Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery
Abstract
:1. Introduction
2. Geology of the Study Area
3. Materials and Methods
3.1. Data Characteristics
3.2. Methods
3.2.1. Dirichlet Process (DP)
3.2.2. Support Vector Machine (SVM)
3.2.3. Spectral Angle Mapper (SAM)
3.2.4. Laboratory Analysis
4. Results and Analysis
4.1. Determining the Training Data
4.2. Detection of the Alteration Zones
4.3. Implementation of the DP Method on the Zeftreh Area
4.4. Implementation of the SVM on ASTER Data
4.5. Implementation of the SAM on ASTER Data
5. Fieldworks
5.1. Petrography Study
5.2. Geochemical Analysis
6. Accuracy Assessment
7. Discussion
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Row | Sample_NO | X (m) | Y (m) | Au (ppb) | Fe | Ag | As | Cu | Mn | Mo | Pb | Sb | Zn |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | S01 | 585,770 | 3,701,063 | <5 | 5855 | 0 | 8.3 | 18 | 178 | 2.1 | 5 | 1.08 | 20 |
2 | S02 | 586,064 | 3,700,766 | <5 | 17,302 | 0 | 8.9 | 5 | 29 | 5.3 | 5 | 1.24 | 53 |
3 | S03 | 589,488 | 3,690,065 | 70 | 13,866 | 0 | 150.7 | 15 | 219 | 6.8 | 63 | 201 | 39 |
4 | S04 | 591,489 | 3,685,219 | 104 | 185,397 | 0 | 289.2 | 467 | 161 | 20.9 | 86 | 1.1 | 138 |
5 | S05 | 591,544 | 3,685,158 | <5 | 21,278 | 0 | 9.1 | 6 | 67 | 2.18 | 6 | 1.29 | 16 |
6 | S06 | 592,703 | 3,683,705 | 7 | 123,344 | 0 | 8.9 | 112 | 564 | 4.8 | 32 | 1.09 | 1195 |
7 | S07 | 592,237 | 3,683,423 | <5 | 14,281 | 0 | 909.2 | 346 | 10,111 | 7.4 | 124 | 1.26 | 3014 |
8 | S08 | 599,089 | 3,684,040 | <5 | 8571 | 0.27 | 16.2 | 6 | 59 | 4 | 7 | 1.02 | 9 |
9 | S09 | 605,129 | 3,675,998 | 60 | 81,444 | 0.35 | 28.1 | 198 | 3464 | 2.27 | 32 | 1.17 | 60 |
10 | S10 | 621,599 | 3,670,515 | <5 | 35,705 | 0.27 | 8.8 | 16 | 110 | 3.4 | 7 | 1.13 | 78 |
11 | S11 | 623,023 | 3,670,502 | <5 | 47,093 | 0.22 | 8.9 | 63 | 1664 | 2.31 | 37 | 1.09 | 234 |
12 | S12 | 631,737 | 3,673,323 | <5 | 15,726 | 0.24 | 11.2 | 28 | 65 | 2.43 | 7 | 1.12 | 18 |
13 | S13 | 632,235 | 3,672,977 | <5 | 27,364 | 0.28 | 8.4 | 40 | 49 | 3.2 | 197 | 1.02 | 119 |
14 | S14 | 632,566 | 3,672,641 | 8 | 77,478 | 0.36 | 36.4 | 12 | 80 | 26.9 | 25 | 1.06 | 23 |
15 | S15 | 615,304 | 3,656,553 | 12 | 37,739 | 0.22 | 8.6 | 8 | 29 | 2.16 | 5 | 1.01 | 22 |
16 | S16 | 615,253 | 3,656,502 | 23 | 20,271 | 0.25 | 8.3 | 40 | 48 | 6.6 | 5 | 0.97 | 19 |
17 | S17 | 615,249 | 3,656,248 | 25 | 42,181 | 0.27 | 120.8 | 10 | 39 | 2.27 | 13 | 1.09 | 22 |
18 | S18 | 616,106 | 3,656,542 | 6 | 17,893 | 0.28 | 8.4 | 24 | 52 | 3.8 | 6 | 1.02 | 17 |
