33.805
342.23
22.7
1.38


ZK0201-80.5-1-3
138.611
116.038
19860.015
34.616
377.23
22.4
0.98


ZK0201-80.5-2-1
55.474
650.269
91604.327
67.992
394.86
31.3
1.16


ZK0201-80.5-2-2
53.947
536.247
95484.701
73.670
321.25
36.9
0.69


ZK0201-80.5-2-4
72.158
821.008
94829.251
96.079
483.12
51.1
0.01


ZK0201-80.5-2-6
94.916
77.478
9832.123
13.114
368.77
11.9
1.23


ZK0201-78.5-1
111.743
3552.786
31406.007
29.852
1431.76
41.2
1.45


ZK0201-78.5-2
132.056
98.932
14833.049
9.759
277.52
61.3
1.77


ZK0201-78.5-3
114.026
90.124
12876.603
12.985
295.44
44.4
0.91


ZK0201-78.5-4
123.500
83.695
12115.709
7.482
217.98
45.7
1.37


ZK0201-78.5-5
113.205
117.532
26137.298
20.684
214.99
42.4
1.25


ZK0201-78.5-6
107.009
1514.220
49439.130
40.094
547.66
39.3
0.95


ZK0301-85.9-1
151.195
1015.118
9440.462
104.574
175
19.9
2.45


ZK0301-85.9-2
337.211
1022.708
11720.164
163.466
180
11.03
1.01


ZK0301-85.9-3
254.302
985.037
11698.354
144.759
144
12.01
0.41


ZK0301-85.9-4
428.566
1242.996
12112.610
179.630
281
10.09
0.64


ZK0301-85.9-5
217.942
705.326
8452.651
111.589
116
16.01
1.63











    • d. Data processing: data processing is performed by using ICPMSDataCal software, including three steps of data importing, data interpretation and data screening, and average contents of major and trace elements Ti, Ni, V, K and Al+Si+Mg in the magnetite are finally obtained, denoted as c(Ti), c(Ni), c(V), c(K), and c(Al+Si+Mg). Wherein c(Ti)=48.341, c(Ni)=1.894, c(V)=1.081, c(K)=106.379, and c(Al+Si+Mg)=11930.329 in the area A. c(Ti)=133.35, c(Ni)=3.69, c(V)=30.45, c(K)=65.59, and c(Al+Si+Mg)=23217.140 in the area B.





Evaluation of the metallogenic potential of the area A:

    • c(Ni) is substituted into F1=−3.1484*c(Ni)+13.301 to calculate a discriminant factor F1=7.340, and when c(V)>F1, it is discriminated that the metallogenic potential is poor, and otherwise, the metallogenic potential is better;
    • c(V) is compared with a discriminant factor F2=2, and when c(V)>2, it is discriminated that the metallogenic potential is poor, and otherwise, the metallogenic potential is better;
    • c(K) is substituted into F3=0.0437*c(K)+0.4093 to calculate a discriminant factor F3=5.060, and when c(V)>F3, it is discriminated that the metallogenic potential is poor, and otherwise, the metallogenic potential is better;
    • c(Ti) is substituted into F4=−115.11*c(Ti)+34361 to calculate a discriminant factor F4=28791.150, and when c(Al+Si+Mg)>F4, it is discriminated that the metallogenic potential is poor, and otherwise, the metallogenic potential is better; and
    • by comparison, when the four discriminant factors all discriminate the metallogenic potential to be better, it is determined that skarn deposits in the area A have a better metallogenic potential.


Evaluation of the metallogenic potential of the area B:

    • c(Ni) is substituted into F1=−3.1484*c(Ni)+13.301 to calculate a discriminant factor F1=1.675, and when c(V)>F1, it is discriminated that the metallogenic potential is poor, and otherwise, the metallogenic potential is better;
    • c(V) is compared with a discriminant factor F2=2, and when c(V)>2, it is discriminated that the metallogenic potential is poor, and otherwise, the metallogenic potential is better;
    • c(K) is substituted into F3=0.0437*c(K)+0.4093 to calculate a discriminant factor F3=3.276, and when c(V)>F3, it is discriminated that the metallogenic potential is poor, and otherwise, the metallogenic potential is better;
    • c(Ti) is substituted into F4=−115.11*c(Ti)+34361 to calculate a discriminant factor F4-19011.572, and when c(Al+Si+Mg)>F4, it is discriminated that the metallogenic potential is poor, and otherwise, the metallogenic potential is better; and
    • by comparison, when the four discriminant factors all discriminate the metallogenic potential to be poor, it is determined that skarn deposits in the area B have a poor metallogenic potential.


According to the calculation results of the discriminant factors F1, F2, F3 and F4, it is discriminated that the metallogenic potential of the area A is greater than that of the area B, which is consistent with the actual field investigation results, further proving the effectiveness of the new method for evaluating the metallogenic potential based on mineral chemistry of the magnetite in the skarn deposit proposed this time.

Claims
  • 1. A method for evaluating a metallogenic potential of a skarn deposit based on the magnetite composition, comprising: (1) collecting geological, geophysical, geochemical, and remote sensing data in a studying area, systematically, and delineating a favorable area for mineralization;(2) collecting magnetite-bearing samples in the favorable area for mineralization by zoning, and describing the lithology, alteration and mineralization characteristics of each sample;(3) selecting representative magnetite samples for chemical analysis to obtain average contents of trace elements Ti, Ni, V, K and Al+Si+Mg, denoted as c(Ti), c(Ni), c(V), c(K), and c(Al+Si+Mg) in ppm; and(4) calculating a discriminant factor F1 by substituting c(Ni) into F1=−3.1484*c(Ni)+13.301, and when c(V)>F1, discriminating as a poor metallogenic potential, and otherwise, discriminating as a better metallogenic potential;comparing c(V) with a discriminant factor F2=2, and when c(V)>2, discriminating as a poor metallogenic potential, and otherwise, discriminating as a better metallogenic potential;calculating a discriminant factor F3 by substituting c(K) into F3=0.0437*c(K)+0.4093, and when c(V)>F3, discriminating as a poor metallogenic potential, and otherwise, discriminating as a better metallogenic potential;calculating a discriminant factor F4 by substituting c(Ti) into F4=−115.11*c(Ti)+34361, and when c(Al+Si+Mg)>F4, discriminating as a poor metallogenic potential, and otherwise, discriminating as a better metallogenic potential; andwhen the four discriminant factors all discriminate the metallogenic potential to be better, determining the skarn deposit in the favorable area for mineralization to have a good metallogenic potential; and discriminating as a poor metallogenic potential in the remaining cases.
  • 2. The method according to claim 1, wherein the sample collecting process in the step 2 comprises recording a drill hole number and a drill hole depth, taking a field picture, and making a detailed field record at each sample collecting position; wherein the number of the samples is not less than five.
  • 3. The method according to claim 1, wherein selecting the representative magnetite samples in the step 3 comprises: grinding the collected samples into laser in-situ targets, observing the characteristics of magnetite corresponding to the collected samples under a microscope, recording the mineral associations and their magnetite morphology in detail, and selecting the representative magnetite samples according to results under the microscope.
  • 4. The method according to claim 1, wherein the chemical analysis in the step 3 comprises: performing in-situ micro-area elemental analysis by using laser ablation inductively coupled plasma mass spectrometry to obtain recorded data for each analytical point.
  • 5. The method according to claim 1, wherein the step 3 further comprises processing the recorded data obtained from the chemical analysis by using data processing software, comprising: {circle around (1)} data importing, namely batch-importing elemental analysis recorded data obtained from in-situ micro-area analytical points of each magnetite sample into ICPMSDataCal software;{circle around (2)} data interpretation, namely obtaining an integral curve of micro-area elements in the samples at each observation point, and adjusting the start time and the end time of the integral curve for each observation point one by one according to a principle of ensuring that a signal range of the integral curve of the selected elements is the flattest and the widest;{circle around (3)} data screening, namely rejecting invalid data according to an abnormal peak of the integral curve of the elements; and{circle around (4)} data exporting, namely summarizing and batch-exporting interpreted and screened micro-area data for each single point into a file in a csv format.
  • 6. The method according to claim 5, wherein the discriminant factors F1, F2, F3, and F4 in the step 4 are obtained by a method comprising the following steps of: (1) collecting magnetite-bearing samples in an area where the skarn deposit is known to have a good metallogenic potential and an area where the skarn deposit is known to have a poor metallogenic potential, respectively;(2) selecting the representative magnetite samples for chemical analysis to obtain average contents of trace elements Ti, Ni, V, K and Al+Si+Mg, denoted as c(Ti), c(Ni), c(V), c(K), and c(Al+Si+Mg) in ppm; and(3) calculating the discriminant factorsperforming diagram projection on data of sample collecting points with c(Ni) as an abscissa and c(V) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F1: F1=−3.1484*c(Ni)+13.301;performing diagram projection on data of sample collecting points with c(Ti) as an abscissa and c(V) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F2: F2=2;performing diagram projection on data of sample collecting points with c(K) as an abscissa and c(V) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F3: F3=0.0437*c(K)+0.4093; andperforming diagram projection on data of sample collecting points with c(Ti) as an abscissa and c(Al+Si+Mg) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F4: F4=−115.11*c(Ti)+34361.
  • 7. The method according to claim 1, wherein the discriminant factors F1, F2, F3, and F4 in the step 4 are obtained by a method comprising the following steps of: (1) collecting magnetite-bearing samples in an area where the skarn deposit is known to have a good metallogenic potential and an area where the skarn deposit is known to have a poor metallogenic potential, respectively;(2) selecting the representative magnetite samples for chemical analysis to obtain average contents of trace elements Ti, Ni, V, K and Al+Si+Mg, denoted as c(Ti), c(Ni), c(V), c(K), and c(Al+Si+Mg) in ppm; and(3) calculating the discriminant factorsperforming diagram projection on data of sample collecting points with c(Ni) as an abscissa and c(V) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F1: F1=−3.1484*c(Ni)+13.301;performing diagram projection on data of sample collecting points with c(Ti) as an abscissa and c(V) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F2: F2=2;performing diagram projection on data of sample collecting points with c(K) as an abscissa and c(V) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F3: F3=0.0437*c(K)+0.4093; andperforming diagram projection on data of sample collecting points with c(Ti) as an abscissa and c(Al+Si+Mg) as an ordinate, and fitting a boundary of the good metallogenic potential and the poor metallogenic potential to calculate the discriminant factor F4: F4=−115.11*c(Ti)+34361.
Priority Claims (1)
Number Date Country Kind
202310184807.4 Feb 2023 CN national