19 | S19 | 616,073 | 3,656,271 | <5 | 43,412 | 0.22 | 8.8 | 56 | 115 | 2.1 | 7 | 1.1 | 37 |
20 | S20 | 626,512 | 3,639,545 | <5 | 18,158 | 0 | 13 | 29 | 36 | 8.1 | 9 | 1.05 | 6 |
21 | S21 | 626,422 | 3,639,297 | 55 | 39,771 | 0 | 61.7 | 509 | 63 | 9.6 | 280 | 1.04 | 16 |
Row | Sample_NO | SiO2 | Al2O3 | CaO | MgO | TiO2 | Fe2O3 | MnO | P2O5 | Na2O | K2O | SrO | L.O.I | Total |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | S01 | 56.24 | 24.19 | 0.71 | 0.66 | 0.75 | 3.14 | <0.1 | 0.48 | <0.1 | 2.24 | <0.1 | 10.70 | 99.11 |
2 | S02 | 49.23 | 22.81 | 2.70 | 1.55 | 0.85 | 6.08 | 0.14 | 0.46 | 1.54 | 3.23 | <0.1 | 10.00 | 98.60 |
3 | S03 | 41.85 | 20.15 | 8.97 | 2.94 | 0.61 | 6.81 | 0.28 | 0.38 | 0.32 | 2.23 | <0.1 | 13.50 | 98.04 |
4 | S04 | 50.16 | 23.24 | 5.66 | 1.58 | 0.43 | 3.43 | <0.1 | 0.32 | <0.1 | 1.94 | <0.1 | 11.30 | 98.06 |
5 | S05 | 47.91 | 23.51 | 6.90 | 1.64 | 0.43 | 4.01 | <0.1 | 0.34 | <0.1 | 2.81 | <0.1 | 11.49 | 99.03 |
6 | S06 | 43.86 | 22.51 | 6.31 | 1.39 | 0.65 | 7.35 | 0.17 | 0.31 | 0.62 | 2.91 | <0.1 | 12.62 | 98.70 |
7 | S07 | 53.06 | 18.94 | 4.31 | 3.26 | 0.71 | 6.49 | 0.26 | 0.42 | 3.68 | 3.58 | <0.1 | 4.14 | 98.85 |
8 | S08 | 43.38 | 18.91 | 8.87 | 2.86 | 0.66 | 6.80 | 0.16 | 0.43 | 1.99 | 2.22 | 0.12 | 12.70 | 99.08 |
9 | S09 | 52.32 | 22.22 | 5.02 | 2.14 | 0.50 | 2.44 | <0.1 | 0.34 | <0.1 | 3.85 | <0.1 | 9.50 | 98.33 |
10 | S10 | 41.42 | 17.01 | 8.66 | 3.44 | 0.77 | 9.28 | 0.33 | 0.57 | 2.56 | 1.85 | 0.10 | 12.98 | 98.98 |
11 | S11 | 44.85 | 21.87 | 4.79 | 2.21 | 0.62 | 7.67 | 0.15 | 0.36 | 0.65 | 3.27 | <0.1 | 12.34 | 98.77 |
12 | S12 | 49.31 | 19.29 | 5.46 | 2.63 | 0.55 | 6.14 | 0.15 | 0.38 | 2.33 | 2.46 | <0.1 | 9.39 | 98.09 |
13 | S13 | 48.28 | 17.41 | 8.81 | 3.28 | 0.69 | 8.44 | 0.18 | 0.52 | 2.97 | 3.62 | 0.19 | 5.15 | 99.56 |
14 | S14 | 44.26 | 17.00 | 6.57 | 3.07 | 0.75 | 8.95 | 0.17 | 0.37 | 2.91 | 3.70 | 0.10 | 9.84 | 97.69 |
15 | S15 | 56.24 | 24.19 | 0.71 | 0.66 | 0.75 | 3.14 | <0.1 | 0.48 | <0.1 | 2.24 | <0.1 | 6.70 | 95.11 |
16 | S16 | 49.23 | 22.81 | 2.70 | 1.55 | 0.85 | 6.08 | 0.14 | 0.46 | 1.54 | 3.23 | <0.1 | 10.00 | 98.60 |
17 | S17 | 41.85 | 20.15 | 8.97 | 2.94 | 0.61 | 6.81 | 0.28 | 0.38 | 0.32 | 2.23 | <0.1 | 13.50 | 98.04 |
18 | S18 | 50.16 | 23.24 | 5.66 | 1.58 | 0.43 | 3.43 | <0.1 | 0.32 | <0.1 | 1.94 | <0.1 | 11.30 | 98.06 |
19 | S19 | 47.91 | 23.51 | 6.90 | 1.64 | 0.43 | 4.01 | <0.1 | 0.34 | <0.1 | 2.81 | <0.1 | 11.49 | 99.03 |
20 | S20 | 43.86 | 22.51 | 6.31 | 1.39 | 0.65 | 7.35 | 0.17 | 0.31 | 0.62 | 2.91 | <0.1 | 12.62 | 98.70 |
21 | S21 | 53.06 | 18.94 | 4.31 | 3.26 | 0.71 | 6.49 | 0.26 | 0.42 | 3.68 | 3.58 | <0.1 | 4.14 | 98.85 |
Samples | Major Phase | Minor Phase | Alteration |
---|---|---|---|
S04 | Quartz, Calcite, Albite | Hematite, Muscovite, Illite, Orthoclase | Phyllic |
S07 | Albite, Quartz, Calcite, Orthoclase | Hematite, Muscovite, Chlorite | Phyllic |
S13 | Quartz, Calcite, Albite, Orthoclase | Hematite, Muscovite, Illite, Kaolinite | Phyllic–Argillic |
S19 | Quartz, Calcite, Albite | Hematite, Muscovite, Kaolinite, Orthoclase | Phyllic–Argillic |
S21 | Quartz, Calcite, Orthoclase, Albite | Montmorillonite, Hematite | Argillic |
S14 | Quartz, Calcite, Albite, Orthoclase | Chlorite, Hornblende, Hematite | Propylitic |
S16 | Quartz, Albite, Calcite | Chlorite, Epidote, Goethite, Hematite | Propylitic |
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Classes | Phyllic | Argillic | Propylitic | Fe-Oxides | Total | User’s Accuracy |
---|---|---|---|---|---|---|
Unclassified | 20 | 46 | 9 | 30 | 105 | |
Phyllic | 172 | 23 | 0 | 0 | 195 | 88.21 |
Argillic | 33 | 795 | 6 | 47 | 881 | 90.24 |
Propylitic | 0 | 3 | 201 | 1 | 205 | 98.05 |
Fe-Oxides | 0 | 17 | 0 | 104 | 121 | 85.95 |
Total | 225 | 884 | 216 | 182 | 1507 | |
Producer’s accuracy | 76.44 | 89.93 | 93.06 | 57.14 | ||
Overall accuracy | 84.4 | |||||
Kappa coefficient | 0.744 |
Classes | Phyllic | Argillic | Propylitic | Fe-Oxides | Total | User’s Accuracy |
---|---|---|---|---|---|---|
Unclassified | 8 | 102 | 47 | 23 | 180 | |
Phyllic | 146 | 107 | 0 | 7 | 260 | 56.15 |
Argillic | 43 | 586 | 0 | 15 | 644 | 90.99 |
Propylitic | 0 | 1 | 128 | 0 | 129 | 99.22 |
Fe-Oxides | 24 | 108 | 9 | 153 | 294 | 52.04 |
Total | 221 | 904 | 184 | 198 | 1507 | |
Producer’s accuracy | 66.06 | 64.82 | 69.57 | 77.27 | ||
Overall accuracy | 67.2 | |||||
Kappa coefficient | 0.52 |
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Yousefi, M.; Tabatabaei, S.H.; Rikhtehgaran, R.; Pour, A.B.; Pradhan, B. Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery. Minerals 2021, 11, 1235. https://doi.org/10.3390/min11111235
Yousefi M, Tabatabaei SH, Rikhtehgaran R, Pour AB, Pradhan B. Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery. Minerals. 2021; 11(11):1235. https://doi.org/10.3390/min11111235
Chicago/Turabian StyleYousefi, Mastoureh, Seyed Hassan Tabatabaei, Reyhaneh Rikhtehgaran, Amin Beiranvand Pour, and Biswajeet Pradhan. 2021. "Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery" Minerals 11, no. 11: 1235. https://doi.org/10.3390/min11111235